quick.tables.R
In ckraner/quick.tasks: Quick implementation of tables, figures, and other helper functions for quick analysis.
###################### QUICK.TABLES ########################
#################### BY CHRIS KRANER #######################
############## NORTHERN ILLINOIS UNIVERSITY ################
######################## 12/2017 ###########################
############################################################
#' Partial contrasts for ANOVA and MANOVA tables
#'
#' Adds Contrasts and Latent Contrasts to nested table
#' for creating overall MANOVA and ANOVA tables.
#'
#' @param my.nested.table Model.
#' @param SS.type Type of sums of squares to report, either 2 or 3. (default = 3)
#' @param adjustment P-value adjustment sent to p.adjust (default = "bonferroni")
#' @param abbrev.length Integer telling how long of a label is too long. Longer than this and will be condensed (default=15)
#' @return Nested table with added rows and information (Unfortunately as character at the moment)
#' @keywords Explore
#' @examples
#' quick.contrast(my.nested.table)
quick.part.cont=function(my.nested.table,SS.type=2,adjustment="bonferroni",abbrev.length=30,latent.cont=F){
#### Get y levels
my.y.levels=dim(my.nested.table[1,4][[1]][[1]])[1]
#### Put in NA rows
temp.colnames=colnames(my.nested.table)
my.nested.table2=cbind(my.nested.table,as.matrix(c(rep(NA,dim(my.nested.table)[1]))))
if(latent.cont){
my.nested.table2=cbind(my.nested.table2,as.matrix(c(rep(NA,dim(my.nested.table)[1]))))
colnames(my.nested.table2)=c(temp.colnames,"Contrasts","Latent Contrasts")
}else{
colnames(my.nested.table2)=c(temp.colnames,"Contrasts")
}
#### For each row besides the null model
for(p in 2:dim(my.nested.table)[1]){
#### Check if should have contrast
if(!is.na(my.nested.table[p,7][[1]]) & my.nested.table[p,7][[1]]>1){
#### If should, make contrast
my.new.df=my.nested.table[p,3][[1]]$model
my.MSE=mean(my.nested.table[p,3][[1]]$residuals^2)
if(SS.type==3){
the.resid.SS=sum(diag(my.nested.table[p,4][[1]][[2]]))
}else{
the.resid.SS=sum(diag(my.nested.table[p,4][[1]][[2]][[1]]))
}
the.resid.df=my.nested.table[p,3][[1]]$df.residual
if(latent.cont){
#### Latent Mean Square Error
my.latent.MSE=NULL
for(j in 1:my.y.levels){
if(i==1){
my.latent.MSE=as.numeric(mean(my.contrast.model$residuals[i]^2))
}else{
my.latent.MSE=c(my.latent.MSE,as.numeric(mean(my.contrast.model$residuals[i]^2)))
}
}
}
#### Get mean responses for variables longer than 2
#### WARN! LEVEL NAMES IS GOING TO BREAK IT!!!! ####
level.name.grep=grep(my.nested.table[p,1],names(my.nested.table[p,3][[1]]$xlevels))
level.names=as.vector(my.nested.table[p,3][[1]]$xlevels[[level.name.grep]])
num.of.contrasts=my.nested.table[p,7][[1]]
count.grep=grep(my.nested.table[p,1],names(my.new.df))
my.count.means=NULL
my.latent.count.means=NULL
my.count.n=NULL
for(j in 1:{num.of.contrasts+1}){
if(j==1){
my.count.means=as.vector(mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1]))
my.count.n=as.vector(dim(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1])[1])
if(latent.cont){
for(E in 1:{my.y.levels}){
my.latent.count.means[[E]]=as.list(mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1][,E]))
}
}
}else{
my.count.means=c(my.count.means,mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1]))
my.count.n=c(my.count.n,dim(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1])[1])
if(latent.cont){
for(E in 1:{my.y.levels}){
my.latent.count.means[[E]]=c(my.latent.count.means[[E]],mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1][,E]))
}
}
}
}
#### Make contrasts
my.contrasts=NULL
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts=c(1,-1,rep(0,num.of.contrasts-1))
}else{
my.contrasts=rbind(my.contrasts,c(1,rep(0,j-1),-1,rep(0,num.of.contrasts-j)))
}
}
#pf(my.f.val,my.df,my.resid.df,lower.tail = F)
#### Compute F values & SS & P-val
my.contrasts.F=NULL
my.contrasts.SSC=NULL
my.contrasts.p=NULL
if(latent.cont){
my.latent.contrasts.F=NULL
my.latent.contrasts.SSC=NULL
my.latent.contrasts.p=NULL
}
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts.I=as.integer(t(as.matrix(my.count.means)))%*%as.integer(as.matrix(my.contrasts[j,]))
my.contrasts.denom=sum(my.contrasts[j,]^2/as.integer(my.count.n))
my.contrasts.SSC=as.list({my.contrasts.I^2}/{my.contrasts.denom})
my.contrasts.F=as.list({my.contrasts.I^2}/{my.MSE*my.contrasts.denom})
my.contrasts.p=as.list(pf(my.contrasts.F[[j]],1,the.resid.df,lower.tail = F))
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.I=as.integer(t(as.matrix(my.latent.count.means[[l]])))%*%as.integer(as.matrix(my.contrasts[j,]))
my.latent.contrasts.SSC[[l]]=as.list({my.latent.contrasts.I^2}/{my.contrasts.denom})
my.latent.contrasts.F[[l]]=as.list({my.latent.contrasts.I^2}/{my.latent.MSE[l]*my.contrasts.denom})
my.latent.contrasts.p[[l]]=as.list(pf(my.latent.contrasts.F[[l]][[j]],1,the.resid.df,lower.tail = F))
}
}
}else{
my.contrasts.I=as.integer(t(as.matrix(my.count.means)))%*%as.integer(as.matrix(my.contrasts[j,]))
my.contrasts.denom=sum(my.contrasts[j,]^2/as.integer(my.count.n))
my.contrasts.SSC=c(my.contrasts.SSC,{my.contrasts.I^2}/{my.contrasts.denom})
my.contrasts.F=c(my.contrasts.F,{my.contrasts.I^2}/{my.MSE*my.contrasts.denom})
my.contrasts.p=c(my.contrasts.p,pf(my.contrasts.F[[j]],1,the.resid.df,lower.tail = F))
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.I=as.integer(t(as.matrix(my.latent.count.means[[l]])))%*%as.integer(as.matrix(my.contrasts[j,]))
my.latent.contrasts.SSC[[l]]=c(my.latent.contrasts.SSC[[l]],{my.latent.contrasts.I^2}/{my.contrasts.denom})
my.latent.contrasts.F[[l]]=c(my.latent.contrasts.F[[l]],{my.latent.contrasts.I^2}/{my.latent.MSE[l]*my.contrasts.denom})
my.latent.contrasts.p[[l]]=c(my.latent.contrasts.p[[l]],pf(my.latent.contrasts.F[[l]][[j]],1,the.resid.df,lower.tail = F))
}
}
}
}
#### Make p-val Adjustments
my.contrasts.p=p.adjust(my.contrasts.p,method=adjustment)
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.p[[l]]=p.adjust(my.latent.contrasts.p[[l]],method=adjustment)
}
}
#### Make rownames
my.contrasts.names=NULL
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts.names=as.list(paste(trimws(level.names[[1]]),"-",trimws(level.names[[j+1]])))
}else{
my.contrasts.names=c(my.contrasts.names,paste(trimws(level.names[[1]]),"-",trimws(level.names[[j+1]])))
}
}
#### Add to table
my.contrasts.4.table=cbind(as.matrix(unlist(my.contrasts.names)),as.matrix(unlist(my.contrasts.F)),as.matrix(unlist(my.contrasts.SSC)),as.matrix(my.contrasts.p))
contr.grep=grep("^Contrasts$",colnames(my.nested.table2))
my.nested.table2[p,contr.grep]=list(my.contrasts.4.table)
if(latent.cont){
my.latent.contrasts.4.table=NULL
for(V in 1:my.y.levels){
if(V==1){
my.latent.contrasts.4.table=cbind(as.matrix(unlist(my.contrasts.names)),as.numeric(as.matrix(unlist(my.latent.contrasts.F[[V]]))),as.matrix(as.numeric(unlist(my.latent.contrasts.SSC[[V]]))),as.matrix(my.latent.contrasts.p[[V]]))
}else{
my.latent.contrasts.4.table=cbind(my.latent.contrasts.4.table,as.matrix(unlist(my.contrasts.names)),as.numeric(as.matrix(unlist(my.latent.contrasts.F[[V]]))),as.matrix(as.numeric(unlist(my.latent.contrasts.SSC[[V]]))),as.matrix(my.latent.contrasts.p[[V]]))
}
}
latent.contr.grep=grep("^Latent Contrasts$",colnames(my.nested.table2))
my.nested.table2[p,latent.contr.grep]=list(my.latent.contrasts.4.table)
}
}
}
return(my.nested.table2)
}
#' Contrast Tables in Pixiedust
#'
#' Beautiful tables using PHIA and PixieDust
#' for lm, glm, and mancova.
#'
#' @param my.model Model.
#' @param SS.type Type of sums of squares to report, either 2 or 3. (default = 3)
#' @param adjustment P-value adjustment sent to p.adjust (default = "bonferroni")
#' @param test.stat Character string giving the type of test statistic to be used (in MANOVA). (default="Wilks")
#' @param abbrev.length Integer telling how long of a label is too long. Longer than this and will be condensed (default=15)
#' @param pix.int Should this be viewable or is this for a document/dashboard? (default=T)
#' @param pix.method Print method. (default="html")
#' @param manova Is this a MAN(C)OVA?
#' @param my.factors If you only want some of the factors, use this. Otherwise, factors are pulled from the regression model
#' @param my.type If you have problems, you can specify the regression type. This is pulled from the model
#' @return Either pixiedust object or code (in HTML or LaTeX) for table
#' @keywords Explore
#' @export
#' @examples
#' quick.contrast(my.model)
quick.contrast = function(my.model,
SS.type = 3,
adjustment = "bonferroni",
test.stat = "Wilks",
abbrev.length = 15,
pix.int = T,
pix.method = "html",
my.factors = my.contrasts,
my.type = my.reg.type,
skip.me=F) {
#### Find type
my.call = as.character(my.model$call)
my.split.call = strsplit(my.call, "\\(")
my.reg.type = my.split.call[[1]][1]
#### Find factors
my.contrasts = names(my.model$contrasts)
if (my.type != "manova" | my.type != "stats::manova") {
#### Find levels
my.x.levels = NULL
for (i in 1:length(my.factors)) {
my.x.levels = c(my.x.levels, my.model$xlevels[[i]])
}
#### Find num non var
my.non.var = length(my.model$coefficients) - length(my.x.levels) - 1 +
length(my.contrasts)
}
library(pixiedust)
library(phia)
if (my.type == "manova" | my.type == "stats::manova") {
my.phia.print = as.data.frame(matrix(ncol = 8, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
fixed = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names",
"Df",
"test stat",
"approx F",
"num Df",
"den Df",
"Pr(>F)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, nrow(my.phia))
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
rownames(my.phia.print) = NULL
if(skip.me){
return(my.phia.print)
}
phia.length = dim(my.phia.print)[1]
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>F)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "test stat", round = 3) %>%
sprinkle(cols = "approx F", round = 3) %>%
sprinkle_colnames(
"",
"Levels",
"df",
"Pillai <br /> Statistic",
"approx <br /> F-value",
"num <br /> df",
"den <br /> df",
"Pr(>F)"
) %>%
sprinkle(cols = 1:8,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:8, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:8,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:8,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:8,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1:2,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1:2,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 7,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 8,
width = 70,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 8, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
} else if (my.type == "lm" | my.type == "stats::lm") {
my.phia.print = as.data.frame(matrix(ncol = 7, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
pairwise = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia = my.phia[-{
dim(my.phia)[1]
}, ]
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names", "Value", "Df", "Sum of Sq", "F", "Pr(>F)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, {
nrow(my.phia)
})
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (my.lengths[this.table.var] == 1) {
my.phia = my.phia[-2, ]
}
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
#print(my.phia.print)
#my.phia.print=my.phia.print[-{dim(my.phia.print)[1]-1},]
rownames(my.phia.print) = NULL
phia.length = dim(my.phia.print)[1]
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>F)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "test stat", round = 3) %>%
sprinkle(cols = "approx F", round = 3) %>%
sprinkle_colnames("",
"Levels",
"Value",
"df",
"Sums of <br /> Squares",
"F-value",
"Pr(>F)") %>%
sprinkle(cols = 1:7,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:7, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:7,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:7,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:7,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 7,
width = 50,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 7, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
} else{
my.phia.print = as.data.frame(matrix(ncol = 6, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
pairwise = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia = my.phia[-{
dim(my.phia)[1]
}, ]
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names", "Value", "Df", "Chisq", "Pr(>Chisq)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, {
nrow(my.phia)
})
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (my.lengths[this.table.var] == 1) {
my.phia = my.phia[-2, ]
}
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
#print(my.phia.print)
#my.phia.print=my.phia.print[-{dim(my.phia.print)[1]-1},]
rownames(my.phia.print) = NULL
phia.length = dim(my.phia.print)[1]
my.phia.print[[6]] = as.numeric(my.phia.print[[6]])
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>Chisq)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "value", round = 3) %>%
sprinkle(cols = "Chisq", round = 3) %>%
sprinkle_colnames("", "Levels", "Value", "df", "Chi-Sq", "Pr(>Chisq)") %>%
sprinkle(cols = 1:6,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:6, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:6,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:6,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:6,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 6, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
}
}
#' Regression Tables in Pixiedust
#'
#' Beautiful tables adding sums of squares
#' and p-value formatting, then giving html or
#' latex output. If interactive and HTML, will show up
#' in viewer.
#'
#' @param my.model Model. NOTE: If have factors, please place them first in the regression.
#' @param VIF include Variable Inflation Factor? (calculated by car::vif) (default=F)
#' @param part.eta If lm, include partial eta square by calculating SS_part/SS_total? (default=F)
#' @param myDF Dataframe, not needed if use data= in call
#' @param my.factor If there are any factors, list them here.
#' @param SS.type Type of sums of squares to report (default = 3)
#' @param pix.int Should this be viewable or is this for a document/dashboard? (default=T)
#' @param pix.method Print method. (default="html")
#' @param type Type of regression? Currently supported: lm, glm (binary), manova
#' @param do.glance Include glance stats?
#' @return Either pixiedust object or code (in HTML or LaTeX) for table
#' @keywords Explore
#' @export
#' @examples
#' quick.reg(my.model, myDF)
quick.reg = function(my.model,
part.eta = F,
VIF = F,
myDF = my.found.df,
SS.type = 2,
abbrev.length = ab.len,
pix.int = T,
pix.method = "html",
type = my.reg.type,
test.stat = "Pillai",
my.factor = NULL,
do.glance=T,
show.footer=T,
adjustment = "bonferroni",
show.contrasts=F,
show.y.contrasts=F,
show.latent=F,
show.intercepts=F,
real.names=T) {
library(pixiedust)
library(broom)
library(car)
library(tidyr)
library(phia)
library(quick.tasks)
library(dplyr)
#### Find type ####
my.reg.type=quick.type(my.model)
#### Set Inits ####
if(type=="ord"){
ab.len=30
library(ordinal)
}else{
ab.len=15
}
#### Get data frame from parent environment ####
my.found.df = eval(parse(text=capture.output(my.model$call$data)),envir = parent.frame())
#print(dim(my.found.df))
if (is.null(my.found.df)) {
stop(paste("No data frame found"))
}
#### for expansion
if (!VIF & !part.eta) {
v.p.len = 8
v.p.rep = 0
} else if (!VIF) {
v.p.len = 9
v.p.rep = 1
} else if (!part.eta) {
v.p.len = 9
v.p.rep = 1
} else{
v.p.len = 10
v.p.rep = 2
}
#### Make factor list ####
#### Use car::Anova to get SS Type 3
if (type == "lm") {
#### ANOVA TABLES ####
my.summary = summary(my.model)
my.coefficients = my.summary$coefficients
my.coefficients = as.data.frame(my.coefficients)
if (type == "ord" & length(my.model$model) == 1) {
my.III.summary = NULL
the.length = length(my.model$y.levels) - 1
} else{
my.III.summary = car::Anova(my.model, type = SS.type)
if (type == "glm" & is.null(my.factor)) {
the.length = dim(my.III.summary)[1] + 1
} else{
the.length = dim(my.III.summary)[1]
}
}
#### Calculate total SS ####
my.total = sum(my.III.summary$`Sum Sq`[2:length(my.III.summary$`Sum Sq`)])
my.df.total = sum(my.III.summary$Df[2:length(my.III.summary$Df)])
total.intercepts = 1
my.rownames = c(abbreviate(rownames(my.summary$coefficients), minlength = abbrev.length),
"Residuals",
"Total")
treat.SS=sum(my.III.summary$`Sum Sq`[2:{length(my.III.summary$`Sum Sq`)-1}])
treat.df=sum(my.III.summary$Df[2:{length(my.III.summary$Df)-1}])
} else if(type == "manova"){
#### Begin MANOVA ####
#### Inits ####
my.envir=environment()
my.nested.table=quick.SSCP(my.model, myDF, SS.type, show.contrasts, show.latent,my.envir)
#### Get treatment ####
treat.model=my.nested.table[dim(my.nested.table)[1],4]
#### Regular totals
treat.total.temp=lapply(lapply(treat.model[[1]][[1]],diag),sum)
treat.total=0
for(W in 2:length(treat.total.temp)){
treat.total=treat.total+treat.total.temp[[W]]
}
treat.total.df=sum(as.numeric(my.nested.table[,7]),na.rm = T)
#### Partial totals
part.treat.total=NULL
for(X in 2:length(treat.model[[1]][[1]])){
if(X==2){
part.treat.total=treat.model[[1]][[1]][[X]]
}else{
part.treat.total=part.treat.total+treat.model[[1]][[1]][[X]]
}
}
#### Latent totals
if(show.latent){
latent.treat.model=my.nested.table[dim(my.nested.table)[1],8]
latent.part.treat.total=NULL
latent.part.total=NULL
for(X in 2:length(latent.treat.model[[1]][[1]])){
if(X==2){
latent.part.treat.total=latent.treat.model[[1]][[1]][[X]]
}else{
latent.part.treat.total=latent.part.treat.total+latent.treat.model[[1]][[1]][[X]]
}
}
}
#### Get residuals ####
#### Regular residuals
if(SS.type==3){
the.resid=sum(diag(treat.model[[1]][[2]]))
#### Partial residuals
part.resid.total=treat.model[[1]][[2]]
}else{
the.resid=sum(diag(treat.model[[1]][[2]][[1]]))
part.resid.total=treat.model[[1]][[2]][[1]]
}
the.resid.df=my.model$df.residual
#### Latent totals
if(show.latent){
if(SS.type==3){
latent.part.resid.total=latent.treat.model[[1]][[2]]
}else{
latent.part.resid.total=latent.treat.model[[1]][[2]][[1]]
}
}
#### Get totals ####
the.total=total.resid+treat.total+sum(diag(treat.model[[1]][[1]][[1]]))
the.total.df=total.resid.df+treat.df+my.y.levels
#### Partial totals
partial.total=part.resid.total+part.treat.total+treat.model[[1]][[1]][[1]]
#### Latent totals
if(show.latent){
latent.part.total=latent.part.treat.total+latent.part.treat.total+latent.treat.model[[1]][[1]][[1]]
}
#### Make dependent variable rownames ####
my.dv.rownames=rownames(part.resid.total)
#### Make basic table ####
#### NEEDS TO BE FIXED ####
my.table.names=c("var","test.stat","f.val","SS","df","mult.df","resid df","p.val")
my.manova.table=as.data.frame(matrix(ncol=v.p.len,nrow=1))
names(my.manova.table)=my.table.names
my.line.var=1
for(i in 1:length(my.SSP.treat)){
if(i==1){
my.i=i
}else{
my.i=2*i-1
}
#### Put in basic line ####
my.treat.err=solve(my.SSP.err)%*%treat.model[[1]][[1]][[i]]
my.test.stat=quick.m.test(my.treat.err,test.stat)
my.SS=sum(diag(treat.model[[1]][[1]][[i]]))
my.df=my.y.levels*ifelse(my.i!=1,as.numeric(my.nested.table[my.i,7]),1)
my.resid.df=ifelse(my.i==1,{my.y.levels*the.resid.df},min({the.resid.df*as.numeric(my.nested.table[my.i,5])-as.numeric(my.nested.table[my.i,5])},my.y.levels*the.resid.df-as.numeric(my.nested.table[my.i,5])))
my.f.val={my.SS/my.df}/{the.resid/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(ifelse(i==1,"(Intercept)",my.nested.table[my.i,1]),my.test.stat,my.f.val,my.SS,ifelse(my.i==1,NA,my.df/my.y.levels),ifelse(my.i==1,my.y.levels,my.df),my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in decomposed factors (y constrasts) ####
#### Intercept
if({show.intercepts & my.i==1}){
for(y in 1:my.y.levels){
my.name=my.dv.rownames[y]
my.test.stat=NA
my.SS=as.numeric(treat.model[[1]][[1]][[i]][y,y])
my.df=1
my.resid.df=the.resid.df
my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
#### Other
if({show.y.contrasts & my.i!=1}){
for(y in 1:my.y.levels){
my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
my.test.stat=NA
my.SS=treat.model[[1]][[1]][[i]][y,y]
my.df=as.numeric(my.nested.table[my.i,7])
my.resid.df=min(abs(my.y.levels*the.resid.df-my.df*the.resid.df-my.df),abs(my.y.levels*the.resid.df-my.df))
my.resid=part.resid.total[y,y]
my.f.val={my.SS/my.df}/{my.resid/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in latents (ANOVA) ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent &my.i!=1){
# my.my.i.temp=my.counter[my.i-1]
my.my.i.temp=my.i
my.y=y
my.name=NA
my.df=as.numeric(my.nested.table[my.i,7])
my.test.stat=NA
my.resid.df=the.resid.df-my.df
my.SS=as.numeric(latent.treat.model[[1]][[1]][[i]][y,1])
my.f.val={my.SS/my.df}/{my.latent.SSP.err[my.y,my.y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
#### Put in latent contrasts ####
if(show.contrasts & show.latent & my.i!=1){
#### Check length
if({!is.na(my.nested.table[my.i,11])}){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:{my.nested.table[my.i,7]}){
my.name=my.nested.table[my.i,11][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,11][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,11][[1]][k,3])
my.test.stat=NA
my.df=1
my.mult.df=NA
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,11][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Put in contrasts ####
#### Not right. Don't have it decomposed this way.
if(show.contrasts & !show.latent & my.i!=1){
#### Check length
if(!is.na(my.nested.table[my.i,9])){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:as.numeric(my.nested.table[my.i,7])){
my.name=my.nested.table[my.i,9][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,9][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,9][[1]][k,3])
my.test.stat=NA
my.df=NA
my.mult.df=my.y.levels
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,9][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
}
}
#### Put in contrasts ####
if(show.contrasts & !show.latent & !show.y.contrasts){
#### Check length
if(!is.na(my.nested.table[my.i,8])){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:as.numeric(my.nested.table[my.i,7])){
my.name=my.nested.table[my.i,8][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,8][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,8][[1]][k,3])
my.test.stat=NA
my.df=NA
my.mult.df=my.y.levels
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,8][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Put in latents (ANOVA) ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent &my.i!=1 & !show.y.contrasts){
#my.my.i.temp=my.counter[my.i-1]
my.my.i.temp=my.i
for(y in 1:{my.y.levels}){
my.y=y
my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
my.df=as.numeric(my.nested.table[my.i,7])
my.test.stat=NA
my.resid.df=the.resid.df-my.df
my.SS=as.numeric(my.nested.table[my.i,8][[1]][[1]][[i]][my.y])
my.f.val={my.SS/my.df}/{my.latent.SSP.err[my.y,my.y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in latent contrasts ####
if(show.contrasts & !{show.contrasts & show.latent}){
#### Check length
if({my.SSP.treat.df[my.i]>1}){
other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:{my.SSP.treat.df[my.i]}){
my.name=my.contrasts.table[[other.manova.grep]][k,1]
my.f.val=my.latent.contrasts.table[[other.manova.grep]][[y]][k,2]
my.SS=my.latent.contrasts.table[[other.manova.grep]][[y]][k,3]
my.test.stat=NA
my.df=1
my.mult.df=NA
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=my.latent.contrasts.table[[other.manova.grep]][[y]][k,4]
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
}
}
#### Put in treatment ####
#### From Type my.i statistics
if(my.i==1){
my.test.stat=quick.m.test(part.treat.total,test.stat)
my.SS=as.numeric(treat.total)
my.df=my.y.levels*as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=my.y.levels*as.numeric(the.resid.df)
my.f.val={my.SS/my.df}/{as.numeric(the.resid.SS)/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c("Treatment Change",my.test.stat,my.f.val,my.SS,{my.df/my.y.levels},my.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Treatment Y Contrasts ####
if(show.y.contrasts){
for(b in 1:my.y.levels){
my.test.stat=NA
my.name=paste(ifelse(real.names,my.dv.rownames[b],b),"|Treatment",sep="")
my.SS=latent.part.treat.total[b]
my.df=the.resid.df
#### Should really be a min statement, but for later...
my.resid.df=my.y.levels*the.resid.df
my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,{my.df/my.y.levels},NA,{my.resid.df/my.y.levels},my.p.val)
my.line.var=my.line.var+1
#### Treatment Latents ####
if(show.latent){
#### Show latent treatments (ANOVAs)
#### Need to change latents to type II
my.SS=sum(weighted.residuals(my.null.model)[,b]^2)-sum(weighted.residuals(my.model)[,b]^2)
my.df=as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=the.resid.df-my.df
my.f.val={my.SS/my.df}/{my.latent.SSP.err[y,y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(NA,NA,my.f.val,my.SS,my.df,NA,{my.resid.df},my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Latents ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent & !show.y.contrasts){
for(b in 1:{my.y.levels}){
my.name=paste(ifelse(real.names,my.dv.rownames[b],b),"|Treatment",sep="")
my.SS=as.numeric(part.treat.total[b])
my.df=as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=the.resid.df-my.df
my.f.val={my.SS/my.df}/{as.numeric(latent.part.resid.total[b])/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),NA,my.f.val,my.SS,my.df,NA,{my.resid.df},my.p.val)
my.line.var=my.line.var+1
}
}
}
}
#### Put in residuals, total change, total ss ####
my.manova.table[my.line.var,]=c("Total Change",NA,NA,as.numeric(treat.total),treat.total.df,{my.y.levels*treat.total.df},NA,NA,rep(NA,v.p.rep))
my.line.var=my.line.var+1
my.manova.table[my.line.var,]=c("Residuals",NA,NA,the.resid,the.resid.df,my.y.levels*the.resid.df,NA,NA,rep(NA,v.p.rep))
my.line.var=my.line.var+1
if(show.y.contrasts){
for(i in 1:my.y.levels){
my.manova.table[my.line.var,]=c(paste(ifelse(real.names,my.dv.rownames[i],i),"|Residual",sep=""),NA,NA,part.resid.total[i,i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
if(show.latent){
my.manova.table[my.line.var,]=c(NA,NA,NA,latent.part.resid.total[i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
}
}
}
if(show.latent & !show.y.contrasts){
for(i in 1:my.y.levels){
my.manova.table[my.line.var,]=c(paste(ifelse(real.names,my.dv.rownames[i],i),"|Residual",sep=""),NA,NA,latent.part.resid.total[i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
}
}
my.manova.table[my.line.var,]=c("Total",NA,NA,the.total,the.total.change.df+1,my.y.levels*the.total.change.df+2,NA,NA,rep(NA,v.p.rep))
if(show.footer){
the.footer=paste(ifelse(dim(my.new.df)[1]==dim(myDF)[1],"Data have same number of rows <br />",paste({dim(myDF)[1]-dim(my.new.df)[1]}," cases deleted due to missingness <br />")),"Method: QR decomposition",if(show.contrasts){paste(" <br />Adjustment: ", adjustment,sep="")},if(show.latent){paste(" <br /> Latent Contrasts")})
}else{
the.footer=NULL
}
quick.table(my.manova.table,"manova",test=test.stat,SS.type = SS.type, abbrev.length = abbrev.length,the.footer = the.footer)
#
# #### Table inits ####
# for(i in 2:dim(my.manova.table)[2]){
# my.manova.table[[i]]=as.numeric(my.manova.table[[i]])
# }
# tmp.change=0
# if(show.latent & show.y.contrasts){
# tmp.change=4
# }else if(show.latent & !show.y.contrasts){
# tmp.change=2
# }else if(!show.latent & show.y.contrasts){
# tmp.change=2
# }
#
# #### Make Dusted table ####
# options(pixie_interactive = pix.int,
# pixie_na_string = "")
# my.dust=pixiedust::dust(my.manova.table)%>%
# sprinkle_na_string()%>%
# sprinkle_print_method(pix.method)%>%
# sprinkle_border(cols=1,rows=1:my.line.var,border="left")%>%
# sprinkle_border(cols={8+v.p.rep},rows=1:my.line.var,border="right")%>%
# sprinkle_border(rows=my.line.var,boder="bottom")%>%
# sprinkle_border(rows=my.line.var-{2+tmp.change}+1,border="top")%>%
# sprinkle_border(cols=1,border="left",part="head")%>%
# sprinkle_border(cols={8+v.p.rep},border="right",part="head")%>%
# sprinkle_border(rows=1,border=c("top","bottom"),part="head")%>%
# sprinkle_border(rows={1+ifelse(show.intercepts,my.y.levels,0)},border="bottom")%>%
# sprinkle_border(rows={ifelse(show.y.contrasts,my.y.levels+1,1)+ifelse(show.latent,my.y.levels+1,1)+
# ifelse(show.intercepts,my.y.levels,0)},
# border="bottom")%>%
# sprinkle_round(cols=2:v.p.len,round=3)%>%
# sprinkle_colnames("Variable",paste(test.stat, "<br /> Test <br /> Statistic",sep=""),
# "F-Value",paste("Type II <br /> Sums of <br /> Squares",sep=""),"dF",
# "Mult <br /> dF","Resid <br /> dF","P-value")%>%
# sprinkle_align(rows=1,halign="center",part="head")%>%
# sprinkle_pad(rows=1:{my.line.var},pad=5)%>%
# sprinkle(cols = "p.val", fn = quote(pvalString(
# value, digits = 3, format = "default"
# )))%>%
# sprinkle_border(rows={1+ifelse(show.intercepts,my.y.levels,0)},border="bottom")
#
# ##### Make glance stats ####
# if(do.glance){
# my_glance_stats=as.data.frame(matrix(ncol=v.p.len,nrow=1))
# my_glance_stats[1,]=c(paste(ifelse(dim(my.new.df)[1]==dim(myDF)[1],"Data have same number of rows <br />",paste({dim(myDF)[1]-dim(my.new.df)[1]}," cases deleted due to missingness <br />")),"Method: QR decomposition",if(show.contrasts){paste(" <br />Adjustment: ", adjustment,sep="")},if(show.latent){paste(" <br /> Latent Contrasts")}),rep(NA,{7+v.p.rep}))
# my.dust=pixiedust::redust(my.dust,my_glance_stats,part="foot")%>%
# sprinkle(merge=T,halign="center",part="foot")
# }
return()
# if (pix.int) {
# return(my.dust)
# } else{
# my.dust.print = print(my.dust, quote = F)[1]
# return(my.dust.print)
# }
#### ETA-SQ Stuff to finish.... ####
# #### Make eta-sq
# my.SSP.err.t=t(my.SSP.err)
#
# eta.sq=NULL
# for(i in 1:length(my.SSP)){
# eta.sq[[i]]=my.SSP.err.t*my.SSP[[i]]
# }
#
# x3 = capture.output(car::Anova(my.model, type = SS.type, test = test.stat))
# my.manova.test = data.frame(matrix(ncol = 7, nrow = 1))
# my.var.temp = 4
# while (my.var.temp < {
# length(x3) - 1
# }) {
# test = strsplit(x3[my.var.temp], "\\s+")
#
# if (length(test[[1]]) == 9) {
# test2 = test[[1]][-9]
# test2 = test2[-7]
#
# } else if (length(test[[1]]) == 8) {
# test2 = test[[1]][-8]
#
# } else{
# test2 = test[[1]]
# }
#
# my.manova.test[{
# my.var.temp - 3
# }, ] = test2
# my.var.temp = my.var.temp + 1
#
# }
# my.summary=summary(my.model)
#### END MANOVA table ####
}else if (type == "glm") {
#### DevAn Table ####
#### Inits ####
new.df=my.model$model
new.model=update(my.model,data=new.df)
null.model=update(new.model,~1)
total.intercepts = 1
#### Check if null model ####
vars.df=new.model$df.null-new.model$df.residual
if(vars.df==0){
stop("This is the null model.")
}
resid.dev=new.model$deviance
resid.df=new.model$df.residual
vars.dev=anova(new.model,test="Chi")$Deviance[-1]
vars.dev.df=anova(new.model,test="Chi")$Df[-1]
vars.dev.p=anova(new.model,test="Chi")$`Pr(>Chi)`[-1]
total.dev=new.model$null.deviance-new.model$deviance
vars.dev.total=sum(vars.dev)
if(length(grep("*",new.model$formula))>0){
my.full.model=new.model
}else{
my.vars=strsplit(as.character(new.model$formula),"~")
my.dep.var=my.vars[[2]]
my.other.vars=names(new.model$model)
my.var.grep=grep(paste("^",my.dep.var,"$",sep=""),my.other.vars)
my.other.vars=my.other.vars[-my.var.grep]
my.formula=paste("~",my.other.vars[1])
for(i in 2:length(my.other.vars)){
my.formula=paste(my.formula,"*",my.other.vars[i],sep="")}
#### FULL MODEL THING ####
my.full.model=update(new.model,my.formula)
}
my.full.model.z=summary(my.full.model)$coefficients[1:total.intercepts,3]^2
my.null.model.z=summary(null.model)$coefficients[1:total.intercepts,3]^2
my.full.dev=sum(my.full.model.z)
my.null.dev=sum(my.null.model.z)
my.int.dev.total=abs(total.intercepts*{my.full.dev-my.null.dev})
my.int.dev=summary(new.model)$coefficients[1:total.intercepts,2]^2
treat.dev=anova(null.model,new.model)$`Deviance`[2]
#vars.dev.df=drop1(new.model,test="Chi")$Df[-1]
#vars.dev.p=drop1(new.model,test="Chi")$`Pr(>Chi)`[-1]
#total.dev=-2*{as.integer(levels(null.model$info$logLik)[1])-as.integer(levels(new.model$info$logLik)[1])}
total.dev.change=treat.dev+my.int.dev.total
total.dev.change.df=total.intercepts*vars.df
total.dev=new.model$null.deviance
resid.dev=total.dev-total.dev.change
vars.dev.df=anova(new.model,test="Chi")$Df
resid.df=new.model$df.residual
total.df=resid.df+vars.df
my.int.dev.df=max({total.intercepts*sum(vars.dev.df,na.rm = T)-sum(vars.dev.df,na.rm=T)},1)
my.int.dev.or=exp(coef(new.model))[1:{total.intercepts}]
my.int.dev.or.confint=exp(confint(new.model,type="Wald"))[1:total.intercepts,]
my.int.names=abbreviate(names(my.int.dev.or),minlength=abbrev.length)
#resid.dev=new.model$deviance
vars.dev=anova(new.model,test="Chi")$Deviance
vars.dev.p=anova(new.model,test="Chi")$`Pr(>Chi)`
vars.names=rownames(anova(new.model,test="Chi"))
#total.dev=new.model$null.deviance-new.model$deviance
vars.dev.total=sum(vars.dev)
#total.df=sum(vars.dev.df,na.rm = T)
vars.or=exp(coef(new.model))
vars.or.confint=exp(confint(new.model,type="Wald"))
my.names=abbreviate(names(vars.or),minlength=abbrev.length)
#vars.or=exp(coef(new.model))[-1]
#vars.or.confint=exp(confint(new.model,type="Wald"))[-1,]
} else if (type == "ord" | type =="glm2") {
new.df=my.model$model
new.model=update(my.model,data=new.df)
null.model=update(new.model,~1)
total.intercepts = null.model$edf
my.summary=summary(new.model)
if(length(grep("*",new.model$formula))>0){
my.full.model=new.model
}else{
my.vars=strsplit(as.character(new.model$formula),"~")
my.dep.var=my.vars[[2]]
my.other.vars=names(new.model$model)
if(length(my.other.vars)>1){
my.var.grep=grep(paste("^",my.dep.var,"$",sep=""),my.other.vars)
my.other.vars=my.other.vars[-my.var.grep]
my.formula=paste("~",my.other.vars[1])
for(i in 2:length(my.other.vars)){
my.formula=paste(my.formula,"*",my.other.vars[i],sep="")}
#### FULL MODEL THING ####
my.full.model=update(new.model,my.formula)
}else{
my.full.model=new.model
}
}
my.full.model.z=summary(my.full.model)$coefficients[1:total.intercepts,3]^2
my.null.model.z=summary(null.model)$coefficients[1:total.intercepts,3]^2
my.full.dev=sum(my.full.model.z)
my.null.dev=sum(my.null.model.z)
my.int.dev.total=abs(total.intercepts*{my.full.dev-my.null.dev})
my.int.dev=summary(new.model)$coefficients[1:total.intercepts,2]^2
vars.df=new.model$edf-null.model$edf
if(vars.df==0){
stop("This is the null model.")
}
vars.dev=summary(new.model)$coefficients[{total.intercepts+1}:dim(summary(new.model)$coefficients)[1],3]^2
vars.dev.p=summary(new.model)$coefficients[{total.intercepts+1}:dim(summary(new.model)$coefficients)[1],4]
vars.dev.df=NULL
weird.var=2
track.var=1
while(weird.var<=dim(new.model$model)[2]){
if(is.factor(new.model$model[[weird.var]])){
vars.dev.df[track.var]=length(levels(new.model$model[[weird.var]]))-1
}else{
vars.dev.df[track.var]=1
}
weird.var=weird.var+1
track.var=track.var+1
}
### interaction effects
weird.var=weird.var+total.intercepts-1
while(weird.var<=dim(summary(new.model)$coefficients)[1]){
vars.dev.df[track.var]=1
weird.var=weird.var+1
track.var=track.var+1
}
vars.df.total=sum(vars.dev.df,na.rm = T)
treat.dev=anova(null.model,new.model)$`LR.stat`[2]
#vars.dev.df=drop1(new.model,test="Chi")$Df[-1]
#vars.dev.p=drop1(new.model,test="Chi")$`Pr(>Chi)`[-1]
#total.dev=-2*{as.integer(levels(null.model$info$logLik)[1])-as.integer(levels(new.model$info$logLik)[1])}
total.dev.change=treat.dev+my.int.dev.total
if(total.intercepts>1){
total.dev.change.df=total.intercepts*vars.df.total
}else{
total.dev.change.df=vars.df.total+total.intercepts
}
resid.dev=-2*new.model$logLik
total.dev=total.dev.change+resid.dev
total.df=dim(myDF)[1]
resid.df=total.df-total.dev.change.df
vars.or=exp(coef(new.model))[{null.model$edf+1}:length(coef(new.model))]
vars.or.confint=exp(confint(new.model,type="Wald"))[{null.model$edf+1}:length(coef(new.model)),]
my.names=abbreviate(names(vars.or),minlength=abbrev.length)
my.int.dev.df=max({total.intercepts*sum(vars.df,na.rm = T)-sum(vars.df,na.rm=T)},1)
my.int.dev.or=exp(coef(new.model))[1:{null.model$edf}]
my.int.dev.or.confint=exp(confint(new.model,type="Wald"))[1:null.model$edf,]
my.int.names=abbreviate(names(my.int.dev.or),minlength=abbrev.length)
} else{
print("Error")
return()
}
if (!VIF & !part.eta) {
v.p.len = 7
v.p.rep = 0
} else if (!VIF) {
v.p.len = 8
v.p.rep = 1
} else if (!part.eta) {
v.p.len = 8
v.p.rep = 1
} else{
v.p.len = 9
v.p.rep = 2
}
#### Make table if not MANOVA ####
my.tables.df = as.data.frame(matrix(ncol = v.p.len, nrow = 1))
if (type == "lm") {
if (!VIF & !part.eta) {
my.std.error = c(my.coefficients$`Std. Error`, NA)
my.estimate = c(my.coefficients$Estimate, NA)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val")
} else if (!part.eta) {
my.VIF = car::vif(my.model)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"VIF")
} else if (!VIF) {
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"p.eta")
} else{
my.VIF = car::vif(my.model)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"p.eta",
"VIF")
}
} else if (type == "glm" & VIF) {
my.VIF = car::vif(my.model)
names(my.tables.df) = c("var",
"p.odd",
"p.odd.2.5",
"p.odd.97.5",
"deviance",
"df",
"p.val",
"VIF")
} else{
names(my.tables.df) = c("var", "p.odd", "p.odd.2.5", "p.odd.97.5", "deviance", "df", "p.val")
}
#### Make the double table entries ####
#### Was very annoying...took my frustration out on
#### Variable names
factor.stupid = NULL
factor.rownames = NULL
num.of.levels = NULL
ord.temp = 0
if (!is.null(my.factor)) {
for (i in 1:length(my.factor)) {
factor.stupid = c(factor.stupid, grep(paste("^", my.factor[i], "$", sep =
""), names(myDF)))
if (type == "lm") {
factor.rownames = c(factor.rownames, grep(
paste("^", my.factor[i], "$", sep = ""),
rownames(my.III.summary)
))
num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# }else if(type=="glm"){
# factor.rownames=c(factor.rownames,{grep(paste("^",my.factor[i],"$",sep=""),rownames(my.III.summary))+1})
# num.of.levels=c(num.of.levels,length(levels(myDF[[factor.stupid[i]]])))
} else{
my.dev.grep=grep(paste("^", my.factor[i], sep = ""),names(new.model$coefficients))[1]
factor.rownames=c(factor.rownames,my.dev.grep)
num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# factor.rownames = c(factor.rownames, {
# grep(paste("^", my.factor[i], "$", sep = ""),
# rownames(my.III.summary)) + total.intercepts + sum(num.of.levels) - ordinal.temp
# })
# num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# ordinal.temp = ordinal.temp + 1
}
}
} else{
factor.rownames = 0L
}
#### Make phia stuff ####
if(show.contrasts){
if(type=="glm" & !is.null(my.factor)){
my.phia.reg=quick.contrast(new.model,skip.me=T,adjustment = adjustment,SS.type = SS.type,abbrev.length = abbrev.length,my.factors = my.factor)
my.phia.rownames=my.phia.reg$names
phia.dev=my.phia.reg$Chisq
}else if(type=="lm" & !is.null(my.factor)){
my.phia.reg=quick.contrast(my.model,skip.me=T,adjustment = adjustment,SS.type = SS.type,abbrev.length = abbrev.length)
my.j=0
my.big.phia=NULL
phia.shift=0
real.shift=0
for(i in 1:dim(my.phia.reg)[1]){
if(!is.na(my.phia.reg[i,1])){
my.j=my.j+1
real.shift=real.shift+phia.shift
my.big.phia[[my.j]]=my.phia.reg[i,2:7]
}else{
my.big.phia[[my.j]][i-real.shift,]=my.phia.reg[i,2:7]
}
phia.shift=phia.shift+1
}
my.phia.rownames=NULL
my.phia.SS=NULL
my.phia.value=NULL
my.phia.F=NULL
my.phia.p=NULL
my.phia.err=NULL
for(i in 1:{my.j}){
my.phia.rownames[[i]]=my.big.phia[[i]]$names
my.phia.SS[[i]]=my.big.phia[[i]]$`Sum of Sq`
my.phia.value[[i]]=my.big.phia[[i]]$Value
my.phia.F[[i]]=my.big.phia[[i]][,5]
my.phia.p[[i]]=my.big.phia[[i]][,6]
}
}else{
}
}
my.factor.var = 1
this.temp.var = 1
this.shift.temp = 1
yet.another.var = 1
my.shift = 0
other.temp = 2
ord.temp = 0
phia.temp=1
if (type == "lm") {
dang.length = length(rownames(my.III.summary))
} else if (type == "glm") {
# dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
# sum(num.of.levels) - length(my.factor)
# }, 0) + 1
dang.length=length(new.model$coefficients)-sum(num.of.levels)+length(num.of.levels)+total.intercepts+1
} else{
# dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
# sum(num.of.levels) - length(my.factor)
# }, 0) + 1
#dang.length=7
dang.length=length(new.model$coefficients)+length(my.factor)+1
}
while (this.shift.temp < dang.length) {
if (is.na(factor.rownames[my.factor.var])) {
my.factor.rownames = 1
} else{
my.factor.rownames = factor.rownames[my.factor.var]
}
#### LOOP ####
if (this.shift.temp == 1) {
i = 1
if(type=="ord" | type=="glm"){
my.tables.df[this.temp.var, ] = c(
"Intercept Change",
NA,
NA,
NA,
my.int.dev.total,
my.int.dev.df,
pchisq(my.int.dev.total,my.int.dev.df,lower.tail = F),
rep(NA, v.p.rep))
this.temp.var=this.temp.var+1
}
while (i <= total.intercepts) {
if (type == "lm") {
my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
my.df = my.III.summary$Df[this.shift.temp]
my.est = my.estimate[this.shift.temp]
my.std.err = my.std.error[this.shift.temp]
my.f.val = my.III.summary$`F value`[this.shift.temp]
my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
my.tables.df[this.temp.var, ] = c(
my.rownames[this.shift.temp],
summary(my.model)[[4]][this.shift.temp,1],
summary(my.model)[[4]][this.shift.temp,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
rep(NA, v.p.rep)
)
} else if(type=="glm"){
#### HERE IS WHERE I LEFT OFF!! ####
# my.or = exp(my.estimate[this.shift.temp])
# my.est = my.estimate[this.shift.temp]
# my.z.val = my.summary$coefficients[this.shift.temp, 3]
# my.std.err = my.std.error[this.shift.temp]
#my.dev=my.III.summary$`LR Chisq`[this.shift.temp]
#my.df=my.III.summary$Df[this.shift.temp]
# my.p.val = my.summary$coefficients[{
# this.shift.temp
#}, 4]
my.tables.df[this.temp.var, ] = c(
paste(names(attr(my.model$model[[i]],"labels"))[1],"-",names(attr(my.model$model[[i]],"labels"))[2],sep=""),
my.int.dev.or[i],
my.int.dev.or.confint[1],
my.int.dev.or.confint[2],
my.int.dev[i],
1,
NA,
rep(NA, v.p.rep))
#this.temp.var=this.temp.var+1
#my.tables.df[this.temp.var,]=c("Treatment",NA,NA,NA,total.dev,total.df,dchisq(total.dev,total.df),rep(NA,v.p.rep))
}else{
my.tables.df[this.temp.var, ] = c(
my.int.names[i],
my.int.dev.or[i],
my.int.dev.or.confint[i,1],
my.int.dev.or.confint[i,2],
my.int.dev[i],
NA,
NA,
rep(NA, v.p.rep))
#this.temp.var=this.temp.var+1
}
this.shift.temp = this.shift.temp + 1
this.temp.var = this.temp.var + 1
i = i + 1
}
if(type=="ord" | type=="glm"){
my.tables.df[this.temp.var,]=c("Treatment Change",NA,NA,NA,treat.dev,vars.df,pchisq(treat.dev,vars.df,lower.tail = F))
this.temp.var=this.temp.var+1
}else{
my.tables.df[this.temp.var,]=c("Treatment",NA,NA,treat.SS,treat.df,glance(my.model)[4],glance(my.model)[5],rep(NA,v.p.rep))
this.temp.var=this.temp.var+1
}
} else if (this.shift.temp == my.factor.rownames) {
if (type == "lm") {
my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
my.df = my.III.summary$Df[this.shift.temp]
my.est = my.estimate[this.shift.temp]
my.std.err = my.std.error[this.shift.temp]
my.z.val = NA
my.f.val = my.III.summary$`F value`[this.shift.temp]
my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
if(!VIF & !part.eta){
my.tables.df[this.temp.var, ] = c(
my.factor[yet.another.var],
NA,
NA,
my.sumsq,
my.df,
my.f.val,
my.p.val,
rep(NA, v.p.rep)
)
}else if(part.eta & !VIF){
my.p.eta = my.sumsq / my.total
my.tables.df[this.temp.var, ] = c(
my.factor[yet.another.var],
NA,
NA,
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.p.eta
)
}else if(!part.eta & VIF){
my.tables.df[this.temp.var, ] = c(
my.factor[yet.another.var],
NA,
NA,
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.VIF[this.shift.temp - 1, 1]
)
}else{
my.p.eta = my.sumsq / my.total
my.tables.df[this.temp.var, ] = c(
my.factor[yet.another.var],
NA,
NA,
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.p.eta,
my.VIF[this.shift.temp - 1, 1]
)
}
}else{
# my.or = NA
# my.est = NA
# my.std.err = NA
# my.dev = my.III.summary$`LR Chisq`[ord.temp]
# my.df = my.III.summary$Df[ord.temp]
# my.p.val = my.III.summary$`Pr(>Chisq)`[ord.temp]
my.tables.df[this.temp.var,]=c(my.factor[this.shift.temp-1],
NA,
NA,
NA,
drop1(new.model,test="Chi")$`LRT`[this.shift.temp],
drop1(new.model,test="Chi")$`Df`[this.shift.temp],
drop1(new.model,test="Chi")$`Pr(>Chi)`[this.shift.temp],
rep(NA,v.p.rep))
#
# my.tables.df[this.temp.var, ] = c(my.factor[yet.another.var],
# my.or,
# my.est,
# my.std.err,
# my.dev,
# my.df,
# my.p.val)
#ord.temp = ord.temp + 1
}
# else{
# my.or = NA
# my.est = NA
# my.std.err = NA
# my.dev = my.III.summary$Chisq[ord.temp]
# my.df = my.III.summary$Df[ord.temp]
# my.p.val = my.III.summary$`Pr(>Chisq)`[ord.temp]
# my.tables.df[this.temp.var, ] = c(my.factor[yet.another.var],
# my.or,
# my.est,
# my.std.err,
# my.dev,
# my.df,
# my.p.val)
# ord.temp = ord.temp + 1
#
# }
yet.another.var = yet.another.var + 1
this.temp.var = this.temp.var + 1
this.shift.temp = this.shift.temp + 1
if(type=="lm"){
other.other.temp = 2
}else{
other.other.temp=1
}
#### INTERACTION EFFECTS? NOT WORRIED YET ####
if ({length(grepl(":", my.summary$coefficients[other.temp, 1])) >
0}) {
#### I think the while should not be +1
if(type=="glm" | type=="ord"){
num.of.levels[my.factor.var]=num.of.levels[my.factor.var]-1
}
while (other.other.temp < {
num.of.levels[my.factor.var] + 1
}) {
if (type == "lm") {
if(show.contrasts){
my.sumsq = NA
my.df = NA
my.est = my.estimate[other.temp]
my.std.err = my.std.error[other.temp]
#### NEED TO FIX ####
my.f.val = {
my.summary$coefficients[other.temp, 3] ^ 2
}
my.p.val = my.summary$coefficients[other.temp, 4]
my.tables.df[this.temp.var, ] = c(
my.phia.rownames[[phia.temp]][other.temp-1],
my.phia.value[[phia.temp]][other.temp-1],
my.est,
my.phia.SS[[phia.temp]][other.temp-1],
1,
my.phia.F[[phia.temp]][other.temp-1],
my.phia.p[[phia.temp]][other.temp-1],
rep(NA, v.p.rep)
)
}
ord.temp=ord.temp + 1
} else if (type == "glm") {
# my.or = exp(my.estimate[other.temp])
# my.est = my.estimate[other.temp]
# my.std.err = my.std.error[other.temp]
# my.z.val = my.summary$coefficients[other.temp, 3]
# my.dev = my.III.summary$`LR Chisq`[other.temp]
# my.df = my.III.summary$Df[other.temp]
# my.p.val = my.summary$coefficients[other.temp, 4]
# my.tables.df[this.temp.var, ] = c(my.rownames[other.temp],
# my.or,
# my.std.err,
# my.z.val,
# NA,
# NA,
# my.p.val)
if(show.contrasts){
my.tables.df[this.temp.var, ] = c(my.phia.rownames[other.temp-1],
vars.or[other.temp-1],
vars.or.confint[other.temp-1,1],
vars.or.confint[other.temp-1,2],
my.phia[other.temp-1,3],
my.phia[other.temp-1,4],
my.phia[other.temp-1,6],rep(NA,v.p.rep))
#this.shift.temp = this.shift.temp + 1
}
ord.temp=ord.temp + 1
} else{
my.or = exp(my.estimate[other.temp + total.intercepts - 1])
my.est = my.estimate[other.temp + total.intercepts - 1]
my.std.err = my.std.error[other.temp + total.intercepts -
1]
my.z.val = my.summary$coefficients[{
other.temp + total.intercepts - 1
}, 3]
#my.dev=my.III.summary$Chisq[other.temp]
#my.df=my.III.summary$Df[other.temp]
my.p.val = my.summary$coefficients[{
other.temp + total.intercepts - 1
}, 4]
if (!VIF) {
my.tables.df[this.temp.var, ] = c(my.rownames[other.temp +
total.intercepts - 1],
my.or,
my.std.err,
my.z.val,
NA,
NA,
my.p.val)
} else{
my.tables.df[this.temp.var, ] = c(my.rownames[other.temp +
total.intercepts - 1],
my.or,
my.std.err,
my.z.val,
NA,
NA,
my.p.val,
my.VIF[this.shift.temp - 1, 1])
}
this.shift.temp = this.shift.temp + 1
}
this.temp.var = this.temp.var + 1
other.temp = other.temp + 1
other.other.temp = other.other.temp + 1
the.length = the.length + 1
}
phia.temp=phia.temp+1
if(!show.contrasts){this.temp.var=this.temp.var-ord.temp}
} else{
}
if (my.factor.var == 1) {
my.shift = {
my.shift + other.temp - 2
}
} else{
my.shift = my.shift + other.temp
}
my.factor.var = my.factor.var + 1
} else{
if (type == "lm") {
my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
my.df = my.III.summary$Df[this.shift.temp]
my.est = my.estimate[this.shift.temp]
my.std.err = my.std.error[this.shift.temp]
my.f.val = my.III.summary$`F value`[this.shift.temp]
my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
if (!VIF & !part.eta) {
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[this.shift.temp],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
my.sumsq,
my.df,
my.f.val,
my.p.val
)
} else if (!part.eta) {
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[this.shift.temp],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.VIF[this.shift.temp - 1, 1]
)
} else if (!VIF) {
my.p.eta = my.sumsq / my.total
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[this.shift.temp],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.p.eta
)
} else{
my.p.eta = my.sumsq / my.total
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[this.shift.temp],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.p.eta,
my.VIF[this.shift.temp - 1, 1]
)
}
ord.temp=ord.temp+1
} else if (type == "glm") {
# if (!is.null(my.factor)) {
# this.shift.temp = this.shift.temp - ord.temp-1
# }
# my.or = exp(my.estimate[this.shift.temp])
# my.est = my.estimate[this.shift.temp]
# my.std.err = my.std.error[this.shift.temp]
# my.z.val = my.summary$coefficients[other.temp, 3]
# my.dev = my.III.summary$`LR Chisq`[ord.temp]
# my.df = my.III.summary$Df[ord.temp]
# my.p.val = my.III.summary$`Pr(>Chisq)`[ord.temp]
if (!VIF) {
# my.tables.df[this.temp.var, ] = c(my.rownames[this.shift.temp],
# my.or,
# my.std.err,
# NA,
# my.dev,
# my.df,
# my.p.val)
my.tables.df[this.temp.var,]=c(names(vars.or)[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
vars.or[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},1],
vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},2],
vars.dev[this.shift.temp],
vars.dev.df[this.shift.temp],
vars.dev.p[this.shift.temp],rep(NA,v.p.len))
} else{
my.tables.df[this.temp.var, ] = c(names(vars.or)[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
vars.or[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},1],
vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},2],
vars.dev[this.shift.temp],
vars.dev.df[this.shift.temp],
vars.dev.p[this.shift.temp],
rep(NA,v.p.len))
}
# if (!is.null(my.factor)) {
# this.shift.temp = this.shift.temp + ord.temp+1
# }
yet.another.var=yet.another.var+1
ord.temp = ord.temp + 1
} else{
if (!is.null(my.factor)) {
this.shift.temp = this.shift.temp - ord.temp
}else{
this.shift.temp=this.shift.temp-total.intercepts
}
my.tables.df[this.temp.var, ] = c(names(vars.or)[this.shift.temp],
vars.or[{this.shift.temp}],
if(!is.null(dim(vars.or.confint))){
vars.or.confint[{this.shift.temp},1]
}else{
vars.or.confint[1]
},
if(!is.null(dim(vars.or.confint))){
vars.or.confint[{this.shift.temp},2]
}else{
vars.or.confint[2]
},
vars.dev[this.shift.temp-ord.temp],
vars.dev.df[this.shift.temp-ord.temp],
vars.dev.p[this.shift.temp-ord.temp],
rep(NA,v.p.len))
if (!is.null(my.factor)) {
this.shift.temp = this.shift.temp + ord.temp
}else{
this.shift.temp=this.shift.temp+total.intercepts
}
#ord.temp = ord.temp + 1
}
this.shift.temp = this.shift.temp + 1
this.temp.var = this.temp.var + 1
}
}
if (type == "lm") {
my.tables.df[this.temp.var, ] = c(
"Residuals",
NA,
NA,
my.III.summary$`Sum Sq`[this.shift.temp],
my.III.summary$Df[this.shift.temp],
NA,
NA,
rep(NA, v.p.rep)
)
my.tables.df[this.temp.var + 1, ] = c("Total Change",
NA,
NA,
my.total,
my.df.total,
rep(NA, v.p.rep + 2))
my.tables.df[this.temp.var+2,]=c("Total SS",NA,NA,my.total+my.III.summary$`Sum Sq`[1],my.df.total+1,rep(NA,v.p.rep+2))
if (!VIF & !part.eta) {
} else if (!VIF) {
my.tables.df$p.eta = as.numeric(my.tables.df$p.eta)
} else if (!part.eta) {
my.tables.df$VIF = as.numeric(my.tables.df$VIF)
} else{
my.tables.df$p.eta = as.numeric(my.tables.df$p.eta)
my.tables.df$VIF = as.numeric(my.tables.df$VIF)
}
my.tables.df$f.val = as.numeric(my.tables.df$f.val)
my.tables.df$est = as.numeric(my.tables.df$est)
my.tables.df$sumsq = as.numeric(my.tables.df$sumsq)
my.tables.df$df = as.numeric(my.tables.df$df)
my.tables.df$std.err = as.numeric(my.tables.df$std.err)
my.tables.df$p.val = as.numeric(my.tables.df$p.val)
} else{
my.tables.df[this.temp.var,]=c("Total Change",NA,NA,NA,total.dev.change,total.dev.change.df,pchisq(total.dev.change,total.dev.change.df,lower.tail = F),rep(NA,v.p.rep))
my.tables.df[this.temp.var+1,]=c("Residuals",NA,NA,NA,resid.dev,resid.df,NA,rep(NA,v.p.rep))
my.tables.df[this.temp.var+2,]=c("Total",NA,NA,NA,total.dev,total.df,NA,rep(NA,v.p.rep))
#my.tables.df[this.temp.var,] = c("Change from Null", NA, NA, NA, ddeviance2, ddf2, fit2)
my.tables.df$p.odd = as.numeric(my.tables.df$p.odd)
my.tables.df$p.odd.2.5 = as.numeric(my.tables.df$p.odd.2.5)
my.tables.df$p.odd.97.5 = as.numeric(my.tables.df$p.odd.97.5)
my.tables.df$deviance=as.numeric(my.tables.df$deviance)
my.tables.df$p.val=as.numeric(my.tables.df$p.val)
if (VIF) {
my.tables.df$VIF = as.numeric(my.tables.df$VIF)
}
}
#### Make custom glance stats ####
if(do.glance){
#### Can eventually make it options
if (type == "lm") {
glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
glance_stats[1,]=c(paste("Method: ","QR Decomposition",if(show.contrasts){paste("<br />Adjustment Method: ",adjustment,sep="")},sep=""),rep(NA,6),rep(NA,v.p.rep))
# glance_stats = broom::glance(my.model)
# glance_stats = tidyr::gather(glance_stats)
#
#
# glance_stats[3:{
# 5 + v.p.rep
# }] = NA
# glance_stats[6 + v.p.rep] = c(glance_stats$key[7:8],
# NA,
# glance_stats$key[9:11],
# NA,
# NA,
# NA,
# NA,
# NA)
# glance_stats[7 + v.p.rep] = c(glance_stats$value[7:8],
# NA,
# glance_stats$value[9:11],
# NA,
# NA,
# NA,
# NA,
# NA)
# glance_stats = glance_stats[-7:-11, ]
# glance_stats = glance_stats[-3, ]
# glance_stats = glance_stats[-5, ]
} else if (type == "glm") {
glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
glance_stats[1,]=c(paste("Family: ",new.model$family$family," <br /> Link: ",new.model$family$link,if(show.contrasts){paste(" <br />Adjustment: ",adjustment,sep="")},sep=""),rep(NA,6),rep(NA,v.p.rep))
# glance_stats = broom::glance(my.model)
# glance_stats = tidyr::gather(glance_stats)
#
#
# glance_stats[[3]] = c("Family: ", "Link: ", rep(NA, 5))
# glance_stats[[4]] = c(my.model$family$family,
# my.model$family$link,
# rep(NA, 5))
# glance_stats[5:{
# 5 + v.p.rep
# }] = NA
# glance_stats[6 + v.p.rep] = c(glance_stats$key[3:6], NA, NA, NA)
# glance_stats[7 + v.p.rep] = c(glance_stats$value[3:6], NA, NA, NA)
# glance_stats[[1]] = c("Null.dev", "Chi-Sq", "dF", "Pr(>Chisq)", NA, NA, NA)
# glance_stats[[2]] = c(
# glance_stats$value[1],
# ddeviance2,
# ddf2,
# pvalString(fit2, digits = 3, format = "default"),
# NA,
# NA,
# NA
# )
# glance_stats = glance_stats[-5:-7, ]
# glance_stats2=as.data.frame(matrix(ncol=7,nrow=1))
# glance_stats2[1,]=c(glance_stats$key[1],glance_stats$value[1],NA,NA,NA,glance_stats$key[6],glance_stats$value[6])
# glance_stats2[2,]=c(glance_stats$key[2],glance_stats$value[2],NA,NA,NA,glance_stats$key[7],glance_stats$value[7])
# glance_stats2[3,]=c(glance_stats$key[3],glance_stats$value[3],NA,NA,NA,glance_stats$key[4],glance_stats$value[4])
# glance_stats=glance_stats2
} else if(type=="ord"){
glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
glance_stats[1,]=c(paste("Family: Ordinal <br /> Link: ",new.model$info$link,sep=""),rep(NA,6),rep(NA,v.p.rep))
# glance_stats[[1]]=c("Null.dev","Chi-Sq","dF","Pr(>Chisq)")
# glance_stats[[2]]=c({{-2*my.model$logLik}-ddeviance2},ddeviance2,ddf2,pvalString(fit2, digits = 3, format = "default"))
# glance_stats[[3]]=c("Family: ","Link: ",NA,NA)
# glance_stats[[4]]=c("ordinal",levels(my.model$info$link),NA,NA)
# glance_stats[[5:{5+v.p.rep}]]=c(NA,NA,NA,NA)
# glance_stats[[{6+v.p.rep}]]=c("logLik","AIC","BIC","deviance")
# glance_stats[[{7+v.p.rep}]]=c(my.model$logLik,as.numeric(levels(my.model$info$AIC)),BIC(my.model),{-2*my.model$logLik})
}else{
}
}
#### For total
the.length = the.length + 1
this.temp.var = this.temp.var + 1
#### Make table ####
options(pixie_interactive = pix.int,
pixie_na_string = "")
if (type == "lm") {
the.length=the.length+2
my.dust = pixiedust::dust(my.tables.df) %>%
sprinkle(cols = "p.val", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle_print_method(pix.method) %>%
sprinkle_na_string() %>%
sprinkle(
rows = 1:the.length,
cols = v.p.len,
border = "right",
border_color = "black"
) %>%
sprinkle(
rows = 1:the.length,
cols = 1,
border = "left",
border_color = "black"
) %>%
sprinkle(
rows=2,
cols=1:v.p.len,
border=c("top","bottom")
)%>%
sprinkle(rows=this.temp.var-1,
border="top")%>%
sprinkle(
rows = 1,
cols = 1:v.p.len,
border = c("top", "bottom"),
border_color = "black",
part = "head"
) %>%
sprinkle(rows=1,cols=1,border="left",part="head")%>%
sprinkle(rows=1,cols=v.p.len,border="right",part="head")%>%
sprinkle(
rows = this.temp.var+1,
cols = 1:v.p.len,
border = "bottom",
border_color = "black"
)
if (!VIF & !part.eta) {
my.dust = my.dust %>%
sprinkle(cols = c("sumsq", "est", "std.err", "f.val"),
round = 2) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
sprinkle_colnames(
"Variable",
"Estimate",
"Std. Error",
paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
"df",
"F-value",
"Pr(>F)"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head") %>%
sprinkle_pad(rows = 1,
pad = 5,
part = "head")
} else if (!VIF) {
my.dust = my.dust %>%
sprinkle(
cols = c("sumsq", "est", "std.err", "f.val", "p.eta"),
round = 2
) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
sprinkle_colnames(
"Variable",
"Estimate",
"Std. Error",
paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
"df",
"F-value",
"Pr(>F)",
"Part <br /> eta"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head") %>%
sprinkle_pad(rows = 1,
pad = 5,
part = "head")
} else if (!part.eta) {
my.dust = my.dust %>%
sprinkle(
cols = c("sumsq", "est", "std.err", "f.val", "VIF"),
round = 2
) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
sprinkle_colnames(
"Variable",
"Estimate",
"Std. Error",
paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
"df",
"F-value",
"Pr(>F)",
"VIF"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head") %>%
sprinkle_pad(rows = 1,
pad = 5,
part = "head")
} else{
my.dust = my.dust %>%
sprinkle(
cols = c("sumsq", "est", "std.err", "f.val", "p.eta", "VIF"),
round = 2
) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
sprinkle_colnames(
"Variable",
"Estimate",
"Std. Error",
paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
"df",
"F-value",
"Pr(>F)",
"Part <br /> eta",
"VIF"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head") %>%
sprinkle_pad(rows = 1,
pad = 5,
part = "head")
}
} else if (type == "glm2") {
my.dust = pixiedust::dust(my.tables.df) %>%
sprinkle(cols = "p.val", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle_print_method(pix.method) %>%
sprinkle_na_string() %>%
sprinkle(cols = 2:5, round = 2) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
sprinkle(cols = 2:{7+v.p.rep},
pad = 5,
part = "head") %>%
sprinkle(
rows = {
this.temp.var - 2
},
cols = 1:v.p.len,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 1:the.length,
cols = 1:v.p.len,
border = "right",
border_color = "black"
) %>%
sprinkle(
rows = 1:the.length,
cols = 1,
border = "left",
border_color = "black"
) %>%
sprinkle(
rows = 1,
cols = 1:v.p.len,
border = c("top", "bottom", "left", "right"),
border_color = "black",
part = "head"
) %>%
sprinkle(
rows = this.temp.var - 1,
cols = 1:v.p.len,
border = "bottom",
border_color = "black"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head")
if (!VIF) {
my.dust = my.dust %>% sprinkle_colnames("Variable",
"Odds Ratio",
"Std. Error",
"z-Value",
"Deviance",
"df",
"p-Value")
} else{
my.dust = my.dust %>% sprinkle_colnames(
"Variable",
"Odds Ratio",
"Std. Error",
"z-Value",
"Deviance",
"df",
"p-Value",
"VIF"
) %>%
sprinkle_round(cols = 8, round = 2)
}
} else{
my.dust = pixiedust::dust(my.tables.df) %>%
sprinkle(cols = "p.val", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle_print_method(pix.method) %>%
sprinkle_na_string() %>%
sprinkle(cols = 2:5, round = 2) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
sprinkle(cols = 2:7,
pad = 5,
part = "head") %>%
sprinkle(
rows = this.temp.var+1,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 1,
cols = 1:2,
border = c("top", "bottom"),
border_color = "black",
part = "head"
) %>%
sprinkle(
rows = 1,
cols = 3:4,
border = c("top", "bottom"),
border_color = "black",
part = "head"
) %>%
sprinkle(
rows = 1,
cols = 5:{7+v.p.rep},
border = c("top", "bottom"),
border_color = "black",
part = "head"
) %>%
sprinkle(
rows = this.temp.var - 1,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 1,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = this.temp.var - 2,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 1+total.intercepts,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 2+total.intercepts,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle_colnames("Variable",
"Odds Ratio",
"Conf. <br /> 2.5%","Int. <br /> 97.5%",
"Deviance",
"df",
"Pr(>Chi)")%>%
sprinkle_border(cols=1,border=c("left","right"))%>%
sprinkle_border(cols={7+v.p.rep},border="right")%>%
sprinkle_border(cols=1,border=c("left","right"),part="head")%>%
sprinkle_border(cols={7+v.p.rep},border="right",part="head")
}
if(do.glance){
if (type == "lm") {
my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
sprinkle_na_string(part = "foot") %>%
sprinkle(rows=1,merge=T,halign="center",part="foot")
# my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
# sprinkle(cols = c(2, {
# 7 + v.p.rep
# }),
# round = 3,
# part = "foot") %>%
# sprinkle(cols = 3:{
# 5 + v.p.rep
# },
# replace = c(rep("", {
# 12 + 4 * v.p.rep
# })),
# part = "foot") %>%
# sprinkle(
# cols = 1,
# replace = c("R-Square", "Adj R-Sq", "F-Statistic", "P-Value"),
# part = "foot"
# ) %>%
# sprinkle(
# cols = 2,
# rows = 4,
# fn = quote(pvalString(
# value, digits = 3, format = "default"
# )),
# part = "foot"
# ) %>%
# sprinkle(
# cols = 1:v.p.len,
# rows = 1,
# halign = "center",
# part = "head"
# ) %>%
# sprinkle_width(cols = 1,
# width = 90,
# width_units = "pt") %>%
# sprinkle_width(cols = 2,
# width = 108,
# width_units = "pt") %>%
# sprinkle_width(cols = 4,
# width = 62,
# width_units = "pt") %>%
# sprinkle_width(cols = 5,
# width = 68,
# width_units = "pt") %>%
# sprinkle_width(cols = 6,
# width = 68,
# width_units = "pt") %>%
# sprinkle_width(cols = 7,
# width = 71,
# width_units = "pt") %>%
# sprinkle(cols = 2,
# halign = "left",
# part = "foot")
} else{
my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
sprinkle_na_string(part = "foot") %>%
sprinkle(rows=1,merge=T,halign="center",part="foot")
}
}
if (pix.int) {
return(my.dust)
} else{
my.dust.print = print(my.dust, quote = F)[1]
return(my.dust.print)
}
}
#' PixieDust Tables for Lavaan
#'
#' Places html tables in viewer for different areas of Lavaan output
#' for SEM. Based on NIU class 11/2017
#'
#' @param myfit Fit from Lavaan package
#' @return NULL
#' @keywords Explore
#' @export
#' @examples
#' quick.lavaan(myfit)
quick.lavaan = function(myfit) {
require(lavaan)
require(pixiedust)
require(tibble)
prev.width = getOption("width")
options(width = 80)
mysummary = capture.output(lavaan::summary(myfit,
standardized = TRUE, rsq = T))
#### Fit Table ####
my.fit.table = as.data.frame(matrix(nrow = 7, ncol = 4))
my.fit.table[1, ] = c("Number of Iterations",
lavInspect(myfit, what = "iterations"),
NA,
NA)
my.fit.table[2, ] = c("Total Observations",
lavInspect(myfit, what = "ntotal"),
NA,
NA)
my.fit.table[3, ] = c(
"Chi-Sq Test of Fit",
round(fitMeasures(myfit)[3], 2),
fitMeasures(myfit)[4],
pixiedust::pval_string(fitMeasures(myfit)[5])
)
my.fit.table[4, ] = c("Comparitive Fit Index", round(fitMeasures(myfit)[9], 3), NA, NA)
my.fit.table[5, ] = c("Tucker-Lewis Index", round(fitMeasures(myfit)[10], 3), NA, NA)
my.fit.table[6, ] = c("RMSEA",
round(fitMeasures(myfit)[23], 3),
NA,
pixiedust::pval_string(fitMeasures(myfit)[26]))
my.fit.table[7, ] = c("SRMR", round(fitMeasures(myfit)[29], 3), NA, NA)
colnames(my.fit.table) = c("Name", "Value", "df", "p-val")
#my.fit.table
#### R Sq ####
my.r2 = lavaan::lavInspect(myfit, what = "r2")
my.r2 = as.data.frame(my.r2)
my.r2 = tibble::rownames_to_column(my.r2)
my.r2$my.r2 = round(my.r2$my.r2, 3)
#my.r2
#### Other tables ####
lavaan.latent = NULL
lavaan.covar = NULL
lavaan.vari = NULL
lavaan.reg = NULL
lavaan.latent.temp = 1
lavaan.temp = 1
while (lavaan.temp < {
length(mysummary) + 1
}) {
### Latent Variables matrix
if (length(grep("Latent Variables:", mysummary[lavaan.temp])) > 0) {
lavaan.temp = lavaan.temp + 2
while ((length(grep("Covariances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("Regressions:", mysummary[lavaan.temp])) == 0) &&
(length(grep("Variances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("R-Square:", mysummary[lavaan.temp])) == 0)) {
str.temp = strsplit(mysummary[lavaan.temp], " ")
if (length(str.temp[[1]]) > 0) {
lavaan.latent[[lavaan.latent.temp]] = list()
for (i in 1:(length(str.temp[[1]]))) {
if (nchar(str.temp[[1]][i]) > 0) {
lavaan.latent[[lavaan.latent.temp]] = c(lavaan.latent[[lavaan.latent.temp]], str.temp[[1]][i])
}
}
lavaan.latent.temp = lavaan.latent.temp + 1
}
lavaan.temp = lavaan.temp + 1
}
if (length(grep("Regressions:", mysummary[lavaan.temp])) != 0) {
#### Regressions Matrix
lavaan.temp = lavaan.temp + 2
lavaan.latent.temp = 1
while ((length(grep("Covariances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("Variances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("R-Square:", mysummary[lavaan.temp])) == 0)) {
str.temp = strsplit(mysummary[lavaan.temp], " ")
if (length(str.temp[[1]]) > 0) {
lavaan.reg[[lavaan.latent.temp]] = list()
for (i in 1:(length(str.temp[[1]]))) {
if (nchar(str.temp[[1]][i]) > 0) {
lavaan.reg[[lavaan.latent.temp]] = c(lavaan.reg[[lavaan.latent.temp]], str.temp[[1]][i])
}
}
lavaan.latent.temp = lavaan.latent.temp + 1
}
lavaan.temp = lavaan.temp + 1
}
}
#### Covariance matrix
if ((length(grep("Covariances:", mysummary[lavaan.temp])) != 0)) {
lavaan.temp = lavaan.temp + 2
lavaan.latent.temp = 1
while ((length(grep("Variances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("R-Square:", mysummary[lavaan.temp])) == 0)) {
str.temp = strsplit(mysummary[lavaan.temp], " ")
if (length(str.temp[[1]]) > 0) {
lavaan.covar[[lavaan.latent.temp]] = list()
for (i in 1:(length(str.temp[[1]]))) {
if (nchar(str.temp[[1]][i]) > 0) {
lavaan.covar[[lavaan.latent.temp]] = c(lavaan.covar[[lavaan.latent.temp]], str.temp[[1]][i])
}
}
lavaan.latent.temp = lavaan.latent.temp + 1
}
lavaan.temp = lavaan.temp + 1
}
}
#### Variance matrix
if ((length(grep("Variances:", mysummary[lavaan.temp])) != 0)) {
lavaan.temp = lavaan.temp + 2
lavaan.latent.temp = 1
while (length(grep("R-Square:", mysummary[lavaan.temp])) == 0) {
str.temp = strsplit(mysummary[lavaan.temp], " ")
if (length(str.temp[[1]]) > 0) {
lavaan.vari[[lavaan.latent.temp]] = list()
for (i in 1:(length(str.temp[[1]]))) {
if (nchar(str.temp[[1]][i]) > 0) {
lavaan.vari[[lavaan.latent.temp]] = c(lavaan.vari[[lavaan.latent.temp]], str.temp[[1]][i])
}
}
lavaan.latent.temp = lavaan.latent.temp + 1
}
lavaan.temp = lavaan.temp + 1
}
}
}
lavaan.temp = lavaan.temp + 1
}
#### lavaan.covar
if (length(lavaan.covar) > 0) {
covar.table = as.data.frame(matrix(nrow = length(lavaan.covar), ncol = 7))
table.count = 1
for (i in 1:length(lavaan.covar)) {
if (length(lavaan.covar[[i]]) < 7) {
covar.table[table.count, ] = c(lavaan.covar[[i]][1], NA, NA, NA, NA, NA, NA)
}
else{
covar.table[table.count, ] = c(
lavaan.covar[[i]][1],
lavaan.covar[[i]][2],
lavaan.covar[[i]][3],
lavaan.covar[[i]][6],
lavaan.covar[[i]][7],
lavaan.covar[[i]][4],
lavaan.covar[[i]][5]
)
}
table.count = table.count + 1
}
colnames(covar.table) = c("Name",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)")
#covar.table
} else{
covar.table = NULL
}
#### lavaan.latent
if (length(lavaan.latent) > 0) {
latent.table = as.data.frame(matrix(nrow = length(lavaan.latent), ncol =
7))
latent.table.count = 1
for (i in 1:length(lavaan.latent)) {
if (length(lavaan.latent[[i]]) < 3) {
latent.table[latent.table.count, ] = c(lavaan.latent[[i]][[1]], NA, NA, NA, NA, NA, NA)
} else if (length(lavaan.latent[[i]]) == 4) {
latent.table[latent.table.count, ] = c(
lavaan.latent[[i]][[1]],
lavaan.latent[[i]][[2]],
NA,
lavaan.latent[[i]][[3]],
lavaan.latent[[i]][[4]],
NA,
NA
)
} else{
latent.table[latent.table.count, ] = c(
lavaan.latent[[i]][[1]],
lavaan.latent[[i]][[2]],
lavaan.latent[[i]][[3]],
lavaan.latent[[i]][[6]],
lavaan.latent[[i]][[7]],
lavaan.latent[[i]][[4]],
lavaan.latent[[i]][[5]]
)
}
latent.table.count = latent.table.count + 1
}
colnames(latent.table) = c("Name",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)")
#latent.table
} else{
latent.table = NULL
}
#### lavaan.vari
if (length(lavaan.vari) > 0) {
vari.table = as.data.frame(matrix(nrow = length(lavaan.vari), ncol = 7))
vari.table.count = 1
for (i in 1:length(lavaan.vari)) {
vari.table[vari.table.count, ] = c(
lavaan.vari[[i]][[1]],
lavaan.vari[[i]][[2]],
lavaan.vari[[i]][[3]],
lavaan.vari[[i]][[6]],
lavaan.vari[[i]][[7]],
lavaan.vari[[i]][[4]],
lavaan.vari[[i]][[5]]
)
vari.table.count = vari.table.count + 1
}
colnames(vari.table) = c("Name",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)")
} else{
vari.table = NULL
}
#### lavaan.reg
if (length(lavaan.reg) > 0) {
reg.table = as.data.frame(matrix(nrow = length(lavaan.reg), ncol = 7))
reg.table.count = 1
for (i in 1:length(lavaan.reg)) {
if (length(lavaan.reg[[i]]) < 7) {
reg.table[reg.table.count, ] = c(lavaan.reg[[i]][1], NA, NA, NA, NA, NA, NA)
}
else{
reg.table[reg.table.count, ] = c(
lavaan.reg[[i]][1],
lavaan.reg[[i]][2],
lavaan.reg[[i]][3],
lavaan.reg[[i]][6],
lavaan.reg[[i]][7],
lavaan.reg[[i]][4],
lavaan.reg[[i]][5]
)
}
reg.table.count = reg.table.count + 1
}
colnames(reg.table) = c("Name",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)")
} else{
reg.table = NULL
}
dusted.fit.table = pixiedust::dust(my.fit.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("", "Value", "df", "P-Val") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.fit.table
latent.table$`P(>|z|)` = as.numeric(latent.table$`P(>|z|)`)
dusted.latent.table = pixiedust::dust(latent.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("Latent",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle(cols = 7, fn = quote(pvalString(value))) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.latent.table
if (length(reg.table) > 0) {
reg.table$`P(>|z|)` = as.numeric(reg.table$`P(>|z|)`)
dusted.reg.table = pixiedust::dust(reg.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("Regression",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle(cols = 7, fn = quote(pvalString(value))) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.reg.table
} else{
dusted.reg.table = NULL
}
if (length(covar.table) > 0) {
covar.table$`P(>|z|)` = as.numeric(covar.table$`P(>|z|)`)
dusted.covar.table = pixiedust::dust(covar.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("Covariance",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle(cols = 7, fn = quote(pvalString(value))) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.covar.table
} else{
dusted.covar.table = NULL
}
if (length(vari.table)) {
vari.table$`P(>|z|)` = as.numeric(vari.table$`P(>|z|)`)
dusted.vari.table = pixiedust::dust(vari.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("Variance",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle(cols = 7, fn = quote(pvalString(value))) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.vari.table
} else{
dusted.vari.table = NULL
}
dusted.r2 = pixiedust::dust(my.r2) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle_align(halign = "center", part = "head") %>%
pixiedust::sprinkle_colnames("Variable", "R^2")
#dusted.r2
# mydustlist=list(dusted.fit.table,dusted.latent.table)
# if(!is.null(dusted.reg.table)){
# mydustlist=list(mydustlist,dusted.reg.table)
# }
# if(!is.null(dusted.covar.table)){
# mydustlist=list(mydustlist,dusted.covar.table)
# }
# if(!is.null(dusted.vari.table)){
# mydustlist=list(mydustlist,dusted.vari.table)
# }
# mydustlist=list(mydustlist,dusted.r2)
mydustlist = list(
dusted.fit.table,
dusted.latent.table,
dusted.reg.table,
dusted.covar.table,
dusted.vari.table,
dusted.r2
)
#print(mydustlist[[1]])
dust.num = 1
while (dust.num < {
length(mydustlist) + 1
}) {
print(mydustlist[[dust.num]])
x = readline(prompt = "Press z and enter to go back, or enter to go forward\n")
if (x != "z") {
dust.num = dust.num + 1
} else{
if (dust.num > 1) {
dust.num = dust.num - 1
}
}
}
options(width = prev.width)
}
ckraner/quick.tasks documentation built on May 24, 2019, 5:02 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
###################### QUICK.TABLES ########################
#################### BY CHRIS KRANER #######################
############## NORTHERN ILLINOIS UNIVERSITY ################
######################## 12/2017 ###########################
############################################################
#' Partial contrasts for ANOVA and MANOVA tables
#'
#' Adds Contrasts and Latent Contrasts to nested table
#' for creating overall MANOVA and ANOVA tables.
#'
#' @param my.nested.table Model.
#' @param SS.type Type of sums of squares to report, either 2 or 3. (default = 3)
#' @param adjustment P-value adjustment sent to p.adjust (default = "bonferroni")
#' @param abbrev.length Integer telling how long of a label is too long. Longer than this and will be condensed (default=15)
#' @return Nested table with added rows and information (Unfortunately as character at the moment)
#' @keywords Explore
#' @examples
#' quick.contrast(my.nested.table)
quick.part.cont=function(my.nested.table,SS.type=2,adjustment="bonferroni",abbrev.length=30,latent.cont=F){
#### Get y levels
my.y.levels=dim(my.nested.table[1,4][[1]][[1]])[1]
#### Put in NA rows
temp.colnames=colnames(my.nested.table)
my.nested.table2=cbind(my.nested.table,as.matrix(c(rep(NA,dim(my.nested.table)[1]))))
if(latent.cont){
my.nested.table2=cbind(my.nested.table2,as.matrix(c(rep(NA,dim(my.nested.table)[1]))))
colnames(my.nested.table2)=c(temp.colnames,"Contrasts","Latent Contrasts")
}else{
colnames(my.nested.table2)=c(temp.colnames,"Contrasts")
}
#### For each row besides the null model
for(p in 2:dim(my.nested.table)[1]){
#### Check if should have contrast
if(!is.na(my.nested.table[p,7][[1]]) & my.nested.table[p,7][[1]]>1){
#### If should, make contrast
my.new.df=my.nested.table[p,3][[1]]$model
my.MSE=mean(my.nested.table[p,3][[1]]$residuals^2)
if(SS.type==3){
the.resid.SS=sum(diag(my.nested.table[p,4][[1]][[2]]))
}else{
the.resid.SS=sum(diag(my.nested.table[p,4][[1]][[2]][[1]]))
}
the.resid.df=my.nested.table[p,3][[1]]$df.residual
if(latent.cont){
#### Latent Mean Square Error
my.latent.MSE=NULL
for(j in 1:my.y.levels){
if(i==1){
my.latent.MSE=as.numeric(mean(my.contrast.model$residuals[i]^2))
}else{
my.latent.MSE=c(my.latent.MSE,as.numeric(mean(my.contrast.model$residuals[i]^2)))
}
}
}
#### Get mean responses for variables longer than 2
#### WARN! LEVEL NAMES IS GOING TO BREAK IT!!!! ####
level.name.grep=grep(my.nested.table[p,1],names(my.nested.table[p,3][[1]]$xlevels))
level.names=as.vector(my.nested.table[p,3][[1]]$xlevels[[level.name.grep]])
num.of.contrasts=my.nested.table[p,7][[1]]
count.grep=grep(my.nested.table[p,1],names(my.new.df))
my.count.means=NULL
my.latent.count.means=NULL
my.count.n=NULL
for(j in 1:{num.of.contrasts+1}){
if(j==1){
my.count.means=as.vector(mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1]))
my.count.n=as.vector(dim(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1])[1])
if(latent.cont){
for(E in 1:{my.y.levels}){
my.latent.count.means[[E]]=as.list(mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1][,E]))
}
}
}else{
my.count.means=c(my.count.means,mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1]))
my.count.n=c(my.count.n,dim(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1])[1])
if(latent.cont){
for(E in 1:{my.y.levels}){
my.latent.count.means[[E]]=c(my.latent.count.means[[E]],mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1][,E]))
}
}
}
}
#### Make contrasts
my.contrasts=NULL
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts=c(1,-1,rep(0,num.of.contrasts-1))
}else{
my.contrasts=rbind(my.contrasts,c(1,rep(0,j-1),-1,rep(0,num.of.contrasts-j)))
}
}
#pf(my.f.val,my.df,my.resid.df,lower.tail = F)
#### Compute F values & SS & P-val
my.contrasts.F=NULL
my.contrasts.SSC=NULL
my.contrasts.p=NULL
if(latent.cont){
my.latent.contrasts.F=NULL
my.latent.contrasts.SSC=NULL
my.latent.contrasts.p=NULL
}
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts.I=as.integer(t(as.matrix(my.count.means)))%*%as.integer(as.matrix(my.contrasts[j,]))
my.contrasts.denom=sum(my.contrasts[j,]^2/as.integer(my.count.n))
my.contrasts.SSC=as.list({my.contrasts.I^2}/{my.contrasts.denom})
my.contrasts.F=as.list({my.contrasts.I^2}/{my.MSE*my.contrasts.denom})
my.contrasts.p=as.list(pf(my.contrasts.F[[j]],1,the.resid.df,lower.tail = F))
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.I=as.integer(t(as.matrix(my.latent.count.means[[l]])))%*%as.integer(as.matrix(my.contrasts[j,]))
my.latent.contrasts.SSC[[l]]=as.list({my.latent.contrasts.I^2}/{my.contrasts.denom})
my.latent.contrasts.F[[l]]=as.list({my.latent.contrasts.I^2}/{my.latent.MSE[l]*my.contrasts.denom})
my.latent.contrasts.p[[l]]=as.list(pf(my.latent.contrasts.F[[l]][[j]],1,the.resid.df,lower.tail = F))
}
}
}else{
my.contrasts.I=as.integer(t(as.matrix(my.count.means)))%*%as.integer(as.matrix(my.contrasts[j,]))
my.contrasts.denom=sum(my.contrasts[j,]^2/as.integer(my.count.n))
my.contrasts.SSC=c(my.contrasts.SSC,{my.contrasts.I^2}/{my.contrasts.denom})
my.contrasts.F=c(my.contrasts.F,{my.contrasts.I^2}/{my.MSE*my.contrasts.denom})
my.contrasts.p=c(my.contrasts.p,pf(my.contrasts.F[[j]],1,the.resid.df,lower.tail = F))
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.I=as.integer(t(as.matrix(my.latent.count.means[[l]])))%*%as.integer(as.matrix(my.contrasts[j,]))
my.latent.contrasts.SSC[[l]]=c(my.latent.contrasts.SSC[[l]],{my.latent.contrasts.I^2}/{my.contrasts.denom})
my.latent.contrasts.F[[l]]=c(my.latent.contrasts.F[[l]],{my.latent.contrasts.I^2}/{my.latent.MSE[l]*my.contrasts.denom})
my.latent.contrasts.p[[l]]=c(my.latent.contrasts.p[[l]],pf(my.latent.contrasts.F[[l]][[j]],1,the.resid.df,lower.tail = F))
}
}
}
}
#### Make p-val Adjustments
my.contrasts.p=p.adjust(my.contrasts.p,method=adjustment)
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.p[[l]]=p.adjust(my.latent.contrasts.p[[l]],method=adjustment)
}
}
#### Make rownames
my.contrasts.names=NULL
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts.names=as.list(paste(trimws(level.names[[1]]),"-",trimws(level.names[[j+1]])))
}else{
my.contrasts.names=c(my.contrasts.names,paste(trimws(level.names[[1]]),"-",trimws(level.names[[j+1]])))
}
}
#### Add to table
my.contrasts.4.table=cbind(as.matrix(unlist(my.contrasts.names)),as.matrix(unlist(my.contrasts.F)),as.matrix(unlist(my.contrasts.SSC)),as.matrix(my.contrasts.p))
contr.grep=grep("^Contrasts$",colnames(my.nested.table2))
my.nested.table2[p,contr.grep]=list(my.contrasts.4.table)
if(latent.cont){
my.latent.contrasts.4.table=NULL
for(V in 1:my.y.levels){
if(V==1){
my.latent.contrasts.4.table=cbind(as.matrix(unlist(my.contrasts.names)),as.numeric(as.matrix(unlist(my.latent.contrasts.F[[V]]))),as.matrix(as.numeric(unlist(my.latent.contrasts.SSC[[V]]))),as.matrix(my.latent.contrasts.p[[V]]))
}else{
my.latent.contrasts.4.table=cbind(my.latent.contrasts.4.table,as.matrix(unlist(my.contrasts.names)),as.numeric(as.matrix(unlist(my.latent.contrasts.F[[V]]))),as.matrix(as.numeric(unlist(my.latent.contrasts.SSC[[V]]))),as.matrix(my.latent.contrasts.p[[V]]))
}
}
latent.contr.grep=grep("^Latent Contrasts$",colnames(my.nested.table2))
my.nested.table2[p,latent.contr.grep]=list(my.latent.contrasts.4.table)
}
}
}
return(my.nested.table2)
}
#' Contrast Tables in Pixiedust
#'
#' Beautiful tables using PHIA and PixieDust
#' for lm, glm, and mancova.
#'
#' @param my.model Model.
#' @param SS.type Type of sums of squares to report, either 2 or 3. (default = 3)
#' @param adjustment P-value adjustment sent to p.adjust (default = "bonferroni")
#' @param test.stat Character string giving the type of test statistic to be used (in MANOVA). (default="Wilks")
#' @param abbrev.length Integer telling how long of a label is too long. Longer than this and will be condensed (default=15)
#' @param pix.int Should this be viewable or is this for a document/dashboard? (default=T)
#' @param pix.method Print method. (default="html")
#' @param manova Is this a MAN(C)OVA?
#' @param my.factors If you only want some of the factors, use this. Otherwise, factors are pulled from the regression model
#' @param my.type If you have problems, you can specify the regression type. This is pulled from the model
#' @return Either pixiedust object or code (in HTML or LaTeX) for table
#' @keywords Explore
#' @export
#' @examples
#' quick.contrast(my.model)
quick.contrast = function(my.model,
SS.type = 3,
adjustment = "bonferroni",
test.stat = "Wilks",
abbrev.length = 15,
pix.int = T,
pix.method = "html",
my.factors = my.contrasts,
my.type = my.reg.type,
skip.me=F) {
#### Find type
my.call = as.character(my.model$call)
my.split.call = strsplit(my.call, "\\(")
my.reg.type = my.split.call[[1]][1]
#### Find factors
my.contrasts = names(my.model$contrasts)
if (my.type != "manova" | my.type != "stats::manova") {
#### Find levels
my.x.levels = NULL
for (i in 1:length(my.factors)) {
my.x.levels = c(my.x.levels, my.model$xlevels[[i]])
}
#### Find num non var
my.non.var = length(my.model$coefficients) - length(my.x.levels) - 1 +
length(my.contrasts)
}
library(pixiedust)
library(phia)
if (my.type == "manova" | my.type == "stats::manova") {
my.phia.print = as.data.frame(matrix(ncol = 8, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
fixed = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names",
"Df",
"test stat",
"approx F",
"num Df",
"den Df",
"Pr(>F)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, nrow(my.phia))
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
rownames(my.phia.print) = NULL
if(skip.me){
return(my.phia.print)
}
phia.length = dim(my.phia.print)[1]
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>F)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "test stat", round = 3) %>%
sprinkle(cols = "approx F", round = 3) %>%
sprinkle_colnames(
"",
"Levels",
"df",
"Pillai <br /> Statistic",
"approx <br /> F-value",
"num <br /> df",
"den <br /> df",
"Pr(>F)"
) %>%
sprinkle(cols = 1:8,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:8, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:8,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:8,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:8,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1:2,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1:2,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 7,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 8,
width = 70,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 8, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
} else if (my.type == "lm" | my.type == "stats::lm") {
my.phia.print = as.data.frame(matrix(ncol = 7, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
pairwise = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia = my.phia[-{
dim(my.phia)[1]
}, ]
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names", "Value", "Df", "Sum of Sq", "F", "Pr(>F)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, {
nrow(my.phia)
})
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (my.lengths[this.table.var] == 1) {
my.phia = my.phia[-2, ]
}
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
#print(my.phia.print)
#my.phia.print=my.phia.print[-{dim(my.phia.print)[1]-1},]
rownames(my.phia.print) = NULL
phia.length = dim(my.phia.print)[1]
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>F)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "test stat", round = 3) %>%
sprinkle(cols = "approx F", round = 3) %>%
sprinkle_colnames("",
"Levels",
"Value",
"df",
"Sums of <br /> Squares",
"F-value",
"Pr(>F)") %>%
sprinkle(cols = 1:7,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:7, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:7,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:7,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:7,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 7,
width = 50,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 7, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
} else{
my.phia.print = as.data.frame(matrix(ncol = 6, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
pairwise = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia = my.phia[-{
dim(my.phia)[1]
}, ]
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names", "Value", "Df", "Chisq", "Pr(>Chisq)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, {
nrow(my.phia)
})
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (my.lengths[this.table.var] == 1) {
my.phia = my.phia[-2, ]
}
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
#print(my.phia.print)
#my.phia.print=my.phia.print[-{dim(my.phia.print)[1]-1},]
rownames(my.phia.print) = NULL
phia.length = dim(my.phia.print)[1]
my.phia.print[[6]] = as.numeric(my.phia.print[[6]])
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>Chisq)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "value", round = 3) %>%
sprinkle(cols = "Chisq", round = 3) %>%
sprinkle_colnames("", "Levels", "Value", "df", "Chi-Sq", "Pr(>Chisq)") %>%
sprinkle(cols = 1:6,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:6, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:6,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:6,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:6,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 6, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
}
}
#' Regression Tables in Pixiedust
#'
#' Beautiful tables adding sums of squares
#' and p-value formatting, then giving html or
#' latex output. If interactive and HTML, will show up
#' in viewer.
#'
#' @param my.model Model. NOTE: If have factors, please place them first in the regression.
#' @param VIF include Variable Inflation Factor? (calculated by car::vif) (default=F)
#' @param part.eta If lm, include partial eta square by calculating SS_part/SS_total? (default=F)
#' @param myDF Dataframe, not needed if use data= in call
#' @param my.factor If there are any factors, list them here.
#' @param SS.type Type of sums of squares to report (default = 3)
#' @param pix.int Should this be viewable or is this for a document/dashboard? (default=T)
#' @param pix.method Print method. (default="html")
#' @param type Type of regression? Currently supported: lm, glm (binary), manova
#' @param do.glance Include glance stats?
#' @return Either pixiedust object or code (in HTML or LaTeX) for table
#' @keywords Explore
#' @export
#' @examples
#' quick.reg(my.model, myDF)
quick.reg = function(my.model,
part.eta = F,
VIF = F,
myDF = my.found.df,
SS.type = 2,
abbrev.length = ab.len,
pix.int = T,
pix.method = "html",
type = my.reg.type,
test.stat = "Pillai",
my.factor = NULL,
do.glance=T,
show.footer=T,
adjustment = "bonferroni",
show.contrasts=F,
show.y.contrasts=F,
show.latent=F,
show.intercepts=F,
real.names=T) {
library(pixiedust)
library(broom)
library(car)
library(tidyr)
library(phia)
library(quick.tasks)
library(dplyr)
#### Find type ####
my.reg.type=quick.type(my.model)
#### Set Inits ####
if(type=="ord"){
ab.len=30
library(ordinal)
}else{
ab.len=15
}
#### Get data frame from parent environment ####
my.found.df = eval(parse(text=capture.output(my.model$call$data)),envir = parent.frame())
#print(dim(my.found.df))
if (is.null(my.found.df)) {
stop(paste("No data frame found"))
}
#### for expansion
if (!VIF & !part.eta) {
v.p.len = 8
v.p.rep = 0
} else if (!VIF) {
v.p.len = 9
v.p.rep = 1
} else if (!part.eta) {
v.p.len = 9
v.p.rep = 1
} else{
v.p.len = 10
v.p.rep = 2
}
#### Make factor list ####
#### Use car::Anova to get SS Type 3
if (type == "lm") {
#### ANOVA TABLES ####
my.summary = summary(my.model)
my.coefficients = my.summary$coefficients
my.coefficients = as.data.frame(my.coefficients)
if (type == "ord" & length(my.model$model) == 1) {
my.III.summary = NULL
the.length = length(my.model$y.levels) - 1
} else{
my.III.summary = car::Anova(my.model, type = SS.type)
if (type == "glm" & is.null(my.factor)) {
the.length = dim(my.III.summary)[1] + 1
} else{
the.length = dim(my.III.summary)[1]
}
}
#### Calculate total SS ####
my.total = sum(my.III.summary$`Sum Sq`[2:length(my.III.summary$`Sum Sq`)])
my.df.total = sum(my.III.summary$Df[2:length(my.III.summary$Df)])
total.intercepts = 1
my.rownames = c(abbreviate(rownames(my.summary$coefficients), minlength = abbrev.length),
"Residuals",
"Total")
treat.SS=sum(my.III.summary$`Sum Sq`[2:{length(my.III.summary$`Sum Sq`)-1}])
treat.df=sum(my.III.summary$Df[2:{length(my.III.summary$Df)-1}])
} else if(type == "manova"){
#### Begin MANOVA ####
#### Inits ####
my.envir=environment()
my.nested.table=quick.SSCP(my.model, myDF, SS.type, show.contrasts, show.latent,my.envir)
#### Get treatment ####
treat.model=my.nested.table[dim(my.nested.table)[1],4]
#### Regular totals
treat.total.temp=lapply(lapply(treat.model[[1]][[1]],diag),sum)
treat.total=0
for(W in 2:length(treat.total.temp)){
treat.total=treat.total+treat.total.temp[[W]]
}
treat.total.df=sum(as.numeric(my.nested.table[,7]),na.rm = T)
#### Partial totals
part.treat.total=NULL
for(X in 2:length(treat.model[[1]][[1]])){
if(X==2){
part.treat.total=treat.model[[1]][[1]][[X]]
}else{
part.treat.total=part.treat.total+treat.model[[1]][[1]][[X]]
}
}
#### Latent totals
if(show.latent){
latent.treat.model=my.nested.table[dim(my.nested.table)[1],8]
latent.part.treat.total=NULL
latent.part.total=NULL
for(X in 2:length(latent.treat.model[[1]][[1]])){
if(X==2){
latent.part.treat.total=latent.treat.model[[1]][[1]][[X]]
}else{
latent.part.treat.total=latent.part.treat.total+latent.treat.model[[1]][[1]][[X]]
}
}
}
#### Get residuals ####
#### Regular residuals
if(SS.type==3){
the.resid=sum(diag(treat.model[[1]][[2]]))
#### Partial residuals
part.resid.total=treat.model[[1]][[2]]
}else{
the.resid=sum(diag(treat.model[[1]][[2]][[1]]))
part.resid.total=treat.model[[1]][[2]][[1]]
}
the.resid.df=my.model$df.residual
#### Latent totals
if(show.latent){
if(SS.type==3){
latent.part.resid.total=latent.treat.model[[1]][[2]]
}else{
latent.part.resid.total=latent.treat.model[[1]][[2]][[1]]
}
}
#### Get totals ####
the.total=total.resid+treat.total+sum(diag(treat.model[[1]][[1]][[1]]))
the.total.df=total.resid.df+treat.df+my.y.levels
#### Partial totals
partial.total=part.resid.total+part.treat.total+treat.model[[1]][[1]][[1]]
#### Latent totals
if(show.latent){
latent.part.total=latent.part.treat.total+latent.part.treat.total+latent.treat.model[[1]][[1]][[1]]
}
#### Make dependent variable rownames ####
my.dv.rownames=rownames(part.resid.total)
#### Make basic table ####
#### NEEDS TO BE FIXED ####
my.table.names=c("var","test.stat","f.val","SS","df","mult.df","resid df","p.val")
my.manova.table=as.data.frame(matrix(ncol=v.p.len,nrow=1))
names(my.manova.table)=my.table.names
my.line.var=1
for(i in 1:length(my.SSP.treat)){
if(i==1){
my.i=i
}else{
my.i=2*i-1
}
#### Put in basic line ####
my.treat.err=solve(my.SSP.err)%*%treat.model[[1]][[1]][[i]]
my.test.stat=quick.m.test(my.treat.err,test.stat)
my.SS=sum(diag(treat.model[[1]][[1]][[i]]))
my.df=my.y.levels*ifelse(my.i!=1,as.numeric(my.nested.table[my.i,7]),1)
my.resid.df=ifelse(my.i==1,{my.y.levels*the.resid.df},min({the.resid.df*as.numeric(my.nested.table[my.i,5])-as.numeric(my.nested.table[my.i,5])},my.y.levels*the.resid.df-as.numeric(my.nested.table[my.i,5])))
my.f.val={my.SS/my.df}/{the.resid/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(ifelse(i==1,"(Intercept)",my.nested.table[my.i,1]),my.test.stat,my.f.val,my.SS,ifelse(my.i==1,NA,my.df/my.y.levels),ifelse(my.i==1,my.y.levels,my.df),my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in decomposed factors (y constrasts) ####
#### Intercept
if({show.intercepts & my.i==1}){
for(y in 1:my.y.levels){
my.name=my.dv.rownames[y]
my.test.stat=NA
my.SS=as.numeric(treat.model[[1]][[1]][[i]][y,y])
my.df=1
my.resid.df=the.resid.df
my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
#### Other
if({show.y.contrasts & my.i!=1}){
for(y in 1:my.y.levels){
my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
my.test.stat=NA
my.SS=treat.model[[1]][[1]][[i]][y,y]
my.df=as.numeric(my.nested.table[my.i,7])
my.resid.df=min(abs(my.y.levels*the.resid.df-my.df*the.resid.df-my.df),abs(my.y.levels*the.resid.df-my.df))
my.resid=part.resid.total[y,y]
my.f.val={my.SS/my.df}/{my.resid/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in latents (ANOVA) ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent &my.i!=1){
# my.my.i.temp=my.counter[my.i-1]
my.my.i.temp=my.i
my.y=y
my.name=NA
my.df=as.numeric(my.nested.table[my.i,7])
my.test.stat=NA
my.resid.df=the.resid.df-my.df
my.SS=as.numeric(latent.treat.model[[1]][[1]][[i]][y,1])
my.f.val={my.SS/my.df}/{my.latent.SSP.err[my.y,my.y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
#### Put in latent contrasts ####
if(show.contrasts & show.latent & my.i!=1){
#### Check length
if({!is.na(my.nested.table[my.i,11])}){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:{my.nested.table[my.i,7]}){
my.name=my.nested.table[my.i,11][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,11][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,11][[1]][k,3])
my.test.stat=NA
my.df=1
my.mult.df=NA
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,11][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Put in contrasts ####
#### Not right. Don't have it decomposed this way.
if(show.contrasts & !show.latent & my.i!=1){
#### Check length
if(!is.na(my.nested.table[my.i,9])){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:as.numeric(my.nested.table[my.i,7])){
my.name=my.nested.table[my.i,9][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,9][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,9][[1]][k,3])
my.test.stat=NA
my.df=NA
my.mult.df=my.y.levels
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,9][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
}
}
#### Put in contrasts ####
if(show.contrasts & !show.latent & !show.y.contrasts){
#### Check length
if(!is.na(my.nested.table[my.i,8])){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:as.numeric(my.nested.table[my.i,7])){
my.name=my.nested.table[my.i,8][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,8][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,8][[1]][k,3])
my.test.stat=NA
my.df=NA
my.mult.df=my.y.levels
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,8][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Put in latents (ANOVA) ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent &my.i!=1 & !show.y.contrasts){
#my.my.i.temp=my.counter[my.i-1]
my.my.i.temp=my.i
for(y in 1:{my.y.levels}){
my.y=y
my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
my.df=as.numeric(my.nested.table[my.i,7])
my.test.stat=NA
my.resid.df=the.resid.df-my.df
my.SS=as.numeric(my.nested.table[my.i,8][[1]][[1]][[i]][my.y])
my.f.val={my.SS/my.df}/{my.latent.SSP.err[my.y,my.y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in latent contrasts ####
if(show.contrasts & !{show.contrasts & show.latent}){
#### Check length
if({my.SSP.treat.df[my.i]>1}){
other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:{my.SSP.treat.df[my.i]}){
my.name=my.contrasts.table[[other.manova.grep]][k,1]
my.f.val=my.latent.contrasts.table[[other.manova.grep]][[y]][k,2]
my.SS=my.latent.contrasts.table[[other.manova.grep]][[y]][k,3]
my.test.stat=NA
my.df=1
my.mult.df=NA
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=my.latent.contrasts.table[[other.manova.grep]][[y]][k,4]
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
}
}
#### Put in treatment ####
#### From Type my.i statistics
if(my.i==1){
my.test.stat=quick.m.test(part.treat.total,test.stat)
my.SS=as.numeric(treat.total)
my.df=my.y.levels*as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=my.y.levels*as.numeric(the.resid.df)
my.f.val={my.SS/my.df}/{as.numeric(the.resid.SS)/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c("Treatment Change",my.test.stat,my.f.val,my.SS,{my.df/my.y.levels},my.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Treatment Y Contrasts ####
if(show.y.contrasts){
for(b in 1:my.y.levels){
my.test.stat=NA
my.name=paste(ifelse(real.names,my.dv.rownames[b],b),"|Treatment",sep="")
my.SS=latent.part.treat.total[b]
my.df=the.resid.df
#### Should really be a min statement, but for later...
my.resid.df=my.y.levels*the.resid.df
my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,{my.df/my.y.levels},NA,{my.resid.df/my.y.levels},my.p.val)
my.line.var=my.line.var+1
#### Treatment Latents ####
if(show.latent){
#### Show latent treatments (ANOVAs)
#### Need to change latents to type II
my.SS=sum(weighted.residuals(my.null.model)[,b]^2)-sum(weighted.residuals(my.model)[,b]^2)
my.df=as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=the.resid.df-my.df
my.f.val={my.SS/my.df}/{my.latent.SSP.err[y,y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(NA,NA,my.f.val,my.SS,my.df,NA,{my.resid.df},my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Latents ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent & !show.y.contrasts){
for(b in 1:{my.y.levels}){
my.name=paste(ifelse(real.names,my.dv.rownames[b],b),"|Treatment",sep="")
my.SS=as.numeric(part.treat.total[b])
my.df=as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=the.resid.df-my.df
my.f.val={my.SS/my.df}/{as.numeric(latent.part.resid.total[b])/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),NA,my.f.val,my.SS,my.df,NA,{my.resid.df},my.p.val)
my.line.var=my.line.var+1
}
}
}
}
#### Put in residuals, total change, total ss ####
my.manova.table[my.line.var,]=c("Total Change",NA,NA,as.numeric(treat.total),treat.total.df,{my.y.levels*treat.total.df},NA,NA,rep(NA,v.p.rep))
my.line.var=my.line.var+1
my.manova.table[my.line.var,]=c("Residuals",NA,NA,the.resid,the.resid.df,my.y.levels*the.resid.df,NA,NA,rep(NA,v.p.rep))
my.line.var=my.line.var+1
if(show.y.contrasts){
for(i in 1:my.y.levels){
my.manova.table[my.line.var,]=c(paste(ifelse(real.names,my.dv.rownames[i],i),"|Residual",sep=""),NA,NA,part.resid.total[i,i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
if(show.latent){
my.manova.table[my.line.var,]=c(NA,NA,NA,latent.part.resid.total[i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
}
}
}
if(show.latent & !show.y.contrasts){
for(i in 1:my.y.levels){
my.manova.table[my.line.var,]=c(paste(ifelse(real.names,my.dv.rownames[i],i),"|Residual",sep=""),NA,NA,latent.part.resid.total[i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
}
}
my.manova.table[my.line.var,]=c("Total",NA,NA,the.total,the.total.change.df+1,my.y.levels*the.total.change.df+2,NA,NA,rep(NA,v.p.rep))
if(show.footer){
the.footer=paste(ifelse(dim(my.new.df)[1]==dim(myDF)[1],"Data have same number of rows <br />",paste({dim(myDF)[1]-dim(my.new.df)[1]}," cases deleted due to missingness <br />")),"Method: QR decomposition",if(show.contrasts){paste(" <br />Adjustment: ", adjustment,sep="")},if(show.latent){paste(" <br /> Latent Contrasts")})
}else{
the.footer=NULL
}
quick.table(my.manova.table,"manova",test=test.stat,SS.type = SS.type, abbrev.length = abbrev.length,the.footer = the.footer)
#
# #### Table inits ####
# for(i in 2:dim(my.manova.table)[2]){
# my.manova.table[[i]]=as.numeric(my.manova.table[[i]])
# }
# tmp.change=0
# if(show.latent & show.y.contrasts){
# tmp.change=4
# }else if(show.latent & !show.y.contrasts){
# tmp.change=2
# }else if(!show.latent & show.y.contrasts){
# tmp.change=2
# }
#
# #### Make Dusted table ####
# options(pixie_interactive = pix.int,
# pixie_na_string = "")
# my.dust=pixiedust::dust(my.manova.table)%>%
# sprinkle_na_string()%>%
# sprinkle_print_method(pix.method)%>%
# sprinkle_border(cols=1,rows=1:my.line.var,border="left")%>%
# sprinkle_border(cols={8+v.p.rep},rows=1:my.line.var,border="right")%>%
# sprinkle_border(rows=my.line.var,boder="bottom")%>%
# sprinkle_border(rows=my.line.var-{2+tmp.change}+1,border="top")%>%
# sprinkle_border(cols=1,border="left",part="head")%>%
# sprinkle_border(cols={8+v.p.rep},border="right",part="head")%>%
# sprinkle_border(rows=1,border=c("top","bottom"),part="head")%>%
# sprinkle_border(rows={1+ifelse(show.intercepts,my.y.levels,0)},border="bottom")%>%
# sprinkle_border(rows={ifelse(show.y.contrasts,my.y.levels+1,1)+ifelse(show.latent,my.y.levels+1,1)+
# ifelse(show.intercepts,my.y.levels,0)},
# border="bottom")%>%
# sprinkle_round(cols=2:v.p.len,round=3)%>%
# sprinkle_colnames("Variable",paste(test.stat, "<br /> Test <br /> Statistic",sep=""),
# "F-Value",paste("Type II <br /> Sums of <br /> Squares",sep=""),"dF",
# "Mult <br /> dF","Resid <br /> dF","P-value")%>%
# sprinkle_align(rows=1,halign="center",part="head")%>%
# sprinkle_pad(rows=1:{my.line.var},pad=5)%>%
# sprinkle(cols = "p.val", fn = quote(pvalString(
# value, digits = 3, format = "default"
# )))%>%
# sprinkle_border(rows={1+ifelse(show.intercepts,my.y.levels,0)},border="bottom")
#
# ##### Make glance stats ####
# if(do.glance){
# my_glance_stats=as.data.frame(matrix(ncol=v.p.len,nrow=1))
# my_glance_stats[1,]=c(paste(ifelse(dim(my.new.df)[1]==dim(myDF)[1],"Data have same number of rows <br />",paste({dim(myDF)[1]-dim(my.new.df)[1]}," cases deleted due to missingness <br />")),"Method: QR decomposition",if(show.contrasts){paste(" <br />Adjustment: ", adjustment,sep="")},if(show.latent){paste(" <br /> Latent Contrasts")}),rep(NA,{7+v.p.rep}))
# my.dust=pixiedust::redust(my.dust,my_glance_stats,part="foot")%>%
# sprinkle(merge=T,halign="center",part="foot")
# }
return()
# if (pix.int) {
# return(my.dust)
# } else{
# my.dust.print = print(my.dust, quote = F)[1]
# return(my.dust.print)
# }
#### ETA-SQ Stuff to finish.... ####
# #### Make eta-sq
# my.SSP.err.t=t(my.SSP.err)
#
# eta.sq=NULL
# for(i in 1:length(my.SSP)){
# eta.sq[[i]]=my.SSP.err.t*my.SSP[[i]]
# }
#
# x3 = capture.output(car::Anova(my.model, type = SS.type, test = test.stat))
# my.manova.test = data.frame(matrix(ncol = 7, nrow = 1))
# my.var.temp = 4
# while (my.var.temp < {
# length(x3) - 1
# }) {
# test = strsplit(x3[my.var.temp], "\\s+")
#
# if (length(test[[1]]) == 9) {
# test2 = test[[1]][-9]
# test2 = test2[-7]
#
# } else if (length(test[[1]]) == 8) {
# test2 = test[[1]][-8]
#
# } else{
# test2 = test[[1]]
# }
#
# my.manova.test[{
# my.var.temp - 3
# }, ] = test2
# my.var.temp = my.var.temp + 1
#
# }
# my.summary=summary(my.model)
#### END MANOVA table ####
}else if (type == "glm") {
#### DevAn Table ####
#### Inits ####
new.df=my.model$model
new.model=update(my.model,data=new.df)
null.model=update(new.model,~1)
total.intercepts = 1
#### Check if null model ####
vars.df=new.model$df.null-new.model$df.residual
if(vars.df==0){
stop("This is the null model.")
}
resid.dev=new.model$deviance
resid.df=new.model$df.residual
vars.dev=anova(new.model,test="Chi")$Deviance[-1]
vars.dev.df=anova(new.model,test="Chi")$Df[-1]
vars.dev.p=anova(new.model,test="Chi")$`Pr(>Chi)`[-1]
total.dev=new.model$null.deviance-new.model$deviance
vars.dev.total=sum(vars.dev)
if(length(grep("*",new.model$formula))>0){
my.full.model=new.model
}else{
my.vars=strsplit(as.character(new.model$formula),"~")
my.dep.var=my.vars[[2]]
my.other.vars=names(new.model$model)
my.var.grep=grep(paste("^",my.dep.var,"$",sep=""),my.other.vars)
my.other.vars=my.other.vars[-my.var.grep]
my.formula=paste("~",my.other.vars[1])
for(i in 2:length(my.other.vars)){
my.formula=paste(my.formula,"*",my.other.vars[i],sep="")}
#### FULL MODEL THING ####
my.full.model=update(new.model,my.formula)
}
my.full.model.z=summary(my.full.model)$coefficients[1:total.intercepts,3]^2
my.null.model.z=summary(null.model)$coefficients[1:total.intercepts,3]^2
my.full.dev=sum(my.full.model.z)
my.null.dev=sum(my.null.model.z)
my.int.dev.total=abs(total.intercepts*{my.full.dev-my.null.dev})
my.int.dev=summary(new.model)$coefficients[1:total.intercepts,2]^2
treat.dev=anova(null.model,new.model)$`Deviance`[2]
#vars.dev.df=drop1(new.model,test="Chi")$Df[-1]
#vars.dev.p=drop1(new.model,test="Chi")$`Pr(>Chi)`[-1]
#total.dev=-2*{as.integer(levels(null.model$info$logLik)[1])-as.integer(levels(new.model$info$logLik)[1])}
total.dev.change=treat.dev+my.int.dev.total
total.dev.change.df=total.intercepts*vars.df
total.dev=new.model$null.deviance
resid.dev=total.dev-total.dev.change
vars.dev.df=anova(new.model,test="Chi")$Df
resid.df=new.model$df.residual
total.df=resid.df+vars.df
my.int.dev.df=max({total.intercepts*sum(vars.dev.df,na.rm = T)-sum(vars.dev.df,na.rm=T)},1)
my.int.dev.or=exp(coef(new.model))[1:{total.intercepts}]
my.int.dev.or.confint=exp(confint(new.model,type="Wald"))[1:total.intercepts,]
my.int.names=abbreviate(names(my.int.dev.or),minlength=abbrev.length)
#resid.dev=new.model$deviance
vars.dev=anova(new.model,test="Chi")$Deviance
vars.dev.p=anova(new.model,test="Chi")$`Pr(>Chi)`
vars.names=rownames(anova(new.model,test="Chi"))
#total.dev=new.model$null.deviance-new.model$deviance
vars.dev.total=sum(vars.dev)
#total.df=sum(vars.dev.df,na.rm = T)
vars.or=exp(coef(new.model))
vars.or.confint=exp(confint(new.model,type="Wald"))
my.names=abbreviate(names(vars.or),minlength=abbrev.length)
#vars.or=exp(coef(new.model))[-1]
#vars.or.confint=exp(confint(new.model,type="Wald"))[-1,]
} else if (type == "ord" | type =="glm2") {
new.df=my.model$model
new.model=update(my.model,data=new.df)
null.model=update(new.model,~1)
total.intercepts = null.model$edf
my.summary=summary(new.model)
if(length(grep("*",new.model$formula))>0){
my.full.model=new.model
}else{
my.vars=strsplit(as.character(new.model$formula),"~")
my.dep.var=my.vars[[2]]
my.other.vars=names(new.model$model)
if(length(my.other.vars)>1){
my.var.grep=grep(paste("^",my.dep.var,"$",sep=""),my.other.vars)
my.other.vars=my.other.vars[-my.var.grep]
my.formula=paste("~",my.other.vars[1])
for(i in 2:length(my.other.vars)){
my.formula=paste(my.formula,"*",my.other.vars[i],sep="")}
#### FULL MODEL THING ####
my.full.model=update(new.model,my.formula)
}else{
my.full.model=new.model
}
}
my.full.model.z=summary(my.full.model)$coefficients[1:total.intercepts,3]^2
my.null.model.z=summary(null.model)$coefficients[1:total.intercepts,3]^2
my.full.dev=sum(my.full.model.z)
my.null.dev=sum(my.null.model.z)
my.int.dev.total=abs(total.intercepts*{my.full.dev-my.null.dev})
my.int.dev=summary(new.model)$coefficients[1:total.intercepts,2]^2
vars.df=new.model$edf-null.model$edf
if(vars.df==0){
stop("This is the null model.")
}
vars.dev=summary(new.model)$coefficients[{total.intercepts+1}:dim(summary(new.model)$coefficients)[1],3]^2
vars.dev.p=summary(new.model)$coefficients[{total.intercepts+1}:dim(summary(new.model)$coefficients)[1],4]
vars.dev.df=NULL
weird.var=2
track.var=1
while(weird.var<=dim(new.model$model)[2]){
if(is.factor(new.model$model[[weird.var]])){
vars.dev.df[track.var]=length(levels(new.model$model[[weird.var]]))-1
}else{
vars.dev.df[track.var]=1
}
weird.var=weird.var+1
track.var=track.var+1
}
### interaction effects
weird.var=weird.var+total.intercepts-1
while(weird.var<=dim(summary(new.model)$coefficients)[1]){
vars.dev.df[track.var]=1
weird.var=weird.var+1
track.var=track.var+1
}
vars.df.total=sum(vars.dev.df,na.rm = T)
treat.dev=anova(null.model,new.model)$`LR.stat`[2]
#vars.dev.df=drop1(new.model,test="Chi")$Df[-1]
#vars.dev.p=drop1(new.model,test="Chi")$`Pr(>Chi)`[-1]
#total.dev=-2*{as.integer(levels(null.model$info$logLik)[1])-as.integer(levels(new.model$info$logLik)[1])}
total.dev.change=treat.dev+my.int.dev.total
if(total.intercepts>1){
total.dev.change.df=total.intercepts*vars.df.total
}else{
total.dev.change.df=vars.df.total+total.intercepts
}
resid.dev=-2*new.model$logLik
total.dev=total.dev.change+resid.dev
total.df=dim(myDF)[1]
resid.df=total.df-total.dev.change.df
vars.or=exp(coef(new.model))[{null.model$edf+1}:length(coef(new.model))]
vars.or.confint=exp(confint(new.model,type="Wald"))[{null.model$edf+1}:length(coef(new.model)),]
my.names=abbreviate(names(vars.or),minlength=abbrev.length)
my.int.dev.df=max({total.intercepts*sum(vars.df,na.rm = T)-sum(vars.df,na.rm=T)},1)
my.int.dev.or=exp(coef(new.model))[1:{null.model$edf}]
my.int.dev.or.confint=exp(confint(new.model,type="Wald"))[1:null.model$edf,]
my.int.names=abbreviate(names(my.int.dev.or),minlength=abbrev.length)
} else{
print("Error")
return()
}
if (!VIF & !part.eta) {
v.p.len = 7
v.p.rep = 0
} else if (!VIF) {
v.p.len = 8
v.p.rep = 1
} else if (!part.eta) {
v.p.len = 8
v.p.rep = 1
} else{
v.p.len = 9
v.p.rep = 2
}
#### Make table if not MANOVA ####
my.tables.df = as.data.frame(matrix(ncol = v.p.len, nrow = 1))
if (type == "lm") {
if (!VIF & !part.eta) {
my.std.error = c(my.coefficients$`Std. Error`, NA)
my.estimate = c(my.coefficients$Estimate, NA)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val")
} else if (!part.eta) {
my.VIF = car::vif(my.model)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"VIF")
} else if (!VIF) {
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"p.eta")
} else{
my.VIF = car::vif(my.model)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"p.eta",
"VIF")
}
} else if (type == "glm" & VIF) {
my.VIF = car::vif(my.model)
names(my.tables.df) = c("var",
"p.odd",
"p.odd.2.5",
"p.odd.97.5",
"deviance",
"df",
"p.val",
"VIF")
} else{
names(my.tables.df) = c("var", "p.odd", "p.odd.2.5", "p.odd.97.5", "deviance", "df", "p.val")
}
#### Make the double table entries ####
#### Was very annoying...took my frustration out on
#### Variable names
factor.stupid = NULL
factor.rownames = NULL
num.of.levels = NULL
ord.temp = 0
if (!is.null(my.factor)) {
for (i in 1:length(my.factor)) {
factor.stupid = c(factor.stupid, grep(paste("^", my.factor[i], "$", sep =
""), names(myDF)))
if (type == "lm") {
factor.rownames = c(factor.rownames, grep(
paste("^", my.factor[i], "$", sep = ""),
rownames(my.III.summary)
))
num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# }else if(type=="glm"){
# factor.rownames=c(factor.rownames,{grep(paste("^",my.factor[i],"$",sep=""),rownames(my.III.summary))+1})
# num.of.levels=c(num.of.levels,length(levels(myDF[[factor.stupid[i]]])))
} else{
my.dev.grep=grep(paste("^", my.factor[i], sep = ""),names(new.model$coefficients))[1]
factor.rownames=c(factor.rownames,my.dev.grep)
num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# factor.rownames = c(factor.rownames, {
# grep(paste("^", my.factor[i], "$", sep = ""),
# rownames(my.III.summary)) + total.intercepts + sum(num.of.levels) - ordinal.temp
# })
# num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# ordinal.temp = ordinal.temp + 1
}
}
} else{
factor.rownames = 0L
}
#### Make phia stuff ####
if(show.contrasts){
if(type=="glm" & !is.null(my.factor)){
my.phia.reg=quick.contrast(new.model,skip.me=T,adjustment = adjustment,SS.type = SS.type,abbrev.length = abbrev.length,my.factors = my.factor)
my.phia.rownames=my.phia.reg$names
phia.dev=my.phia.reg$Chisq
}else if(type=="lm" & !is.null(my.factor)){
my.phia.reg=quick.contrast(my.model,skip.me=T,adjustment = adjustment,SS.type = SS.type,abbrev.length = abbrev.length)
my.j=0
my.big.phia=NULL
phia.shift=0
real.shift=0
for(i in 1:dim(my.phia.reg)[1]){
if(!is.na(my.phia.reg[i,1])){
my.j=my.j+1
real.shift=real.shift+phia.shift
my.big.phia[[my.j]]=my.phia.reg[i,2:7]
}else{
my.big.phia[[my.j]][i-real.shift,]=my.phia.reg[i,2:7]
}
phia.shift=phia.shift+1
}
my.phia.rownames=NULL
my.phia.SS=NULL
my.phia.value=NULL
my.phia.F=NULL
my.phia.p=NULL
my.phia.err=NULL
for(i in 1:{my.j}){
my.phia.rownames[[i]]=my.big.phia[[i]]$names
my.phia.SS[[i]]=my.big.phia[[i]]$`Sum of Sq`
my.phia.value[[i]]=my.big.phia[[i]]$Value
my.phia.F[[i]]=my.big.phia[[i]][,5]
my.phia.p[[i]]=my.big.phia[[i]][,6]
}
}else{
}
}
my.factor.var = 1
this.temp.var = 1
this.shift.temp = 1
yet.another.var = 1
my.shift = 0
other.temp = 2
ord.temp = 0
phia.temp=1
if (type == "lm") {
dang.length = length(rownames(my.III.summary))
} else if (type == "glm") {
# dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
# sum(num.of.levels) - length(my.factor)
# }, 0) + 1
dang.length=length(new.model$coefficients)-sum(num.of.levels)+length(num.of.levels)+total.intercepts+1
} else{
# dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
# sum(num.of.levels) - length(my.factor)
# }, 0) + 1
#dang.length=7
dang.length=length(new.model$coefficients)+length(my.factor)+1
}
while (this.shift.temp < dang.length) {
if (is.na(factor.rownames[my.factor.var])) {
my.factor.rownames = 1
} else{
my.factor.rownames = factor.rownames[my.factor.var]
}
#### LOOP ####
if (this.shift.temp == 1) {
i = 1
if(type=="ord" | type=="glm"){
my.tables.df[this.temp.var, ] = c(
"Intercept Change",
NA,
NA,
NA,
my.int.dev.total,
my.int.dev.df,
pchisq(my.int.dev.total,my.int.dev.df,lower.tail = F),
rep(NA, v.p.rep))
this.temp.var=this.temp.var+1
}
while (i <= total.intercepts) {
if (type == "lm") {
my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
my.df = my.III.summary$Df[this.shift.temp]
my.est = my.estimate[this.shift.temp]
my.std.err = my.std.error[this.shift.temp]
my.f.val = my.III.summary$`F value`[this.shift.temp]
my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
my.tables.df[this.temp.var, ] = c(
my.rownames[this.shift.temp],
summary(my.model)[[4]][this.shift.temp,1],
summary(my.model)[[4]][this.shift.temp,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
rep(NA, v.p.rep)
)
} else if(type=="glm"){
#### HERE IS WHERE I LEFT OFF!! ####
# my.or = exp(my.estimate[this.shift.temp])
# my.est = my.estimate[this.shift.temp]
# my.z.val = my.summary$coefficients[this.shift.temp, 3]
# my.std.err = my.std.error[this.shift.temp]
#my.dev=my.III.summary$`LR Chisq`[this.shift.temp]
#my.df=my.III.summary$Df[this.shift.temp]
# my.p.val = my.summary$coefficients[{
# this.shift.temp
#}, 4]
my.tables.df[this.temp.var, ] = c(
paste(names(attr(my.model$model[[i]],"labels"))[1],"-",names(attr(my.model$model[[i]],"labels"))[2],sep=""),
my.int.dev.or[i],
my.int.dev.or.confint[1],
my.int.dev.or.confint[2],
my.int.dev[i],
1,
NA,
rep(NA, v.p.rep))
#this.temp.var=this.temp.var+1
#my.tables.df[this.temp.var,]=c("Treatment",NA,NA,NA,total.dev,total.df,dchisq(total.dev,total.df),rep(NA,v.p.rep))
}else{
my.tables.df[this.temp.var, ] = c(
my.int.names[i],
my.int.dev.or[i],
my.int.dev.or.confint[i,1],
my.int.dev.or.confint[i,2],
my.int.dev[i],
NA,
NA,
rep(NA, v.p.rep))
#this.temp.var=this.temp.var+1
}
this.shift.temp = this.shift.temp + 1
this.temp.var = this.temp.var + 1
i = i + 1
}
if(type=="ord" | type=="glm"){
my.tables.df[this.temp.var,]=c("Treatment Change",NA,NA,NA,treat.dev,vars.df,pchisq(treat.dev,vars.df,lower.tail = F))
this.temp.var=this.temp.var+1
}else{
my.tables.df[this.temp.var,]=c("Treatment",NA,NA,treat.SS,treat.df,glance(my.model)[4],glance(my.model)[5],rep(NA,v.p.rep))
this.temp.var=this.temp.var+1
}
} else if (this.shift.temp == my.factor.rownames) {
if (type == "lm") {
my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
my.df = my.III.summary$Df[this.shift.temp]
my.est = my.estimate[this.shift.temp]
my.std.err = my.std.error[this.shift.temp]
my.z.val = NA
my.f.val = my.III.summary$`F value`[this.shift.temp]
my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
if(!VIF & !part.eta){
my.tables.df[this.temp.var, ] = c(
my.factor[yet.another.var],
NA,
NA,
my.sumsq,
my.df,
my.f.val,
my.p.val,
rep(NA, v.p.rep)
)
}else if(part.eta & !VIF){
my.p.eta = my.sumsq / my.total
my.tables.df[this.temp.var, ] = c(
my.factor[yet.another.var],
NA,
NA,
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.p.eta
)
}else if(!part.eta & VIF){
my.tables.df[this.temp.var, ] = c(
my.factor[yet.another.var],
NA,
NA,
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.VIF[this.shift.temp - 1, 1]
)
}else{
my.p.eta = my.sumsq / my.total
my.tables.df[this.temp.var, ] = c(
my.factor[yet.another.var],
NA,
NA,
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.p.eta,
my.VIF[this.shift.temp - 1, 1]
)
}
}else{
# my.or = NA
# my.est = NA
# my.std.err = NA
# my.dev = my.III.summary$`LR Chisq`[ord.temp]
# my.df = my.III.summary$Df[ord.temp]
# my.p.val = my.III.summary$`Pr(>Chisq)`[ord.temp]
my.tables.df[this.temp.var,]=c(my.factor[this.shift.temp-1],
NA,
NA,
NA,
drop1(new.model,test="Chi")$`LRT`[this.shift.temp],
drop1(new.model,test="Chi")$`Df`[this.shift.temp],
drop1(new.model,test="Chi")$`Pr(>Chi)`[this.shift.temp],
rep(NA,v.p.rep))
#
# my.tables.df[this.temp.var, ] = c(my.factor[yet.another.var],
# my.or,
# my.est,
# my.std.err,
# my.dev,
# my.df,
# my.p.val)
#ord.temp = ord.temp + 1
}
# else{
# my.or = NA
# my.est = NA
# my.std.err = NA
# my.dev = my.III.summary$Chisq[ord.temp]
# my.df = my.III.summary$Df[ord.temp]
# my.p.val = my.III.summary$`Pr(>Chisq)`[ord.temp]
# my.tables.df[this.temp.var, ] = c(my.factor[yet.another.var],
# my.or,
# my.est,
# my.std.err,
# my.dev,
# my.df,
# my.p.val)
# ord.temp = ord.temp + 1
#
# }
yet.another.var = yet.another.var + 1
this.temp.var = this.temp.var + 1
this.shift.temp = this.shift.temp + 1
if(type=="lm"){
other.other.temp = 2
}else{
other.other.temp=1
}
#### INTERACTION EFFECTS? NOT WORRIED YET ####
if ({length(grepl(":", my.summary$coefficients[other.temp, 1])) >
0}) {
#### I think the while should not be +1
if(type=="glm" | type=="ord"){
num.of.levels[my.factor.var]=num.of.levels[my.factor.var]-1
}
while (other.other.temp < {
num.of.levels[my.factor.var] + 1
}) {
if (type == "lm") {
if(show.contrasts){
my.sumsq = NA
my.df = NA
my.est = my.estimate[other.temp]
my.std.err = my.std.error[other.temp]
#### NEED TO FIX ####
my.f.val = {
my.summary$coefficients[other.temp, 3] ^ 2
}
my.p.val = my.summary$coefficients[other.temp, 4]
my.tables.df[this.temp.var, ] = c(
my.phia.rownames[[phia.temp]][other.temp-1],
my.phia.value[[phia.temp]][other.temp-1],
my.est,
my.phia.SS[[phia.temp]][other.temp-1],
1,
my.phia.F[[phia.temp]][other.temp-1],
my.phia.p[[phia.temp]][other.temp-1],
rep(NA, v.p.rep)
)
}
ord.temp=ord.temp + 1
} else if (type == "glm") {
# my.or = exp(my.estimate[other.temp])
# my.est = my.estimate[other.temp]
# my.std.err = my.std.error[other.temp]
# my.z.val = my.summary$coefficients[other.temp, 3]
# my.dev = my.III.summary$`LR Chisq`[other.temp]
# my.df = my.III.summary$Df[other.temp]
# my.p.val = my.summary$coefficients[other.temp, 4]
# my.tables.df[this.temp.var, ] = c(my.rownames[other.temp],
# my.or,
# my.std.err,
# my.z.val,
# NA,
# NA,
# my.p.val)
if(show.contrasts){
my.tables.df[this.temp.var, ] = c(my.phia.rownames[other.temp-1],
vars.or[other.temp-1],
vars.or.confint[other.temp-1,1],
vars.or.confint[other.temp-1,2],
my.phia[other.temp-1,3],
my.phia[other.temp-1,4],
my.phia[other.temp-1,6],rep(NA,v.p.rep))
#this.shift.temp = this.shift.temp + 1
}
ord.temp=ord.temp + 1
} else{
my.or = exp(my.estimate[other.temp + total.intercepts - 1])
my.est = my.estimate[other.temp + total.intercepts - 1]
my.std.err = my.std.error[other.temp + total.intercepts -
1]
my.z.val = my.summary$coefficients[{
other.temp + total.intercepts - 1
}, 3]
#my.dev=my.III.summary$Chisq[other.temp]
#my.df=my.III.summary$Df[other.temp]
my.p.val = my.summary$coefficients[{
other.temp + total.intercepts - 1
}, 4]
if (!VIF) {
my.tables.df[this.temp.var, ] = c(my.rownames[other.temp +
total.intercepts - 1],
my.or,
my.std.err,
my.z.val,
NA,
NA,
my.p.val)
} else{
my.tables.df[this.temp.var, ] = c(my.rownames[other.temp +
total.intercepts - 1],
my.or,
my.std.err,
my.z.val,
NA,
NA,
my.p.val,
my.VIF[this.shift.temp - 1, 1])
}
this.shift.temp = this.shift.temp + 1
}
this.temp.var = this.temp.var + 1
other.temp = other.temp + 1
other.other.temp = other.other.temp + 1
the.length = the.length + 1
}
phia.temp=phia.temp+1
if(!show.contrasts){this.temp.var=this.temp.var-ord.temp}
} else{
}
if (my.factor.var == 1) {
my.shift = {
my.shift + other.temp - 2
}
} else{
my.shift = my.shift + other.temp
}
my.factor.var = my.factor.var + 1
} else{
if (type == "lm") {
my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
my.df = my.III.summary$Df[this.shift.temp]
my.est = my.estimate[this.shift.temp]
my.std.err = my.std.error[this.shift.temp]
my.f.val = my.III.summary$`F value`[this.shift.temp]
my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
if (!VIF & !part.eta) {
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[this.shift.temp],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
my.sumsq,
my.df,
my.f.val,
my.p.val
)
} else if (!part.eta) {
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[this.shift.temp],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.VIF[this.shift.temp - 1, 1]
)
} else if (!VIF) {
my.p.eta = my.sumsq / my.total
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[this.shift.temp],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.p.eta
)
} else{
my.p.eta = my.sumsq / my.total
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[this.shift.temp],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,1],
summary(my.model)[[4]][this.shift.temp+ord.temp-1,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
my.p.eta,
my.VIF[this.shift.temp - 1, 1]
)
}
ord.temp=ord.temp+1
} else if (type == "glm") {
# if (!is.null(my.factor)) {
# this.shift.temp = this.shift.temp - ord.temp-1
# }
# my.or = exp(my.estimate[this.shift.temp])
# my.est = my.estimate[this.shift.temp]
# my.std.err = my.std.error[this.shift.temp]
# my.z.val = my.summary$coefficients[other.temp, 3]
# my.dev = my.III.summary$`LR Chisq`[ord.temp]
# my.df = my.III.summary$Df[ord.temp]
# my.p.val = my.III.summary$`Pr(>Chisq)`[ord.temp]
if (!VIF) {
# my.tables.df[this.temp.var, ] = c(my.rownames[this.shift.temp],
# my.or,
# my.std.err,
# NA,
# my.dev,
# my.df,
# my.p.val)
my.tables.df[this.temp.var,]=c(names(vars.or)[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
vars.or[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},1],
vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},2],
vars.dev[this.shift.temp],
vars.dev.df[this.shift.temp],
vars.dev.p[this.shift.temp],rep(NA,v.p.len))
} else{
my.tables.df[this.temp.var, ] = c(names(vars.or)[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
vars.or[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1}],
vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},1],
vars.or.confint[{this.shift.temp+sum(vars.dev.df[1:{this.shift.temp-1}],na.rm = T)-yet.another.var+1},2],
vars.dev[this.shift.temp],
vars.dev.df[this.shift.temp],
vars.dev.p[this.shift.temp],
rep(NA,v.p.len))
}
# if (!is.null(my.factor)) {
# this.shift.temp = this.shift.temp + ord.temp+1
# }
yet.another.var=yet.another.var+1
ord.temp = ord.temp + 1
} else{
if (!is.null(my.factor)) {
this.shift.temp = this.shift.temp - ord.temp
}else{
this.shift.temp=this.shift.temp-total.intercepts
}
my.tables.df[this.temp.var, ] = c(names(vars.or)[this.shift.temp],
vars.or[{this.shift.temp}],
if(!is.null(dim(vars.or.confint))){
vars.or.confint[{this.shift.temp},1]
}else{
vars.or.confint[1]
},
if(!is.null(dim(vars.or.confint))){
vars.or.confint[{this.shift.temp},2]
}else{
vars.or.confint[2]
},
vars.dev[this.shift.temp-ord.temp],
vars.dev.df[this.shift.temp-ord.temp],
vars.dev.p[this.shift.temp-ord.temp],
rep(NA,v.p.len))
if (!is.null(my.factor)) {
this.shift.temp = this.shift.temp + ord.temp
}else{
this.shift.temp=this.shift.temp+total.intercepts
}
#ord.temp = ord.temp + 1
}
this.shift.temp = this.shift.temp + 1
this.temp.var = this.temp.var + 1
}
}
if (type == "lm") {
my.tables.df[this.temp.var, ] = c(
"Residuals",
NA,
NA,
my.III.summary$`Sum Sq`[this.shift.temp],
my.III.summary$Df[this.shift.temp],
NA,
NA,
rep(NA, v.p.rep)
)
my.tables.df[this.temp.var + 1, ] = c("Total Change",
NA,
NA,
my.total,
my.df.total,
rep(NA, v.p.rep + 2))
my.tables.df[this.temp.var+2,]=c("Total SS",NA,NA,my.total+my.III.summary$`Sum Sq`[1],my.df.total+1,rep(NA,v.p.rep+2))
if (!VIF & !part.eta) {
} else if (!VIF) {
my.tables.df$p.eta = as.numeric(my.tables.df$p.eta)
} else if (!part.eta) {
my.tables.df$VIF = as.numeric(my.tables.df$VIF)
} else{
my.tables.df$p.eta = as.numeric(my.tables.df$p.eta)
my.tables.df$VIF = as.numeric(my.tables.df$VIF)
}
my.tables.df$f.val = as.numeric(my.tables.df$f.val)
my.tables.df$est = as.numeric(my.tables.df$est)
my.tables.df$sumsq = as.numeric(my.tables.df$sumsq)
my.tables.df$df = as.numeric(my.tables.df$df)
my.tables.df$std.err = as.numeric(my.tables.df$std.err)
my.tables.df$p.val = as.numeric(my.tables.df$p.val)
} else{
my.tables.df[this.temp.var,]=c("Total Change",NA,NA,NA,total.dev.change,total.dev.change.df,pchisq(total.dev.change,total.dev.change.df,lower.tail = F),rep(NA,v.p.rep))
my.tables.df[this.temp.var+1,]=c("Residuals",NA,NA,NA,resid.dev,resid.df,NA,rep(NA,v.p.rep))
my.tables.df[this.temp.var+2,]=c("Total",NA,NA,NA,total.dev,total.df,NA,rep(NA,v.p.rep))
#my.tables.df[this.temp.var,] = c("Change from Null", NA, NA, NA, ddeviance2, ddf2, fit2)
my.tables.df$p.odd = as.numeric(my.tables.df$p.odd)
my.tables.df$p.odd.2.5 = as.numeric(my.tables.df$p.odd.2.5)
my.tables.df$p.odd.97.5 = as.numeric(my.tables.df$p.odd.97.5)
my.tables.df$deviance=as.numeric(my.tables.df$deviance)
my.tables.df$p.val=as.numeric(my.tables.df$p.val)
if (VIF) {
my.tables.df$VIF = as.numeric(my.tables.df$VIF)
}
}
#### Make custom glance stats ####
if(do.glance){
#### Can eventually make it options
if (type == "lm") {
glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
glance_stats[1,]=c(paste("Method: ","QR Decomposition",if(show.contrasts){paste("<br />Adjustment Method: ",adjustment,sep="")},sep=""),rep(NA,6),rep(NA,v.p.rep))
# glance_stats = broom::glance(my.model)
# glance_stats = tidyr::gather(glance_stats)
#
#
# glance_stats[3:{
# 5 + v.p.rep
# }] = NA
# glance_stats[6 + v.p.rep] = c(glance_stats$key[7:8],
# NA,
# glance_stats$key[9:11],
# NA,
# NA,
# NA,
# NA,
# NA)
# glance_stats[7 + v.p.rep] = c(glance_stats$value[7:8],
# NA,
# glance_stats$value[9:11],
# NA,
# NA,
# NA,
# NA,
# NA)
# glance_stats = glance_stats[-7:-11, ]
# glance_stats = glance_stats[-3, ]
# glance_stats = glance_stats[-5, ]
} else if (type == "glm") {
glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
glance_stats[1,]=c(paste("Family: ",new.model$family$family," <br /> Link: ",new.model$family$link,if(show.contrasts){paste(" <br />Adjustment: ",adjustment,sep="")},sep=""),rep(NA,6),rep(NA,v.p.rep))
# glance_stats = broom::glance(my.model)
# glance_stats = tidyr::gather(glance_stats)
#
#
# glance_stats[[3]] = c("Family: ", "Link: ", rep(NA, 5))
# glance_stats[[4]] = c(my.model$family$family,
# my.model$family$link,
# rep(NA, 5))
# glance_stats[5:{
# 5 + v.p.rep
# }] = NA
# glance_stats[6 + v.p.rep] = c(glance_stats$key[3:6], NA, NA, NA)
# glance_stats[7 + v.p.rep] = c(glance_stats$value[3:6], NA, NA, NA)
# glance_stats[[1]] = c("Null.dev", "Chi-Sq", "dF", "Pr(>Chisq)", NA, NA, NA)
# glance_stats[[2]] = c(
# glance_stats$value[1],
# ddeviance2,
# ddf2,
# pvalString(fit2, digits = 3, format = "default"),
# NA,
# NA,
# NA
# )
# glance_stats = glance_stats[-5:-7, ]
# glance_stats2=as.data.frame(matrix(ncol=7,nrow=1))
# glance_stats2[1,]=c(glance_stats$key[1],glance_stats$value[1],NA,NA,NA,glance_stats$key[6],glance_stats$value[6])
# glance_stats2[2,]=c(glance_stats$key[2],glance_stats$value[2],NA,NA,NA,glance_stats$key[7],glance_stats$value[7])
# glance_stats2[3,]=c(glance_stats$key[3],glance_stats$value[3],NA,NA,NA,glance_stats$key[4],glance_stats$value[4])
# glance_stats=glance_stats2
} else if(type=="ord"){
glance_stats=as.data.frame(matrix(ncol={7+v.p.rep},nrow=1))
glance_stats[1,]=c(paste("Family: Ordinal <br /> Link: ",new.model$info$link,sep=""),rep(NA,6),rep(NA,v.p.rep))
# glance_stats[[1]]=c("Null.dev","Chi-Sq","dF","Pr(>Chisq)")
# glance_stats[[2]]=c({{-2*my.model$logLik}-ddeviance2},ddeviance2,ddf2,pvalString(fit2, digits = 3, format = "default"))
# glance_stats[[3]]=c("Family: ","Link: ",NA,NA)
# glance_stats[[4]]=c("ordinal",levels(my.model$info$link),NA,NA)
# glance_stats[[5:{5+v.p.rep}]]=c(NA,NA,NA,NA)
# glance_stats[[{6+v.p.rep}]]=c("logLik","AIC","BIC","deviance")
# glance_stats[[{7+v.p.rep}]]=c(my.model$logLik,as.numeric(levels(my.model$info$AIC)),BIC(my.model),{-2*my.model$logLik})
}else{
}
}
#### For total
the.length = the.length + 1
this.temp.var = this.temp.var + 1
#### Make table ####
options(pixie_interactive = pix.int,
pixie_na_string = "")
if (type == "lm") {
the.length=the.length+2
my.dust = pixiedust::dust(my.tables.df) %>%
sprinkle(cols = "p.val", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle_print_method(pix.method) %>%
sprinkle_na_string() %>%
sprinkle(
rows = 1:the.length,
cols = v.p.len,
border = "right",
border_color = "black"
) %>%
sprinkle(
rows = 1:the.length,
cols = 1,
border = "left",
border_color = "black"
) %>%
sprinkle(
rows=2,
cols=1:v.p.len,
border=c("top","bottom")
)%>%
sprinkle(rows=this.temp.var-1,
border="top")%>%
sprinkle(
rows = 1,
cols = 1:v.p.len,
border = c("top", "bottom"),
border_color = "black",
part = "head"
) %>%
sprinkle(rows=1,cols=1,border="left",part="head")%>%
sprinkle(rows=1,cols=v.p.len,border="right",part="head")%>%
sprinkle(
rows = this.temp.var+1,
cols = 1:v.p.len,
border = "bottom",
border_color = "black"
)
if (!VIF & !part.eta) {
my.dust = my.dust %>%
sprinkle(cols = c("sumsq", "est", "std.err", "f.val"),
round = 2) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
sprinkle_colnames(
"Variable",
"Estimate",
"Std. Error",
paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
"df",
"F-value",
"Pr(>F)"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head") %>%
sprinkle_pad(rows = 1,
pad = 5,
part = "head")
} else if (!VIF) {
my.dust = my.dust %>%
sprinkle(
cols = c("sumsq", "est", "std.err", "f.val", "p.eta"),
round = 2
) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
sprinkle_colnames(
"Variable",
"Estimate",
"Std. Error",
paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
"df",
"F-value",
"Pr(>F)",
"Part <br /> eta"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head") %>%
sprinkle_pad(rows = 1,
pad = 5,
part = "head")
} else if (!part.eta) {
my.dust = my.dust %>%
sprinkle(
cols = c("sumsq", "est", "std.err", "f.val", "VIF"),
round = 2
) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
sprinkle_colnames(
"Variable",
"Estimate",
"Std. Error",
paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
"df",
"F-value",
"Pr(>F)",
"VIF"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head") %>%
sprinkle_pad(rows = 1,
pad = 5,
part = "head")
} else{
my.dust = my.dust %>%
sprinkle(
cols = c("sumsq", "est", "std.err", "f.val", "p.eta", "VIF"),
round = 2
) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7, 8), pad = 5) %>%
sprinkle_colnames(
"Variable",
"Estimate",
"Std. Error",
paste("Type ", SS.type, "<br /> Sums of Sq", sep = ""),
"df",
"F-value",
"Pr(>F)",
"Part <br /> eta",
"VIF"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head") %>%
sprinkle_pad(rows = 1,
pad = 5,
part = "head")
}
} else if (type == "glm2") {
my.dust = pixiedust::dust(my.tables.df) %>%
sprinkle(cols = "p.val", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle_print_method(pix.method) %>%
sprinkle_na_string() %>%
sprinkle(cols = 2:5, round = 2) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
sprinkle(cols = 2:{7+v.p.rep},
pad = 5,
part = "head") %>%
sprinkle(
rows = {
this.temp.var - 2
},
cols = 1:v.p.len,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 1:the.length,
cols = 1:v.p.len,
border = "right",
border_color = "black"
) %>%
sprinkle(
rows = 1:the.length,
cols = 1,
border = "left",
border_color = "black"
) %>%
sprinkle(
rows = 1,
cols = 1:v.p.len,
border = c("top", "bottom", "left", "right"),
border_color = "black",
part = "head"
) %>%
sprinkle(
rows = this.temp.var - 1,
cols = 1:v.p.len,
border = "bottom",
border_color = "black"
) %>%
sprinkle_align(rows = 1,
halign = "center",
part = "head")
if (!VIF) {
my.dust = my.dust %>% sprinkle_colnames("Variable",
"Odds Ratio",
"Std. Error",
"z-Value",
"Deviance",
"df",
"p-Value")
} else{
my.dust = my.dust %>% sprinkle_colnames(
"Variable",
"Odds Ratio",
"Std. Error",
"z-Value",
"Deviance",
"df",
"p-Value",
"VIF"
) %>%
sprinkle_round(cols = 8, round = 2)
}
} else{
my.dust = pixiedust::dust(my.tables.df) %>%
sprinkle(cols = "p.val", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle_print_method(pix.method) %>%
sprinkle_na_string() %>%
sprinkle(cols = 2:5, round = 2) %>%
sprinkle(cols = c(2, 3, 4, 5, 6, 7), pad = 5) %>%
sprinkle(cols = 2:7,
pad = 5,
part = "head") %>%
sprinkle(
rows = this.temp.var+1,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 1,
cols = 1:2,
border = c("top", "bottom"),
border_color = "black",
part = "head"
) %>%
sprinkle(
rows = 1,
cols = 3:4,
border = c("top", "bottom"),
border_color = "black",
part = "head"
) %>%
sprinkle(
rows = 1,
cols = 5:{7+v.p.rep},
border = c("top", "bottom"),
border_color = "black",
part = "head"
) %>%
sprinkle(
rows = this.temp.var - 1,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 1,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = this.temp.var - 2,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 1+total.intercepts,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle(
rows = 2+total.intercepts,
cols = 1:7,
border = "bottom",
border_color = "black"
) %>%
sprinkle_colnames("Variable",
"Odds Ratio",
"Conf. <br /> 2.5%","Int. <br /> 97.5%",
"Deviance",
"df",
"Pr(>Chi)")%>%
sprinkle_border(cols=1,border=c("left","right"))%>%
sprinkle_border(cols={7+v.p.rep},border="right")%>%
sprinkle_border(cols=1,border=c("left","right"),part="head")%>%
sprinkle_border(cols={7+v.p.rep},border="right",part="head")
}
if(do.glance){
if (type == "lm") {
my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
sprinkle_na_string(part = "foot") %>%
sprinkle(rows=1,merge=T,halign="center",part="foot")
# my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
# sprinkle(cols = c(2, {
# 7 + v.p.rep
# }),
# round = 3,
# part = "foot") %>%
# sprinkle(cols = 3:{
# 5 + v.p.rep
# },
# replace = c(rep("", {
# 12 + 4 * v.p.rep
# })),
# part = "foot") %>%
# sprinkle(
# cols = 1,
# replace = c("R-Square", "Adj R-Sq", "F-Statistic", "P-Value"),
# part = "foot"
# ) %>%
# sprinkle(
# cols = 2,
# rows = 4,
# fn = quote(pvalString(
# value, digits = 3, format = "default"
# )),
# part = "foot"
# ) %>%
# sprinkle(
# cols = 1:v.p.len,
# rows = 1,
# halign = "center",
# part = "head"
# ) %>%
# sprinkle_width(cols = 1,
# width = 90,
# width_units = "pt") %>%
# sprinkle_width(cols = 2,
# width = 108,
# width_units = "pt") %>%
# sprinkle_width(cols = 4,
# width = 62,
# width_units = "pt") %>%
# sprinkle_width(cols = 5,
# width = 68,
# width_units = "pt") %>%
# sprinkle_width(cols = 6,
# width = 68,
# width_units = "pt") %>%
# sprinkle_width(cols = 7,
# width = 71,
# width_units = "pt") %>%
# sprinkle(cols = 2,
# halign = "left",
# part = "foot")
} else{
my.dust = pixiedust::redust(my.dust, glance_stats, part = "foot") %>%
sprinkle_na_string(part = "foot") %>%
sprinkle(rows=1,merge=T,halign="center",part="foot")
}
}
if (pix.int) {
return(my.dust)
} else{
my.dust.print = print(my.dust, quote = F)[1]
return(my.dust.print)
}
}
#' PixieDust Tables for Lavaan
#'
#' Places html tables in viewer for different areas of Lavaan output
#' for SEM. Based on NIU class 11/2017
#'
#' @param myfit Fit from Lavaan package
#' @return NULL
#' @keywords Explore
#' @export
#' @examples
#' quick.lavaan(myfit)
quick.lavaan = function(myfit) {
require(lavaan)
require(pixiedust)
require(tibble)
prev.width = getOption("width")
options(width = 80)
mysummary = capture.output(lavaan::summary(myfit,
standardized = TRUE, rsq = T))
#### Fit Table ####
my.fit.table = as.data.frame(matrix(nrow = 7, ncol = 4))
my.fit.table[1, ] = c("Number of Iterations",
lavInspect(myfit, what = "iterations"),
NA,
NA)
my.fit.table[2, ] = c("Total Observations",
lavInspect(myfit, what = "ntotal"),
NA,
NA)
my.fit.table[3, ] = c(
"Chi-Sq Test of Fit",
round(fitMeasures(myfit)[3], 2),
fitMeasures(myfit)[4],
pixiedust::pval_string(fitMeasures(myfit)[5])
)
my.fit.table[4, ] = c("Comparitive Fit Index", round(fitMeasures(myfit)[9], 3), NA, NA)
my.fit.table[5, ] = c("Tucker-Lewis Index", round(fitMeasures(myfit)[10], 3), NA, NA)
my.fit.table[6, ] = c("RMSEA",
round(fitMeasures(myfit)[23], 3),
NA,
pixiedust::pval_string(fitMeasures(myfit)[26]))
my.fit.table[7, ] = c("SRMR", round(fitMeasures(myfit)[29], 3), NA, NA)
colnames(my.fit.table) = c("Name", "Value", "df", "p-val")
#my.fit.table
#### R Sq ####
my.r2 = lavaan::lavInspect(myfit, what = "r2")
my.r2 = as.data.frame(my.r2)
my.r2 = tibble::rownames_to_column(my.r2)
my.r2$my.r2 = round(my.r2$my.r2, 3)
#my.r2
#### Other tables ####
lavaan.latent = NULL
lavaan.covar = NULL
lavaan.vari = NULL
lavaan.reg = NULL
lavaan.latent.temp = 1
lavaan.temp = 1
while (lavaan.temp < {
length(mysummary) + 1
}) {
### Latent Variables matrix
if (length(grep("Latent Variables:", mysummary[lavaan.temp])) > 0) {
lavaan.temp = lavaan.temp + 2
while ((length(grep("Covariances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("Regressions:", mysummary[lavaan.temp])) == 0) &&
(length(grep("Variances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("R-Square:", mysummary[lavaan.temp])) == 0)) {
str.temp = strsplit(mysummary[lavaan.temp], " ")
if (length(str.temp[[1]]) > 0) {
lavaan.latent[[lavaan.latent.temp]] = list()
for (i in 1:(length(str.temp[[1]]))) {
if (nchar(str.temp[[1]][i]) > 0) {
lavaan.latent[[lavaan.latent.temp]] = c(lavaan.latent[[lavaan.latent.temp]], str.temp[[1]][i])
}
}
lavaan.latent.temp = lavaan.latent.temp + 1
}
lavaan.temp = lavaan.temp + 1
}
if (length(grep("Regressions:", mysummary[lavaan.temp])) != 0) {
#### Regressions Matrix
lavaan.temp = lavaan.temp + 2
lavaan.latent.temp = 1
while ((length(grep("Covariances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("Variances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("R-Square:", mysummary[lavaan.temp])) == 0)) {
str.temp = strsplit(mysummary[lavaan.temp], " ")
if (length(str.temp[[1]]) > 0) {
lavaan.reg[[lavaan.latent.temp]] = list()
for (i in 1:(length(str.temp[[1]]))) {
if (nchar(str.temp[[1]][i]) > 0) {
lavaan.reg[[lavaan.latent.temp]] = c(lavaan.reg[[lavaan.latent.temp]], str.temp[[1]][i])
}
}
lavaan.latent.temp = lavaan.latent.temp + 1
}
lavaan.temp = lavaan.temp + 1
}
}
#### Covariance matrix
if ((length(grep("Covariances:", mysummary[lavaan.temp])) != 0)) {
lavaan.temp = lavaan.temp + 2
lavaan.latent.temp = 1
while ((length(grep("Variances:", mysummary[lavaan.temp])) == 0) &&
(length(grep("R-Square:", mysummary[lavaan.temp])) == 0)) {
str.temp = strsplit(mysummary[lavaan.temp], " ")
if (length(str.temp[[1]]) > 0) {
lavaan.covar[[lavaan.latent.temp]] = list()
for (i in 1:(length(str.temp[[1]]))) {
if (nchar(str.temp[[1]][i]) > 0) {
lavaan.covar[[lavaan.latent.temp]] = c(lavaan.covar[[lavaan.latent.temp]], str.temp[[1]][i])
}
}
lavaan.latent.temp = lavaan.latent.temp + 1
}
lavaan.temp = lavaan.temp + 1
}
}
#### Variance matrix
if ((length(grep("Variances:", mysummary[lavaan.temp])) != 0)) {
lavaan.temp = lavaan.temp + 2
lavaan.latent.temp = 1
while (length(grep("R-Square:", mysummary[lavaan.temp])) == 0) {
str.temp = strsplit(mysummary[lavaan.temp], " ")
if (length(str.temp[[1]]) > 0) {
lavaan.vari[[lavaan.latent.temp]] = list()
for (i in 1:(length(str.temp[[1]]))) {
if (nchar(str.temp[[1]][i]) > 0) {
lavaan.vari[[lavaan.latent.temp]] = c(lavaan.vari[[lavaan.latent.temp]], str.temp[[1]][i])
}
}
lavaan.latent.temp = lavaan.latent.temp + 1
}
lavaan.temp = lavaan.temp + 1
}
}
}
lavaan.temp = lavaan.temp + 1
}
#### lavaan.covar
if (length(lavaan.covar) > 0) {
covar.table = as.data.frame(matrix(nrow = length(lavaan.covar), ncol = 7))
table.count = 1
for (i in 1:length(lavaan.covar)) {
if (length(lavaan.covar[[i]]) < 7) {
covar.table[table.count, ] = c(lavaan.covar[[i]][1], NA, NA, NA, NA, NA, NA)
}
else{
covar.table[table.count, ] = c(
lavaan.covar[[i]][1],
lavaan.covar[[i]][2],
lavaan.covar[[i]][3],
lavaan.covar[[i]][6],
lavaan.covar[[i]][7],
lavaan.covar[[i]][4],
lavaan.covar[[i]][5]
)
}
table.count = table.count + 1
}
colnames(covar.table) = c("Name",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)")
#covar.table
} else{
covar.table = NULL
}
#### lavaan.latent
if (length(lavaan.latent) > 0) {
latent.table = as.data.frame(matrix(nrow = length(lavaan.latent), ncol =
7))
latent.table.count = 1
for (i in 1:length(lavaan.latent)) {
if (length(lavaan.latent[[i]]) < 3) {
latent.table[latent.table.count, ] = c(lavaan.latent[[i]][[1]], NA, NA, NA, NA, NA, NA)
} else if (length(lavaan.latent[[i]]) == 4) {
latent.table[latent.table.count, ] = c(
lavaan.latent[[i]][[1]],
lavaan.latent[[i]][[2]],
NA,
lavaan.latent[[i]][[3]],
lavaan.latent[[i]][[4]],
NA,
NA
)
} else{
latent.table[latent.table.count, ] = c(
lavaan.latent[[i]][[1]],
lavaan.latent[[i]][[2]],
lavaan.latent[[i]][[3]],
lavaan.latent[[i]][[6]],
lavaan.latent[[i]][[7]],
lavaan.latent[[i]][[4]],
lavaan.latent[[i]][[5]]
)
}
latent.table.count = latent.table.count + 1
}
colnames(latent.table) = c("Name",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)")
#latent.table
} else{
latent.table = NULL
}
#### lavaan.vari
if (length(lavaan.vari) > 0) {
vari.table = as.data.frame(matrix(nrow = length(lavaan.vari), ncol = 7))
vari.table.count = 1
for (i in 1:length(lavaan.vari)) {
vari.table[vari.table.count, ] = c(
lavaan.vari[[i]][[1]],
lavaan.vari[[i]][[2]],
lavaan.vari[[i]][[3]],
lavaan.vari[[i]][[6]],
lavaan.vari[[i]][[7]],
lavaan.vari[[i]][[4]],
lavaan.vari[[i]][[5]]
)
vari.table.count = vari.table.count + 1
}
colnames(vari.table) = c("Name",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)")
} else{
vari.table = NULL
}
#### lavaan.reg
if (length(lavaan.reg) > 0) {
reg.table = as.data.frame(matrix(nrow = length(lavaan.reg), ncol = 7))
reg.table.count = 1
for (i in 1:length(lavaan.reg)) {
if (length(lavaan.reg[[i]]) < 7) {
reg.table[reg.table.count, ] = c(lavaan.reg[[i]][1], NA, NA, NA, NA, NA, NA)
}
else{
reg.table[reg.table.count, ] = c(
lavaan.reg[[i]][1],
lavaan.reg[[i]][2],
lavaan.reg[[i]][3],
lavaan.reg[[i]][6],
lavaan.reg[[i]][7],
lavaan.reg[[i]][4],
lavaan.reg[[i]][5]
)
}
reg.table.count = reg.table.count + 1
}
colnames(reg.table) = c("Name",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)")
} else{
reg.table = NULL
}
dusted.fit.table = pixiedust::dust(my.fit.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("", "Value", "df", "P-Val") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.fit.table
latent.table$`P(>|z|)` = as.numeric(latent.table$`P(>|z|)`)
dusted.latent.table = pixiedust::dust(latent.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("Latent",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle(cols = 7, fn = quote(pvalString(value))) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.latent.table
if (length(reg.table) > 0) {
reg.table$`P(>|z|)` = as.numeric(reg.table$`P(>|z|)`)
dusted.reg.table = pixiedust::dust(reg.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("Regression",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle(cols = 7, fn = quote(pvalString(value))) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.reg.table
} else{
dusted.reg.table = NULL
}
if (length(covar.table) > 0) {
covar.table$`P(>|z|)` = as.numeric(covar.table$`P(>|z|)`)
dusted.covar.table = pixiedust::dust(covar.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("Covariance",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle(cols = 7, fn = quote(pvalString(value))) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.covar.table
} else{
dusted.covar.table = NULL
}
if (length(vari.table)) {
vari.table$`P(>|z|)` = as.numeric(vari.table$`P(>|z|)`)
dusted.vari.table = pixiedust::dust(vari.table) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_colnames("Variance",
"Estimate",
"Std.Err",
"Std.lv",
"Std.all",
"z-value",
"P(>|z|)") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle(cols = 7, fn = quote(pvalString(value))) %>%
pixiedust::sprinkle_align(halign = "center", part = "head")
#dusted.vari.table
} else{
dusted.vari.table = NULL
}
dusted.r2 = pixiedust::dust(my.r2) %>%
pixiedust::sprinkle_na_string(na_string = "") %>%
pixiedust::sprinkle_print_method(print_method = "html") %>%
pixiedust::sprinkle_border(border = "all") %>%
pixiedust::sprinkle_border(border = "all", part = "head") %>%
pixiedust::sprinkle_pad(pad = 7) %>%
pixiedust::sprinkle_pad(pad = 7, part = "head") %>%
pixiedust::sprinkle_align(halign = "center", part = "head") %>%
pixiedust::sprinkle_colnames("Variable", "R^2")
#dusted.r2
# mydustlist=list(dusted.fit.table,dusted.latent.table)
# if(!is.null(dusted.reg.table)){
# mydustlist=list(mydustlist,dusted.reg.table)
# }
# if(!is.null(dusted.covar.table)){
# mydustlist=list(mydustlist,dusted.covar.table)
# }
# if(!is.null(dusted.vari.table)){
# mydustlist=list(mydustlist,dusted.vari.table)
# }
# mydustlist=list(mydustlist,dusted.r2)
mydustlist = list(
dusted.fit.table,
dusted.latent.table,
dusted.reg.table,
dusted.covar.table,
dusted.vari.table,
dusted.r2
)
#print(mydustlist[[1]])
dust.num = 1
while (dust.num < {
length(mydustlist) + 1
}) {
print(mydustlist[[dust.num]])
x = readline(prompt = "Press z and enter to go back, or enter to go forward\n")
if (x != "z") {
dust.num = dust.num + 1
} else{
if (dust.num > 1) {
dust.num = dust.num - 1
}
}
}
options(width = prev.width)
}
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