#' ANOVA and ANODE tables in HTML
#'
#' Beautiful tables in HTML. Creates advanced ANOVA, ANODE, and MANOVA
#' tables reporting relevant variable changes, treatment changes, and intercept
#' diagnostics. Also can report factor contrasts within the table. For MANOVA tables,
#' latent variable ANCOVAs can also be calculated within the MANOVA table. Normally
#' only requires my.model and my.factor.
#'
#' This package combines [summary], [anova], and [car::Anova] to create ANOVA, ANODE,
#' and MANOVA tables. On top of providing normal information about the current model,
#' this package can show contrasts for multi-level factors. In addition to the current
#' model, treatment changes from null provide basic fit diagnostics.
#'
#' For models of class [lm], a basic ANOVA table will be created with Type II sums of
#' squares.
#'
#' @param my.model Model to be tested. Currently supported are lm, glm, clm, manova.
#' @param my.factor If there are any factors, list them here.
#' @param show.contrasts If there are factors with df>1, show partial contrasts? (default = F). For lm and glm, this is done using Phia, for manova, it is calculated from SSCP matrices.
#' @param adjustment Adjustment type to be performed on contrats. Uses p.adjust. (default = "bonferroni")
#' @param marginality Should SS or deviance for the intercept be neglected? (default = T) If set to F, this is a diagnostic tool to see if the effects of the treatment are at least similar in magnitude to the effects of the intercepts.
#' @param do.return Return the quick.table or just display it? (default=T)
#' @param abbrev.length Abbreviation length for rownames. Default is 30 for clm, 15 otherwise.
#' @param show.footer Show footer including missingness, method, and link and adjustment information if appropriate (default = T)
#' @param SS.type Type of sums of squares (or deviance) to report. Currently, only type II is reported. (default = 2)
#' @param myDF Backup in case can't figure out data frame.
#' @param type Backup in case can't figure out type.
#' @return Either quick.table or invisible()
#' @keywords Explore
#' @export
#' @examples
#' quick.reg(wine.ord)
quick.reg = function(my.model,
my.factor = NULL,
show.contrasts=F,
adjustment = "bonferroni",
marginality=T,
do.return=T,
abbrev.length = ab.len,
show.footer=T,
SS.type = 2,
myDF=my.found.df,
type=my.reg.type,
...) {
library(car)
library(tidyr)
library(phia)
library(quick.tasks)
library(dplyr)
#### Find type ####
#my.reg.type=quick.type(my.model)
UseMethod("quick.reg",my.model)
}
#' MANOVA tables in HTML
#'
#' quick.reg method for class "manova".
#'
#' Need to put some description of method here.
#'
#' @param my.model Model to be tested. Currently supported are lm, glm, clm, manova.
#' @param my.factor If there are any factors, list them here.
#' @param test.stat MANOVA test statistic to use. (default = "Pillai"). Wilks, Pillai, Roy supported.
#' @param show.contrasts If there are factors with df>1, show partial contrasts? (default = F). For lm and glm, this is done using Phia, for manova, it is calculated from SSCP matrices.
#' @param adjustment Adjustment type to be performed on contrats. Uses p.adjust. (default = "bonferroni")
#' @param show.y.contrasts Show how variables load on each variable? (default = F). NOTE: This is NOT the same as latent variables what are latent ANOVAs.
#' @param show.latent Show latent variables? (default = F). This is equivalent to running an ANOVA for each independent variable while making the dependent variables as uncorrelated as possible.
#' @param show.intercepts Show the individual intercepts? (default = F).
#' @param real.names Use real names for intercepts in contrasts or use intercept number? (default = T). This does not change intercept names if show.intercepts is on.
#' @param part.eta NOT YET SUPPORTED
#' @param VIF NOT YET SUPPORTED
#' @param marginality Should SS or deviance for the intercept be neglected? (default = T) If set to F, this is a diagnostic tool to see if the effects of the treatment are at least similar in magnitude to the effects of the intercepts.
#' @param do.return Return the quick.table or just display it? (default=T)
#' @param abbrev.length Abbreviation length for rownames. Default is 30 for clm, 15 otherwise.
#' @param show.footer Show footer including missingness, method, and link and adjustment information if appropriate (default = T)
#' @param SS.type Type of sums of squares (or deviance) to report. Currently, only type II is reported. (default = 2)
#' @param myDF Backup in case can't figure out data frame.
#' @param type Backup in case can't figure out type.
#' @return Either quick.table or invisible()
#' @keywords Explore
#' @export
#' @examples
#' quick.reg.manova(my.manova)
quick.reg.manova = function(my.model,
my.factor = NULL,
test.stat = "Pillai",
show.contrasts=F,
adjustment = "bonferroni",
show.y.contrasts=F,
show.latent=F,
show.intercepts=F,
real.names=T,
part.eta = F,
VIF = F,
marginality=T,
do.return=T,
abbrev.length = ab.len,
show.footer=T,
SS.type=2,
myDF = my.found.df,
type = my.reg.type
) {
#### Find type ####
my.reg.type="manova"
#### 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
}
#### Begin MANOVA ####
#### Inits ####
my.y.levels=dim(my.model$model[[1]])[2]
my.new.df=my.model$model
my.envir=environment()
SS.type = 2
my.nested.table=quick.SSCP(my.model, myDF, marginality=T, show.contrasts, show.latent,my.envir,adjustment)
#### Get treatment ####
treat.model=my.nested.table[dim(my.nested.table)[1],4]
my.null.model=my.nested.table[1,3]
#### 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(!marginality){
the.resid=sum(diag(treat.model[[1]][[2]][[1]]))
#### Partial residuals
part.resid.total=treat.model[[1]][[2]][[1]]
}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){
latent.part.resid.total=latent.treat.model[[1]][[2]]
}
#### Get totals ####
the.total=the.resid+treat.total+if(!marginality){sum(diag(treat.model[[1]][[1]][[1]]))}else{0}
the.total.df=the.resid.df+treat.total.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(treat.model[[1]][[1]])){
if(i==1){
my.i=i
}else{
my.i=2*i-1
}
#### Put in basic line ####
my.treat.err=solve(treat.model[[1]][[2]][[1]])%*%treat.model[[1]][[1]][[i]]
my.test.stat=ifelse({i==1 & marginality},NA,quick.m.test(my.treat.err,test.stat))
my.SS=ifelse({i==1 & marginality},0,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=ifelse({i==1 & marginality},NA,{my.SS/my.df}/{the.resid/the.resid.df})
my.p.val=ifelse({i==1 & marginality},NA,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(treat.model[[1]][[2]][[1]]))/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}/{latent.treat.model[[1]][[2]][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$xlevels))
for(k in 1:{my.nested.table[my.i,7]}){
my.name=my.nested.table[my.i,11][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,11][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,11][[1]][k,3])
my.test.stat=NA
my.df=1
my.mult.df=NA
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,11][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Put in contrasts ####
#### HERE ####
#### Not right. Don't have it decomposed this way.
if(show.contrasts & !show.latent & my.i!=1){
#### Check length
if(!is.na(my.nested.table[my.i,8])){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:as.numeric(my.nested.table[my.i,7])){
my.name=my.nested.table[my.i,8][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,8][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,8][[1]][k,3])
my.test.stat=NA
my.df=NA
my.mult.df=my.y.levels
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,8][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
}
}
#### Put in contrasts ####
if(show.contrasts & !show.latent & !show.y.contrasts){
#### Check length
if(!is.na(my.nested.table[my.i,8])){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:as.numeric(my.nested.table[my.i,7])){
my.name=my.nested.table[my.i,8][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,8][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,8][[1]][k,3])
my.test.stat=NA
my.df=NA
my.mult.df=my.y.levels
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,8][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Put in latents (ANOVA) ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent &my.i!=1 & !show.y.contrasts){
#my.my.i.temp=my.counter[my.i-1]
my.my.i.temp=my.i
for(y in 1:{my.y.levels}){
my.y=y
my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
my.df=as.numeric(my.nested.table[my.i,7])
my.test.stat=NA
my.resid.df=the.resid.df-my.df
my.SS=as.numeric(my.nested.table[my.i,8][[1]][[1]][[i]][my.y])
#### FIX HERE!!!! ####
my.f.val={my.SS/my.df}/{latent.treat.model[[1]][[2]][my.y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in latent contrasts ####
if(show.contrasts & !{show.contrasts & show.latent}){
#### Check length
if({as.numeric(my.nested.table[my.i,7])>1}){
other.manova.grep=grep(paste("^",names(treat.model[[1]][[1]])[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:{as.numeric(my.nested.table[my.i,7])}){
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)/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=as.numeric(treat.total.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(treat.model[[1]][[2]][[1]]))/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.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=abs(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}/{latent.treat.model[[1]][[2]][b]/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)+ifelse(marginality,0,sum(diag(treat.model[[1]][[1]][[1]])))},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.resid.df+treat.total.df,my.y.levels*{the.resid.df+treat.total.df},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
}
my.html.table=quick.table(my.manova.table,"manova",test=test.stat,marginality=marginality, abbrev.length = abbrev.length,the.footer = the.footer)
if(do.return){
return(invisible(my.html.table))
}else{
return(invisible(NULL))
}
#### 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 ####
}
#' ANOVA and ANODE tables in HTML
#'
#' quick.reg method for "lm", "glm", and "clm" classes
#'
#' @param my.model Model to be tested. Currently supported are lm, glm, clm, manova.
#' @param my.factor If there are any factors, list them here.
#' @param show.contrasts If there are factors with df>1, show partial contrasts? (default = F). For lm and glm, this is done using Phia, for manova, it is calculated from SSCP matrices.
#' @param adjustment Adjustment type to be performed on contrats. Uses p.adjust. (default = "bonferroni")
#' @param show.intercepts Show information regarding threshold values for binomial or ordinal regression? (default = T).
#' @param show.int.change Show the change in deviance for the intercept? (default = F). This is a diagnostic as to whether your link function is appropriate and/or if you can generalize to the null model.
#' @param part.eta Show partial eta square for lm? (default = F).
#' @param VIF Show variable inflation factor? (default = F).
#' @param marginality Should SS or deviance for the intercept be neglected? (default = T) If set to F, this is a diagnostic tool to see if the effects of the treatment are at least similar in magnitude to the effects of the intercepts.
#' @param do.return Return the quick.table or just display it? (default=T)
#' @param abbrev.length Abbreviation length for rownames. Default is 30 for clm, 15 otherwise.
#' @param show.footer Show footer including missingness, method, and link and adjustment information if appropriate (default = T)
#' @param SS.type Type of sums of squares (or deviance) to report. Currently, only type II is reported. (default = 2)
#' @param myDF Backup in case can't figure out data frame.
#' @param type Backup in case can't figure out type.
#' @return Either quick.table or invisible()
#' @keywords Explore
#' @export
#' @examples
#' quick.reg(wine.ord)
quick.reg.default = function(my.model,
my.factor = NULL,
show.contrasts=F,
adjustment = "bonferroni",
show.intercepts=T,
show.int.change=F,
part.eta = F,
VIF = F,
marginality=T,
do.return=T,
abbrev.length = ab.len,
show.footer=T,
SS.type=2,
myDF = my.found.df,
type = my.reg.type) {
#### Find type ####
#my.reg.type=quick.type(my.model)
my.reg.type=class(my.model)[1]
if(type=="clm"){
type="ord"
}
#### 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$data)),envir = .GlobalEnv)
#print(dim(my.found.df))
if (is.null(my.found.df)) {
stop(paste("No data frame found"))
}
SS.type = 2
my.new.df=my.model$model
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$y.levels) - 1
} else{
my.III.summary = car::Anova(my.model, type = ifelse(marginality,2,3))
if (type == "glm" & is.null(my.factor)) {
the.length = dim(my.III.summary)[1] + 1
} else{
the.length = dim(my.III.summary)[1]
}
}
#### Calculate total SS ####
my.total = sum(my.III.summary$`Sum Sq`[ifelse(marginality,1,1):length(my.III.summary$`Sum Sq`)])
my.df.total = sum(my.III.summary$Df[ifelse(marginality,1,2):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$`Sum Sq`[ifelse(marginality,1,2):{length(my.III.summary$`Sum Sq`)-1}])
treat.df=sum(my.III.summary$Df[ifelse(marginality,1,2):{length(my.III.summary$Df)-1}])
my.total.change=treat.SS+ifelse(marginality,0,my.III.summary$`Sum Sq`[1])
my.df.total.change=treat.df+ifelse(marginality,0,1)
}else if (type == "glm") {
#### DevAn Table ####
#### Inits ####
new.df=my.model$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)$coefficients[1:total.intercepts,3]^2
my.full.dev=sum(my.full.model.z)
my.null.dev=sum(my.null.model.z)
treat.dev=anova(null.model,new.model)$`Deviance`[2]
my.null.dev.total=summary(null.model)$coefficients[1]^2
my.full.dev.total=sum(summary(new.model)$coefficients[1:total.intercepts,3]^2)
my.int.dev.total=abs(my.null.dev.total-my.full.dev.total)
my.int.dev=summary(new.model)$coefficients[1:total.intercepts,3]^2
#vars.dev.df=drop1(new.model,test="Chi")$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$logLik)[1])}
total.dev.change=treat.dev+my.int.dev.total
total.dev.change.df=vars.df+1
total.dev=new.model$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)$coefficients[1:total.intercepts,3]^2
my.full.dev=sum(my.full.model.z)
my.null.dev=sum(my.null.model.z)
my.int.dev.total=abs(total.intercepts*{my.null.dev-my.full.dev})
my.int.dev=summary(new.model)$coefficients[1:total.intercepts,3]^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$logLik)[1])}
total.dev.change=treat.dev+ifelse(marginality,0,sum(my.int.dev))
if(total.intercepts>1){
total.dev.change.df=total.intercepts*vars.df.total
}else{
total.dev.change.df=vars.df.total+total.intercepts
}
total.dev=-2*null.model$logLik
resid.dev=-2*new.model$logLik-ifelse(marginality,0,sum(my.int.dev))
total.df=dim(myDF)[1]
resid.df=my.model$df.residual
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 (VIF & type == "glm") {
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))+ifelse(marginality,1,0)
} 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$coefficients)-sum(num.of.levels)+length(num.of.levels)+total.intercepts+1
if(is.null(my.factor)){
dang.length=dang.length-1
}
} else{
# dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
# sum(num.of.levels) - length(my.factor)
# }, 0) + 1
#dang.length=7
dang.length=length(new.model$coefficients)+length(my.factor)+1-ifelse(show.intercepts,0,total.intercepts-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"){
if(show.int.change){
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
}
# print(sum(my.int.dev))
# print(total.intercepts)
my.tables.df[this.temp.var, ] = c(
"(Intercept)",
NA,
NA,
NA,
ifelse(marginality,NA,sum(my.int.dev)),
total.intercepts,
ifelse(marginality,NA,pchisq(sum(my.int.dev),total.intercepts,lower.tail = F)),
rep(NA, v.p.rep))
this.temp.var=this.temp.var+1
}
if(type=="lm" | show.intercepts){
while (i <= total.intercepts) {
if (type == "lm") {
if(marginality){
# 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(
"(Intercept)",
NA,
NA,
NA,
1,
NA,
NA,
rep(NA, v.p.rep)
)
}else{
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],
NA,
NA,
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],
1,
pchisq(my.int.dev[i],1,lower.tail = F),
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
}
}else{
this.shift.temp = this.shift.temp + 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),rep(NA,v.p.rep))
this.temp.var=this.temp.var+1
}else{
my.tables.df[this.temp.var,]=c("Treatment Change",NA,NA,treat.SS,treat.df,my.summary$fstatistic[1],pf(my.summary$fstatistic[1],my.summary$fstatistic[2],my.summary$fstatistic[3],lower.tail=F),rep(NA,v.p.rep))
this.temp.var=this.temp.var+1
}
} else if (ifelse({type=="lm" & marginality},this.shift.temp-1,this.shift.temp) %in% my.factor.rownames) {
if (type == "lm") {
my.sumsq = my.III.summary$`Sum Sq`[ifelse(marginality,this.shift.temp-1,this.shift.temp)]
my.df = my.III.summary$Df[ifelse(marginality,this.shift.temp-1,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$`F value`[ifelse(marginality,this.shift.temp-1,this.shift.temp)]
my.p.val = my.III.summary$`Pr(>F)`[ifelse(marginality,this.shift.temp-1,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$`Sum Sq`[ifelse(marginality,this.shift.temp-1,this.shift.temp)]
my.df = my.III.summary$Df[ifelse(marginality,this.shift.temp-1,this.shift.temp)]
my.est = my.estimate[ifelse(SS.type==3,this.shift.temp-1,{this.shift.temp+sum(num.of.levels[1:{my.factor.var-1}])-my.factor.var})]
my.std.err = my.std.error[ifelse(SS.type==3,this.shift.temp-1,{this.shift.temp+sum(num.of.levels[1:{my.factor.var-1}])-my.factor.var})]
my.f.val = my.III.summary$`F value`[ifelse(marginality,this.shift.temp-1,this.shift.temp)]
my.p.val = my.III.summary$`Pr(>F)`[ifelse(marginality,this.shift.temp-1,this.shift.temp)]
if (!VIF & !part.eta) {
my.tables.df[this.temp.var, ] = c(
rownames(my.III.summary)[ifelse(marginality,this.shift.temp-1,this.shift.temp)],
my.est,
my.std.err,
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+ord.temp-1],
my.est,
my.std.err,
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+ord.temp-1],
my.est,
my.std.err,
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],
my.est,
my.std.err,
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],
car::vif(my.model)[this.shift.temp-1],
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-ifelse(!show.intercepts,1,{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+ifelse(!show.intercepts,1,{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("Total Change",
NA,
NA,
my.total.change,
my.df.total.change,
{{my.total.change/my.df.total.change}/{my.III.summary$`Sum Sq`[ifelse(marginality,this.shift.temp-1,this.shift.temp)]/my.III.summary$Df[ifelse(marginality,this.shift.temp-1,this.shift.temp)]}},
pf({{my.total.change/my.df.total.change}/{my.III.summary$`Sum Sq`[ifelse(marginality,this.shift.temp-1,this.shift.temp)]/my.III.summary$Df[ifelse(marginality,this.shift.temp-1,this.shift.temp)]}},my.df.total.change,my.III.summary$Df[ifelse(marginality,this.shift.temp-1,this.shift.temp)],lower.tail=F),
rep(NA, v.p.rep))
my.tables.df[this.temp.var+1, ] = c(
"Residuals",
NA,
NA,
my.III.summary$`Sum Sq`[ifelse(marginality,this.shift.temp-1,this.shift.temp)],
my.III.summary$Df[ifelse(marginality,this.shift.temp-1,this.shift.temp)],
NA,
NA,
rep(NA, v.p.rep)
)
my.tables.df[this.temp.var+2,]=c("Total",NA,NA,my.total,my.df.total,rep(NA,v.p.rep+2))
if (!VIF & !part.eta) {
} else if (!VIF) {
my.tables.df$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$VIF)
}
}
if(type=="lm"){
if(show.footer){
the.footer=paste(ifelse(dim(my.new.df)[1]==dim(myDF)[1],"Data have same number of rows <br />",paste({dim(myDF)[1]-dim(my.new.df)[1]}," cases deleted due to missingness <br />")),"Method: QR decomposition",if(show.contrasts){paste(" <br />Adjustment: ", adjustment,sep="")})
}else{
the.footer=NULL
}
my.html.table=quick.table(my.tables.df,"lm",marginality=marginality, abbrev.length = abbrev.length,the.footer = the.footer)
}else if(type=="glm" | type=="ord"){
if(show.footer){
the.footer=paste(ifelse(dim(my.new.df)[1]==dim(myDF)[1],"Data have same number of rows <br />",paste({dim(myDF)[1]-dim(my.new.df)[1]}," cases deleted due to missingness <br />")),"Family: ",ifelse(type=="glm",new.model$family$family,"Ordinal")," <br /> Link: ",ifelse(type=="glm",new.model$family$link,levels(my.model$info$link)),if(show.contrasts){paste(" <br />Adjustment: ",adjustment,sep="")})
}else{
the.footer=NULL
}
my.html.table=quick.table(my.tables.df,"glm",marginality=marginality, abbrev.length = abbrev.length,the.footer = the.footer,VIF=VIF)
}
if(do.return){
return(invisible(my.html.table))
}else{
return(invisible(NULL))
}
}
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