Nothing
ea2<-
function(data, design=1, alpha=0.05, cov=4, list=FALSE, p.adjust=1, plot=2)
{
list=ifelse(list==FALSE,1,2)
pres=function(m){
r=resid(m)
r=scale(r)
t=1:length(r)
g1=function(r){boxplot(r,col="grey80",ylab="Standardized residuals", main="Box plot for residuals")}
g2=function(r){plot(r~t, pch="",ylim=c(-4,4), ylab="Standardized residuals", xlab="Sequence data", main="Standardized residuals vs Sequence data", axes=FALSE);axis(2,c(-4,-3.5,-3,-2.5,-2,-1,0,1,2,2.5,3,3.5,4));abline(h=2.5, lty=2);abline(h=-2.5,lty=2);abline(h=3.5, lty=2, col=2);abline(h=-3.5,lty=2, col=2); text(2.5,2.7, "2.5 z-score");text(2.5,-2.7, "-2.5 z-score");text(2.5,3.7, "3.5 z-score");text(2.5,-3.7, "-3.5 z-score");text(t,r,labels=1:length(r))}
a=qqnorm(r,plot.it = FALSE)
a1=a$x;a2=a$y;rownames(a2)=NULL; a3=sqrt((a$y)^2);rownames(a3)=NULL; a4=1:length(r)
d=data.frame(a1,a2,a3,a4)
do=d[order(d[,3], decreasing=TRUE),]
d1=do[1,c(1,2)]
d2=do[2,c(1,2)]
d3=do[3,c(1,2)]
n1=as.character(do[1,4])
n2=as.character(do[2,4])
n3=as.character(do[3,4])
g3=function(r){qqnorm(r, ylab="Standardized residuals", xlab="Theoretical quantiles", main="Standardized residuals vs Theoretical quantiles");qqline(r, col = "grey50");text(d1,n1,adj=-0.5,col=2, cex=0.8);text(d2,n2,adj=-0.5,col=2, cex=0.8);text(d3,n3,adj=-0.5,col=2,cex=0.8)}
g=list(g1,g2,g3)
g[[plot]](r)
}
sk=function(means, df1, QME, nrep, alpha=0.05){
sk1=function(means, df1, QME, nrep, alpha=alpha) {
means=sort(means,decreasing=TRUE)
n=1:(length(means)-1)
n=as.list(n)
f=function(n){list(means[c(1:n)],means[-c(1:n)])}
g=lapply(n, f)
b1=function(x){(sum(g[[x]][[1]])^2)/length(g[[x]][[1]]) +
(sum(g[[x]][[2]])^2)/length(g[[x]][[2]])-
(sum(c(g[[x]][[1]],g[[x]][[2]]))^2)/length(c(g[[x]][[1]],g[[x]][[2]]))}
p=1:length(g)
values=sapply(p,b1)
minimo=min(values); maximo=max(values)
alfa=(1/(length(means)+df1))*(sum((means-mean(means))^2)+(df1*QME/nrep))
lambda=(pi/(2*(pi-2)))*(maximo/alfa)
vq=qchisq((alpha),lower.tail=FALSE, df=length(means)/(pi-2))
ll=1:length(values); da=data.frame(ll,values); da=da[order(-values),]
ran=da$ll[1]
r=g[[ran]]; r=as.list(r)
i=ifelse(vq>lambda|length(means)==1, 1,2)
means=list(means)
res=list(means, r)
return(res[[i]])
}
u=sk1(means, df1, QME, nrep, alpha=alpha)
u=lapply(u, sk1, df1=df1, QME=QME, nrep=nrep, alpha=alpha)
sk2=function(u){
v1=function(...){c(u[[1]])};v2=function(...){c(u[[1]],u[[2]])};v3=function(...){c(u[[1]],u[[2]],u[[3]])}
v4=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]])}; v5=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]])}
v6=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]])}
v7=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]])}
v8=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]])}
v9=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]])}
v10=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]])}
v11=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]])}
v12=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]], u[[12]])}
v13=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]], u[[12]], u[[13]])}
v14=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]], u[[12]], u[[13]], u[[14]])}
v15=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]], u[[12]], u[[13]], u[[14]], u[[15]])}
lv=list(v1,v2,v3,v4,v5,v6,v7,v8,v9,v10,v11,v12,v13,v14,v15)
l=length(u)
ti=lv[[l]]
u=ti()
u=lapply(u, sk1, df1=df1, QME=QME, nrep=nrep, alpha=alpha)
return(u)
}
u=sk2(u);u=sk2(u);u=sk2(u);u=sk2(u);u=sk2(u)
u=sk2(u);u=sk2(u);u=sk2(u);u=sk2(u);u=sk2(u)
v1=function(...){c(u[[1]])};v2=function(...){c(u[[1]],u[[2]])};v3=function(...){c(u[[1]],u[[2]],u[[3]])}
v4=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]])}; v5=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]])}
v6=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]])}
v7=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]])}
v8=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]])}
v9=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]])}
v10=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]])}
v11=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]])}
v12=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]], u[[12]])}
v13=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]], u[[12]], u[[13]])}
v14=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]], u[[12]], u[[13]], u[[14]])}
v15=function(...){c(u[[1]],u[[2]],u[[3]],u[[4]],u[[5]],u[[6]],u[[7]],u[[8]],u[[9]],u[[10]],u[[11]], u[[12]], u[[13]], u[[14]], u[[15]])}
lv=list(v1,v2,v3,v4,v5,v6,v7,v8,v9,v10,v11,v12,v13,v14,v15)
l=length(u)
ti=lv[[l]]
u=ti()
rp=u
l2=lapply(rp, length)
l2=unlist(l2)
rp2=rep(letters[1:length(rp)], l2)
return(rp2)
}
cv <- function(x) {
sd = (deviance(x)/df.residual(x))^0.5
mm = mean(fitted(x))
r = 100 * sd/mm
return(round(r, 2))
}
fr=function(m,data){
r=resid(m); names(r)=1:length(r); rp=scale(r)[,1]
s <- shapiro.test(r)
b1<- bartlett.test(r~factor_1, data=data)
b2<- bartlett.test(r~factor_2, data=data)
b3<- bartlett.test(r~treatments, data=data)
cvf=cv(m)
rd=as.data.frame((sort(sqrt(r^2),decreasing=TRUE)))
rl=as.list(rownames(rd))
r1=rl[[1]];r2=rl[[2]];r3=rl[[3]]
d=data.frame(round(s$"p.value",4),round(b1$"p.value",4),round(b2$"p.value",4),round(b3$"p.value",4), round(cvf,2),as.numeric(r1),as.numeric(r2),as.numeric(r3)); d=t(d)
rownames(d)=c("p.value Shapiro-Wilk test","p.value Bartlett test (factor_1)","p.value Bartlett test (factor_2)","p.value Bartlett test (treatments)","coefficient of variation (%)", "first value most discrepant","second value most discrepant","third value most discrepant")
colnames(d)="values"
l=list("residual analysis"=d,"residuals"=r,"standardized residuals"=rp)
return(l)}
fr2=function(m,data){
data=data.frame(data,nr=c(1:length(data$response)))
data2<-na.omit(data)
r<-resid(m); names(r)=1:length(r); rp=scale(r)[,1]
s<-shapiro.test(r)
b1<- bartlett.test(r~plot, data=data2)
b2<- bartlett.test(r~split.plot, data=data2)
b3<- bartlett.test(r~treatments, data=data2)
a1=AIC(m);a1=as.numeric(a1);a1=round(a1,4)
b11=BIC(m);b11=as.numeric(b11);b11=round(b11,4)
rd=as.data.frame((sort(sqrt(r^2),decreasing=TRUE)))
rl=as.list(rownames(rd))
r1=rl[[1]];r2=rl[[2]];r3=rl[[3]]
d=data.frame(round(s$"p.value",4),round(b1$"p.value",4),round(b2$"p.value",4),round(b3$"p.value",4), a1,b11,as.numeric(r1),as.numeric(r2),as.numeric(r3)); d=t(d)
rownames(d)=c("p.value Shapiro-Wilk test","p.value Bartlett test (plot)","p.value Bartlett test (split.plot)","p.value Bartlett test (plot*split.plot)","AIC","BIC", "first value most discrepant","second value most discrepant","third value most discrepant")
colnames(d)="values"
l=list("residual analysis"=d,"residuals"=r,"standardized residuals"=rp)
return(l)}
fr3=function(m,data){
r=resid(m); names(r)=1:length(r); rp=scale(r)[,1]
s <- shapiro.test(r)
b1<- bartlett.test(r~factor_1, data=data)
b2<- bartlett.test(r~factor_2, data=data)
b3<- bartlett.test(r~factor_3, data=data)
cvf=cv(m)
rd=as.data.frame((sort(sqrt(r^2),decreasing=TRUE)))
rl=as.list(rownames(rd))
r1=rl[[1]];r2=rl[[2]];r3=rl[[3]]
d=data.frame(round(s$"p.value",4),round(b1$"p.value",4),round(b2$"p.value",4),round(b3$"p.value",4), round(cvf,2),as.numeric(r1),as.numeric(r2),as.numeric(r3)); d=t(d)
rownames(d)=c("p.value Shapiro-Wilk test","p.value Bartlett test (factor_1)","p.value Bartlett test (factor_2)","p.value Bartlett test (factor_3)","coefficient of variation (%)", "first value most discrepant","second value most discrepant","third value most discrepant")
colnames(d)="values"
l=list("residual analysis"=d,"residuals"=r,"standardized residuals"=rp)
return(l)}
fr4=function(m,data){
data=data.frame(data,nr=c(1:length(data$response)))
data2<-na.omit(data)
r<-resid(m);names(r)=1:length(r); rp=scale(r)[,1]
s<-shapiro.test(r)
b1<- bartlett.test(r~factor_1, data=data2)
b2<- bartlett.test(r~factor_2, data=data2)
b3<- bartlett.test(r~factor_3, data=data2)
a1=AIC(m);a1=as.numeric(a1);a1=round(a1,4)
b11=BIC(m);b11=as.numeric(b11);b11=round(b11,4)
names(r)=data2$nr
rd=as.data.frame((sort(sqrt(r^2),decreasing=TRUE)))
rl=as.list(rownames(rd))
r1=rl[[1]];r2=rl[[2]];r3=rl[[3]]
d=data.frame(round(s$"p.value",4),round(b1$"p.value",4),round(b2$"p.value",4),round(b3$"p.value",4), a1,b11,as.numeric(r1),as.numeric(r2),as.numeric(r3)); d=t(d)
rownames(d)=c("p.value Shapiro-Wilk test","p.value Bartlett test (factor_1)","p.value Bartlett test (factor_2)","p.value Bartlett test (factor_3)","AIC","BIC", "first value most discrepant","second value most discrepant","third value most discrepant")
colnames(d)="values"
l=list("residual analysis"=d,"residuals"=r,"standardized residuals"=rp)
return(l)}
fa2=function(a,m){
ress=c(m$df.residual,sum(m$residuals^2),sum(m$residuals^2)/m$df.residual,NA,NA)
res=a; d=data.frame(res[-1,]); d=data.frame(d[,1],d[,2],d[,2]/d[,1],d[,5],d[,6])
;d=rbind(d,ress);d=round(d,4);d1=d[,5]; d2=ifelse(d1<0.001, "<0.001", d1);
d2=d2[-length(d2)];d2=c(d2,"-"); d=d[,-5];d=data.frame(d,d2);d[is.na(d)] <- "-"
names(d)=c("df", "type III SS", "mean square", "F value", "p>F"); rownames(d)=c(rownames(res[-1,]),"residuals")
return(d)
}
fmm=function(ma,dff){
ma=data.frame(ma,co=ma[,1])
ma=ma[order(ma[,2], decreasing=TRUE),]
j=ma[,1];j=as.character(j)
aux <- combn(j, 2)
w <- apply(aux, 2, paste, collapse = " - ")
jj=ma[,2]
auxj <- combn(jj, 2)
yi=auxj[1,]-auxj[2,]
jjj=ma$standard.error^2
auxjj <- combn(jjj, 2)
si=sqrt((auxjj[1,]+auxjj[2,])/2)
yx=yi/si; yx=yx^2; yx=sqrt(yx)
nmeans=length(ma[,1])
ft=function(yx, nmeans){1-ptukey(yx,nmeans, dff)}
st=ft(yx,nmeans)
st=round(st,4)
fs=function(ns){s=2:ns;return(s)}
ns=nmeans:2
se=sapply(ns, fs)
ns=unlist(se)
ssnk=ft(yx, ns)
ssnk=round(ssnk,4)
sd=1-ptukey(yx,ns, dff)^(1/(ns-1))
sd=round(sd,4)
yxx=yi/(si*sqrt(2))
vt=1-pt(yxx,dff); vt=vt*2
vt=round(vt,4)
lp=list("none","holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr")
pf=p.adjust(vt, lp[[p.adjust]])
ggs=data.frame(w,round(yi,4),st, ssnk, sd, round(pf,4))
nam=list("p(t)","p(t)adjust.holm", "p(t)adjust.hochberg", "p(t)adjust.hommel", "p(t)adjust.bonferroni", "p(t)adjust.BH", "p(t)adjust.BY","p(t)adjust.fdr")
colnames(ggs)=c("pair", "contrast","p(tukey)", "p(snk)", "p(duncan)", nam[[p.adjust]])
return(ggs)
}
fm=function(ma,dff){
ma=data.frame(ma,co=ma[,1])
ma=ma[order(ma[,2], decreasing=TRUE),]
j=ma[,1];j=as.character(j)
aux <- combn(j, 2)
w <- apply(aux, 2, paste, collapse = " - ")
jj=ma[,2]
auxj <- combn(jj, 2)
yi=auxj[1,]-auxj[2,]
jjj=ma$sem^2
auxjj <- combn(jjj, 2)
si=sqrt((auxjj[1,]+auxjj[2,])/2)
yx=yi/si; yx=yx^2; yx=sqrt(yx)
nmeans=length(ma[,1])
ftt=function(yx, nmeans){1-ptukey(yx,nmeans, dff)}
st=ftt(yx,nmeans)
st=round(st,4)
fs=function(ns){s=2:ns;return(s)}
ns=nmeans:2
se=sapply(ns, fs)
ns=unlist(se)
ssnk=ftt(yx, ns)
ssnk=round(ssnk,4)
sd=1-ptukey(yx,ns, dff)^(1/(ns-1))
sd=round(sd,4)
yxx=yi/(si*sqrt(2))
vt=1-pt(yxx,dff); vt=vt*2
vt=round(vt,4)
lp=list("none","holm", "hochberg", "hommel", "bonferroni", "BH", "BY","fdr")
pf=p.adjust(vt, lp[[p.adjust]])
ggs=data.frame(w,round(yi,4),st, ssnk, sd, round(pf,4))
nam=list("p(t)","p(t)adjust.holm", "p(t)adjust.hochberg", "p(t)adjust.hommel", "p(t)adjust.bonferroni", "p(t)adjust.BH", "p(t)adjust.BY","p(t)adjust.fdr")
colnames(ggs)=c("pair", "contrast","p(tukey)", "p(snk)", "p(duncan)", nam[[p.adjust]])
return(ggs)
}
ft=function(test, alpha=0.05){
level=alpha
tes1=test[,3]
tes2=test[,4]
tes3=test[,5]
tes4=test[,6]
names(tes1)=test$pair
names(tes2)=test$pair
names(tes3)=test$pair
names(tes4)=test$pair
tes1=ifelse(tes1<=level,TRUE,FALSE)
tes2=ifelse(tes2<=level,TRUE,FALSE)
tes3=ifelse(tes3<=level,TRUE,FALSE)
tes4=ifelse(tes4<=level,TRUE,FALSE)
x1=tes1;x2=tes2;x3=tes3;x4=tes4
inab <- function(x, Letters=c(letters, LETTERS), separator=".", decreasing = decreasing){
obj_x <- deparse(substitute(x))
namx <- names(x)
namx <- gsub(" ", "", names(x))
if(length(namx) != length(x))
stop("Names required for ", obj_x)
split_names <- strsplit(namx, "-")
stopifnot( sapply(split_names, length) == 2 )
comps <- t(as.matrix(as.data.frame(split_names)))
rownames(comps) <- names(x)
lvls <- unique(as.vector(comps))
n <- length(lvls)
lmat <- array(TRUE, dim=c(n,1), dimnames=list(lvls, NULL) )
if( sum(x) == 0 ){
ltrs <- rep(get_letters(1, Letters=Letters, separator=separator), length(lvls) )
names(ltrs) <- lvls
colnames(lmat) <- ltrs[1]
msl <- ltrs
ret <- list(Letters=ltrs, monospacedLetters=msl, LetterMatrix=lmat)
return(ret)
}
else{
signifs <- comps[x,,drop=FALSE]
absorb <- function(m){
for(j in 1:(ncol(m)-1)){
for(k in (j+1):ncol(m)){
if( all(m[which(m[,k]),k] & m[which(m[,k]),j]) ){
m <- m[,-k, drop=FALSE]
return(absorb(m))
}
else if( all(m[which(m[,j]),k] & m[which(m[,j]),j]) ){
m <- m[,-j, drop=FALSE]
return(absorb(m))
}
}
}
return(m)
}
for( i in 1:nrow(signifs) ){
tmpcomp <- signifs[i,]
wassert <- which(lmat[tmpcomp[1],] & lmat[tmpcomp[2],])
if(any(wassert)){
tmpcols <- lmat[,wassert,drop=FALSE]
tmpcols[tmpcomp[2],] <- FALSE
lmat[tmpcomp[1],wassert] <- FALSE
lmat <- cbind(lmat, tmpcols)
colnames(lmat) <- get_letters( ncol(lmat), Letters=Letters,
separator=separator)
if(ncol(lmat) > 1){
lmat <- absorb(lmat)
colnames(lmat) <- get_letters( ncol(lmat), Letters=Letters,
separator=separator )
}
}
}
}
lmat <- lmat[,order(apply(lmat, 2, sum))]
lmat <- sweepLetters(lmat)
lmat <- lmat[,names(sort(apply(lmat,2, function(x) return(min(which(x))))))]
colnames(lmat) <- get_letters( ncol(lmat), Letters=Letters,
separator=separator)
lmat <- lmat[,order(apply(lmat, 2, sum))]
lmat <- sweepLetters(lmat)
lmat <- lmat[,names(sort(apply(lmat,2, function(x) return(min(which(x)))),
decreasing = decreasing))]
colnames(lmat) <- get_letters( ncol(lmat), Letters=Letters,
separator=separator)
ltrs <- apply(lmat,1,function(x) return(paste(names(x)[which(x)], sep="", collapse="") ) )
msl <- matrix(ncol=ncol(lmat), nrow=nrow(lmat))
for( i in 1:nrow(lmat) ){
msl[i,which(lmat[i,])] <- colnames(lmat)[which(lmat[i,])]
absent <- which(!lmat[i,])
if( length(absent) < 2 ){
if( length(absent) == 0 )
next
else{
msl[i,absent] <- paste( rep(" ", nchar(colnames(lmat)[absent])), collapse="" )
}
}
else{
msl[i,absent] <- unlist( lapply( sapply( nchar(colnames(lmat)[absent]),
function(x) return(rep( " ",x)) ),
paste, collapse="") )
}
}
msl <- apply(msl, 1, paste, collapse="")
names(msl) <- rownames(lmat)
ret <- list(Letters=ltrs)
return(ret)
}
sweepLetters <- function(mat, start.col=1, Letters=c(letters, LETTERS), separator="."){
stopifnot( all(start.col %in% 1:ncol(mat)) )
locked <- matrix(rep(0,ncol(mat)*nrow(mat)), ncol=ncol(mat))
cols <- 1:ncol(mat)
cols <- cols[c( start.col, cols[-start.col] )]
if( any(is.na(cols) ) )
cols <- cols[-which(is.na(cols))]
for( i in cols){
tmp <- matrix(rep(0,ncol(mat)*nrow(mat)), ncol=ncol(mat))
tmp[which(mat[,i]),] <- mat[which(mat[,i]),]
one <- which(tmp[,i]==1)
if( all(apply(tmp[,-i,drop=FALSE], 1, function(x) return( any(x==1) ))) ){
}
for( j in one ){
if( locked[j,i] == 1 ){
next
}
chck <- 0
lck <- list()
for( k in one ){
if( j==k ){
next
}
else{
rows <- tmp[c(j,k),]
dbl <- rows[1,] & rows[2,]
hit <- which(dbl)
hit <- hit[-which(hit==i)]
dbl <- rows[1,-i,drop=FALSE] & rows[2,-i,drop=FALSE]
if( any(dbl) ){
chck <- chck + 1
lck[[chck]] <- list(c(j,hit[length(hit)]), c(k,hit[length(hit)]))
}
}
}
if( (chck == (length(one)-1)) && chck != 0 ){
for( k in 1:length(lck) ){
locked[ lck[[k]][[1]][1], lck[[k]][[1]][2] ] <- 1
locked[ lck[[k]][[2]][1], lck[[k]][[2]][2] ] <- 1
}
mat[j,i] <- FALSE
}
}
if(all(mat[,i]==FALSE)){
mat <- mat[,-i,drop=FALSE]
colnames(mat) <- get_letters( ncol(mat), Letters=Letters, separator=separator)
return(sweepLetters(mat, Letters=Letters, separator=separator))
}
}
onlyF <- apply(mat, 2, function(x) return(all(!x)))
if( any(onlyF) ){
mat <- mat[,-which(onlyF),drop=FALSE]
colnames(mat) <- get_letters( ncol(mat), Letters=Letters, separator=separator)
}
return( mat )
}
get_letters <- function( n, Letters=c(letters, LETTERS), separator="." ){
n.complete <- floor(n / length(Letters))
n.partial <- n %% length(Letters)
lett <- character()
separ=""
if( n.complete > 0 ){
for( i in 1:n.complete ){
lett <- c(lett, paste(separ, Letters, sep="") )
separ <- paste( separ, separator, sep="" )
}
}
if(n.partial > 0 )
lett <- c(lett, paste(separ, Letters[1:n.partial], sep="") )
return(lett)
}
decreasing=FALSE;jjj1=inab(x1, decreasing = decreasing,); jjj1=jjj1[[1]]
jjj2=inab(x2, decreasing = decreasing,); jjj2=jjj2[[1]]
jjj3=inab(x3, decreasing = decreasing,); jjj3=jjj3[[1]]
jjj4=inab(x4, decreasing = decreasing,); jjj4=jjj4[[1]]
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
hgy=data.frame(jjj1,jjj2,jjj3,jjj4); names(hgy)=c("tukey","snk","duncan",nam[[p.adjust]])
return(hgy)
}
fsd1=function(data)
{
s=split(data,data$factor_1)
f=function(x){sd(s[[x]]$response, na.rm=TRUE)}
sds=sapply(1:length(s), f); sds=round(sds,4)
return(sds)
}
fsd2=function(data)
{
s=split(data,data$factor_2)
f=function(x){sd(s[[x]]$response, na.rm=TRUE)}
sds=sapply(1:length(s), f); sds=round(sds,4)
return(sds)
}
fsd3=function(data)
{
s=split(data,data$treatment)
f=function(x){sd(s[[x]]$response, na.rm=TRUE)}
sds=sapply(1:length(s), f); sds=round(sds,4)
return(sds)
}
f1<-function(data,cov){
names(data)=c("factor_1","factor_2","response")
data<-data.frame(factor_1=factor(data$factor_1), factor_2=factor(data$factor_2), response=data$response)
m<-aov(response~factor_1*factor_2,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum))
m1<-aov(response~-1+factor_1+factor_2+factor_1*factor_2,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum))
m2<-aov(response~-1+ factor_2+factor_1+factor_2*factor_1,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum))
a<-anova(m)
a2 <- drop1(m,.~.,test="F")
a3 <- a2
a3<-fa2(a3,m)
treatments=interaction(data$factor_1,data$factor_2)
data<-data.frame(data,treatments)
m3<-aov(response~-1+treatments,data=data, contrasts=list(treatments=contr.sum))
data2<-na.omit(data)
res=fr(m,data2)
adjusted.mean<-round(coef(m1)[1:nlevels(data$factor_1)],4)
sem<-round(sderrs(m1)[1:nlevels(data$factor_1)],4)
sd=fsd1(data)
factor_1<-levels(data$factor_1)
means1=adjusted.mean; names(means1)=factor_1
maf1=data.frame(factor_1,adjusted.mean,sd, sem)
rownames(maf1)=NULL
adjusted.mean<-round(coef(m2)[1:nlevels(data$factor_2)],4)
sem<-round(sderrs(m2) [1:nlevels(data$factor_2)],4)
factor_2<-levels(data$factor_2)
means2=adjusted.mean; names(means2)=factor_2
sd=fsd2(data)
maf2=data.frame(factor_2,adjusted.mean,sd,sem)
rownames(maf2)=NULL
adjusted.mean<-round(coef(m3),4)
sem<-round(sderrs(m3),4)
treatment<-levels(data$t)
sd=fsd3(data)
mat=data.frame(treatment,adjusted.mean,sd,sem)
rownames(mat)=NULL
dff=df.residual(m)
test1=fm(maf1,dff)
test2=fm(maf2,dff)
nrep1=length(data2[,1])/length(factor_1)
nrep2=length(data2[,1])/length(factor_2)
nrep3=length(data2[,1])/length(treatment)
QME=deviance(m)/dff
scott_knott=sk(means1, dff, QME, nrep1, alpha)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1, scott_knott)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
scott_knott=sk(means2, dff, QME, nrep2, alpha)
mf2=data.frame(maf2,groups2, scott_knott)
rownames(mf2) = NULL
c1=rep(1:nlevels(data$factor_2), each=nlevels(data$factor_1))
cs1=split(mat, c1)
test3=lapply(cs1, fm, dff=dff); n1=rep("factor_1 in", nlevels(data$factor_2));n11=data.frame(n1,levels(data$factor_2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_2))
cs2=split(mat, c2)
test4=lapply(cs2, fm, dff=dff); n2=rep("factor_2 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf)
i=1:length(mft1)
ft1=function(i){a=mft1[[i]][,2];names(a)=mft1[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups3[x],scott_knott[x])}
xx1=1:length(groups3)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups3)
dd<-function(x){l=data.frame(mft1[[x]],ftr[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_2)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf)
i=1:length(mft2)
ft1=function(i){a=mft2[[i]][,2];names(a)=mft2[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups4[x],scott_knott[x])}
xx1=1:length(groups4)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups4)
ddd<-function(x){l=data.frame(mft2[[x]],ftr[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
l<-list(a3,mf1, test1, mf2,test2,mft1,test3, mft2, test4, res)
names(l)= list("Analysis of variance", "Adjusted means (factor 1)", "Multiple comparison test (factor 1)","Adjusted means (factor 2)", "Multiple comparison test (factor 2)", "Adjusted means (factor 1 in levels of factor 2)", "Multiple comparison test (factor 1 in levels of factor 2)", "Adjusted means (factor 2 in levels of factor 1)", "Multiple comparison test (factor 2 in levels of factor 1)","Residual analysis")
pres(m)
return(l)}
f2<-function(data, cov){
names(data)=c("factor_1","factor_2","blocks","response")
data<-data.frame(factor_1=factor(data$factor_1), factor_2=factor(data$factor_2), blocks=factor(data$blocks), response=data$response)
m<-aov(response~factor_1*factor_2+blocks,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, blocks=contr.sum))
m1<-aov(response~-1+factor_1+factor_2+factor_1*factor_2+blocks,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, blocks=contr.sum))
m2<-aov(response~-1+ factor_2+factor_1+factor_2*factor_1+blocks,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, blocks=contr.sum))
a<-anova(m)
a2 <- drop1(m,.~.,test="F")
a3 <- a2
a3<-fa2(a3,m)
treatments=interaction(data$factor_1,data$factor_2)
data<-data.frame(data,treatments)
m3<-aov(response~-1+treatments+blocks,data=data, contrasts=list(treatments=contr.sum, blocks=contr.sum))
data2<-na.omit(data)
res=fr(m,data2)
adjusted.mean<-round(coef(m1)[1:nlevels(data$factor_1)],4)
sem<-round(sderrs(m1)[1:nlevels(data$factor_1)],4)
sd=fsd1(data)
factor_1<-levels(data$factor_1)
means1=adjusted.mean; names(means1)=factor_1
maf1=data.frame(factor_1,adjusted.mean,sd, sem)
rownames(maf1)=NULL
adjusted.mean<-round(coef(m2)[1:nlevels(data$factor_2)],4)
sem<-round(sderrs(m2)[1:nlevels(data$factor_2)],4)
sd=fsd2(data)
factor_2<-levels(data$factor_2)
means2=adjusted.mean; names(means2)=factor_2
maf2=data.frame(factor_2,adjusted.mean,sd, sem)
rownames(maf2)=NULL
adjusted.mean<-round(coef(m3)[1:nlevels(data$treatments)],4)
treatment<-levels(data$treatments)
sem<-round(sderrs(m3)[1:nlevels(data$treatments)],4)
sd=fsd3(data)
mat=data.frame(treatment,adjusted.mean,sd,sem)
rownames(mat)=NULL
dff=df.residual(m)
test1=fm(maf1,dff)
test2=fm(maf2,dff)
nrep1=length(data2[,1])/length(factor_1)
nrep2=length(data2[,1])/length(factor_2)
nrep3=length(data2[,1])/length(treatment)
QME=deviance(m)/dff
scott_knott=sk(means1, dff, QME, nrep1, alpha)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1, scott_knott)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
scott_knott=sk(means2, dff, QME, nrep2, alpha)
mf2=data.frame(maf2,groups2, scott_knott)
rownames(mf2) = NULL
c1=rep(1:nlevels(data$factor_2), each=nlevels(data$factor_1))
cs1=split(mat, c1)
test3=lapply(cs1, fm, dff=dff); n1=rep("factor_1 in", nlevels(data$factor_2));n11=data.frame(n1,levels(data$factor_2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_2))
cs2=split(mat, c2)
test4=lapply(cs2, fm, dff=dff); n2=rep("factor_2 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf)
i=1:length(mft1)
ft1=function(i){a=mft1[[i]][,2];names(a)=mft1[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups3[x],scott_knott[x])}
xx1=1:length(groups3)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups3)
dd<-function(x){l=data.frame(mft1[[x]],ftr[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_2)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf)
i=1:length(mft2)
ft1=function(i){a=mft2[[i]][,2];names(a)=mft2[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups4[x],scott_knott[x])}
xx1=1:length(groups4)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups4)
ddd<-function(x){l=data.frame(mft2[[x]],ftr[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
l<-list(a3,mf1, test1, mf2,test2,mft1,test3, mft2, test4, res)
names(l)= list("Analysis of variance", "Adjusted means (factor 1)", "Multiple comparison test (factor 1)","Adjusted means (factor 2)", "Multiple comparison test (factor 2)", "Adjusted means (factor 1 in levels of factor 2)", "Multiple comparison test (factor 1 in levels of factor 2)", "Adjusted means (factor 2 in levels of factor 1)", "Multiple comparison test (factor 2 in levels of factor 1)","Residual analysis")
pres(m)
return(l)
}
f3<-function(data, cov){
names(data)=c("factor_1","factor_2","rows","cols","response")
data<-data.frame(factor_1=factor(data$factor_1), factor_2=factor(data$factor_2), rows=factor(data$rows), columns=factor(data$cols), response=data$response)
m<-aov(response~factor_1*factor_2+ rows+columns,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, rows=contr.sum, columns=contr.sum))
m1<-aov(response~-1+factor_1+factor_2+factor_1*factor_2+ rows+columns,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, rows=contr.sum, columns=contr.sum))
m2<-aov(response~-1+ factor_2+factor_1+factor_2*factor_1+ rows+columns,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, rows=contr.sum, columns=contr.sum))
a<-anova(m)
a2 <- drop1(m,.~.,test="F")
a3 <- a2
a3<-fa2(a3,m)
treatments=interaction(data$factor_1,data$factor_2)
data<-data.frame(data,treatments)
m3<-aov(response~-1+treatments +rows+columns,data=data, contrasts=list(treatments=contr.sum, rows=contr.sum, columns=contr.sum))
data2<-na.omit(data)
res=fr(m,data2)
adjusted.mean<-round(coef(m1)[1:nlevels(data$factor_1)],4)
sem<-round(sderrs(m1)[1:nlevels(data$factor_1)],4)
sd=fsd1(data)
factor_1<-levels(data$factor_1)
means1=adjusted.mean; names(means1)=factor_1
maf1=data.frame(factor_1,adjusted.mean,sd, sem)
rownames(maf1)=NULL
adjusted.mean<-round(coef(m2)[1:nlevels(data$factor_2)],4)
sem<-round(sderrs(m2)[1:nlevels(data$factor_2)],4)
sd=fsd2(data)
factor_2<-levels(data$factor_2)
means2=adjusted.mean; names(means2)=factor_2
maf2=data.frame(factor_2,adjusted.mean,sd, sem)
rownames(maf2)=NULL
adjusted.mean<-round(coef(m3)[1:nlevels(data$treatments)],4)
treatment<-levels(data$treatments)
sem<-round(sderrs(m3)[1:nlevels(data$treatments)],4)
sd=fsd3(data)
mat=data.frame(treatment,adjusted.mean,sd,sem)
rownames(mat)=NULL
dff=df.residual(m)
nrep1=length(data2[,1])/length(factor_1)
nrep2=length(data2[,1])/length(factor_2)
nrep3=length(data2[,1])/length(treatment)
QME=deviance(m)/dff
scott_knott=sk(means1, dff, QME, nrep1, alpha)
test1=fm(maf1,dff)
test2=fm(maf2,dff)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1, scott_knott)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
scott_knott=sk(means2, dff, QME, nrep1, alpha)
mf2=data.frame(maf2,groups2, scott_knott)
rownames(mf2) = NULL
c1=rep(1:nlevels(data$factor_2), each=nlevels(data$factor_1))
cs1=split(mat, c1)
test3=lapply(cs1, fm, dff=dff); n1=rep("factor_1 in", nlevels(data$factor_2));n11=data.frame(n1,levels(data$factor_2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_2))
cs2=split(mat, c2)
test4=lapply(cs2, fm, dff=dff); n2=rep("factor_2 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf)
i=1:length(mft1)
ft1=function(i){a=mft1[[i]][,2];names(a)=mft1[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups3[x],scott_knott[x])}
xx1=1:length(groups3)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups3)
dd<-function(x){l=data.frame(mft1[[x]],ftr[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_2)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf)
i=1:length(mft2)
ft1=function(i){a=mft2[[i]][,2];names(a)=mft2[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups4[x],scott_knott[x])}
xx1=1:length(groups4)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups4)
ddd<-function(x){l=data.frame(mft2[[x]],ftr[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
l<-list(a3,mf1, test1, mf2,test2,mft1,test3, mft2, test4, res)
names(l)= list("Analysis of variance", "Adjusted means (factor 1)", "Multiple comparison test (factor 1)","Adjusted means (factor 2)", "Multiple comparison test (factor 2)", "Adjusted means (factor 1 in levels of factor 2)", "Multiple comparison test (factor 1 in levels of factor 2)", "Adjusted means (factor 2 in levels of factor 1)", "Multiple comparison test (factor 2 in levels of factor 1)","Residual analysis")
pres(m)
return(l)
}
f4<-function(data, cov){
gg<-cov
names(data)<-c("plot","rep","split.plot","response")
data<-data.frame(plot=factor(data$plot), rep=factor(data$rep), split.plot=factor(data$split.plot), response=data$response)
subject<-interaction(data$plot,data$rep)
treatments<-interaction(data$plot,data$split.plot)
data<-data.frame(data, subject,treatments)
UN<-corSymm(form=~1|subject)
AR<-corAR1(form=~1|subject)
ARH<-corAR1(form=~1|subject)
CS<- corCompSymm (form=~1|subject)
CAR<-corCAR1(form=~split.plot|subject)
o1<-list(AR,ARH, CAR, CS, UN)
cor<-o1[[gg]]
UN1<-varIdent(form=~1|split.plot)
AR1<-NULL
ARH1<- varIdent(form=~1|split.plot)
CS1<-NULL
CAR1<-NULL
o2<-list(AR1,ARH1, CAR1, CS1, UN1)
var<-o2[[gg]]
m<-lme(response~plot*split.plot, random=~1|subject, correlation=cor, weights=var, data<-data, na.action=na.omit, contrasts=list(plot=contr.sum, split.plot=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
b<-anova(m, type="marginal")[-1,]
m1<-lme(response~-1+ treatments, random=~1|subject, data=data, na.action=na.omit, contrasts=list(treatments=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
am<-fixef(m1)[1:nlevels(data$treatments)]
c<-data.frame(levels(treatments), round(am,4),
round(sderrs(m1)[1:nlevels(data$treatments)],4))
colnames(c)<-c("plot*split.plot","adjusted.means", "standard.error")
rownames(c)<-NULL
df1<- anova(m) $"denDF"[2]
df2<- anova(m) $"denDF"[1]
mm1<-lme(response~-1+plot*split.plot, random=~1|subject, data=data, na.action=na.omit, contrasts=list(plot=contr.sum, split.plot=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
mm2<-lme(response~-1+split.plot*plot, random=~1|subject, data=data, na.action=na.omit, contrasts=list(split.plot=contr.sum, plot=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
a11<-data.frame(levels(data$plot), round(fixef(mm1)[1:nlevels(data$plot)],4), round(sderrs(mm1)[1:nlevels(data$plot)],4))
a22<-data.frame(levels(data$split.plot), round(fixef(mm2)[1:nlevels(data$split.plot)],4), round(sderrs(mm2)[1:nlevels(data$split.plot)],4))
#################################################
# MS
msa=sqrt((m$sigma^2)/(nlevels(data[,1])*(nrow(data)/nlevels(data$treatments))))
msa=rep(msa,nlevels(data[,3]))
a222<-data.frame(levels(data$split.plot), round(fixef(mm2)[1:nlevels(data$split.plot)],4), round(msa,4))
a22<-data.frame(levels(data$split.plot), round(fixef(mm2)[1:nlevels(data$split.plot)],4), round(sderrs(mm2)[1:nlevels(data$split.plot)],4))
#################################################
mal=lm(response~plot+subject, data=data)
ano=anova(mal)
vara=anova(mal)[[2,3]]
varb=m$sigma^2
J=nlevels(data$split.plot)-1
df1a=((vara+(J*varb))^2)/( ((vara^2)/df1)+(((J*varb)^2)/df2))
colnames(a11)<-c("plot","adjusted.mean", "standard.error")
rownames(a11)<-NULL
colnames(a22)<-c("split.plot","adjusted.mean", "standard.error")
rownames(a22)<-NULL
colnames(a222)<-c("split.plot","adjusted.mean", "standard.error")
rownames(a222)<-NULL
res=fr2(m,data)
test1=fmm(a11,df1)
test2=fmm(a222,df2)
groups1=ft(test1, alpha); a11=a11[order(a11[,2], decreasing=TRUE),]
mf1=data.frame(a11,groups1);rownames(mf1) = NULL
groups2=ft(test2, alpha); a22=a22[order(a22[,2], decreasing=TRUE),]
mf2=data.frame(a22,groups2);rownames(mf2) = NULL
c1=rep(1:nlevels(data$split.plot), each=nlevels(data$plot))
cs1=split(c, c1)
#### trocados os dfs
test3=lapply(cs1, fmm, dff=df1a); n1=rep("plot in", nlevels(data$split.plot));n11=data.frame(n1,levels(data$split.plot))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$plot), nlevels(data$split.plot))
cs2=split(c, c2)
###############
msa2=sqrt((m$sigma^2)/(nrow(data)/nlevels(data$treatments)))
msa2=rep(msa2,nlevels(data$treatments))
c222<-data.frame(levels(treatments), round(am,4), round(msa2,4))
colnames(c222)<-c("plot*split.plot","adjusted.mean", "standard.error")
rownames(c222)<-NULL
cs222=split(c222, c2)
#######################
test4=lapply(cs222, fmm, dff=df2); n2=rep("split.plot in", nlevels(data$plot));n22=data.frame(n2,levels(data$plot))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft1[[x]],groups3[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$split.plot)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft2[[x]],groups4[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$plot)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
cva=round(sqrt(vara)*100/mean(data$response, na.rm = TRUE),4); vara=round(vara,4)
cvb=round(sqrt(varb)*100/mean(data$response, na.rm = TRUE),4);varb=round(varb,4)
ress=rbind(res[[1]],"Mean Square of Error a"=vara, "Mean Square of Error b"=varb, "Coefficient of Variation a"=cva,"Coefficient of Variation b"=cvb)
res=list("values"=ress,"residuals"=res[[2]],"standardized residuals"=res[[3]])
l=list(b,mf1,test1,mf2,test2,mft1,test3,mft2,test4, res)
names(l)=c("Marginal anova (Type III Sum of Squares)","Adjusted means (plot)", "Multiple comparison test (plot)","Adjusted means (split.plot)", "Multiple comparison test (split.plot)", "Adjusted means (plot in levels of split.plot)", "Multiple comparison test (plot in levels of split.plot)", "Adjusted means (split.plot in levels of plot)", "Multiple comparison test (split.plot in levels of plot)","Residual analysis")
pres(m)
return(l)
}
f5<-function(data, cov){
gg<-cov
names(data)<-c("plot","block","split.plot","response")
data<-data.frame(plot=factor(data$plot), block=factor(data$block), split.plot=factor(data$split.plot), response=data$response)
subject<-interaction(data$plot,data$block)
treatments<-interaction(data$plot,data$split.plot)
data<-data.frame(data, subject,treatments)
UN<-corSymm(form=~1|subject)
AR<-corAR1(form=~1|subject)
ARH<-corAR1(form=~1|subject)
CS<- corCompSymm (form=~1|subject)
CAR<-corCAR1(form=~split.plot|subject)
o1<-list(AR,ARH, CAR, CS, UN)
cor<-o1[[gg]]
UN1<-varIdent(form=~1|split.plot)
AR1<-NULL
ARH1<- varIdent(form=~1|split.plot)
CS1<-NULL
CAR1<-NULL
o2<-list(AR1,ARH1, CAR1, CS1, UN1)
var<-o2[[gg]]
m<-lme(response~plot*split.plot+block, random=~1|subject, correlation=cor, weights=var, data<-data, na.action=na.omit, contrasts=list(plot=contr.sum, split.plot=contr.sum, block=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
b<-anova(m, type="marginal")[-1,]
m1<-lme(response~-1+ treatments+block, random=~1|subject, data=data, na.action=na.omit, contrasts=list(treatments=contr.sum, block=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
am<-fixef(m1)[1:nlevels(data$ treatments)]
c<-data.frame(levels(treatments), round(am,4), round(sderrs(m1)[1:nlevels(data$ treatments)],4))
colnames(c)<-c("plot*split.plot","adjusted.mean", "standard.error")
rownames(c)<-NULL
df1<- anova(m) $"denDF"[2]
df2<- anova(m) $"denDF"[1]
mm1<-lme(response~-1+plot*split.plot+block, random=~1|subject, data=data, na.action=na.omit, contrasts=list(plot=contr.sum, split.plot=contr.sum, block=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
mm2<-lme(response~-1+split.plot*plot+block, random=~1|subject, data=data, na.action=na.omit, contrasts=list(split.plot=contr.sum, plot=contr.sum, block=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
a11<-data.frame(levels(data$plot), round(fixef(mm1)[1:nlevels(data$plot)],4), round(sderrs(mm1)[1:nlevels(data$plot)],4))
#################################################
# MS
msa=sqrt((m$sigma^2)/(nlevels(data[,1])*(nrow(data)/nlevels(data$treatments))))
msa=rep(msa,nlevels(data[,3]))
a222<-data.frame(levels(data$split.plot), round(fixef(mm2)[1:nlevels(data$split.plot)],4), round(msa,4))
a22<-data.frame(levels(data$split.plot), round(fixef(mm2)[1:nlevels(data$split.plot)],4), round(sderrs(mm2)[1:nlevels(data$split.plot)],4))
#################################################
mal=lm(response~block*plot, data=data)
ano=anova(mal)
vara=anova(mal)[[3,3]]
varb=m$sigma^2
J=nlevels(data$split.plot)-1
df1a=((vara+(J*varb))^2)/( ((vara^2)/df1)+(((J*varb)^2)/df2))
colnames(a11)<-c("plot","adjusted.mean", "standard.error")
rownames(a11)<-NULL
colnames(a22)<-c("split.plot","adjusted.mean", "standard.error")
rownames(a22)<-NULL
colnames(a222)<-c("split.plot","adjusted.mean", "standard.error")
rownames(a222)<-NULL
res=fr2(m,data)
test1=fmm(a11,df1)
test2=fmm(a222,df2)
groups1=ft(test1, alpha); a11=a11[order(a11[,2], decreasing=TRUE),]
mf1=data.frame(a11,groups1);rownames(mf1) = NULL
groups2=ft(test2, alpha); a22=a22[order(a22[,2], decreasing=TRUE),]
mf2=data.frame(a22,groups2);rownames(mf2) = NULL
c1=rep(1:nlevels(data$split.plot), each=nlevels(data$plot))
cs1=split(c, c1)
#### trocados os dfs
test3=lapply(cs1, fmm, dff=df1a); n1=rep("plot in", nlevels(data$split.plot));n11=data.frame(n1,levels(data$split.plot))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$plot), nlevels(data$split.plot))
cs2=split(c, c2)
###############
msa2=sqrt((m$sigma^2)/(nrow(data)/nlevels(data$treatments)))
msa2=rep(msa2,nlevels(data$treatments))
c222<-data.frame(levels(treatments), round(am,4), round(msa2,4))
colnames(c222)<-c("plot*split.plot","adjusted.mean", "standard.error")
rownames(c222)<-NULL
cs222=split(c222, c2)
#######################
test4=lapply(cs222, fmm, dff=df2); n2=rep("split.plot in", nlevels(data$plot));n22=data.frame(n2,levels(data$plot))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft1[[x]],groups3[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$split.plot)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft2[[x]],groups4[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$plot)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
cva=round(sqrt(vara)*100/mean(data$response, na.rm = TRUE),4); vara=round(vara,4)
cvb=round(sqrt(varb)*100/mean(data$response, na.rm = TRUE),4);varb=round(varb,4)
ress=rbind(res[[1]],"Mean Square of Error a"=vara, "Mean Square of Error b"=varb, "Coefficient of Variation a"=cva,"Coefficient of Variation b"=cvb)
res=list("values"=ress,"residuals"=res[[2]],"standardized residuals"=res[[3]])
l=list(b,mf1,test1,mf2,test2,mft1,test3,mft2,test4, res)
names(l)=c("Marginal anova (Type III Sum of Squares)","Adjusted means (plot)", "Multiple comparison test (plot)","Adjusted means (split.plot)", "Multiple comparison test (split.plot)", "Adjusted means (plot in levels of split.plot)", "Multiple comparison test (plot in levels of split.plot)", "Adjusted means (split.plot in levels of plot)", "Multiple comparison test (split.plot in levels of plot)","Residual analysis")
pres(m)
return(l)
}
f6<-function(data, cov){
gg<-cov
names(data)<-c("plot","row", "column", "split.plot","response")
data<-data.frame(plot=factor(data$plot), row=factor(data$row), column=factor(data$column), split.plot=factor(data$split.plot), response=data$response)
subject<-interaction(data$plot,data$column)
treatments<-interaction(data$plot,data$split.plot)
data<-data.frame(data, subject,treatments)
UN<-corSymm(form=~1|subject)
AR<-corAR1(form=~1|subject)
ARH<-corAR1(form=~1|subject)
CS<- corCompSymm (form=~1|subject)
CAR<-corCAR1(form=~split.plot|subject)
o1<-list(AR,ARH, CAR, CS, UN)
cor<-o1[[gg]]
UN1<-varIdent(form=~1|split.plot)
AR1<-NULL
ARH1<- varIdent(form=~1|split.plot)
CS1<-NULL
CAR1<-NULL
o2<-list(AR1,ARH1, CAR1, CS1, UN1)
var<-o2[[gg]]
m<-lme(response~plot*split.plot+row+column, random=~1|subject, correlation=cor, weights=var, data<-data, na.action=na.omit, contrasts=list(plot=contr.sum, split.plot=contr.sum, column=contr.sum, row=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
b<-anova(m, type="marginal")[-1,]
m1<-lme(response~-1+ treatments+row+column, random=~1|subject, data=data, na.action=na.omit, contrasts=list(treatments=contr.sum, column=contr.sum, row=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
am<-fixef(m1)[1:nlevels(data$treatments)]
c<-data.frame(levels(treatments), round(am,4), round(sderrs(m1)[1:nlevels(data$ treatments)],4))
colnames(c)<-c("plot*split.plot","adjusted.mean", "standard.error")
rownames(c)<-NULL
df1<- anova(m) $"denDF"[2]
df2<- anova(m) $"denDF"[1]
mm1<-lme(response~-1+plot*split.plot+row+column, random=~1|subject, data=data, na.action=na.omit, contrasts=list(plot=contr.sum, split.plot=contr.sum, column=contr.sum, row=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
mm2<-lme(response~-1+split.plot*plot+row+column, random=~1|subject, data=data, na.action=na.omit, contrasts=list(split.plot=contr.sum, plot=contr.sum, column=contr.sum, row=contr.sum), correlation=cor, weights=var, control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"))
a11<-data.frame(levels(data$plot),
round( fixef(mm1)[1:nlevels(data$plot)], 4),
round(sderrs(mm1)[1:nlevels(data$plot)], 4))
a22<-data.frame(levels(data$split.plot),
round( fixef(mm2)[1:nlevels(data$split.plot)],4),
round(sderrs(mm2)[1:nlevels(data$split.plot)],4))
#################################################
# MS
msa=sqrt((m$sigma^2)/(nlevels(data[,1])*(nrow(data)/nlevels(data$treatments))))
msa=rep(msa,nlevels(data[,4]))
a222<-data.frame(levels(data$split.plot), round(fixef(mm2)[1:nlevels(data$split.plot)],4), round(msa,4))
a22<-data.frame(levels(data$split.plot), round(fixef(mm2)[1:nlevels(data$split.plot)],4), round(sderrs(mm2)[1:nlevels(data$split.plot)],4))
#################################################
mal=lm(response~row+column+plot+subject, data=data)
ano=anova(mal)
vara=anova(mal)[[4,3]]
varb=m$sigma^2
J=nlevels(data$split.plot)-1
df1a=((vara+(J*varb))^2)/( ((vara^2)/df1)+(((J*varb)^2)/df2))
colnames(a11)<-c("plot","adjusted.mean", "standard.error")
rownames(a11)<-NULL
colnames(a22)<-c("split.plot","adjusted.mean", "standard.error")
rownames(a22)<-NULL
colnames(a222)<-c("split.plot","adjusted.mean", "standard.error")
rownames(a222)<-NULL
res=fr2(m,data)
test1=fmm(a11,df1)
test2=fmm(a222,df2)
groups1=ft(test1, alpha); a11=a11[order(a11[,2], decreasing=TRUE),]
mf1=data.frame(a11,groups1);rownames(mf1) = NULL
groups2=ft(test2, alpha); a22=a22[order(a22[,2], decreasing=TRUE),]
mf2=data.frame(a22,groups2);rownames(mf2) = NULL
c1=rep(1:nlevels(data$split.plot), each=nlevels(data$plot))
cs1=split(c, c1)
#### trocados os dfs
test3=lapply(cs1, fmm, dff=df1a); n1=rep("plot in", nlevels(data$split.plot));n11=data.frame(n1,levels(data$split.plot))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$plot), nlevels(data$split.plot))
cs2=split(c, c2)
###############
msa2=sqrt((m$sigma^2)/(nrow(data)/nlevels(data$treatments)))
msa2=rep(msa2,nlevels(data$treatments))
c222<-data.frame(levels(treatments), round(am,4), round(msa2,4))
colnames(c222)<-c("plot*split.plot","adjusted.mean", "standard.error")
rownames(c222)<-NULL
cs222=split(c222, c2)
#######################
test4=lapply(cs222, fmm, dff=df2); n2=rep("split.plot in", nlevels(data$plot));n22=data.frame(n2,levels(data$plot))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft1[[x]],groups3[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$split.plot)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft2[[x]],groups4[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$plot)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
cva=round(sqrt(vara)*100/mean(data$response, na.rm = TRUE),4); vara=round(vara,4)
cvb=round(sqrt(varb)*100/mean(data$response, na.rm = TRUE),4);varb=round(varb,4)
ress=rbind(res[[1]],"Mean Square of Error a"=vara, "Mean Square of Error b"=varb, "Coefficient of Variation a"=cva,"Coefficient of Variation b"=cvb)
res=list("values"=ress,"residuals"=res[[2]],"standardized residuals"=res[[3]])
l=list(b,mf1,test1,mf2,test2,mft1,test3,mft2,test4, res)
names(l)=c("Marginal anova (Type III Sum of Squares)","Adjusted means (plot)", "Multiple comparison test (plot)","Adjusted means (split.plot)", "Multiple comparison test (split.plot)", "Adjusted means (plot in levels of split.plot)", "Multiple comparison test (plot in levels of split.plot)", "Adjusted means (split.plot in levels of plot)", "Multiple comparison test (split.plot in levels of plot)","Residual analysis")
pres(m)
return(l)
}
f7<-function(data, cov){
names(data)=c("factor_1","factor_2", "factor_3", "response")
data<-data.frame(factor_1=factor(data$factor_1), factor_2=factor(data$factor_2), factor_3=factor(data$factor_3), response=data$response)
m<-aov(response~factor_1*factor_2*factor_3,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum))
m1<-aov(response~-1+ factor_1*factor_2*factor_3,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum))
m2<-aov(response~-1+ factor_2*factor_1*factor_3,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum))
m3<-aov(response~-1+ factor_3*factor_1*factor_2,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum))
a2 <- drop1(m,.~.,test="F")
a3 <- a2
a3<-fa2(a3,m)
data2<-na.omit(data)
res=fr3(m,data2)
treatments_f1f2=interaction(data$factor_1,data$factor_2)
treatments_f1f3=interaction(data$factor_1,data$factor_3)
treatments_f2f3=interaction(data$factor_2,data$factor_3)
treatments_f1f2f3=interaction(data$factor_1,data$factor_2, data$factor_3)
treatments_f2f1f3=interaction(data$factor_2,data$factor_1, data$factor_3)
treatments_f3f1f2=interaction(data$factor_3,data$factor_1, data$factor_2)
data<-data.frame(data,treatments_f1f2, treatments_f1f3, treatments_f2f3, treatments_f1f2f3, treatments_f2f1f3, treatments_f3f1f2)
m4<-aov(response~-1+treatments_f1f2+factor_3*treatments_f1f2,data=data, contrasts=list(treatments_f1f2=contr.sum, factor_3=contr.sum))
m5<-aov(response~-1+treatments_f1f3+factor_2*treatments_f1f3,data=data, contrasts=list(treatments_f1f3=contr.sum, factor_2=contr.sum))
m6<-aov(response~-1+treatments_f2f3+factor_1*treatments_f2f3,data=data, contrasts=list(treatments_f2f3=contr.sum, factor_1=contr.sum))
m7<-aov(response~-1+treatments_f1f2f3,data=data, contrasts=list(treatments_f1f2f3=contr.sum))
m8<-aov(response~-1+treatments_f2f1f3,data=data, contrasts=list(treatments_f2f1f3=contr.sum))
m9<-aov(response~-1+treatments_f3f1f2,data=data, contrasts=list(treatments_f3f1f2=contr.sum))
adjusted.mean <-round( coef(m1)[1:nlevels(data$factor_1)],4)
standard.error<-round(sderrs(m1)[1:nlevels(data$factor_1)], 4)
factor_1<-levels(data$factor_1)
maf1=data.frame(factor_1,adjusted.mean,standard.error)
rownames(maf1)=NULL
adjusted.mean <-round( coef(m2)[1:nlevels(data$factor_2)],4)
standard.error<-round(sderrs(m2)[1:nlevels(data$factor_2)],4)
factor_2<-levels(data$factor_2)
maf2=data.frame(factor_2,adjusted.mean,standard.error)
rownames(maf2)=NULL
adjusted.mean<- round( coef(m3)[1:nlevels(data$factor_3)],4)
standard.error<-round(sderrs(m3)[1:nlevels(data$factor_3)],4)
factor_3<-levels(data$factor_3)
maf3=data.frame(factor_3,adjusted.mean,standard.error)
rownames(maf3)=NULL
adjusted.mean<-round(coef(m4)[1:nlevels(data$treatments_f1f2)],4)
standard.error<-round(sderrs(m4)[1:nlevels(data$treatments_f1f2)],4)
treatments_f1f2<-levels(data$treatments_f1f2)
mat1=data.frame(treatments_f1f2,adjusted.mean,standard.error)
rownames(mat1)=NULL
adjusted.mean<-round(coef(m5)[1:nlevels(data$treatments_f1f3)],4)
standard.error<-round(sderrs(m5)[1:nlevels(data$treatments_f1f3)],4)
treatments_f1f3<-levels(data$treatments_f1f3)
mat2=data.frame(treatments_f1f3,adjusted.mean,standard.error)
rownames(mat2)=NULL
adjusted.mean<-round(coef(m6)[1:nlevels(data$treatments_f2f3)],4)
standard.error<-round(sderrs(m6)[1:nlevels(data$treatments_f2f3)],4)
treatments_f2f3<-levels(data$treatments_f2f3)
mat3=data.frame(treatments_f2f3,adjusted.mean,standard.error)
rownames(mat3)=NULL
adjusted.mean<-round(coef(m7)[1:nlevels(data$treatments_f1f2f3)],4)
standard.error<-round(sderrs(m7)[1:nlevels(data$treatments_f1f2f3)],4)
treatments_f1f2f3<-levels(data$treatments_f1f2f3)
mat4=data.frame(treatments_f1f2f3,adjusted.mean,standard.error)
rownames(mat4)=NULL
dff=df.residual(m)
test1=fmm(maf1,dff)
test2=fmm(maf2,dff)
test3=fmm(maf3,dff)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
mf2=data.frame(maf2,groups2)
rownames(mf2) = NULL
groups3=ft(test3, alpha); maf3=maf3[order(maf3[,2], decreasing=TRUE),]
mf3=data.frame(maf3,groups3)
rownames(mf3) = NULL
c1=rep(1:nlevels(data$factor_2), each=nlevels(data$factor_1))
cs1=split(mat1, c1)
test4=lapply(cs1, fmm, dff=dff); n1=rep("factor_1 in", nlevels(data$factor_2));n11=data.frame(n1,levels(data$factor_2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test4)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_2))
cs2=split(mat1, c2)
test5=lapply(cs2, fmm, dff=dff); n2=rep("factor_2 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test5)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups4=lapply(test4, ft, alpha)
mft1=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft1[[x]],groups4[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_2)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups5=lapply(test5, ft, alpha)
mft2=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft2[[x]],groups5[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
c1=rep(1:nlevels(data$factor_3), each=nlevels(data$factor_1))
cs1=split(mat2, c1)
test6=lapply(cs1, fmm, dff=dff); n1=rep("factor_1 in", nlevels(data$factor_3));n11=data.frame(n1,levels(data$factor_3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test6)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_3))
cs2=split(mat2, c2)
test7=lapply(cs2, fmm, dff=dff); n2=rep("factor_3 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test7)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups6=lapply(test6, ft, alpha)
mft3=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft3[[x]],groups6[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_3)))
mft3=lapply(nf1,dd)
names(mft3)=n11
groups7=lapply(test7, ft, alpha)
mft4=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft4[[x]],groups7[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft4=lapply(nf2,ddd)
names(mft4)=n22
c1=rep(1:nlevels(data$factor_3), each=nlevels(data$factor_2))
cs1=split(mat3, c1)
test8=lapply(cs1, fmm, dff=dff); n1=rep("factor_2 in", nlevels(data$factor_3));n11=data.frame(n1,levels(data$factor_3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test8)=n11
c2=rep(1:nlevels(data$factor_2), nlevels(data$factor_3))
cs2=split(mat3, c2)
test9=lapply(cs2, fmm, dff=dff); n2=rep("factor_3 in", nlevels(data$factor_2));n22=data.frame(n2,levels(data$factor_2))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test9)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups8=lapply(test8, ft, alpha)
mft5=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft5[[x]],groups8[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_3)))
mft5=lapply(nf1,dd)
names(mft5)=n11
groups9=lapply(test9, ft, alpha)
mft6=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft6[[x]],groups9[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_2)))
mft6=lapply(nf2,ddd)
names(mft6)=n22
c1=rep(1:nlevels(data$treatments_f2f3), each=nlevels(data$factor_1))
cs1=split(mat4, c1)
test10=lapply(cs1, fmm, dff=dff); n1=rep("factor_1 in", nlevels(data$treatments_f2f3));n11=data.frame(n1,levels(data$treatments_f2f3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test10)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups10=lapply(test10, ft, alpha)
mft7=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft7[[x]],groups10[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f2f3)))
mft7=lapply(nf1,dd)
names(mft7)=n11
c1=rep(1:nlevels(data$factor_1), nlevels(data$factor_2));c1=rep(c1,nlevels(data$factor_3))
s=rep(0:(nlevels(data$factor_3)-1), each=nlevels(data$treatments_f1f2)); oi=max(c1); s=oi*s; c1=c1+s
cs1=split(mat4, c1)
test11=lapply(cs1, fmm, dff=dff); n1=rep("factor_2 in", nlevels(data$treatments_f1f3));n11=data.frame(n1,levels(data$treatments_f1f3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test11)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups11=lapply(test11, ft, alpha)
mft8=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft8[[x]],groups11[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f1f3)))
mft8=lapply(nf1,dd)
names(mft8)=n11
c1=rep(1:nlevels(data$treatments_f1f2), nlevels(data$factor_3))
cs1=split(mat4, c1)
test12=lapply(cs1, fmm, dff=dff); n1=rep("factor_3 in", nlevels(data$treatments_f1f2));n11=data.frame(n1,levels(data$treatments_f1f2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test12)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups12=lapply(test12, ft, alpha)
mft9=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft9[[x]],groups12[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f1f2)))
mft9=lapply(nf1,dd)
names(mft9)=n11
l<-list(a3, mf1, test1, mf2,test2,mf3,test3, mft1,test4,mft2,test5, mft3,test6,mft4,test7, mft5,test8,mft6,test9, mft7,test10, mft8,test11, mft9,test12, res)
names(l)= list("Analysis of variance", "Adjusted means (factor 1)", "Multiple comparison test (factor 1)","Adjusted means (factor 2)", "Multiple comparison test (factor 2)", "Adjusted means (factor 3)", "Multiple comparison test (factor 3)", "Adjusted means (factor 1 in levels of factor 2)", "Multiple comparison test (factor 1 in levels of factor 2)", "Adjusted means (factor 2 in levels of factor 1)", "Multiple comparison test (factor 2 in levels of factor 1)","Adjusted means (factor 1 in levels of factor 3)", "Multiple comparison test (factor 1 in levels of factor 3)", "Adjusted means (factor 3 in levels of factor 1)", "Multiple comparison test (factor 3 in levels of factor 1)","Adjusted means (factor 2 in levels of factor 3)", "Multiple comparison test (factor 2 in levels of factor 3)", "Adjusted means (factor 3 in levels of factor 2)", "Multiple comparison test (factor 3 in levels of factor 2)","Adjusted means (factor 1 in levels of treatments factor2*factor3)", "Multiple comparison test (factor 1 in levels of treatments factor2*factor3)","Adjusted means (factor 2 in levels of treatments factor1*factor3)", "Multiple comparison test (factor 2 in levels of treatments factor1*factor3)", "Adjusted means (factor 3 in levels of treatments factor1*factor2)","Multiple comparison test (factor 3 in levels of treatments factor1*factor2)","Residual analysis")
pres(m)
return(l)}
f8<-function(data, cov){
names(data)=c("factor_1","factor_2", "factor_3", "blocks","response")
data<-data.frame(factor_1=factor(data$factor_1), factor_2=factor(data$factor_2), factor_3=factor(data$factor_3), blocks=factor(data$blocks), response=data$response)
m<-aov(response~factor_1*factor_2*factor_3+ blocks,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum, blocks=contr.sum))
m1<-aov(response~-1+ factor_1*factor_2*factor_3+ blocks,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum, blocks=contr.sum))
m2<-aov(response~-1+ factor_2*factor_1*factor_3+ blocks,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum, blocks=contr.sum))
m3<-aov(response~-1+ factor_3*factor_1*factor_2+ blocks,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum, blocks=contr.sum))
a2 <- drop1(m,.~.,test="F")
a3 <- a2
a3<-fa2(a3,m)
data2<-na.omit(data)
res=fr3(m,data2)
treatments_f1f2=interaction(data$factor_1,data$factor_2)
treatments_f1f3=interaction(data$factor_1,data$factor_3)
treatments_f2f3=interaction(data$factor_2,data$factor_3)
treatments_f1f2f3=interaction(data$factor_1,data$factor_2, data$factor_3)
treatments_f2f1f3=interaction(data$factor_2,data$factor_1, data$factor_3)
treatments_f3f1f2=interaction(data$factor_3,data$factor_1, data$factor_2)
data<-data.frame(data,treatments_f1f2, treatments_f1f3, treatments_f2f3, treatments_f1f2f3, treatments_f2f1f3, treatments_f3f1f2)
m4<-aov(response~-1+treatments_f1f2+factor_3*treatments_f1f2+ blocks,data=data, contrasts=list(treatments_f1f2=contr.sum, factor_3=contr.sum, blocks=contr.sum))
m5<-aov(response~-1+treatments_f1f3+factor_2*treatments_f1f3+ blocks,data=data, contrasts=list(treatments_f1f3=contr.sum, factor_2=contr.sum, blocks=contr.sum))
m6<-aov(response~-1+treatments_f2f3+factor_1*treatments_f2f3+blocks,data=data, contrasts=list(treatments_f2f3=contr.sum, factor_1=contr.sum, blocks=contr.sum))
m7<-aov(response~-1+treatments_f1f2f3+ blocks,data=data, contrasts=list(treatments_f1f2f3=contr.sum, blocks=contr.sum))
m8<-aov(response~-1+treatments_f2f1f3+ blocks,data=data, contrasts=list(treatments_f2f1f3=contr.sum, blocks=contr.sum))
m9<-aov(response~-1+treatments_f3f1f2+ blocks,data=data, contrasts=list(treatments_f3f1f2=contr.sum, blocks=contr.sum))
adjusted.mean<-round(coef(m1)[1:nlevels(data$factor_1)],4)
standard.error<-round(sderrs(m1) [1:nlevels(data$factor_1)], 4)
factor_1<-levels(data$factor_1)
maf1=data.frame(factor_1,adjusted.mean,standard.error)
rownames(maf1)=NULL
adjusted.mean <-round( coef(m2)[1:nlevels(data$factor_2)],4)
standard.error<-round(sderrs(m2)[1:nlevels(data$factor_2)],4)
factor_2<-levels(data$factor_2)
maf2=data.frame(factor_2,adjusted.mean,standard.error)
rownames(maf2)=NULL
adjusted.mean<-round(coef(m3) [1:nlevels(data$factor_3)],4)
standard.error<-round(sderrs(m3)[1:nlevels(data$factor_3)],4)
factor_3<-levels(data$factor_3)
maf3=data.frame(factor_3,adjusted.mean,standard.error)
rownames(maf3)=NULL
adjusted.mean<-round(coef(m4)[1:nlevels(data$treatments_f1f2)],4)
standard.error<-round(sderrs(m4)[1:nlevels(data$treatments_f1f2)],4)
treatments_f1f2<-levels(data$treatments_f1f2)
mat1=data.frame(treatments_f1f2,adjusted.mean,standard.error)
rownames(mat1)=NULL
adjusted.mean<-round(coef(m5)[1:nlevels(data$treatments_f1f3)],4)
standard.error<-round(sderrs(m5)[1:nlevels(data$treatments_f1f3)],4)
treatments_f1f3<-levels(data$treatments_f1f3)
mat2=data.frame(treatments_f1f3,adjusted.mean,standard.error)
rownames(mat2)=NULL
adjusted.mean<-round(coef(m6)[1:nlevels(data$treatments_f2f3)],4)
standard.error<-round(sderrs(m6)[1:nlevels(data$treatments_f2f3)],4)
treatments_f2f3<-levels(data$treatments_f2f3)
mat3=data.frame(treatments_f2f3,adjusted.mean,standard.error)
rownames(mat3)=NULL
adjusted.mean<-round(coef(m7)[1:nlevels(data$treatments_f1f2f3)],4)
standard.error<-round(sderrs(m7)[1:nlevels(data$treatments_f1f2f3)],4)
treatments_f1f2f3<-levels(data$treatments_f1f2f3)
mat4=data.frame(treatments_f1f2f3,adjusted.mean,standard.error)
rownames(mat4)=NULL
dff=df.residual(m)
test1=fmm(maf1,dff)
test2=fmm(maf2,dff)
test3=fmm(maf3,dff)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
mf2=data.frame(maf2,groups2)
rownames(mf2) = NULL
groups3=ft(test3, alpha); maf3=maf3[order(maf3[,2], decreasing=TRUE),]
mf3=data.frame(maf3,groups3)
rownames(mf3) = NULL
c1=rep(1:nlevels(data$factor_2), each=nlevels(data$factor_1))
cs1=split(mat1, c1)
test4=lapply(cs1, fmm, dff=dff); n1=rep("factor_1 in", nlevels(data$factor_2));n11=data.frame(n1,levels(data$factor_2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test4)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_2))
cs2=split(mat1, c2)
test5=lapply(cs2, fmm, dff=dff); n2=rep("factor_2 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test5)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups4=lapply(test4, ft, alpha)
mft1=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft1[[x]],groups4[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_2)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups5=lapply(test5, ft, alpha)
mft2=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft2[[x]],groups5[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
c1=rep(1:nlevels(data$factor_3), each=nlevels(data$factor_1))
cs1=split(mat2, c1)
test6=lapply(cs1, fmm, dff=dff); n1=rep("factor_1 in", nlevels(data$factor_3));n11=data.frame(n1,levels(data$factor_3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test6)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_3))
cs2=split(mat2, c2)
test7=lapply(cs2, fmm, dff=dff); n2=rep("factor_3 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test7)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups6=lapply(test6, ft, alpha)
mft3=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft3[[x]],groups6[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_3)))
mft3=lapply(nf1,dd)
names(mft3)=n11
groups7=lapply(test7, ft, alpha)
mft4=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft4[[x]],groups7[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft4=lapply(nf2,ddd)
names(mft4)=n22
c1=rep(1:nlevels(data$factor_3), each=nlevels(data$factor_2))
cs1=split(mat3, c1)
test8=lapply(cs1, fmm, dff=dff); n1=rep("factor_2 in", nlevels(data$factor_3));n11=data.frame(n1,levels(data$factor_3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test8)=n11
c2=rep(1:nlevels(data$factor_2), nlevels(data$factor_3))
cs2=split(mat3, c2)
test9=lapply(cs2, fmm, dff=dff); n2=rep("factor_3 in", nlevels(data$factor_2));n22=data.frame(n2,levels(data$factor_2))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test9)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups8=lapply(test8, ft, alpha)
mft5=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft5[[x]],groups8[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_3)))
mft5=lapply(nf1,dd)
names(mft5)=n11
groups9=lapply(test9, ft, alpha)
mft6=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft6[[x]],groups9[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_2)))
mft6=lapply(nf2,ddd)
names(mft6)=n22
c1=rep(1:nlevels(data$treatments_f2f3), each=nlevels(data$factor_1))
cs1=split(mat4, c1)
test10=lapply(cs1, fmm, dff=dff); n1=rep("factor_1 in", nlevels(data$treatments_f2f3));n11=data.frame(n1,levels(data$treatments_f2f3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test10)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups10=lapply(test10, ft, alpha)
mft7=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft7[[x]],groups10[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f2f3)))
mft7=lapply(nf1,dd)
names(mft7)=n11
c1=rep(1:nlevels(data$factor_1), nlevels(data$factor_2));c1=rep(c1,nlevels(data$factor_3))
s=rep(0:(nlevels(data$factor_3)-1), each=nlevels(data$treatments_f1f2)); oi=max(c1); s=oi*s; c1=c1+s
cs1=split(mat4, c1)
test11=lapply(cs1, fmm, dff=dff); n1=rep("factor_2 in", nlevels(data$treatments_f1f3));n11=data.frame(n1,levels(data$treatments_f1f3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test11)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups11=lapply(test11, ft, alpha)
mft8=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft8[[x]],groups11[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f1f3)))
mft8=lapply(nf1,dd)
names(mft8)=n11
c1=rep(1:nlevels(data$treatments_f1f2), nlevels(data$factor_3))
cs1=split(mat4, c1)
test12=lapply(cs1, fmm, dff=dff); n1=rep("factor_3 in", nlevels(data$treatments_f1f2));n11=data.frame(n1,levels(data$treatments_f1f2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test12)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups12=lapply(test12, ft, alpha)
mft9=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft9[[x]],groups12[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f1f2)))
mft9=lapply(nf1,dd)
names(mft9)=n11
l<-list(a3, mf1, test1, mf2,test2,mf3,test3, mft1,test4,mft2,test5, mft3,test6,mft4,test7, mft5,test8,mft6,test9, mft7,test10, mft8,test11, mft9,test12, res)
names(l)= list("Analysis of variance", "Adjusted means (factor 1)", "Multiple comparison test (factor 1)","Adjusted means (factor 2)", "Multiple comparison test (factor 2)", "Adjusted means (factor 3)", "Multiple comparison test (factor 3)", "Adjusted means (factor 1 in levels of factor 2)", "Multiple comparison test (factor 1 in levels of factor 2)", "Adjusted means (factor 2 in levels of factor 1)", "Multiple comparison test (factor 2 in levels of factor 1)","Adjusted means (factor 1 in levels of factor 3)", "Multiple comparison test (factor 1 in levels of factor 3)", "Adjusted means (factor 3 in levels of factor 1)", "Multiple comparison test (factor 3 in levels of factor 1)","Adjusted means (factor 2 in levels of factor 3)", "Multiple comparison test (factor 2 in levels of factor 3)", "Adjusted means (factor 3 in levels of factor 2)", "Multiple comparison test (factor 3 in levels of factor 2)","Adjusted means (factor 1 in levels of treatments factor2*factor3)", "Multiple comparison test (factor 1 in levels of treatments factor2*factor3)","Adjusted means (factor 2 in levels of treatments factor1*factor3)", "Multiple comparison test (factor 2 in levels of treatments factor1*factor3)", "Adjusted means (factor 3 in levels of treatments factor1*factor2)","Multiple comparison test (factor 3 in levels of treatments factor1*factor2)","Residual analysis")
pres(m)
return(l)
}
f9<-function(data, cov){
gg=cov
names(data)=c("factor_1","factor_2", "factor_3","rep","response")
data<-data.frame(factor_1=factor(data$factor_1), factor_2=factor(data$factor_2), factor_3=factor(data$factor_3), rep=factor(data$rep), response=data$response)
subject<-interaction(data$factor_1,data$factor_2,data$rep)
data<-data.frame(data,subject)
UN<-corSymm(form=~1|subject)
AR<-corAR1(form=~1|subject)
ARH<-corAR1(form=~1|subject)
CS<- corCompSymm (form=~1|subject)
CAR<-corCAR1(form=~factor_3|subject)
o1<-list(AR,ARH, CAR, CS, UN)
cor<-o1[[gg]]
UN1<-varIdent(form=~1|factor_3)
AR1<-NULL
ARH1<- varIdent(form=~1|factor_3)
CS1<-NULL
CAR1<-NULL
o2<-list(AR1,ARH1, CAR1, CS1, UN1)
var<-o2[[gg]]
m<- lme(response~factor_1*factor_2*factor_3, random=~1|subject, correlation=cor, weights=var , data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m1<-lme(response~-1+ factor_1*factor_2*factor_3, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m2<-lme(response~-1+ factor_2*factor_1*factor_3, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m3<-lme(response~-1+ factor_3*factor_1*factor_2, random=~1|subject, correlation=cor, weights=var , data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
a3<-anova(m, type="marginal")[-1,]
df1<- anova(m) $"denDF"[2]
df2<- anova(m) $"denDF"[1]
treatments_f1f2=interaction(data$factor_1,data$factor_2)
treatments_f1f3=interaction(data$factor_1,data$factor_3)
treatments_f2f3=interaction(data$factor_2,data$factor_3)
treatments_f1f2f3=interaction(data$factor_1,data$factor_2, data$factor_3)
treatments_f2f1f3=interaction(data$factor_2,data$factor_1, data$factor_3)
treatments_f3f1f2=interaction(data$factor_3,data$factor_1, data$factor_2)
data<-data.frame(data,treatments_f1f2, treatments_f1f3, treatments_f2f3, treatments_f1f2f3, treatments_f2f1f3, treatments_f3f1f2)
m4<-lme(response~-1+treatments_f1f2+factor_3*treatments_f1f2, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f1f2=contr.sum, factor_3=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m5<-lme(response~-1+treatments_f1f3+factor_2*treatments_f1f3, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f1f3=contr.sum, factor_2=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m6<-lme(response~-1+treatments_f2f3+factor_1*treatments_f2f3, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f2f3=contr.sum, factor_1=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m7<-lme(response~-1+treatments_f1f2f3, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f1f2f3=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m8<-lme(response~-1+treatments_f2f1f3, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f2f1f3=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m9<-lme(response~-1+treatments_f3f1f2, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f3f1f2=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
res=fr4(m,data)
adjusted.mean<-round(fixef(m1)[1:nlevels(data$factor_1)],4)
standard.error<-round(sderrs(m1)[1:nlevels(data$factor_1)], 4)
factor_1<-levels(data$factor_1)
maf1=data.frame(factor_1,adjusted.mean,standard.error)
rownames(maf1)=NULL
adjusted.mean<-round(fixef(m2) [1:nlevels(data$factor_2)],4)
standard.error<-round(sderrs(m2)[1:nlevels(data$factor_2)],4)
factor_2<-levels(data$factor_2)
maf2=data.frame(factor_2,adjusted.mean,standard.error)
rownames(maf2)=NULL
adjusted.mean<-round(fixef(m3) [1:nlevels(data$factor_3)],4)
standard.error<-round(sderrs(m3)[1:nlevels(data$factor_3)],4)
factor_3<-levels(data$factor_3)
maf3=data.frame(factor_3,adjusted.mean,standard.error)
rownames(maf3)=NULL
adjusted.mean<-round(fixef(m4)[1:nlevels(data$treatments_f1f2)],4)
standard.error<-round(sderrs(m4)[1:nlevels(data$treatments_f1f2)],4)
treatments_f1f2<-levels(data$treatments_f1f2)
mat1=data.frame(treatments_f1f2,adjusted.mean,standard.error)
rownames(mat1)=NULL
adjusted.mean<-round(fixef(m5)[1:nlevels(data$treatments_f1f3)],4)
standard.error<-round(sderrs(m5)[1:nlevels(data$treatments_f1f3)],4)
treatments_f1f3<-levels(data$treatments_f1f3)
mat2=data.frame(treatments_f1f3,adjusted.mean,standard.error)
rownames(mat2)=NULL
adjusted.mean<-round(fixef(m6)[1:nlevels(data$treatments_f2f3)],2)
standard.error<-round(sderrs(m6)[1:nlevels(data$treatments_f2f3)],2)
treatments_f2f3<-levels(data$treatments_f2f3)
mat3=data.frame(treatments_f2f3,adjusted.mean,standard.error)
rownames(mat3)=NULL
adjusted.mean<-round(fixef(m7)[1:nlevels(data$treatments_f1f2f3)],4)
standard.error<-round(sderrs(m7)[1:nlevels(data$treatments_f1f2f3)],4)
treatments_f1f2f3<-levels(data$treatments_f1f2f3)
mat4=data.frame(treatments_f1f2f3,adjusted.mean,standard.error)
rownames(mat4)=NULL
test1=fmm(maf1,df1)
test2=fmm(maf2,df1)
test3=fmm(maf3,df2)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
mf2=data.frame(maf2,groups2)
rownames(mf2) = NULL
groups3=ft(test3, alpha); maf3=maf3[order(maf3[,2], decreasing=TRUE),]
mf3=data.frame(maf3,groups3)
rownames(mf3) = NULL
c1=rep(1:nlevels(data$factor_2), each=nlevels(data$factor_1))
cs1=split(mat1, c1)
test4=lapply(cs1, fmm, dff=df1); n1=rep("factor_1 in", nlevels(data$factor_2));n11=data.frame(n1,levels(data$factor_2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test4)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_2))
cs2=split(mat1, c2)
test5=lapply(cs2, fmm, dff=df1); n2=rep("factor_2 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test5)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups4=lapply(test4, ft, alpha)
mft1=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft1[[x]],groups4[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_2)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups5=lapply(test5, ft, alpha)
mft2=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft2[[x]],groups5[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
c1=rep(1:nlevels(data$factor_3), each=nlevels(data$factor_1))
cs1=split(mat2, c1)
test6=lapply(cs1, fmm, dff=df2); n1=rep("factor_1 in", nlevels(data$factor_3));n11=data.frame(n1,levels(data$factor_3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test6)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_3))
cs2=split(mat2, c2)
test7=lapply(cs2, fmm, dff=df2); n2=rep("factor_3 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test7)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups6=lapply(test6, ft, alpha)
mft3=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft3[[x]],groups6[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_3)))
mft3=lapply(nf1,dd)
names(mft3)=n11
groups7=lapply(test7, ft, alpha)
mft4=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft4[[x]],groups7[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft4=lapply(nf2,ddd)
names(mft4)=n22
c1=rep(1:nlevels(data$factor_3), each=nlevels(data$factor_2))
cs1=split(mat3, c1)
test8=lapply(cs1, fmm, dff=df2); n1=rep("factor_2 in", nlevels(data$factor_3));n11=data.frame(n1,levels(data$factor_3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test8)=n11
c2=rep(1:nlevels(data$factor_2), nlevels(data$factor_3))
cs2=split(mat3, c2)
test9=lapply(cs2, fmm, dff=df2); n2=rep("factor_3 in", nlevels(data$factor_2));n22=data.frame(n2,levels(data$factor_2))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test9)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups8=lapply(test8, ft, alpha)
mft5=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft5[[x]],groups8[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_3)))
mft5=lapply(nf1,dd)
names(mft5)=n11
groups9=lapply(test9, ft, alpha)
mft6=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft6[[x]],groups9[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_2)))
mft6=lapply(nf2,ddd)
names(mft6)=n22
c1=rep(1:nlevels(data$treatments_f2f3), each=nlevels(data$factor_1))
cs1=split(mat4, c1)
test10=lapply(cs1, fmm, dff=df2); n1=rep("factor_1 in", nlevels(data$treatments_f2f3));n11=data.frame(n1,levels(data$treatments_f2f3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test10)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups10=lapply(test10, ft, alpha)
mft7=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft7[[x]],groups10[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f2f3)))
mft7=lapply(nf1,dd)
names(mft7)=n11
c1=rep(1:nlevels(data$factor_1), nlevels(data$factor_2));c1=rep(c1,nlevels(data$factor_3))
s=rep(0:(nlevels(data$factor_3)-1), each=nlevels(data$treatments_f1f2)); oi=max(c1); s=oi*s; c1=c1+s
cs1=split(mat4, c1)
test11=lapply(cs1, fmm, dff=df2); n1=rep("factor_2 in", nlevels(data$treatments_f1f3));n11=data.frame(n1,levels(data$treatments_f1f3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test11)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups11=lapply(test11, ft, alpha)
mft8=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft8[[x]],groups11[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f1f3)))
mft8=lapply(nf1,dd)
names(mft8)=n11
c1=rep(1:nlevels(data$treatments_f1f2), nlevels(data$factor_3))
cs1=split(mat4, c1)
test12=lapply(cs1, fmm, dff=df2); n1=rep("factor_3 in", nlevels(data$treatments_f1f2));n11=data.frame(n1,levels(data$treatments_f1f2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test12)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups12=lapply(test12, ft, alpha)
mft9=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft9[[x]],groups12[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f1f2)))
mft9=lapply(nf1,dd)
names(mft9)=n11
l<-list(a3, mf1, test1, mf2,test2,mf3,test3, mft1,test4,mft2,test5, mft3,test6,mft4,test7, mft5,test8,mft6,test9, mft7,test10, mft8,test11, mft9,test12, res)
names(l)= list("Marginal anova (Type III Sum of Squares)", "Adjusted means (factor 1)", "Multiple comparison test (factor 1)","Adjusted means (factor 2)", "Multiple comparison test (factor 2)", "Adjusted means (factor 3)", "Multiple comparison test (factor 3)", "Adjusted means (factor 1 in levels of factor 2)", "Multiple comparison test (factor 1 in levels of factor 2)", "Adjusted means (factor 2 in levels of factor 1)", "Multiple comparison test (factor 2 in levels of factor 1)","Adjusted means (factor 1 in levels of factor 3)", "Multiple comparison test (factor 1 in levels of factor 3)", "Adjusted means (factor 3 in levels of factor 1)", "Multiple comparison test (factor 3 in levels of factor 1)","Adjusted means (factor 2 in levels of factor 3)", "Multiple comparison test (factor 2 in levels of factor 3)", "Adjusted means (factor 3 in levels of factor 2)", "Multiple comparison test (factor 3 in levels of factor 2)","Adjusted means (factor 1 in levels of treatments factor2*factor3)", "Multiple comparison test (factor 1 in levels of treatments factor2*factor3)","Adjusted means (factor 2 in levels of treatments factor1*factor3)", "Multiple comparison test (factor 2 in levels of treatments factor1*factor3)", "Adjusted means (factor 3 in levels of treatments factor1*factor2)","Multiple comparison test (factor 3 in levels of treatments factor1*factor2)","Residual analysis")
pres(m)
return(l)
}
f10<-function(data, cov){
gg=cov
names(data)=c("factor_1","factor_2", "factor_3","blocks","response")
data<-data.frame(factor_1=factor(data$factor_1), factor_2=factor(data$factor_2), factor_3=factor(data$factor_3), blocks=factor(data$blocks), response=data$response)
subject<-interaction(data$factor_1,data$factor_2,data$blocks)
data<-data.frame(data,subject)
UN<-corSymm(form=~1|subject)
AR<-corAR1(form=~1|subject)
ARH<-corAR1(form=~1|subject)
CS<- corCompSymm (form=~1|subject)
CAR<-corCAR1(form=~factor_3|subject)
o1<-list(AR,ARH, CAR, CS, UN)
cor<-o1[[gg]]
UN1<-varIdent(form=~1|factor_3)
AR1<-NULL
ARH1<- varIdent(form=~1|factor_3)
CS1<-NULL
CAR1<-NULL
o2<-list(AR1,ARH1, CAR1, CS1, UN1)
var<-o2[[gg]]
m<- lme(response~factor_1*factor_2*factor_3+blocks, random=~1|subject, correlation=cor, weights=var , data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m1<-lme(response~-1+ factor_1*factor_2*factor_3+blocks, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m2<-lme(response~-1+ factor_2*factor_1*factor_3+blocks, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m3<-lme(response~-1+ factor_3*factor_1*factor_2+blocks, random=~1|subject, correlation=cor, weights=var , data=data, contrasts=list(factor_1=contr.sum, factor_2=contr.sum, factor_3=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
a3<-anova(m, type="marginal")[-1,]
df1<- anova(m) $"denDF"[2]
df2<- anova(m) $"denDF"[1]
treatments_f1f2=interaction(data$factor_1,data$factor_2)
treatments_f1f3=interaction(data$factor_1,data$factor_3)
treatments_f2f3=interaction(data$factor_2,data$factor_3)
treatments_f1f2f3=interaction(data$factor_1,data$factor_2, data$factor_3)
treatments_f2f1f3=interaction(data$factor_2,data$factor_1, data$factor_3)
treatments_f3f1f2=interaction(data$factor_3,data$factor_1, data$factor_2)
data<-data.frame(data,treatments_f1f2, treatments_f1f3, treatments_f2f3, treatments_f1f2f3, treatments_f2f1f3, treatments_f3f1f2)
m4<-lme(response~-1+treatments_f1f2+factor_3*treatments_f1f2+blocks, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f1f2=contr.sum, factor_3=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m5<-lme(response~-1+treatments_f1f3+factor_2*treatments_f1f3+blocks, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f1f3=contr.sum, factor_2=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m6<-lme(response~-1+treatments_f2f3+factor_1*treatments_f2f3+blocks, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f2f3=contr.sum, factor_1=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m7<-lme(response~-1+treatments_f1f2f3+blocks, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f1f2f3=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m8<-lme(response~-1+treatments_f2f1f3+blocks, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f2f1f3=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
m9<-lme(response~-1+treatments_f3f1f2+blocks, random=~1|subject, correlation=cor, weights=var ,data=data, contrasts=list(treatments_f3f1f2=contr.sum, blocks=contr.sum), control=lmeControl(maxIter =6000, msMaxIter=6000, niterEM=2000, opt="optim"),na.action=na.omit)
res=fr4(m,data)
adjusted.mean<-round(fixef(m1)[1:nlevels(data$factor_1)],4)
standard.error<-round(sderrs(m1)[1:nlevels(data$factor_1)], 4)
factor_1<-levels(data$factor_1)
maf1=data.frame(factor_1,adjusted.mean,standard.error)
rownames(maf1)=NULL
adjusted.mean<-round(fixef(m2) [1:nlevels(data$factor_2)],4)
standard.error<-round(sderrs(m2)[1:nlevels(data$factor_2)],4)
factor_2<-levels(data$factor_2)
maf2=data.frame(factor_2,adjusted.mean,standard.error)
rownames(maf2)=NULL
adjusted.mean<-round(fixef(m3) [1:nlevels(data$factor_3)],4)
standard.error<-round(sderrs(m3)[1:nlevels(data$factor_3)],4)
factor_3<-levels(data$factor_3)
maf3=data.frame(factor_3,adjusted.mean,standard.error)
rownames(maf3)=NULL
adjusted.mean<-round(fixef(m4)[1:nlevels(data$treatments_f1f2)],4)
standard.error<-round(sderrs(m4)[1:nlevels(data$treatments_f1f2)],4)
treatments_f1f2<-levels(data$treatments_f1f2)
mat1=data.frame(treatments_f1f2,adjusted.mean,standard.error)
rownames(mat1)=NULL
adjusted.mean<-round(fixef(m5)[1:nlevels(data$treatments_f1f3)],4)
standard.error<-round(sderrs(m5)[1:nlevels(data$treatments_f1f3)],4)
treatments_f1f3<-levels(data$treatments_f1f3)
mat2=data.frame(treatments_f1f3,adjusted.mean,standard.error)
rownames(mat2)=NULL
adjusted.mean<-round(fixef(m6)[1:nlevels(data$treatments_f2f3)],2)
standard.error<-round(sderrs(m6)[1:nlevels(data$treatments_f2f3)],2)
treatments_f2f3<-levels(data$treatments_f2f3)
mat3=data.frame(treatments_f2f3,adjusted.mean,standard.error)
rownames(mat3)=NULL
adjusted.mean<-round(fixef(m7)[1:nlevels(data$treatments_f1f2f3)],4)
standard.error<-round(sderrs(m7)[1:nlevels(data$treatments_f1f2f3)],4)
treatments_f1f2f3<-levels(data$treatments_f1f2f3)
mat4=data.frame(treatments_f1f2f3,adjusted.mean,standard.error)
rownames(mat4)=NULL
test1=fmm(maf1,df1)
test2=fmm(maf2,df1)
test3=fmm(maf3,df2)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
mf2=data.frame(maf2,groups2)
rownames(mf2) = NULL
groups3=ft(test3, alpha); maf3=maf3[order(maf3[,2], decreasing=TRUE),]
mf3=data.frame(maf3,groups3)
rownames(mf3) = NULL
c1=rep(1:nlevels(data$factor_2), each=nlevels(data$factor_1))
cs1=split(mat1, c1)
test4=lapply(cs1, fmm, dff=df1); n1=rep("factor_1 in", nlevels(data$factor_2));n11=data.frame(n1,levels(data$factor_2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test4)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_2))
cs2=split(mat1, c2)
test5=lapply(cs2, fmm, dff=df1); n2=rep("factor_2 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test5)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups4=lapply(test4, ft, alpha)
mft1=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft1[[x]],groups4[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_2)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups5=lapply(test5, ft, alpha)
mft2=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft2[[x]],groups5[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
c1=rep(1:nlevels(data$factor_3), each=nlevels(data$factor_1))
cs1=split(mat2, c1)
test6=lapply(cs1, fmm, dff=df2); n1=rep("factor_1 in", nlevels(data$factor_3));n11=data.frame(n1,levels(data$factor_3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test6)=n11
c2=rep(1:nlevels(data$factor_1), nlevels(data$factor_3))
cs2=split(mat2, c2)
test7=lapply(cs2, fmm, dff=df2); n2=rep("factor_3 in", nlevels(data$factor_1));n22=data.frame(n2,levels(data$factor_1))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test7)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups6=lapply(test6, ft, alpha)
mft3=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft3[[x]],groups6[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_3)))
mft3=lapply(nf1,dd)
names(mft3)=n11
groups7=lapply(test7, ft, alpha)
mft4=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft4[[x]],groups7[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_1)))
mft4=lapply(nf2,ddd)
names(mft4)=n22
c1=rep(1:nlevels(data$factor_3), each=nlevels(data$factor_2))
cs1=split(mat3, c1)
test8=lapply(cs1, fmm, dff=df2); n1=rep("factor_2 in", nlevels(data$factor_3));n11=data.frame(n1,levels(data$factor_3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test8)=n11
c2=rep(1:nlevels(data$factor_2), nlevels(data$factor_3))
cs2=split(mat3, c2)
test9=lapply(cs2, fmm, dff=df2); n2=rep("factor_3 in", nlevels(data$factor_2));n22=data.frame(n2,levels(data$factor_2))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test9)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups8=lapply(test8, ft, alpha)
mft5=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft5[[x]],groups8[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$factor_3)))
mft5=lapply(nf1,dd)
names(mft5)=n11
groups9=lapply(test9, ft, alpha)
mft6=lapply(cs2,csf);
ddd<-function(x){l=data.frame(mft6[[x]],groups9[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$factor_2)))
mft6=lapply(nf2,ddd)
names(mft6)=n22
c1=rep(1:nlevels(data$treatments_f2f3), each=nlevels(data$factor_1))
cs1=split(mat4, c1)
test10=lapply(cs1, fmm, dff=df2); n1=rep("factor_1 in", nlevels(data$treatments_f2f3));n11=data.frame(n1,levels(data$treatments_f2f3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test10)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups10=lapply(test10, ft, alpha)
mft7=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft7[[x]],groups10[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f2f3)))
mft7=lapply(nf1,dd)
names(mft7)=n11
c1=rep(1:nlevels(data$factor_1), nlevels(data$factor_2));c1=rep(c1,nlevels(data$factor_3))
s=rep(0:(nlevels(data$factor_3)-1), each=nlevels(data$treatments_f1f2)); oi=max(c1); s=oi*s; c1=c1+s
cs1=split(mat4, c1)
test11=lapply(cs1, fmm, dff=df2); n1=rep("factor_2 in", nlevels(data $treatments_f1f3));n11=data.frame(n1,levels(data$treatments_f1f3))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test11)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups11=lapply(test11, ft, alpha)
mft8=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft8[[x]],groups11[[x]]); return(l)}; nf1=as.list(c (1:nlevels(data$treatments_f1f3)))
mft8=lapply(nf1,dd)
names(mft8)=n11
c1=rep(1:nlevels(data$treatments_f1f2), nlevels(data$factor_3))
cs1=split(mat4, c1)
test12=lapply(cs1, fmm, dff=df2); n1=rep("factor_3 in", nlevels(data$treatments_f1f2));n11=data.frame(n1,levels(data$treatments_f1f2))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test12)=n11
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups12=lapply(test12, ft, alpha)
mft9=lapply(cs1,csf);
dd<-function(x){l=data.frame(mft9[[x]],groups12[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$treatments_f1f2)))
mft9=lapply(nf1,dd)
names(mft9)=n11
l<-list(a3, mf1, test1, mf2,test2,mf3,test3, mft1,test4,mft2,test5, mft3,test6,mft4,test7, mft5,test8,mft6,test9, mft7,test10, mft8,test11, mft9,test12, res)
names(l)= list("Marginal anova (Type III Sum of Squares)", "Adjusted means (factor 1)", "Multiple comparison test (factor 1)","Adjusted means (factor 2)", "Multiple comparison test (factor 2)", "Adjusted means (factor 3)", "Multiple comparison test (factor 3)", "Adjusted means (factor 1 in levels of factor 2)", "Multiple comparison test (factor 1 in levels of factor 2)", "Adjusted means (factor 2 in levels of factor 1)", "Multiple comparison test (factor 2 in levels of factor 1)","Adjusted means (factor 1 in levels of factor 3)", "Multiple comparison test (factor 1 in levels of factor 3)", "Adjusted means (factor 3 in levels of factor 1)", "Multiple comparison test (factor 3 in levels of factor 1)","Adjusted means (factor 2 in levels of factor 3)", "Multiple comparison test (factor 2 in levels of factor 3)", "Adjusted means (factor 3 in levels of factor 2)", "Multiple comparison test (factor 3 in levels of factor 2)","Adjusted means (factor 1 in levels of treatments factor2*factor3)", "Multiple comparison test (factor 1 in levels of treatments factor2*factor3)","Adjusted means (factor 2 in levels of treatments factor1*factor3)", "Multiple comparison test (factor 2 in levels of treatments factor1*factor3)", "Adjusted means (factor 3 in levels of treatments factor1*factor2)","Multiple comparison test (factor 3 in levels of treatments factor1*factor2)","Residual analysis")
pres(m)
return(l)
}
f11<-function(data, cov){
names(data)=c("factor_1","factor_2","blocks","response")
data<-data.frame(treatment=factor(data$factor_1), experiment=factor(data$factor_2), block=factor(data$blocks), response=data$response)
m<-aov(response~treatment*experiment+experiment/block,data=data, contrasts=list(treatment=contr.sum, experiment=contr.sum, block=contr.sum))
m1<-aov(response~-1+treatment+experiment+ treatment*experiment +experiment/block,data=data, contrasts=list(treatment=contr.sum, experiment=contr.sum, block=contr.sum))
m2<-aov(response~-1+ experiment+treatment+ treatment*experiment +experiment/block , data=data, contrasts=list(treatment=contr.sum, experiment=contr.sum, block=contr.sum))
a<-anova(m)
a2 <- drop1(m,.~.,test="F")
a3 <- a2
a3<-fa2(a3,m)
interaction=interaction(data$treatment,data$experiment)
data<-data.frame(data,interaction)
m3<-aov(response~-1+interaction+experiment/block,data=data, contrasts=list(interaction=contr.sum, block=contr.sum))
data2<-na.omit(data)
fr11=function(m,data){
r=resid(m)
s <- shapiro.test(r)
b1<- bartlett.test(r~treatment, data=data)
b2<- bartlett.test(r~experiment, data=data)
b3<- bartlett.test(r~interaction, data=data)
cvf=cv(m)
rd=as.data.frame((sort(sqrt(r^2),decreasing=TRUE)))
rl=as.list(rownames(rd))
r1=rl[[1]];r2=rl[[2]];r3=rl[[3]]
d=data.frame(round(s$"p.value",4),round(b1$"p.value",4),round(b2$"p.value",4),round(b3$"p.value",4), round(cvf,2),as.numeric(r1),as.numeric(r2),as.numeric(r3)); d=t(d)
rownames(d)=c("p.value Shapiro-Wilk test","p.value Bartlett test (treatment)","p.value Bartlett test (experiment)","p.value Bartlett test (interaction)","coefficient of variation (%)", "first value most discrepant","second value most discrepant","third value most discrepant")
colnames(d)="values"
return(d)}
res=fr11(m,data2)
adjusted.mean<-round(coef(m1)[1:nlevels(data$treatment)],4)
standard.error<-round(sderrs(m1)[1:nlevels(data$treatment)], 4)
treatment<-levels(data$treatment)
means1=adjusted.mean; names(means1)=treatment
maf1=data.frame(treatment,adjusted.mean,standard.error)
rownames(maf1)=NULL
adjusted.mean<-coef(m2)[1:nlevels(data$experiment)]
standard.error<-round(sderrs(m2)[1:nlevels(data$experiment)],4)
experiment<-levels(data$experiment)
means2=adjusted.mean; names(means2)=experiment
maf2=data.frame(experiment,adjusted.mean,standard.error)
rownames(maf2)=NULL
adjusted.mean<-round(coef(m3)[1:nlevels(data$interaction)],4)
standard.error<-round(sderrs(m3)[1:nlevels(data$interaction)],4)
interaction<-levels(data$interaction)
means3=adjusted.mean; names(means3)=treatment
mat=data.frame(interaction,adjusted.mean,standard.error)
rownames(mat)=NULL
dff=df.residual(m)
test1=fmm(maf1,dff)
test2=fmm(maf2,dff)
nrep1=length(data2[,1])/length(treatment)
nrep2=length(data2[,1])/length(experiment)
nrep3=length(data2[,1])/length(interaction)
QME=deviance(m)/dff
scott_knott=sk(means1, dff, QME, nrep1, alpha)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1, scott_knott)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
scott_knott=sk(means2, dff, QME, nrep2, alpha)
mf2=data.frame(maf2,groups2, scott_knott)
rownames(mf2) = NULL
c1=rep(1:nlevels(data$experiment), each=nlevels(data$treatment))
cs1=split(mat, c1)
test3=lapply(cs1, fmm, dff=dff); n1=rep("treatment in", nlevels(data$experiment));n11=data.frame(n1,levels(data$experiment))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$treatment), nlevels(data$experiment))
cs2=split(mat, c2)
test4=lapply(cs2, fmm, dff=dff); n2=rep("experiment in", nlevels(data$treatment));n22=data.frame(n2,levels(data$treatment))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf)
i=1:length(mft1)
ft1=function(i){a=mft1[[i]][,2];names(a)=mft1[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups3[x],scott_knott[x])}
xx1=1:length(groups3)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups3)
dd<-function(x){l=data.frame(mft1[[x]],ftr[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$experiment)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf)
i=1:length(mft2)
ft1=function(i){a=mft2[[i]][,2];names(a)=mft2[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups4[x],scott_knott[x])}
xx1=1:length(groups4)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups4)
ddd<-function(x){l=data.frame(mft2[[x]],ftr[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$treatment)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
l<-list(a3,mf1, test1, mf2,test2,mft1,test3, mft2, test4, res)
names(l)= list("Analysis of variance", "Adjusted means (treatment)", "Multiple comparison test (treatment)","Adjusted means (experiment)", "Multiple comparison test (experiment)", "Adjusted means (treatment in levels of experiment)", "Multiple comparison test (treatment in levels of experiment)", "Adjusted means (experiment in levels treatment)", "Multiple comparison test (experiment in levels treatment)","Residual analysis")
pres(m)
return(l)
}
f12<-function(data, cov){
names(data) = c("treatments", "squares", "rows", "cols",
"response")
data <- data.frame(treatments = factor(data$treatments),
squares = factor(data$squares), rows = factor(data$rows),
columns = factor(data$cols), response = data$response)
m <- aov(response ~ treatments*squares + squares/rows + columns,
data = data, contrasts = list(treatments = contr.sum,
squares = contr.sum, rows = contr.sum, columns = contr.sum))
m1 <- aov(response ~ -1 + treatments + treatments*squares +squares/rows +
columns, data = data, contrasts = list(treatments = contr.sum,
squares = contr.sum, rows = contr.sum, columns = contr.sum))
m2 <- aov(response ~ -1 + squares+treatments + treatments*squares +squares/rows +
columns, data = data, contrasts = list(treatments = contr.sum,
squares = contr.sum, rows = contr.sum, columns = contr.sum))
a<-anova(m)
a2 <- drop1(m,.~.,test="F")
a3 <- a2
a3<-fa2(a3,m)
interaction=interaction(data$treatments,data$squares)
data<-data.frame(data,interaction)
m3<- aov(response ~ -1 + interaction +squares/rows +
columns, data = data, contrasts = list(interaction = contr.sum, rows = contr.sum, columns = contr.sum))
data2<-na.omit(data)
fr12=function(m,data){
r=resid(m)
s <- shapiro.test(r)
b1<- bartlett.test(r~treatments, data=data)
b2<- bartlett.test(r~squares, data=data)
b3<- bartlett.test(r~interaction, data=data)
cvf=cv(m)
rd=as.data.frame((sort(sqrt(r^2),decreasing=TRUE)))
rl=as.list(rownames(rd))
r1=rl[[1]];r2=rl[[2]];r3=rl[[3]]
d=data.frame(round(s$"p.value",4),round(b1$"p.value",4),round(b2$"p.value",4),round(b3$"p.value",4), round(cvf,2),as.numeric(r1),as.numeric(r2),as.numeric(r3)); d=t(d)
rownames(d)=c("p.value Shapiro-Wilk test","p.value Bartlett test (treatments)","p.value Bartlett test (squares)","p.value Bartlett test (interaction)","coefficient of variation (%)", "first value most discrepant","second value most discrepant","third value most discrepant")
colnames(d)="values"
return(d)}
res=fr12(m,data2)
adjusted.mean<-round(coef(m1)[1:nlevels(data$treatments)],4)
standard.error<-round(sderrs(m1)[1:nlevels(data$treatments)], 4)
treatment<-levels(data$treatments)
means1=adjusted.mean; names(means1)=treatment
maf1=data.frame(treatment,adjusted.mean,standard.error)
rownames(maf1)=NULL
adjusted.mean<-coef(m2)[1:nlevels(data$squares)]
standard.error<-round(sderrs(m2)[1:nlevels(data$squares)],4)
square<-levels(data$squares)
means2=adjusted.mean; names(means2)=square
maf2=data.frame(square,adjusted.mean,standard.error)
rownames(maf2)=NULL
adjusted.mean<-round(coef(m3)[1:nlevels(data$interaction)],4)
standard.error<-round(sderrs(m3)[1:nlevels(data$interaction)],4)
interaction<-levels(data$interaction)
means3=adjusted.mean; names(means3)=interaction
mat=data.frame(interaction,adjusted.mean,standard.error)
rownames(mat)=NULL
dff=df.residual(m)
test1=fmm(maf1,dff)
test2=fmm(maf2,dff)
nrep1=length(data2[,1])/length(treatment)
nrep2=length(data2[,1])/length(square)
nrep3=length(data2[,1])/length(interaction)
QME=deviance(m)/dff
scott_knott=sk(means1, dff, QME, nrep1, alpha)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1, scott_knott)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
scott_knott=sk(means2, dff, QME, nrep2, alpha)
mf2=data.frame(maf2,groups2, scott_knott)
rownames(mf2) = NULL
c1=rep(1:nlevels(data$squares), each=nlevels(data$treatments))
cs1=split(mat, c1)
test3=lapply(cs1, fmm, dff=dff); n1=rep("treatments in", nlevels(data$squares));n11=data.frame(n1,levels(data$squares))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$treatments), nlevels(data$squares))
cs2=split(mat, c2)
test4=lapply(cs2, fmm, dff=dff); n2=rep("squares in", nlevels(data$treatments));n22=data.frame(n2,levels(data$treatments))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf)
i=1:length(mft1)
ft1=function(i){a=mft1[[i]][,2];names(a)=mft1[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups3[x],scott_knott[x])}
xx1=1:length(groups3)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups3)
dd<-function(x){l=data.frame(mft1[[x]],ftr[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$squares)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf)
i=1:length(mft2)
ft1=function(i){a=mft2[[i]][,2];names(a)=mft2[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups4[x],scott_knott[x])}
xx1=1:length(groups4)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups4)
ddd<-function(x){l=data.frame(mft2[[x]],ftr[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$treatments)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
l<-list(a3,mf1, test1, mf2,test2,mft1,test3, mft2, test4, res)
names(l)= list("Analysis of variance", "Adjusted means (treatments)", "Multiple comparison test (treatments)","Adjusted means (squares)", "Multiple comparison test (squares)", "Adjusted means (treatments in levels of squares)", "Multiple comparison test (treatments in levels of squares)", "Adjusted means (squares in levels treatments)", "Multiple comparison test (squares in levels treatments)","Residual analysis")
pres(m)
return(l)
}
f13<-function(data, cov){
names(data) = c("treatments", "squares", "rows", "cols",
"response")
data <- data.frame(treatments = factor(data$treatments),
squares = factor(data$squares), rows = factor(data$rows),
columns = factor(data$cols), response = data$response)
m <- aov(response ~ treatments*squares + squares/rows + squares/columns,
data = data, contrasts = list(treatments = contr.sum,
squares = contr.sum, rows = contr.sum, columns = contr.sum))
m1 <- aov(response ~ -1 + treatments + treatments*squares +squares/rows +
squares/columns, data = data, contrasts = list(treatments = contr.sum,
squares = contr.sum, rows = contr.sum, columns = contr.sum))
m2 <- aov(response ~ -1 + squares+treatments + treatments*squares +squares/rows +
squares/columns, data = data, contrasts = list(treatments = contr.sum,
squares = contr.sum, rows = contr.sum, columns = contr.sum))
a<-anova(m)
a2 <- drop1(m,.~.,test="F")
a3 <- a2
a3<-fa2(a3,m)
interaction=interaction(data$treatments,data$squares)
data<-data.frame(data,interaction)
m3<- aov(response ~ -1 + interaction +squares/rows +
columns, data = data, contrasts = list(interaction = contr.sum, rows = contr.sum, columns = contr.sum))
data2<-na.omit(data)
fr12=function(m,data){
r=resid(m)
s <- shapiro.test(r)
b1<- bartlett.test(r~treatments, data=data)
b2<- bartlett.test(r~squares, data=data)
b3<- bartlett.test(r~interaction, data=data)
cvf=cv(m)
rd=as.data.frame((sort(sqrt(r^2),decreasing=TRUE)))
rl=as.list(rownames(rd))
r1=rl[[1]];r2=rl[[2]];r3=rl[[3]]
d=data.frame(round(s$"p.value",4),round(b1$"p.value",4),round(b2$"p.value",4),round(b3$"p.value",4), round(cvf,2),as.numeric(r1),as.numeric(r2),as.numeric(r3)); d=t(d)
rownames(d)=c("p.value Shapiro-Wilk test","p.value Bartlett test (treatments)","p.value Bartlett test (squares)","p.value Bartlett test (interaction)","coefficient of variation (%)", "first value most discrepant","second value most discrepant","third value most discrepant")
colnames(d)="values"
return(d)}
res=fr12(m,data2)
adjusted.mean<-round(coef(m1)[1:nlevels(data$treatments)],4)
standard.error<-round(sderrs(m1)[1:nlevels(data$treatments)], 4)
treatment<-levels(data$treatments)
means1=adjusted.mean; names(means1)=treatment
maf1=data.frame(treatment,adjusted.mean,standard.error)
rownames(maf1)=NULL
adjusted.mean<-coef(m2)[1:nlevels(data$squares)]
standard.error<-round(sderrs(m2)[1:nlevels(data$squares)],4)
square<-levels(data$squares)
means2=adjusted.mean; names(means2)=square
maf2=data.frame(square,adjusted.mean,standard.error)
rownames(maf2)=NULL
adjusted.mean<-round(coef(m3)[1:nlevels(data$interaction)],4)
standard.error<-round(sderrs(m3)[1:nlevels(data$interaction)],4)
interaction<-levels(data$interaction)
means3=adjusted.mean; names(means3)=interaction
mat=data.frame(interaction,adjusted.mean,standard.error)
rownames(mat)=NULL
dff=df.residual(m)
test1=fmm(maf1,dff)
test2=fmm(maf2,dff)
nrep1=length(data2[,1])/length(treatment)
nrep2=length(data2[,1])/length(square)
nrep3=length(data2[,1])/length(interaction)
QME=deviance(m)/dff
scott_knott=sk(means1, dff, QME, nrep1, alpha)
groups1=ft(test1, alpha); maf1=maf1[order(maf1[,2], decreasing=TRUE),]
mf1=data.frame(maf1,groups1, scott_knott)
rownames(mf1) = NULL
groups2=ft(test2, alpha); maf2=maf2[order(maf2[,2], decreasing=TRUE),]
scott_knott=sk(means2, dff, QME, nrep2, alpha)
mf2=data.frame(maf2,groups2, scott_knott)
rownames(mf2) = NULL
c1=rep(1:nlevels(data$squares), each=nlevels(data$treatments))
cs1=split(mat, c1)
test3=lapply(cs1, fmm, dff=dff); n1=rep("treatments in", nlevels(data$squares));n11=data.frame(n1,levels(data$squares))
n11 <- apply(t(n11), 2, paste, collapse = " ")
names(test3)=n11
c2=rep(1:nlevels(data$treatments), nlevels(data$squares))
cs2=split(mat, c2)
test4=lapply(cs2, fmm, dff=dff); n2=rep("squares in", nlevels(data$treatments));n22=data.frame(n2,levels(data$treatments))
n22 <- apply(t(n22), 2, paste, collapse = " ")
names(test4)=n22
csf=function(x){a=x[order(x[,2], decreasing=TRUE),];return(a)}
groups3=lapply(test3, ft, alpha)
mft1=lapply(cs1,csf)
i=1:length(mft1)
ft1=function(i){a=mft1[[i]][,2];names(a)=mft1[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups3[x],scott_knott[x])}
xx1=1:length(groups3)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups3)
dd<-function(x){l=data.frame(mft1[[x]],ftr[[x]]); return(l)}; nf1=as.list(c(1:nlevels(data$squares)))
mft1=lapply(nf1,dd)
names(mft1)=n11
groups4=lapply(test4, ft, alpha)
mft2=lapply(cs2,csf)
i=1:length(mft2)
ft1=function(i){a=mft2[[i]][,2];names(a)=mft2[[i]][,1];return(a)}
lap1=lapply(i, ft1)
scott_knott=lapply(lap1, sk, df1=dff, QME=QME, nrep=nrep3, alpha=alpha)
names(scott_knott)=rep("scott_knott",length(scott_knott))
ftt1=function(x){a=data.frame(groups4[x],scott_knott[x])}
xx1=1:length(groups4)
ftr=lapply(xx1, ftt1)
nam=list("t","t.adjust.holm", "t.adjust.hochberg", "t.adjust.hommel", "t.adjust.bonferroni", "t.adjust.BH", "t.adjust.BY","t.adjust.fdr")
on=c("tukey","snk","duncan",nam[[p.adjust]],"scott_knott");for(i in xx1){names(ftr[[i]])=on}
names(ftr)=names(groups4)
ddd<-function(x){l=data.frame(mft2[[x]],ftr[[x]]); return(l)}; nf2=as.list(c(1:nlevels(data$treatments)))
mft2=lapply(nf2,ddd)
names(mft2)=n22
l<-list(a3,mf1, test1, mf2,test2,mft1,test3, mft2, test4, res)
names(l)= list("Analysis of variance", "Adjusted means (treatments)", "Multiple comparison test (treatments)","Adjusted means (squares)", "Multiple comparison test (squares)", "Adjusted means (treatments in levels of squares)", "Multiple comparison test (treatments in levels of squares)", "Adjusted means (squares in levels treatments)", "Multiple comparison test (squares in levels treatments)","Residual analysis")
pres(m)
return(l)
}
de1=c(1,2); de2=c(1,2,3);de3=c(1,2,3,4); de4=c(1,2,3);de5=c(1,2,3);de6=c(1,2,3,4);de7=c(1,2,3);de8=c(1,2,3,4)
de9=c(1,2,3,4);de10=c(1,2,3,4); de11=c(1,2,3); de12=c(1,2,3,4); de13=c(1,2,3,4)
de=list(de1,de2,de3,de4,de5,de6, de7,de8,de9,de10,de11,de12,de13)
de=de[[design]]
d=as.list(data)
d1=d[de]
d2=d[-de]
f=function(h){data.frame(d1,d2[h])}
h=length(d2)
h=1:h
l=lapply(h, f)
l2=list(f1,f2,f3,f4,f5,f6,f7,f8,f9,f10,f11,f12,f13)
fun=l2[[design]]
li1=lapply(l, fun, cov)
names(li1)=names(d2)
li=list(fun(data, cov),li1)
li=li[[list]]
return(li)
}
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