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backup.R
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#' Regression Tables in Pixiedust
#'
#' Beautiful tables adding sums of squares
#' and p-value formatting, then giving html or
#' latex output. If interactive and HTML, will show up
#' in viewer.
#'
#' @param my.model Model. NOTE: If have factors, please place them first in the regression.
#' @param VIF include Variable Inflation Factor? (calculated by car::vif) (default=F)
#' @param part.eta If lm, include partial eta square by calculating SS_part/SS_total? (default=F)
#' @param myDF Dataframe, not needed if use data= in call
#' @param my.factor If there are any factors, list them here.
#' @param SS.type Type of sums of squares to report (default = 3)
#' @param pix.int Should this be viewable or is this for a document/dashboard? (default=T)
#' @param pix.method Print method. (default="html")
#' @param type Type of regression? Currently supported: lm, glm (binary), manova
#' @param do.glance Include glance stats?
#' @return Either pixiedust object or code (in HTML or LaTeX) for table
#' @keywords Explore
#' @export
#' @examples
#' quick.reg(my.model, myDF)
quick.reg = function(my.model,
part.eta = F,
VIF = F,
myDF = my.found.df,
SS.type = 3,
abbrev.length = ab.len,
pix.int = T,
pix.method = "html",
type = my.reg.type,
test.stat = "Pillai",
my.factor = NULL,
do.glance=T,
show.footer=T,
adjustment = "bonferroni",
show.contrasts=F,
show.y.contrasts=F,
show.latent=F,
show.intercepts=F,
real.names=T,
do.return=T) {
library(pixiedust)
library(broom)
library(car)
library(tidyr)
library(phia)
library(quick.tasks)
library(dplyr)
#### Find type ####
my.reg.type=quick.type(my.model)
#### Set Inits ####
if(type=="ord"){
ab.len=30
library(ordinal)
}else{
ab.len=15
}
#### Get data frame from parent environment ####
my.found.df = eval(parse(text=capture.output(my.model$call$data)),envir = parent.frame())
#print(dim(my.found.df))
if (is.null(my.found.df)) {
stop(paste("No data frame found"))
}
#### for expansion
if (!VIF & !part.eta) {
v.p.len = 8
v.p.rep = 0
} else if (!VIF) {
v.p.len = 9
v.p.rep = 1
} else if (!part.eta) {
v.p.len = 9
v.p.rep = 1
} else{
v.p.len = 10
v.p.rep = 2
}
#### Make factor list ####
#### Use car::Anova to get SS Type 3
if (type == "lm") {
#### ANOVA TABLES ####
my.summary = summary(my.model)
my.coefficients = my.summary$coefficients
my.coefficients = as.data.frame(my.coefficients)
if (type == "ord" & length(my.model$model) == 1) {
my.III.summary = NULL
the.length = length(my.model$y.levels) - 1
} else{
my.III.summary = car::Anova(my.model, type = SS.type)
if (type == "glm" & is.null(my.factor)) {
the.length = dim(my.III.summary)[1] + 1
} else{
the.length = dim(my.III.summary)[1]
}
}
#### Calculate total SS ####
my.total = sum(my.III.summary$`Sum Sq`[2:length(my.III.summary$`Sum Sq`)])
my.df.total = sum(my.III.summary$Df[2:length(my.III.summary$Df)])
total.intercepts = 1
my.rownames = c(abbreviate(rownames(my.summary$coefficients), minlength = abbrev.length),
"Residuals",
"Total")
treat.SS=sum(my.III.summary$`Sum Sq`[2:{length(my.III.summary$`Sum Sq`)-1}])
treat.df=sum(my.III.summary$Df[2:{length(my.III.summary$Df)-1}])
} else if(type == "manova"){
#### Begin MANOVA ####
#### Inits ####
my.envir=environment()
my.nested.table=quick.SSCP(my.model, myDF, SS.type, show.contrasts, show.latent,my.envir)
#### Get treatment ####
treat.model=my.nested.table[dim(my.nested.table)[1],4]
#### Regular totals
treat.total.temp=lapply(lapply(treat.model[[1]][[1]],diag),sum)
treat.total=0
for(W in 2:length(treat.total.temp)){
treat.total=treat.total+treat.total.temp[[W]]
}
treat.total.df=sum(as.numeric(my.nested.table[,7]),na.rm = T)
#### Partial totals
part.treat.total=NULL
for(X in 2:length(treat.model[[1]][[1]])){
if(X==2){
part.treat.total=treat.model[[1]][[1]][[X]]
}else{
part.treat.total=part.treat.total+treat.model[[1]][[1]][[X]]
}
}
#### Latent totals
if(show.latent){
latent.treat.model=my.nested.table[dim(my.nested.table)[1],8]
latent.part.treat.total=NULL
latent.part.total=NULL
for(X in 2:length(latent.treat.model[[1]][[1]])){
if(X==2){
latent.part.treat.total=latent.treat.model[[1]][[1]][[X]]
}else{
latent.part.treat.total=latent.part.treat.total+latent.treat.model[[1]][[1]][[X]]
}
}
}
#### Get residuals ####
#### Regular residuals
if(SS.type==3){
the.resid=sum(diag(treat.model[[1]][[2]]))
#### Partial residuals
part.resid.total=treat.model[[1]][[2]]
}else{
the.resid=sum(diag(treat.model[[1]][[2]][[1]]))
part.resid.total=treat.model[[1]][[2]][[1]]
}
the.resid.df=my.model$df.residual
#### Latent totals
if(show.latent){
if(SS.type==3){
latent.part.resid.total=latent.treat.model[[1]][[2]]
}else{
latent.part.resid.total=latent.treat.model[[1]][[2]][[1]]
}
}
#### Get totals ####
the.total=total.resid+treat.total+sum(diag(treat.model[[1]][[1]][[1]]))
the.total.df=total.resid.df+treat.df+my.y.levels
#### Partial totals
partial.total=part.resid.total+part.treat.total+treat.model[[1]][[1]][[1]]
#### Latent totals
if(show.latent){
latent.part.total=latent.part.treat.total+latent.part.treat.total+latent.treat.model[[1]][[1]][[1]]
}
#### Make dependent variable rownames ####
my.dv.rownames=rownames(part.resid.total)
#### Make basic table ####
#### NEEDS TO BE FIXED ####
my.table.names=c("var","test.stat","f.val","SS","df","mult.df","resid df","p.val")
my.manova.table=as.data.frame(matrix(ncol=v.p.len,nrow=1))
names(my.manova.table)=my.table.names
my.line.var=1
for(i in 1:length(my.SSP.treat)){
if(i==1){
my.i=i
}else{
my.i=2*i-1
}
#### Put in basic line ####
my.treat.err=solve(my.SSP.err)%*%treat.model[[1]][[1]][[i]]
my.test.stat=quick.m.test(my.treat.err,test.stat)
my.SS=sum(diag(treat.model[[1]][[1]][[i]]))
my.df=my.y.levels*ifelse(my.i!=1,as.numeric(my.nested.table[my.i,7]),1)
my.resid.df=ifelse(my.i==1,{my.y.levels*the.resid.df},min({the.resid.df*as.numeric(my.nested.table[my.i,5])-as.numeric(my.nested.table[my.i,5])},my.y.levels*the.resid.df-as.numeric(my.nested.table[my.i,5])))
my.f.val={my.SS/my.df}/{the.resid/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(ifelse(i==1,"(Intercept)",my.nested.table[my.i,1]),my.test.stat,my.f.val,my.SS,ifelse(my.i==1,NA,my.df/my.y.levels),ifelse(my.i==1,my.y.levels,my.df),my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in decomposed factors (y constrasts) ####
#### Intercept
if({show.intercepts & my.i==1}){
for(y in 1:my.y.levels){
my.name=my.dv.rownames[y]
my.test.stat=NA
my.SS=as.numeric(treat.model[[1]][[1]][[i]][y,y])
my.df=1
my.resid.df=the.resid.df
my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
#### Other
if({show.y.contrasts & my.i!=1}){
for(y in 1:my.y.levels){
my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
my.test.stat=NA
my.SS=treat.model[[1]][[1]][[i]][y,y]
my.df=as.numeric(my.nested.table[my.i,7])
my.resid.df=min(abs(my.y.levels*the.resid.df-my.df*the.resid.df-my.df),abs(my.y.levels*the.resid.df-my.df))
my.resid=part.resid.total[y,y]
my.f.val={my.SS/my.df}/{my.resid/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in latents (ANOVA) ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent &my.i!=1){
# my.my.i.temp=my.counter[my.i-1]
my.my.i.temp=my.i
my.y=y
my.name=NA
my.df=as.numeric(my.nested.table[my.i,7])
my.test.stat=NA
my.resid.df=the.resid.df-my.df
my.SS=as.numeric(latent.treat.model[[1]][[1]][[i]][y,1])
my.f.val={my.SS/my.df}/{my.latent.SSP.err[my.y,my.y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
#### Put in latent contrasts ####
if(show.contrasts & show.latent & my.i!=1){
#### Check length
if({!is.na(my.nested.table[my.i,11])}){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:{my.nested.table[my.i,7]}){
my.name=my.nested.table[my.i,11][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,11][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,11][[1]][k,3])
my.test.stat=NA
my.df=1
my.mult.df=NA
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,11][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Put in contrasts ####
#### Not right. Don't have it decomposed this way.
if(show.contrasts & !show.latent & my.i!=1){
#### Check length
if(!is.na(my.nested.table[my.i,9])){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:as.numeric(my.nested.table[my.i,7])){
my.name=my.nested.table[my.i,9][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,9][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,9][[1]][k,3])
my.test.stat=NA
my.df=NA
my.mult.df=my.y.levels
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,9][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
}
}
#### Put in contrasts ####
if(show.contrasts & !show.latent & !show.y.contrasts){
#### Check length
if(!is.na(my.nested.table[my.i,8])){
#other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:as.numeric(my.nested.table[my.i,7])){
my.name=my.nested.table[my.i,8][[1]][k,1]
my.f.val=as.numeric(my.nested.table[my.i,8][[1]][k,2])
my.SS=as.numeric(my.nested.table[my.i,8][[1]][k,3])
my.test.stat=NA
my.df=NA
my.mult.df=my.y.levels
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=as.numeric(my.nested.table[my.i,8][[1]][k,4])
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Put in latents (ANOVA) ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent &my.i!=1 & !show.y.contrasts){
#my.my.i.temp=my.counter[my.i-1]
my.my.i.temp=my.i
for(y in 1:{my.y.levels}){
my.y=y
my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
my.df=as.numeric(my.nested.table[my.i,7])
my.test.stat=NA
my.resid.df=the.resid.df-my.df
my.SS=as.numeric(my.nested.table[my.i,8][[1]][[1]][[i]][my.y])
my.f.val={my.SS/my.df}/{my.latent.SSP.err[my.y,my.y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in latent contrasts ####
if(show.contrasts & !{show.contrasts & show.latent}){
#### Check length
if({my.SSP.treat.df[my.i]>1}){
other.manova.grep=grep(paste("^",names(my.SSP.treat)[my.i],"$",sep=""),names(my.model$xlevels))
for(k in 1:{my.SSP.treat.df[my.i]}){
my.name=my.contrasts.table[[other.manova.grep]][k,1]
my.f.val=my.latent.contrasts.table[[other.manova.grep]][[y]][k,2]
my.SS=my.latent.contrasts.table[[other.manova.grep]][[y]][k,3]
my.test.stat=NA
my.df=1
my.mult.df=NA
my.resid.df=the.resid.df-my.y.levels+1
my.p.val=my.latent.contrasts.table[[other.manova.grep]][[y]][k,4]
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,my.mult.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
}
}
}
#### Put in treatment ####
#### From Type my.i statistics
if(my.i==1){
my.test.stat=quick.m.test(part.treat.total,test.stat)
my.SS=as.numeric(treat.total)
my.df=my.y.levels*as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=my.y.levels*as.numeric(the.resid.df)
my.f.val={my.SS/my.df}/{as.numeric(the.resid.SS)/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c("Treatment Change",my.test.stat,my.f.val,my.SS,{my.df/my.y.levels},my.df,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Treatment Y Contrasts ####
if(show.y.contrasts){
for(b in 1:my.y.levels){
my.test.stat=NA
my.name=paste(ifelse(real.names,my.dv.rownames[b],b),"|Treatment",sep="")
my.SS=latent.part.treat.total[b]
my.df=the.resid.df
#### Should really be a min statement, but for later...
my.resid.df=my.y.levels*the.resid.df
my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,{my.df/my.y.levels},NA,{my.resid.df/my.y.levels},my.p.val)
my.line.var=my.line.var+1
#### Treatment Latents ####
if(show.latent){
#### Show latent treatments (ANOVAs)
#### Need to change latents to type II
my.SS=sum(weighted.residuals(my.null.model)[,b]^2)-sum(weighted.residuals(my.model)[,b]^2)
my.df=as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=the.resid.df-my.df
my.f.val={my.SS/my.df}/{my.latent.SSP.err[y,y]/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(NA,NA,my.f.val,my.SS,my.df,NA,{my.resid.df},my.p.val)
my.line.var=my.line.var+1
}
}
}
#### Latents ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent & !show.y.contrasts){
for(b in 1:{my.y.levels}){
my.name=paste(ifelse(real.names,my.dv.rownames[b],b),"|Treatment",sep="")
my.SS=as.numeric(part.treat.total[b])
my.df=as.numeric(treat.total.df)
#### Should really be a min statement, but for later...
my.resid.df=the.resid.df-my.df
my.f.val={my.SS/my.df}/{as.numeric(latent.part.resid.total[b])/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),NA,my.f.val,my.SS,my.df,NA,{my.resid.df},my.p.val)
my.line.var=my.line.var+1
}
}
}
}
#### Put in residuals, total change, total ss ####
my.manova.table[my.line.var,]=c("Total Change",NA,NA,as.numeric(treat.total),treat.total.df,{my.y.levels*treat.total.df},NA,NA,rep(NA,v.p.rep))
my.line.var=my.line.var+1
my.manova.table[my.line.var,]=c("Residuals",NA,NA,the.resid,the.resid.df,my.y.levels*the.resid.df,NA,NA,rep(NA,v.p.rep))
my.line.var=my.line.var+1
if(show.y.contrasts){
for(i in 1:my.y.levels){
my.manova.table[my.line.var,]=c(paste(ifelse(real.names,my.dv.rownames[i],i),"|Residual",sep=""),NA,NA,part.resid.total[i,i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
if(show.latent){
my.manova.table[my.line.var,]=c(NA,NA,NA,latent.part.resid.total[i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
}
}
}
if(show.latent & !show.y.contrasts){
for(i in 1:my.y.levels){
my.manova.table[my.line.var,]=c(paste(ifelse(real.names,my.dv.rownames[i],i),"|Residual",sep=""),NA,NA,latent.part.resid.total[i],the.resid.df,NA,NA,NA)
my.line.var=my.line.var+1
}
}
my.manova.table[my.line.var,]=c("Total",NA,NA,the.total,the.total.change.df+1,my.y.levels*the.total.change.df+2,NA,NA,rep(NA,v.p.rep))
if(show.footer){
the.footer=paste(ifelse(dim(my.new.df)[1]==dim(myDF)[1],"Data have same number of rows <br />",paste({dim(myDF)[1]-dim(my.new.df)[1]}," cases deleted due to missingness <br />")),"Method: QR decomposition",if(show.contrasts){paste(" <br />Adjustment: ", adjustment,sep="")},if(show.latent){paste(" <br /> Latent Contrasts")})
}else{
the.footer=NULL
}
my.html.table=quick.table(my.manova.table,"manova",test=test.stat,SS.type = SS.type, abbrev.length = abbrev.length,the.footer = the.footer)
if(do.return){
return(my.html.table)
}else{}
#### ETA-SQ Stuff to finish.... ####
# #### Make eta-sq
# my.SSP.err.t=t(my.SSP.err)
#
# eta.sq=NULL
# for(i in 1:length(my.SSP)){
# eta.sq[[i]]=my.SSP.err.t*my.SSP[[i]]
# }
#
# x3 = capture.output(car::Anova(my.model, type = SS.type, test = test.stat))
# my.manova.test = data.frame(matrix(ncol = 7, nrow = 1))
# my.var.temp = 4
# while (my.var.temp < {
# length(x3) - 1
# }) {
# test = strsplit(x3[my.var.temp], "\\s+")
#
# if (length(test[[1]]) == 9) {
# test2 = test[[1]][-9]
# test2 = test2[-7]
#
# } else if (length(test[[1]]) == 8) {
# test2 = test[[1]][-8]
#
# } else{
# test2 = test[[1]]
# }
#
# my.manova.test[{
# my.var.temp - 3
# }, ] = test2
# my.var.temp = my.var.temp + 1
#
# }
# my.summary=summary(my.model)
#### END MANOVA table ####
}else if (type == "glm") {
#### DevAn Table ####
#### Inits ####
new.df=my.model$model
new.model=update(my.model,data=new.df)
null.model=update(new.model,~1)
total.intercepts = 1
#### Check if null model ####
vars.df=new.model$df.null-new.model$df.residual
if(vars.df==0){
stop("This is the null model.")
}
resid.dev=new.model$deviance
resid.df=new.model$df.residual
vars.dev=anova(new.model,test="Chi")$Deviance[-1]
vars.dev.df=anova(new.model,test="Chi")$Df[-1]
vars.dev.p=anova(new.model,test="Chi")$`Pr(>Chi)`[-1]
total.dev=new.model$null.deviance-new.model$deviance
vars.dev.total=sum(vars.dev)
if(length(grep("*",new.model$formula))>0){
my.full.model=new.model
}else{
my.vars=strsplit(as.character(new.model$formula),"~")
my.dep.var=my.vars[[2]]
my.other.vars=names(new.model$model)
my.var.grep=grep(paste("^",my.dep.var,"$",sep=""),my.other.vars)
my.other.vars=my.other.vars[-my.var.grep]
my.formula=paste("~",my.other.vars[1])
for(i in 2:length(my.other.vars)){
my.formula=paste(my.formula,"*",my.other.vars[i],sep="")}
#### FULL MODEL THING ####
my.full.model=update(new.model,my.formula)
}
my.full.model.z=summary(my.full.model)$coefficients[1:total.intercepts,3]^2
my.null.model.z=summary(null.model)$coefficients[1:total.intercepts,3]^2
my.full.dev=sum(my.full.model.z)
my.null.dev=sum(my.null.model.z)
my.int.dev.total=abs(total.intercepts*{my.full.dev-my.null.dev})
my.int.dev=summary(new.model)$coefficients[1:total.intercepts,2]^2
treat.dev=anova(null.model,new.model)$`Deviance`[2]
#vars.dev.df=drop1(new.model,test="Chi")$Df[-1]
#vars.dev.p=drop1(new.model,test="Chi")$`Pr(>Chi)`[-1]
#total.dev=-2*{as.integer(levels(null.model$info$logLik)[1])-as.integer(levels(new.model$info$logLik)[1])}
total.dev.change=treat.dev+my.int.dev.total
total.dev.change.df=total.intercepts*vars.df
total.dev=new.model$null.deviance
resid.dev=total.dev-total.dev.change
vars.dev.df=anova(new.model,test="Chi")$Df
resid.df=new.model$df.residual
total.df=resid.df+vars.df
my.int.dev.df=max({total.intercepts*sum(vars.dev.df,na.rm = T)-sum(vars.dev.df,na.rm=T)},1)
my.int.dev.or=exp(coef(new.model))[1:{total.intercepts}]
my.int.dev.or.confint=exp(confint(new.model,type="Wald"))[1:total.intercepts,]
my.int.names=abbreviate(names(my.int.dev.or),minlength=abbrev.length)
#resid.dev=new.model$deviance
vars.dev=anova(new.model,test="Chi")$Deviance
vars.dev.p=anova(new.model,test="Chi")$`Pr(>Chi)`
vars.names=rownames(anova(new.model,test="Chi"))
#total.dev=new.model$null.deviance-new.model$deviance
vars.dev.total=sum(vars.dev)
#total.df=sum(vars.dev.df,na.rm = T)
vars.or=exp(coef(new.model))
vars.or.confint=exp(confint(new.model,type="Wald"))
my.names=abbreviate(names(vars.or),minlength=abbrev.length)
#vars.or=exp(coef(new.model))[-1]
#vars.or.confint=exp(confint(new.model,type="Wald"))[-1,]
} else if (type == "ord" | type =="glm2") {
new.df=my.model$model
new.model=update(my.model,data=new.df)
null.model=update(new.model,~1)
total.intercepts = null.model$edf
my.summary=summary(new.model)
if(length(grep("*",new.model$formula))>0){
my.full.model=new.model
}else{
my.vars=strsplit(as.character(new.model$formula),"~")
my.dep.var=my.vars[[2]]
my.other.vars=names(new.model$model)
if(length(my.other.vars)>1){
my.var.grep=grep(paste("^",my.dep.var,"$",sep=""),my.other.vars)
my.other.vars=my.other.vars[-my.var.grep]
my.formula=paste("~",my.other.vars[1])
for(i in 2:length(my.other.vars)){
my.formula=paste(my.formula,"*",my.other.vars[i],sep="")}
#### FULL MODEL THING ####
my.full.model=update(new.model,my.formula)
}else{
my.full.model=new.model
}
}
my.full.model.z=summary(my.full.model)$coefficients[1:total.intercepts,3]^2
my.null.model.z=summary(null.model)$coefficients[1:total.intercepts,3]^2
my.full.dev=sum(my.full.model.z)
my.null.dev=sum(my.null.model.z)
my.int.dev.total=abs(total.intercepts*{my.full.dev-my.null.dev})
my.int.dev=summary(new.model)$coefficients[1:total.intercepts,2]^2
vars.df=new.model$edf-null.model$edf
if(vars.df==0){
stop("This is the null model.")
}
vars.dev=summary(new.model)$coefficients[{total.intercepts+1}:dim(summary(new.model)$coefficients)[1],3]^2
vars.dev.p=summary(new.model)$coefficients[{total.intercepts+1}:dim(summary(new.model)$coefficients)[1],4]
vars.dev.df=NULL
weird.var=2
track.var=1
while(weird.var<=dim(new.model$model)[2]){
if(is.factor(new.model$model[[weird.var]])){
vars.dev.df[track.var]=length(levels(new.model$model[[weird.var]]))-1
}else{
vars.dev.df[track.var]=1
}
weird.var=weird.var+1
track.var=track.var+1
}
### interaction effects
weird.var=weird.var+total.intercepts-1
while(weird.var<=dim(summary(new.model)$coefficients)[1]){
vars.dev.df[track.var]=1
weird.var=weird.var+1
track.var=track.var+1
}
vars.df.total=sum(vars.dev.df,na.rm = T)
treat.dev=anova(null.model,new.model)$`LR.stat`[2]
#vars.dev.df=drop1(new.model,test="Chi")$Df[-1]
#vars.dev.p=drop1(new.model,test="Chi")$`Pr(>Chi)`[-1]
#total.dev=-2*{as.integer(levels(null.model$info$logLik)[1])-as.integer(levels(new.model$info$logLik)[1])}
total.dev.change=treat.dev+my.int.dev.total
if(total.intercepts>1){
total.dev.change.df=total.intercepts*vars.df.total
}else{
total.dev.change.df=vars.df.total+total.intercepts
}
resid.dev=-2*new.model$logLik
total.dev=total.dev.change+resid.dev
total.df=dim(myDF)[1]
resid.df=total.df-total.dev.change.df
vars.or=exp(coef(new.model))[{null.model$edf+1}:length(coef(new.model))]
vars.or.confint=exp(confint(new.model,type="Wald"))[{null.model$edf+1}:length(coef(new.model)),]
my.names=abbreviate(names(vars.or),minlength=abbrev.length)
my.int.dev.df=max({total.intercepts*sum(vars.df,na.rm = T)-sum(vars.df,na.rm=T)},1)
my.int.dev.or=exp(coef(new.model))[1:{null.model$edf}]
my.int.dev.or.confint=exp(confint(new.model,type="Wald"))[1:null.model$edf,]
my.int.names=abbreviate(names(my.int.dev.or),minlength=abbrev.length)
} else{
print("Error")
return()
}
if (!VIF & !part.eta) {
v.p.len = 7
v.p.rep = 0
} else if (!VIF) {
v.p.len = 8
v.p.rep = 1
} else if (!part.eta) {
v.p.len = 8
v.p.rep = 1
} else{
v.p.len = 9
v.p.rep = 2
}
#### Make table if not MANOVA ####
my.tables.df = as.data.frame(matrix(ncol = v.p.len, nrow = 1))
if (type == "lm") {
if (!VIF & !part.eta) {
my.std.error = c(my.coefficients$`Std. Error`, NA)
my.estimate = c(my.coefficients$Estimate, NA)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val")
} else if (!part.eta) {
my.VIF = car::vif(my.model)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"VIF")
} else if (!VIF) {
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"p.eta")
} else{
my.VIF = car::vif(my.model)
names(my.tables.df) = c("rownames",
"sumsq",
"df",
"est",
"std.err",
"f.val",
"p.val",
"p.eta",
"VIF")
}
} else if (type == "glm" & VIF) {
my.VIF = car::vif(my.model)
names(my.tables.df) = c("var",
"p.odd",
"p.odd.2.5",
"p.odd.97.5",
"deviance",
"df",
"p.val",
"VIF")
} else{
names(my.tables.df) = c("var", "p.odd", "p.odd.2.5", "p.odd.97.5", "deviance", "df", "p.val")
}
#### Make the double table entries ####
#### Was very annoying...took my frustration out on
#### Variable names
factor.stupid = NULL
factor.rownames = NULL
num.of.levels = NULL
ord.temp = 0
if (!is.null(my.factor)) {
for (i in 1:length(my.factor)) {
factor.stupid = c(factor.stupid, grep(paste("^", my.factor[i], "$", sep =
""), names(myDF)))
if (type == "lm") {
factor.rownames = c(factor.rownames, grep(
paste("^", my.factor[i], "$", sep = ""),
rownames(my.III.summary)
))
num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# }else if(type=="glm"){
# factor.rownames=c(factor.rownames,{grep(paste("^",my.factor[i],"$",sep=""),rownames(my.III.summary))+1})
# num.of.levels=c(num.of.levels,length(levels(myDF[[factor.stupid[i]]])))
} else{
my.dev.grep=grep(paste("^", my.factor[i], sep = ""),names(new.model$coefficients))[1]
factor.rownames=c(factor.rownames,my.dev.grep)
num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# factor.rownames = c(factor.rownames, {
# grep(paste("^", my.factor[i], "$", sep = ""),
# rownames(my.III.summary)) + total.intercepts + sum(num.of.levels) - ordinal.temp
# })
# num.of.levels = c(num.of.levels, length(levels(myDF[[factor.stupid[i]]])))
# ordinal.temp = ordinal.temp + 1
}
}
} else{
factor.rownames = 0L
}
#### Make phia stuff ####
if(show.contrasts){
if(type=="glm" & !is.null(my.factor)){
my.phia.reg=quick.contrast(new.model,skip.me=T,adjustment = adjustment,SS.type = SS.type,abbrev.length = abbrev.length,my.factors = my.factor)
my.phia.rownames=my.phia.reg$names
phia.dev=my.phia.reg$Chisq
}else if(type=="lm" & !is.null(my.factor)){
my.phia.reg=quick.contrast(my.model,skip.me=T,adjustment = adjustment,SS.type = SS.type,abbrev.length = abbrev.length)
my.j=0
my.big.phia=NULL
phia.shift=0
real.shift=0
for(i in 1:dim(my.phia.reg)[1]){
if(!is.na(my.phia.reg[i,1])){
my.j=my.j+1
real.shift=real.shift+phia.shift
my.big.phia[[my.j]]=my.phia.reg[i,2:7]
}else{
my.big.phia[[my.j]][i-real.shift,]=my.phia.reg[i,2:7]
}
phia.shift=phia.shift+1
}
my.phia.rownames=NULL
my.phia.SS=NULL
my.phia.value=NULL
my.phia.F=NULL
my.phia.p=NULL
my.phia.err=NULL
for(i in 1:{my.j}){
my.phia.rownames[[i]]=my.big.phia[[i]]$names
my.phia.SS[[i]]=my.big.phia[[i]]$`Sum of Sq`
my.phia.value[[i]]=my.big.phia[[i]]$Value
my.phia.F[[i]]=my.big.phia[[i]][,5]
my.phia.p[[i]]=my.big.phia[[i]][,6]
}
}else{
}
}
my.factor.var = 1
this.temp.var = 1
this.shift.temp = 1
yet.another.var = 1
my.shift = 0
other.temp = 2
ord.temp = 0
phia.temp=1
if (type == "lm") {
dang.length = length(rownames(my.III.summary))
} else if (type == "glm") {
# dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
# sum(num.of.levels) - length(my.factor)
# }, 0) + 1
dang.length=length(new.model$coefficients)-sum(num.of.levels)+length(num.of.levels)+total.intercepts+1
} else{
# dang.length = total.intercepts + length(rownames(my.III.summary)) + max({
# sum(num.of.levels) - length(my.factor)
# }, 0) + 1
#dang.length=7
dang.length=length(new.model$coefficients)+length(my.factor)+1
}
while (this.shift.temp < dang.length) {
if (is.na(factor.rownames[my.factor.var])) {
my.factor.rownames = 1
} else{
my.factor.rownames = factor.rownames[my.factor.var]
}
#### LOOP ####
if (this.shift.temp == 1) {
i = 1
if(type=="ord" | type=="glm"){
my.tables.df[this.temp.var, ] = c(
"Intercept Change",
NA,
NA,
NA,
my.int.dev.total,
my.int.dev.df,
pchisq(my.int.dev.total,my.int.dev.df,lower.tail = F),
rep(NA, v.p.rep))
this.temp.var=this.temp.var+1
}
while (i <= total.intercepts) {
if (type == "lm") {
my.sumsq = my.III.summary$`Sum Sq`[this.shift.temp]
my.df = my.III.summary$Df[this.shift.temp]
my.est = my.estimate[this.shift.temp]
my.std.err = my.std.error[this.shift.temp]
my.f.val = my.III.summary$`F value`[this.shift.temp]
my.p.val = my.III.summary$`Pr(>F)`[this.shift.temp]
my.tables.df[this.temp.var, ] = c(
my.rownames[this.shift.temp],
summary(my.model)[[4]][this.shift.temp,1],
summary(my.model)[[4]][this.shift.temp,2],
my.sumsq,
my.df,
my.f.val,
my.p.val,
rep(NA, v.p.rep)
)
} else if(type=="glm"){
#### HERE IS WHERE I LEFT OFF!! ####
# my.or = exp(my.estimate[this.shift.temp])
# my.est = my.estimate[this.shift.temp]
# my.z.val = my.summary$coefficients[this.shift.temp, 3]
# my.std.err = my.std.error[this.shift.temp]
#my.dev=my.III.summary$`LR Chisq`[this.shift.temp]
#my.df=my.III.summary$Df[this.shift.temp]
# my.p.val = my.summary$coefficients[{
# this.shift.temp
#}, 4]
my.tables.df[this.temp.var, ] = c(
paste(names(attr(my.model$model[[i]],"labels"))[1],"-",names(attr(my.model$model[[i]],"labels"))[2],sep=""),
my.int.dev.or[i],
my.int.dev.or.confint[1],
my.int.dev.or.confint[2],
my.int.dev[i],
1,
NA,
rep(NA, v.p.rep))
#this.temp.var=this.temp.var+1
#my.tables.df[this.temp.var,]=c("Treatment",NA,NA,NA,total.dev,total.df,dchisq(total.dev,total.df),rep(NA,v.p.rep))
}else{
my.tables.df[this.temp.var, ] = c(
my.int.names[i],
my.int.dev.or[i],
my.int.dev.or.confint[i,1],
my.int.dev.or.confint[i,2],
my.int.dev[i],
NA,
NA,
rep(NA, v.p.rep))
#this.temp.var=this.temp.var+1
}
this.shift.temp = this.shift.temp + 1
this.temp.var = this.temp.var + 1