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quick.tables.R
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###################### QUICK.TABLES ########################
#################### BY CHRIS KRANER #######################
############## NORTHERN ILLINOIS UNIVERSITY ################
######################## 12/2017 ###########################
############################################################
#' Partial contrasts for ANOVA and MANOVA tables
#'
#' Adds Contrasts and Latent Contrasts to nested table
#' for creating overall MANOVA and ANOVA tables.
#'
#' @param my.nested.table Model.
#' @param SS.type Type of sums of squares to report, either 2 or 3. (default = 3)
#' @param adjustment P-value adjustment sent to p.adjust (default = "bonferroni")
#' @param abbrev.length Integer telling how long of a label is too long. Longer than this and will be condensed (default=15)
#' @return Nested table with added rows and information (Unfortunately as character at the moment)
#' @keywords Explore
#' @examples
#' quick.contrast(my.nested.table)
quick.part.cont=function(my.nested.table,SS.type=2,adjustment="bonferroni",abbrev.length=30,latent.cont=F){
#### Get y levels
my.y.levels=dim(my.nested.table[1,4][[1]][[1]])[1]
#### Put in NA rows
temp.colnames=colnames(my.nested.table)
my.nested.table2=cbind(my.nested.table,as.matrix(c(rep(NA,dim(my.nested.table)[1]))))
if(latent.cont){
my.nested.table2=cbind(my.nested.table2,as.matrix(c(rep(NA,dim(my.nested.table)[1]))))
colnames(my.nested.table2)=c(temp.colnames,"Contrasts","Latent Contrasts")
}else{
colnames(my.nested.table2)=c(temp.colnames,"Contrasts")
}
#### For each row besides the null model
for(p in 2:dim(my.nested.table)[1]){
#### Check if should have contrast
if(!is.na(my.nested.table[p,7][[1]]) & my.nested.table[p,7][[1]]>1){
#### If should, make contrast
my.new.df=my.nested.table[p,3][[1]]$model
my.MSE=mean(my.nested.table[p,3][[1]]$residuals^2)
if(SS.type==3){
the.resid.SS=sum(diag(my.nested.table[p,4][[1]][[2]]))
}else{
the.resid.SS=sum(diag(my.nested.table[p,4][[1]][[2]][[1]]))
}
the.resid.df=my.nested.table[p,3][[1]]$df.residual
if(latent.cont){
#### Latent Mean Square Error
my.latent.MSE=NULL
for(j in 1:my.y.levels){
if(i==1){
my.latent.MSE=as.numeric(mean(my.contrast.model$residuals[i]^2))
}else{
my.latent.MSE=c(my.latent.MSE,as.numeric(mean(my.contrast.model$residuals[i]^2)))
}
}
}
#### Get mean responses for variables longer than 2
#### WARN! LEVEL NAMES IS GOING TO BREAK IT!!!! ####
level.name.grep=grep(my.nested.table[p,1],names(my.nested.table[p,3][[1]]$xlevels))
level.names=as.vector(my.nested.table[p,3][[1]]$xlevels[[level.name.grep]])
num.of.contrasts=my.nested.table[p,7][[1]]
count.grep=grep(my.nested.table[p,1],names(my.new.df))
my.count.means=NULL
my.latent.count.means=NULL
my.count.n=NULL
for(j in 1:{num.of.contrasts+1}){
if(j==1){
my.count.means=as.vector(mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1]))
my.count.n=as.vector(dim(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1])[1])
if(latent.cont){
for(E in 1:{my.y.levels}){
my.latent.count.means[[E]]=as.list(mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1][,E]))
}
}
}else{
my.count.means=c(my.count.means,mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1]))
my.count.n=c(my.count.n,dim(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1])[1])
if(latent.cont){
for(E in 1:{my.y.levels}){
my.latent.count.means[[E]]=c(my.latent.count.means[[E]],mean(my.new.df[which(my.new.df[[count.grep]]==level.names[j]),1][,E]))
}
}
}
}
#### Make contrasts
my.contrasts=NULL
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts=c(1,-1,rep(0,num.of.contrasts-1))
}else{
my.contrasts=rbind(my.contrasts,c(1,rep(0,j-1),-1,rep(0,num.of.contrasts-j)))
}
}
#pf(my.f.val,my.df,my.resid.df,lower.tail = F)
#### Compute F values & SS & P-val
my.contrasts.F=NULL
my.contrasts.SSC=NULL
my.contrasts.p=NULL
if(latent.cont){
my.latent.contrasts.F=NULL
my.latent.contrasts.SSC=NULL
my.latent.contrasts.p=NULL
}
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts.I=as.integer(t(as.matrix(my.count.means)))%*%as.integer(as.matrix(my.contrasts[j,]))
my.contrasts.denom=sum(my.contrasts[j,]^2/as.integer(my.count.n))
my.contrasts.SSC=as.list({my.contrasts.I^2}/{my.contrasts.denom})
my.contrasts.F=as.list({my.contrasts.I^2}/{my.MSE*my.contrasts.denom})
my.contrasts.p=as.list(pf(my.contrasts.F[[j]],1,the.resid.df,lower.tail = F))
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.I=as.integer(t(as.matrix(my.latent.count.means[[l]])))%*%as.integer(as.matrix(my.contrasts[j,]))
my.latent.contrasts.SSC[[l]]=as.list({my.latent.contrasts.I^2}/{my.contrasts.denom})
my.latent.contrasts.F[[l]]=as.list({my.latent.contrasts.I^2}/{my.latent.MSE[l]*my.contrasts.denom})
my.latent.contrasts.p[[l]]=as.list(pf(my.latent.contrasts.F[[l]][[j]],1,the.resid.df,lower.tail = F))
}
}
}else{
my.contrasts.I=as.integer(t(as.matrix(my.count.means)))%*%as.integer(as.matrix(my.contrasts[j,]))
my.contrasts.denom=sum(my.contrasts[j,]^2/as.integer(my.count.n))
my.contrasts.SSC=c(my.contrasts.SSC,{my.contrasts.I^2}/{my.contrasts.denom})
my.contrasts.F=c(my.contrasts.F,{my.contrasts.I^2}/{my.MSE*my.contrasts.denom})
my.contrasts.p=c(my.contrasts.p,pf(my.contrasts.F[[j]],1,the.resid.df,lower.tail = F))
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.I=as.integer(t(as.matrix(my.latent.count.means[[l]])))%*%as.integer(as.matrix(my.contrasts[j,]))
my.latent.contrasts.SSC[[l]]=c(my.latent.contrasts.SSC[[l]],{my.latent.contrasts.I^2}/{my.contrasts.denom})
my.latent.contrasts.F[[l]]=c(my.latent.contrasts.F[[l]],{my.latent.contrasts.I^2}/{my.latent.MSE[l]*my.contrasts.denom})
my.latent.contrasts.p[[l]]=c(my.latent.contrasts.p[[l]],pf(my.latent.contrasts.F[[l]][[j]],1,the.resid.df,lower.tail = F))
}
}
}
}
#### Make p-val Adjustments
my.contrasts.p=p.adjust(my.contrasts.p,method=adjustment)
if(latent.cont){
for(l in 1:{my.y.levels}){
my.latent.contrasts.p[[l]]=p.adjust(my.latent.contrasts.p[[l]],method=adjustment)
}
}
#### Make rownames
my.contrasts.names=NULL
for(j in 1:{num.of.contrasts}){
if(j==1){
my.contrasts.names=as.list(paste(trimws(level.names[[1]]),"-",trimws(level.names[[j+1]])))
}else{
my.contrasts.names=c(my.contrasts.names,paste(trimws(level.names[[1]]),"-",trimws(level.names[[j+1]])))
}
}
#### Add to table
my.contrasts.4.table=cbind(as.matrix(unlist(my.contrasts.names)),as.matrix(unlist(my.contrasts.F)),as.matrix(unlist(my.contrasts.SSC)),as.matrix(my.contrasts.p))
contr.grep=grep("^Contrasts$",colnames(my.nested.table2))
my.nested.table2[p,contr.grep]=list(my.contrasts.4.table)
if(latent.cont){
my.latent.contrasts.4.table=NULL
for(V in 1:my.y.levels){
if(V==1){
my.latent.contrasts.4.table=cbind(as.matrix(unlist(my.contrasts.names)),as.numeric(as.matrix(unlist(my.latent.contrasts.F[[V]]))),as.matrix(as.numeric(unlist(my.latent.contrasts.SSC[[V]]))),as.matrix(my.latent.contrasts.p[[V]]))
}else{
my.latent.contrasts.4.table=cbind(my.latent.contrasts.4.table,as.matrix(unlist(my.contrasts.names)),as.numeric(as.matrix(unlist(my.latent.contrasts.F[[V]]))),as.matrix(as.numeric(unlist(my.latent.contrasts.SSC[[V]]))),as.matrix(my.latent.contrasts.p[[V]]))
}
}
latent.contr.grep=grep("^Latent Contrasts$",colnames(my.nested.table2))
my.nested.table2[p,latent.contr.grep]=list(my.latent.contrasts.4.table)
}
}
}
return(my.nested.table2)
}
#' Contrast Tables in Pixiedust
#'
#' Beautiful tables using PHIA and PixieDust
#' for lm, glm, and mancova.
#'
#' @param my.model Model.
#' @param SS.type Type of sums of squares to report, either 2 or 3. (default = 3)
#' @param adjustment P-value adjustment sent to p.adjust (default = "bonferroni")
#' @param test.stat Character string giving the type of test statistic to be used (in MANOVA). (default="Wilks")
#' @param abbrev.length Integer telling how long of a label is too long. Longer than this and will be condensed (default=15)
#' @param pix.int Should this be viewable or is this for a document/dashboard? (default=T)
#' @param pix.method Print method. (default="html")
#' @param manova Is this a MAN(C)OVA?
#' @param my.factors If you only want some of the factors, use this. Otherwise, factors are pulled from the regression model
#' @param my.type If you have problems, you can specify the regression type. This is pulled from the model
#' @return Either pixiedust object or code (in HTML or LaTeX) for table
#' @keywords Explore
#' @export
#' @examples
#' quick.contrast(my.model)
quick.contrast = function(my.model,
SS.type = 3,
adjustment = "bonferroni",
test.stat = "Wilks",
abbrev.length = 15,
pix.int = T,
pix.method = "html",
my.factors = my.contrasts,
my.type = my.reg.type,
skip.me=F) {
#### Find type
my.call = as.character(my.model$call)
my.split.call = strsplit(my.call, "\\(")
my.reg.type = my.split.call[[1]][1]
#### Find factors
my.contrasts = names(my.model$contrasts)
if (my.type != "manova" | my.type != "stats::manova") {
#### Find levels
my.x.levels = NULL
for (i in 1:length(my.factors)) {
my.x.levels = c(my.x.levels, my.model$xlevels[[i]])
}
#### Find num non var
my.non.var = length(my.model$coefficients) - length(my.x.levels) - 1 +
length(my.contrasts)
}
library(pixiedust)
library(phia)
if (my.type == "manova" | my.type == "stats::manova") {
my.phia.print = as.data.frame(matrix(ncol = 8, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
fixed = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names",
"Df",
"test stat",
"approx F",
"num Df",
"den Df",
"Pr(>F)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, nrow(my.phia))
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
rownames(my.phia.print) = NULL
if(skip.me){
return(my.phia.print)
}
phia.length = dim(my.phia.print)[1]
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>F)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "test stat", round = 3) %>%
sprinkle(cols = "approx F", round = 3) %>%
sprinkle_colnames(
"",
"Levels",
"df",
"Pillai <br /> Statistic",
"approx <br /> F-value",
"num <br /> df",
"den <br /> df",
"Pr(>F)"
) %>%
sprinkle(cols = 1:8,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:8, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:8,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:8,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:8,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1:2,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1:2,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 7,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 8,
width = 70,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 8, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
} else if (my.type == "lm" | my.type == "stats::lm") {
my.phia.print = as.data.frame(matrix(ncol = 7, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
pairwise = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia = my.phia[-{
dim(my.phia)[1]
}, ]
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names", "Value", "Df", "Sum of Sq", "F", "Pr(>F)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, {
nrow(my.phia)
})
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (my.lengths[this.table.var] == 1) {
my.phia = my.phia[-2, ]
}
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
#print(my.phia.print)
#my.phia.print=my.phia.print[-{dim(my.phia.print)[1]-1},]
rownames(my.phia.print) = NULL
phia.length = dim(my.phia.print)[1]
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>F)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "test stat", round = 3) %>%
sprinkle(cols = "approx F", round = 3) %>%
sprinkle_colnames("",
"Levels",
"Value",
"df",
"Sums of <br /> Squares",
"F-value",
"Pr(>F)") %>%
sprinkle(cols = 1:7,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:7, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:7,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:7,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:7,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 7,
width = 50,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 7, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
} else{
my.phia.print = as.data.frame(matrix(ncol = 6, nrow = 1))
my.lengths = NULL
this.table.var = 1
while (this.table.var < {
length(my.factors) + 1
}) {
my.phia = phia::testInteractions(
my.model,
pairwise = my.factors[this.table.var],
adjustment = adjustment,
abbrev.levels = abbrev.length
)
my.phia = my.phia[-{
dim(my.phia)[1]
}, ]
my.phia$names = attr(my.phia, "row.names")
my.phia = my.phia[c("names", "Value", "Df", "Chisq", "Pr(>Chisq)")]
attr(my.phia, "class") = attr(my.phia, "class")[-1]
my.lengths = c(my.lengths, {
nrow(my.phia)
})
this.stuff = c(my.factors[this.table.var], NA)
if (my.lengths[this.table.var] > 2) {
for (i in 1:{
my.lengths[this.table.var] - 2
}) {
this.stuff = c(this.stuff, NA)
}
}
my.phia = cbind(this.stuff, my.phia)
if (my.lengths[this.table.var] == 1) {
my.phia = my.phia[-2, ]
}
if (this.table.var == 1) {
my.phia.print = my.phia
} else{
my.phia.print = rbind(my.phia.print, my.phia)
}
this.table.var = this.table.var + 1
#print(my.phia.print)
}
#print(my.phia.print)
#my.phia.print=my.phia.print[-{dim(my.phia.print)[1]-1},]
rownames(my.phia.print) = NULL
phia.length = dim(my.phia.print)[1]
my.phia.print[[6]] = as.numeric(my.phia.print[[6]])
options(pixie_interactive = pix.int)
my.phia.pixie = pixiedust::dust(my.phia.print) %>%
sprinkle_print_method(pix.method) %>%
sprinkle(cols = "Pr(>Chisq)", fn = quote(pvalString(
value, digits = 3, format = "default"
))) %>%
sprinkle(cols = "value", round = 3) %>%
sprinkle(cols = "Chisq", round = 3) %>%
sprinkle_colnames("", "Levels", "Value", "df", "Chi-Sq", "Pr(>Chisq)") %>%
sprinkle(cols = 1:6,
rows = {
sum(my.lengths)
},
border = "bottom") %>%
sprinkle(cols = 1:6, pad = 10) %>%
sprinkle(cols = 1,
rows = 1:{
sum(my.lengths)
},
border = "left") %>%
sprinkle(
cols = 3:6,
rows = 1:{
sum(my.lengths)
},
border = c("right", "left")
) %>%
sprinkle(
cols = 1:6,
rows = 1,
border = c("top", "bottom"),
part = "head"
) %>%
sprinkle(
cols = 1,
rows = 1,
border = "left",
part = "head"
) %>%
sprinkle(
cols = 3:6,
rows = 1,
border = c("right", "left"),
part = "head"
) %>%
sprinkle_na_string(na_string = "") %>%
sprinkle_width(
cols = 1,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(
cols = 2,
rows = 1,
width = 70,
width_units = "pt"
) %>%
sprinkle_width(cols = 3,
width = 50,
width_units = "pt") %>%
sprinkle_width(cols = 4,
width = 30,
width_units = "pt") %>%
sprinkle_width(cols = 5,
width = 60,
width_units = "pt") %>%
sprinkle_width(cols = 6,
width = 50,
width_units = "pt") %>%
sprinkle(rows = 1,
halign = "center",
part = "head")
adj.method = as.data.frame(matrix(ncol = 6, nrow = 1))
adj.method[1, ] = c(paste("p adjustment method: ", adjustment, sep =
""),
NA,
NA,
NA,
NA,
NA)
my.phia.pixie = redust(my.phia.pixie, adj.method, part = "foot") %>%
sprinkle(merge = T,
halign = "center",
part = "foot")
if (pix.int & !skip.me) {
return(my.phia.pixie)
} else if(!pix.int & !skip.me){
my.phia.pixie = print(my.phia.pixie, quote = F)[1]
return(my.phia.pixie)
}else{
return(my.phia.print)
}
}
}
#' Regression Tables in Pixiedust
#'
#' Beautiful tables adding sums of squares
#' and p-value formatting, then giving html or
#' latex output. If interactive and HTML, will show up
#' in viewer.
#'
#' @param my.model Model. NOTE: If have factors, please place them first in the regression.
#' @param VIF include Variable Inflation Factor? (calculated by car::vif) (default=F)
#' @param part.eta If lm, include partial eta square by calculating SS_part/SS_total? (default=F)
#' @param myDF Dataframe, not needed if use data= in call
#' @param my.factor If there are any factors, list them here.
#' @param SS.type Type of sums of squares to report (default = 3)
#' @param pix.int Should this be viewable or is this for a document/dashboard? (default=T)
#' @param pix.method Print method. (default="html")
#' @param type Type of regression? Currently supported: lm, glm (binary), manova
#' @param do.glance Include glance stats?
#' @return Either pixiedust object or code (in HTML or LaTeX) for table
#' @keywords Explore
#' @export
#' @examples
#' quick.reg(my.model, myDF)
quick.reg = function(my.model,
part.eta = F,
VIF = F,
myDF = my.found.df,
SS.type = 2,
abbrev.length = ab.len,
pix.int = T,
pix.method = "html",
type = my.reg.type,
test.stat = "Pillai",
my.factor = NULL,
do.glance=T,
show.footer=T,
adjustment = "bonferroni",
show.contrasts=F,
show.y.contrasts=F,
show.latent=F,
show.intercepts=F,
real.names=T) {
library(pixiedust)
library(broom)
library(car)
library(tidyr)
library(phia)
library(quick.tasks)
library(dplyr)
#### Find type ####
my.reg.type=quick.type(my.model)
#### Set Inits ####
if(type=="ord"){
ab.len=30
library(ordinal)
}else{
ab.len=15
}
#### Get data frame from parent environment ####
my.found.df = eval(parse(text=capture.output(my.model$call$data)),envir = parent.frame())
#print(dim(my.found.df))
if (is.null(my.found.df)) {
stop(paste("No data frame found"))
}
#### for expansion
if (!VIF & !part.eta) {
v.p.len = 8
v.p.rep = 0
} else if (!VIF) {
v.p.len = 9
v.p.rep = 1
} else if (!part.eta) {
v.p.len = 9
v.p.rep = 1
} else{
v.p.len = 10
v.p.rep = 2
}
#### Make factor list ####
#### Use car::Anova to get SS Type 3
if (type == "lm") {
#### ANOVA TABLES ####
my.summary = summary(my.model)
my.coefficients = my.summary$coefficients
my.coefficients = as.data.frame(my.coefficients)
if (type == "ord" & length(my.model$model) == 1) {
my.III.summary = NULL
the.length = length(my.model$y.levels) - 1
} else{
my.III.summary = car::Anova(my.model, type = SS.type)
if (type == "glm" & is.null(my.factor)) {
the.length = dim(my.III.summary)[1] + 1
} else{
the.length = dim(my.III.summary)[1]
}
}
#### Calculate total SS ####
my.total = sum(my.III.summary$`Sum Sq`[2:length(my.III.summary$`Sum Sq`)])
my.df.total = sum(my.III.summary$Df[2:length(my.III.summary$Df)])
total.intercepts = 1
my.rownames = c(abbreviate(rownames(my.summary$coefficients), minlength = abbrev.length),
"Residuals",
"Total")
treat.SS=sum(my.III.summary$`Sum Sq`[2:{length(my.III.summary$`Sum Sq`)-1}])
treat.df=sum(my.III.summary$Df[2:{length(my.III.summary$Df)-1}])
} else if(type == "manova"){
#### Begin MANOVA ####
#### Inits ####
my.envir=environment()
my.nested.table=quick.SSCP(my.model, myDF, SS.type, show.contrasts, show.latent,my.envir)
#### Get treatment ####
treat.model=my.nested.table[dim(my.nested.table)[1],4]
#### Regular totals
treat.total.temp=lapply(lapply(treat.model[[1]][[1]],diag),sum)
treat.total=0
for(W in 2:length(treat.total.temp)){
treat.total=treat.total+treat.total.temp[[W]]
}
treat.total.df=sum(as.numeric(my.nested.table[,7]),na.rm = T)
#### Partial totals
part.treat.total=NULL
for(X in 2:length(treat.model[[1]][[1]])){
if(X==2){
part.treat.total=treat.model[[1]][[1]][[X]]
}else{
part.treat.total=part.treat.total+treat.model[[1]][[1]][[X]]
}
}
#### Latent totals
if(show.latent){
latent.treat.model=my.nested.table[dim(my.nested.table)[1],8]
latent.part.treat.total=NULL
latent.part.total=NULL
for(X in 2:length(latent.treat.model[[1]][[1]])){
if(X==2){
latent.part.treat.total=latent.treat.model[[1]][[1]][[X]]
}else{
latent.part.treat.total=latent.part.treat.total+latent.treat.model[[1]][[1]][[X]]
}
}
}
#### Get residuals ####
#### Regular residuals
if(SS.type==3){
the.resid=sum(diag(treat.model[[1]][[2]]))
#### Partial residuals
part.resid.total=treat.model[[1]][[2]]
}else{
the.resid=sum(diag(treat.model[[1]][[2]][[1]]))
part.resid.total=treat.model[[1]][[2]][[1]]
}
the.resid.df=my.model$df.residual
#### Latent totals
if(show.latent){
if(SS.type==3){
latent.part.resid.total=latent.treat.model[[1]][[2]]
}else{
latent.part.resid.total=latent.treat.model[[1]][[2]][[1]]
}
}
#### Get totals ####
the.total=total.resid+treat.total+sum(diag(treat.model[[1]][[1]][[1]]))
the.total.df=total.resid.df+treat.df+my.y.levels
#### Partial totals
partial.total=part.resid.total+part.treat.total+treat.model[[1]][[1]][[1]]
#### Latent totals
if(show.latent){
latent.part.total=latent.part.treat.total+latent.part.treat.total+latent.treat.model[[1]][[1]][[1]]
}
#### Make dependent variable rownames ####
my.dv.rownames=rownames(part.resid.total)
#### Make basic table ####
#### NEEDS TO BE FIXED ####
my.table.names=c("var","test.stat","f.val","SS","df","mult.df","resid df","p.val")
my.manova.table=as.data.frame(matrix(ncol=v.p.len,nrow=1))
names(my.manova.table)=my.table.names
my.line.var=1
for(i in 1:length(my.SSP.treat)){
if(i==1){
my.i=i
}else{
my.i=2*i-1
}
#### Put in basic line ####
my.treat.err=solve(my.SSP.err)%*%treat.model[[1]][[1]][[i]]
my.test.stat=quick.m.test(my.treat.err,test.stat)
my.SS=sum(diag(treat.model[[1]][[1]][[i]]))
my.df=my.y.levels*ifelse(my.i!=1,as.numeric(my.nested.table[my.i,7]),1)
my.resid.df=ifelse(my.i==1,{my.y.levels*the.resid.df},min({the.resid.df*as.numeric(my.nested.table[my.i,5])-as.numeric(my.nested.table[my.i,5])},my.y.levels*the.resid.df-as.numeric(my.nested.table[my.i,5])))
my.f.val={my.SS/my.df}/{the.resid/the.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(ifelse(i==1,"(Intercept)",my.nested.table[my.i,1]),my.test.stat,my.f.val,my.SS,ifelse(my.i==1,NA,my.df/my.y.levels),ifelse(my.i==1,my.y.levels,my.df),my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in decomposed factors (y constrasts) ####
#### Intercept
if({show.intercepts & my.i==1}){
for(y in 1:my.y.levels){
my.name=my.dv.rownames[y]
my.test.stat=NA
my.SS=as.numeric(treat.model[[1]][[1]][[i]][y,y])
my.df=1
my.resid.df=the.resid.df
my.f.val={my.SS/my.df}/{sum(diag(my.SSP.err))/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(my.name,my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
}
}
#### Other
if({show.y.contrasts & my.i!=1}){
for(y in 1:my.y.levels){
my.name=paste(ifelse(real.names,my.dv.rownames[y],y),"|",my.nested.table[my.i,1],sep="")
my.test.stat=NA
my.SS=treat.model[[1]][[1]][[i]][y,y]
my.df=as.numeric(my.nested.table[my.i,7])
my.resid.df=min(abs(my.y.levels*the.resid.df-my.df*the.resid.df-my.df),abs(my.y.levels*the.resid.df-my.df))
my.resid=part.resid.total[y,y]
my.f.val={my.SS/my.df}/{my.resid/my.resid.df}
my.p.val=pf(my.f.val,my.df,my.resid.df,lower.tail = F)
my.manova.table[my.line.var,]=c(abbreviate(my.name,abbrev.length),my.test.stat,my.f.val,my.SS,my.df,NA,my.resid.df,my.p.val)
my.line.var=my.line.var+1
#### Put in latents (ANOVA) ####
my.counter=NULL
for(r in 2:my.y.levels){
my.counter=c(my.counter,r)
}
my.counter=c(my.counter,1)
if(show.latent &my.i!=1){
# my.my.i.temp=my.counter[my.i-1]
my.my.i.temp=my.i
my.y=y
my.name=NA