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interActive_OLS.R
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#Server
options(digits=9)
options(warn=-1) #Suppress Warnings
options(shiny.maxRequestSize=20*1024^2) #max file size = 20 MB
require(ggplot2)
require(RColorBrewer)
require(foreign)
require(pander)
# server.R
# HELPER FUNCTIONS --------------------------------------------------------
#lighten() coverts a color to 75% transparency. This is used for creating shaded confidence regions.
lighten <- function(col,
pct=0.75,
alpha=1){
if (abs(pct)>1) {
warning("invalid pct in lighten(); must be between 0 and 1.")
pcol <- col2rgb(col)/255
} else {
col <- col2rgb(col)/255
if (pct>0) {
pcol <- col + pct*(1-col)
} else {
pcol <- col*pct
}
}
rgb(pcol[1], pcol[2], pcol[3], alpha)
}
#extract() is used later in extracting coefficients for regions-of-signifiance analysis.
extract <- function(MOD, regressor) {
tmp = numeric()
tmp[1] <- MOD$coef[regressor] #run it, and put the coefficient in cell [i,2] of a table
tmp[2] <- summary(MOD)$coef[regressor,2] #run it, and put the standard error in cell [i,3] of a table
tmp[3] <- confint(MOD)[regressor,1] #run it, and put the lower limit of my 95% CI in cell [i,4] of a table
tmp[4] <- confint(MOD)[regressor,2] #same for upper limit
return(tmp)
}
# END HELPER FUNCTIONS ----------------------------------------------------
server<-function(input, output, session) {
values<-reactiveValues() #defines empty list that will take model-produced values
plotelements<-reactiveValues() #defines empty list that will take model-produced values
# DATAFRAME UPLOAD CODE ---------------------------------------------------
df<-reactive({
if (!is.null(input$file1)){
if(tools::file_ext(input$file1) == "csv"){
read.csv(input$file1$datapath, header = TRUE)
}
else if (tools::file_ext(input$file1) == "xlsx"){
file.rename(input$file1$datapath,
paste(input$file1$datapath, ".xlsx", sep=""))
as.data.frame(readxl::read_excel(paste(input$file1$datapath, ".xlsx", sep=""), 1,col_names = TRUE))
}
else if (tools::file_ext(input$file1) == "xls"){
file.rename(input$file1$datapath,
paste(input$file1$datapath, ".xls", sep=""))
as.data.frame(readxl::read_xls(paste(input$file1$datapath, ".xls", sep=""), 1,col_names = TRUE))
}
else if (tools::file_ext(input$file1) == "sav"){
foreign::read.spss(input$file1$datapath, to.data.frame=TRUE)
}
}
})
# User Interface (UI) code ------------------------------------------------
#Categorical moderator option
output$ui.categorical <- renderUI ({
if (is.null(input$file1))
return()
checkboxInput(inputId="cat", label = h5("Categorical Moderator"),
value=FALSE)
})
# Continuous Variable Scaling Options -----------------------------------------------
#Scaling for the X predictor
output$ui.scalex <- renderUI ({
if (is.null(input$file1))
return()
radioButtons(inputId="scalex", label = NULL, inline = TRUE,
c("Raw X" = "raw", "Centered X" = "cen", "Standardized X" = "std"),
selected = "cen")
})
#Scaling for the Z predictor
output$ui.scalez <- renderUI ({
if (is.null(input$file1))
return()
radioButtons(inputId="scalez", label = NULL, inline = TRUE,
c("Raw Z" = "raw", "Centered Z" = "cen", "Standardized Z" = "std"),
selected = "cen")
})
#Scaling for the dependent variable
output$ui.scaledv <- renderUI ({
if (is.null(input$file1))
return()
radioButtons(inputId="scaledv", label = NULL, inline = TRUE,
c("Raw Y" = "raw", "Centered Y" = "cen", "Standardized Y" = "std"),
selected = "raw")
})
#Model Selection Options ------------------------------------
#Specify covariates for the model
output$ui.covars <- renderUI ({
if (is.null(input$file1))
return()
selectInput(inputId = "covars",
label = "Choose your covariate(s)\n(control variables):",
choices = colnames(df()),
selected = "",
multiple = TRUE,
selectize = TRUE)
})
#Specify focal variable (x-axis)
output$ui.foc <- renderUI ({
if (is.null(input$file1))
return()
selectInput("foc",
label = "Choose your focal (x-axis) variable:",
choices = colnames(df()),
selected = "")
})
# Specify moderator variable
output$ui.mod <- renderUI ({
if (is.null(input$file1))
return()
selectInput("mod",
label = "Choose your moderator (legend) variable:",
choices = colnames(df()),
selected = "")
})
#Specify outcome variable (appearing on y-axis)
output$ui.dv <- renderUI ({
if (is.null(input$file1))
return()
selectInput("dv",
label = "Choose your dependent (y-axis) variable:",
choices = colnames(df()),
selected = "")
})
# specifying polynomial terms (currently accomodates quadratic effects only)
output$ui.poly <- renderUI ({
if (is.null(input$file1))
return()
radioButtons(inputId="poly", label = h4("Form of Focal Predictor:"),
c("Linear" = "lin", "Quadratic" = "quad"),
selected = "lin")
})
output$ui.navals<-renderUI ({
if (is.null(input$file1))
return()
textInput(inputId="naval",
label="Missing (NA) values code:",
value = "NA",
width = "100%")
})
# Defining customization UI options for small multiples plot ---------------------
#X axis label
output$ui.xlab<-renderUI ({
if (is.null(input$file1))
return()
textInput(inputId="xaxislab",
label="X-Axis label:",
value=input$foc)
})
#Y axis label
output$ui.ylab<-renderUI ({
if (is.null(input$file1))
return()
textInput(inputId="yaxislab",
label="Y-Axis label:",
value=input$dv)
})
output$ui.title<-renderUI ({
if (is.null(input$file1))
return()
textInput(inputId="title",
label="Title of Graph:",
value=paste("Level of Moderator (",input$mod,")"))
})
output$ui.greyscale <- renderUI ({
if (is.null(input$file1))
return()
checkboxInput(inputId="greyscale", label = h5("Greyscale"),
value=FALSE)
})
#Define "run analysis" button
output$ui.go <- renderUI ({
if (is.null(input$file1))
return()
actionButton("go","Run Analysis", width = "100%", icon = icon("fa fa-thumbs-up"))
})
# Defining customization UI options for regions of significance plot --------------
#X axis label
output$ui.xlab.ros<-renderUI ({
if (is.null(input$file1))
return()
textInput(inputId="xaxislab.ros",
label="X-Axis label:",
value=input$mod)
})
#Y axis label
output$ui.ylab.ros<-renderUI ({
if (is.null(input$file1))
return()
textInput(inputId="yaxislab.ros",
label="Y-Axis label:",
value=paste("Simple Slope of ",input$foc))
})
#Plot title
output$ui.title.ros<-renderUI ({
if (is.null(input$file1))
return()
textInput(inputId="rostitle",
label="Title of Graph:",
value="")
})
#Produce a table of data descriptives
output$varnames <- renderTable({
if (is.null(input$file1))
return()
if (is.null(input$go))
return()
desc<- as.data.frame(psych::describe(df()))[,c("mean","sd","min","max","skew")]
varlist<-cbind(colnames(df()),desc)
colnames(varlist)<-c("Variable",colnames(desc))
varlist
})
observeEvent(input$go, { #Once the "go" button is hit, InterActive looks at all the ui input and runs the model.
updateTabsetPanel(session, "inTabset",
selected = "Plot")
# Define small multiple moderator values (from left to right)
#Small multiple 1
output$ui.sm1<-renderUI ({
if (is.null(input$file1))
return()
if (input$cat==TRUE) return()
textInput(inputId="sm1",
label="Multiple 1",
value= -2,
width = "80%")
})
#Small multiple 2
output$ui.sm2<-renderUI ({
if (is.null(input$file1))
return()
if (input$cat==TRUE) return()
textInput(inputId="sm2",
label="Multiple 2",
value= -1,
width="80%")
})
#Small multiple 3
output$ui.sm3<-renderUI ({
if (is.null(input$file1))
return()
if (input$cat==TRUE) return()
textInput(inputId="sm3",
label="Mulitple 3",
value= 0,
width="80%")
})
#Small multiple 4
output$ui.sm4<-renderUI ({
if (is.null(input$file1))
return()
if (input$cat==TRUE) return()
textInput(inputId="sm4",
label="Multiple 4",
value= 1,
width="80%")
})
#Small multiple 5
output$ui.sm5<-renderUI ({
if (is.null(input$file1))
return()
if (input$cat==TRUE) return()
textInput(inputId="sm5",
label="Multiple 5",
value= 2,
width="80%")
})
output$ui.downloadplot <- renderUI ({
if (is.null(input$file1))
return()
downloadButton('downloadPlot', 'Download Plot')
})
#Define small multiples for binary categorical moderator
output$ui.catsm1<-renderUI ({
if (is.null(input$file1))
return()
if (input$cat==FALSE)
return()
mod <- input$mod
mod.seq<-na.omit(unique(df()[,mod]))
textInput(inputId="catsm1",
label="Category 0",
value= paste("Category ",mod.seq[1]),
width = "50%")
})
output$ui.catsm2<-renderUI ({
if (is.null(input$file1))
return()
if (input$cat==FALSE)
return()
mod <- input$mod
mod.seq<-na.omit(unique(df()[,mod]))
textInput(inputId="catsm2",
label="Category 1",
value= paste("Category ",mod.seq[2]),
width = "50%")
})
# Simple slopes analysis and plot creation code -----------------------------------
output$modplot <- renderPlot({
if (is.null(input$file1))
return(NULL)
# validate(
# need(values$staticplot != "", "Please wait...")
# )
#Defining plot template function
interactive.plot<-function(data, dfpoints, plotdf){
ggplot() +
geom_point(data=dfpoints, aes(x=pred,y=y),size = .75, alpha = .5) +
geom_ribbon(data=plotdf, aes(x=focal.seq, y=pe, ymin = lower, ymax = upper, fill = level), alpha = .25) +
geom_line(data = plotdf, aes(focal.seq, pe, fill=level, color=level), size=1) +
ylim(min(c(plotdf[,"lower"],min(data[,dv]))),max(c(plotdf[,"upper"],max(data[,dv])))) +
xlim(min(data[,foc], na.rm = TRUE),max(data[,foc], na.rm = TRUE)) +
#thematic specifications of my graphic
theme(text=element_text(family="Helvetica",size=10, color="black"),
legend.position="none",
panel.background = element_blank(),
legend.background = element_rect(fill = "white"),
legend.title=element_blank(),
legend.key = element_rect(fill = "white"),
panel.grid.minor = element_blank(),
axis.text.x=element_text(colour="black"),
axis.title.x=element_text(size=14),
axis.text.y=element_text(colour="black"),
axis.title.y=element_text(size=14),
axis.line = element_line(size=.4),
panel.grid.major = element_blank(),
plot.background=element_rect(fill='white')) +
labs(fill = mod, color = mod, linetype=mod) +
labs(x = foc, y = dv) +
coord_cartesian() +
ggtitle(paste0("Level of Moderator (",mod,")")) +
geom_hline(yintercept=c(max(data[,dv], na.rm = TRUE),min(data[,dv], na.rm = TRUE)),linetype=2) +
facet_grid(~level) +
# theme(strip.text.x = element_text(size = 12, colour = "black")) +
scale_color_manual(values=df.cols$col) +
scale_fill_manual(values=df.cols$colslight) +
theme(plot.title = element_text(family="Helvetica", face="bold", size=14, hjust=0, color="black"))
}
# Analyses code -----------------------------------------------------------
dftrue<-plotelements$data<-df() #retain an original copy
#Replace all missing values in dataset with NA
dftrue[dftrue==input$naval]<-NA
data<-dftrue #make a copy of the data for variable rescaling and analyses
mod <- input$mod #define moderator
foc <- input$foc #define focal predictor
dv <- input$dv #define outcome variable
data<-as.data.frame(data.matrix(data))
#Re-scale variables based on user input
if (input$scalex == "cen"){
data[,foc] <- scale(data[,foc], scale = FALSE, center = TRUE)
}
if (input$scalez == "cen" && input$cat==FALSE){
data[,mod] <- scale(data[,mod], scale = FALSE, center = TRUE)
}
if (input$scaledv == "cen"){
data[,dv] <- scale(data[,dv], scale = FALSE, center = TRUE)
}
if (input$scalex == "std"){
data[,foc] <- scale(data[,foc], scale = TRUE)
}
if (input$scalez == "std" && input$cat==FALSE){
data[,mod] <- scale(data[,mod], scale = TRUE)
}
if (input$scaledv == "std"){
data[,dv] <- scale(data[,dv], scale = TRUE)
}
values$modlevel<-input$modlevel
if(input$cat == FALSE) (values$modlevel<- mean(data[,mod], na.rm = TRUE))
modlevel <- values$modlevel
dfcoefs<-data #copy data for creating summary table of coefficients. Reflects scaling of X, Z, and Y.
if(input$cat==FALSE){data[,mod]<-data[,mod]-values$modlevel}
covarstemp<-input$covars
covars<-covarstemp[!covarstemp %in% c(foc,mod)]
cov<-paste(covars, collapse=" + ")
values$covars<-covars
if (input$poly == "quad"){
focmod<-paste("+",
foc,"+",
"I(",foc,"^2)","+",
mod,"+",
foc,"*",
mod, "+",
"I(",foc,"^2)","*",
mod)
}
else{
focmod<-paste("+",foc,"+",mod,"+",foc,"*",mod)}
dep<-paste(input$dv, "~")
values$form<-as.formula(paste(dep,cov,focmod))
values$m<-plotelements$model<-lm(values$form,dfcoefs, na.action = na.omit)
values$model<-as.formula(paste(dep,cov,focmod))
m <- values$m
mtrue <- values$mtrue
data[,mod] <- as.numeric(data[,mod])
# Computing the crossover point
values$co<- -(m$coefficients[mod]/(m$coefficients[paste0(foc,":",mod)]))
if (input$poly == "quad")values$co<- -9999
cov<-paste(covars, collapse="+")
if (input$poly == "quad"){
focmod2<-paste("+",
"s.foc","+",
"I(","s.foc","^2)","+",
"s.mod","+",
"s.foc","*",
"s.mod", "+",
"I(","s.foc","^2)","*",
"s.mod")
}
else{
focmod2<-paste("+","s.foc","+","s.mod","+","s.foc","*","s.mod")
}
dep2<-paste(input$dv, "~")
if(input$cat==TRUE){
values$form2<-as.formula(paste(dep2,cov,focmod2))
values$model2<-as.formula(paste(dep2,cov,focmod2))
data$s.foc<-data[,foc]
data$s.mod <- data[,mod]-min(unique(data[,mod]),na.rm=TRUE)
values$m.cat0<-lm(values$form2,data, na.action = na.omit)
data$s.mod <- data[,mod]-max(unique(data[,mod]),na.rm=TRUE)
m.cat1<-lm(values$form2,data, na.action = na.omit)
}
else{
values$dep2<-dep2
values$cov<-cov
values$focmod2<-focmod2
values$form2<-as.formula(paste(dep2,cov,focmod2))
values$model2<-as.formula(paste(dep2,cov,focmod2))
}
#Results table creation code
m <- values$m
resultstable<-cbind(names(m$coefficients),c(" ",round(QuantPsyc::lm.beta(m),3)),round(summary(m)$coefficients,3))
colnames(resultstable)<-c(" ","Beta","b","SE","t","Pr(>|t|)")
#Note: lm.beta will not produce correct beta value for the interaction coefficient, so this is computed on it's own below.
# beta <- b * sx/sy
resultstable[paste0(foc,":",mod),"Beta"]<-round(m$coefficients[paste0(foc,":",mod)]*sd(m$model[,foc]*m$model[,mod],na.rm=T)/sd(m$model[,dv],na.rm=T),3)
values$resultstable<-resultstable
# Regions-of-significance analyses and marginal effects plot code ---------------------------------------------------------
if(input$cat == FALSE){
df.ros<-as.data.frame(data.matrix(dftrue))
df.ros[,foc] <- scale(df.ros[,foc])
df.ros[,mod] <- scale(df.ros[,mod])
hyp.Z <- seq(-3,3,.05)
ros <- sapply(hyp.Z, function(i) {
df.ros[,mod] <- round(df.ros[,mod],2) - i
mod <- lm(values$form,df.ros, na.action = na.omit)
tmp <- c(i, extract(mod, foc))})
ros <- data.frame(t(ros))
colnames(ros) <- c("hyp.Z","pe.X","SE","LL","UL")
ros$significance[(ros$LL*ros$UL) > 0] <- "sig"
ros$significance[(ros$LL*ros$UL) <= 0] <- "not sig"
ros$sign[ros$pe.X<0]<-"neg"
ros$sign[ros$pe.X>=0]<-"pos"
values$ros<-plotelements$ros<-ros[c("hyp.Z","pe.X","LL","UL")]
#Creating the RoS plot
rosplot <- ggplot() +
geom_ribbon(data = ros, aes(hyp.Z, pe.X), ymin = ros$LL, ymax = ros$UL,
fill=brewer.pal(3,"Greys")[3], alpha = .25) +
geom_line(data = ros, aes(hyp.Z, pe.X), color="Black", size = 1.25) +
scale_x_continuous(breaks = c(-3,-2,-1,0,1,2,3),labels = c(-3,-2,-1,0,1,2,3),limits = c(-3,3)) +
ylim(min(ros$LL),max(ros$UL)) +
geom_hline(yintercept = 0) +
xlab(input$xaxislab.ros) +
ylab(input$yaxislab.ros) +
geom_rug(data = df.ros, aes(x=df.ros[,mod])) +
theme_bw() +
theme(text=element_text(family="Helvetica",size=12, color="black"))
values$text.negneg<-values$text.negpos<-values$text.posneg<-values$text.pospos<-paste("")
if(m$coefficients[paste0(foc,":",mod)]<0){
if("not sig"%in%ros[which(ros$sign=="neg"),"significance"]==TRUE &
"sig"%in%ros[which(ros$sign=="neg"),"significance"]==TRUE ){
sigline.negneg<-min(ros[which(ros$significance=="sig" & ros$sign=="neg"),"hyp.Z"])
rosplot <- rosplot +
geom_vline(xintercept = sigline.negneg, linetype="dashed")
pct.negneg<-(length(df.ros[which(df.ros[,mod]>sigline.negneg),mod])/length(df.ros[,mod]))*100
values$text.negneg<-paste0("The simple slope of ",input$foc," on ",input$dv," is significant and negative when ",input$mod," is ",sigline.negneg," standard deviations away from the mean or further. ",round(pct.negneg,2),"% of observations are within this region.\n")
}
if("not sig"%in%ros[which(ros$sign=="pos"),"significance"]==TRUE &
"sig"%in%ros[which(ros$sign=="pos"),"significance"]==TRUE){
sigline.negpos<-max(ros[which(ros$significance=="sig" & ros$sign=="pos"),"hyp.Z"])
rosplot <- rosplot +
geom_vline(xintercept = sigline.negpos, linetype="dashed")
pct.negpos<-(length(df.ros[which(df.ros[,mod]<sigline.negpos),mod])/length(df.ros[,mod]))*100
values$text.negpos<-paste0("The simple slope of ",input$foc," on ",input$dv," is significant and positive when ",input$mod," is ",sigline.negpos," standard deviations away from the mean or further. ",round(pct.negpos,2),"% of observations in ",input$mod," are within this region.\n")
}
}
else{
if("not sig"%in%ros[which(ros$sign=="neg"),"significance"]==TRUE &
"sig"%in%ros[which(ros$sign=="neg"),"significance"]==TRUE ){
sigline.posneg<-max(ros[which(ros$significance=="sig" & ros$sign=="neg"),"hyp.Z"])
rosplot <- rosplot +
geom_vline(xintercept = sigline.posneg, linetype="dashed")
pct.posneg<-(length(df.ros[which(df.ros[,mod]<sigline.posneg),mod])/length(df.ros[,mod]))*100
values$text.posneg<-paste0("The simple slope of ",input$foc," on ",input$dv," is significant and negative when ",input$mod," is ",sigline.posneg," standard deviations away from the mean or further. ",round(pct.posneg,2),"% of observations in ",input$mod," are within this region.\n")
}
if("not sig"%in%ros[which(ros$sign=="pos"),"significance"]==TRUE &
"sig"%in%ros[which(ros$sign=="pos"),"significance"]==TRUE){
sigline.pospos<-min(ros[which(ros$significance=="sig" & ros$sign=="pos"),"hyp.Z"])
rosplot <- rosplot +
geom_vline(xintercept = sigline.pospos, linetype="dashed")
pct.pospos<-(length(df.ros[which(df.ros[,mod]>sigline.pospos),mod])/length(df.ros[,mod]))*100
values$text.pospos<-paste0("The simple slope of ",input$foc," on ",input$dv," is significant and positive when ",input$mod," is ",sigline.pospos," standard deviations away from the mean or further. ",round(pct.pospos,2),"% of observations in ",input$mod," are within this region.\n")
}
}
values$rostext<-paste0(values$text.negneg,"\n",values$text.negpos,"\n",values$text.posneg,"\n",values$text.pospos)
values$rosplot<-rosplot
plotelements$rosplot<-rosplot + xlab("Moderator") + ylab("Simple Slope of Focal Predictor")
}
# Creating plot data (simple slope estimates and confidence regions) for each small multiple --------------
focal.seq <- sort(c(-1*sd(data[,foc],na.rm=T),
mean(data[,foc],na.rm=T),
1*sd(data[,foc],na.rm=T),
seq(min(data[,foc], na.rm = TRUE),
max(data[,foc], na.rm = TRUE),
.1*sd(data[,foc],na.rm=T))))
if(values$co <= max(focal.seq) & values$co >= min(focal.seq)){
focal.seq<-sort(c(focal.seq,values$co))
}
if (input$scalez == "raw"){
sm1<-mean(dftrue[,mod], na.rm=TRUE) + as.numeric(input$sm1)*(sd(dftrue[,mod], na.rm=TRUE))
sm2<-mean(dftrue[,mod], na.rm=TRUE) + as.numeric(input$sm2)*(sd(dftrue[,mod], na.rm=TRUE))
sm3<-mean(dftrue[,mod], na.rm=TRUE) + as.numeric(input$sm3)*(sd(dftrue[,mod], na.rm=TRUE))
sm4<-mean(dftrue[,mod], na.rm=TRUE) + as.numeric(input$sm4)*(sd(dftrue[,mod], na.rm=TRUE))
sm5<-mean(dftrue[,mod], na.rm=TRUE) + as.numeric(input$sm5)*(sd(dftrue[,mod], na.rm=TRUE))
}
if (input$scalez == "cen"){
sm1<-as.numeric(input$sm1)*(sd(scale(dftrue[,mod],scale=F,center=T), na.rm=TRUE))
sm2<-as.numeric(input$sm2)*(sd(scale(dftrue[,mod],scale=F,center=T), na.rm=TRUE))
sm3<-as.numeric(input$sm3)*(sd(scale(dftrue[,mod],scale=F,center=T), na.rm=TRUE))
sm4<-as.numeric(input$sm4)*(sd(scale(dftrue[,mod],scale=F,center=T), na.rm=TRUE))
sm5<-as.numeric(input$sm5)*(sd(scale(dftrue[,mod],scale=F,center=T), na.rm=TRUE))
}
if (input$scalez == "std"){
sm1<-as.numeric(input$sm1)*(sd(scale(dftrue[,mod],scale=T,center=T), na.rm=TRUE))
sm2<-as.numeric(input$sm2)*(sd(scale(dftrue[,mod],scale=T,center=T), na.rm=TRUE))
sm3<-as.numeric(input$sm3)*(sd(scale(dftrue[,mod],scale=T,center=T), na.rm=TRUE))
sm4<-as.numeric(input$sm4)*(sd(scale(dftrue[,mod],scale=T,center=T), na.rm=TRUE))
sm5<-as.numeric(input$sm5)*(sd(scale(dftrue[,mod],scale=T,center=T), na.rm=TRUE))
#NOTE: the sd of the scaled moderator will be 1, so (e.g.) sm1<-as.numeric(input$sm1)*1.
}
xi<-rep(0,length(values$m$coefficients))
matxi1<-as.data.frame(matrix(xi,length(focal.seq),length(xi)))
names(matxi1)<-names(values$m$coefficients)
if(length(covars)==1){
covmeans<-mean(data[,covars],na.rm=TRUE)
matxi1[,covars]<-rep(covmeans,length(focal.seq))
values$covmeans<-covmeans
}
else if (length(covars)>1){
covmeans<-lapply(data[,covars],mean, na.rm=TRUE)
for (i in 1:length(covmeans)){matxi1[,covars[i]]<-rep(covmeans[i],ncol(matxi1))}
values$covmeans<-covmeans
}
matxi1[,foc]<-focal.seq
matxi1$`(Intercept)`<-rep(1,nrow(matxi1))
values$matxi1<-matxi1
matxi2<-matxi3<-matxi4<-matxi5<-matxi1
if(input$cat ==TRUE){
mod.seq<-sort(unique(data[,mod]))
matxi1[,mod]<-rep(mod.seq[1],nrow(matxi1))
matxi2[,mod]<-rep(mod.seq[2],nrow(matxi2))
matxi<-rbind(matxi1,matxi2)
}
else{
mod.seq <- c(as.numeric(sm1),
as.numeric(sm2),
as.numeric(sm3),
as.numeric(sm4),
as.numeric(sm5))
matxi1[,mod]<-rep(mod.seq[1],nrow(matxi1))
matxi2[,mod]<-rep(mod.seq[2],nrow(matxi2))
matxi3[,mod]<-rep(mod.seq[3],nrow(matxi3))
matxi4[,mod]<-rep(mod.seq[4],nrow(matxi4))
matxi5[,mod]<-rep(mod.seq[5],nrow(matxi5))
matxi<-rbind(matxi1,matxi2,matxi3,matxi4,matxi5)
}
mod.input <- c(as.numeric(input$sm1),
as.numeric(input$sm2),
as.numeric(input$sm3),
as.numeric(input$sm4),
as.numeric(input$sm5))
matxi[,paste0(foc,":",mod)]<-matxi[,foc]*as.numeric(matxi[,mod])
Xmat<-as.data.frame(cbind(rep(1,nrow(values$m$model)),
values$m$model[,-1]))
colnames(Xmat)<-c("(Intercept)",colnames(values$m$model[,-1]))
Xmat[,paste0(foc,":",mod)]<-(Xmat[,foc])*(as.numeric(Xmat[,mod]))
if(input$poly == "quad") {
matxi[,paste0("I(",foc,"^2)")]<-matxi[,foc]*matxi[,foc]
matxi[,paste0("I(",foc,"^2):",mod)]<-matxi[,foc]*matxi[,foc]*as.numeric(matxi[,mod])
Xmat[,paste0("I(",foc,"^2)")]<-(Xmat[,foc])*(Xmat[,foc])
Xmat[,paste0("I(",foc,"^2):",mod)]<-as.numeric(Xmat[,mod])*(Xmat[,foc])*(Xmat[,foc])
}
Xmat<-as.matrix(Xmat)
lapply(Xmat,as.numeric)
values$matxi<-plotelements$matxi<-matxi
values$Xmat<-plotelements$Xmat<-Xmat
matquant<-solve((t(Xmat) %*% Xmat))
values$matquant<-matquant
y<-values$m$model[,1]
A<-solve((t(Xmat) %*% Xmat))
values$A<-A
beta <- A %*% t(Xmat) %*% y
values$beta<-beta
resids <- y - (Xmat %*% beta)
values$resids
sig<-sqrt(sum(resids^2) / (nrow(Xmat)-ncol(Xmat)))
values$sig
pes<-as.matrix(matxi)%*%beta
values$pes<-pes
SEys<-diag(sig*(sqrt(as.matrix(matxi)%*%matquant%*%t(as.matrix(matxi)))))#Warning is generated because of square-rooting negative values in the off-diagonals of the covariance matrix. This is OK since we are only taking the diagonals of this matrix, which will always be positive.
fac <- qt(.975,values$m$df.residual)
ylower<-pes - fac*SEys
yupper<-pes + fac*SEys
df.staticplot<-as.data.frame(cbind(focal.seq,matxi[,mod],pes,ylower,yupper))
names(df.staticplot)<-c("focal.seq","modlevel","pe","lower","upper")
df.staticplot<-as.data.frame(df.staticplot)
pval<-level<-list()
data$s.foc<-data[,foc]
#percentiles corresponding with each small multiple value
perc.sm1<-ecdf(scale(data[,mod]))(as.numeric(input$sm1))
perc.sm2<-ecdf(scale(data[,mod]))(as.numeric(input$sm2))
perc.sm3<-ecdf(scale(data[,mod]))(as.numeric(input$sm3))
perc.sm4<-ecdf(scale(data[,mod]))(as.numeric(input$sm4))
perc.sm5<-ecdf(scale(data[,mod]))(as.numeric(input$sm5))
percentiles<-100*c(perc.sm1,perc.sm2,perc.sm3,perc.sm4,perc.sm5)
for (k in 1:length(mod.seq)){
# data$s.mod<-data[,mod]-mod.seq[k]
if (input$scalez == "raw"){data$s.mod<-dftrue[,mod]-mod.seq[k]}
if (input$scalez == "cen"){data$s.mod<-scale(dftrue[,mod],center=T,scale=F)-mod.seq[k]}
if (input$scalez == "std"){data$s.mod<-scale(dftrue[,mod],scale=T)-mod.seq[k]}
lm<-lm(values$form2,data, na.action = na.omit)
pval[[k]]<-rep(summary(lm)$coefficients["s.foc","Pr(>|t|)"],length(focal.seq))
if(input$poly=="lin"){
leveltemp<-rep(paste0("\nb = ",
round(lm$coefficients["s.foc"],2),"\n95% CI = [",
round(confint(lm)["s.foc","2.5 %"],2),", ",
round(confint(lm)["s.foc","97.5 %"],2),"]"),length(focal.seq))
}else(leveltemp<-rep("",length(focal.seq)))
if(input$cat==TRUE){level[[k]]<-paste0("Category ",mod.seq[k],leveltemp)}
else(level[[k]]<-paste0(mod.input[k]," SD (PTCL = ",round(percentiles[k],2),")",leveltemp))
# else(level[[k]]<-paste0(mod.seq[k]/sd(data[,mod],na.rm=TRUE)," SD (PTCL = ",round(percentiles[k],2),")",leveltemp))
# else{
# if (input$scalez == "raw"){
# level[[k]]<-paste0(round(mod.seq[k],2)," (PTCL = ",round(percentiles[k],2),")",leveltemp)
# }
# if (input$scalez == "cen" | input$scalez == "std"){
# level[[k]]<-paste0(mod.seq[k]/sd(data[,mod],na.rm=TRUE)," SD (PTCL = ",round(percentiles[k],2),")",leveltemp)
# }
# }
}
df.staticplot$p.val<-do.call(c,pval)
df.staticplot$level<-do.call(c,level)
values$df.staticplot<- df.staticplot
#Define bluescale and greyscale color palletes
bluespal<-c("#08306B","#2171B5","#4292C6","#9ECAE1","#C6DBEF")
greyspal<-c("#000000","#252525","#525252","#737373","#969696")
names(bluespal)<-c("less001","less01","less05","less10","greater10")
names(greyspal)<-c("less001","less01","less05","less10","greater10")
if(input$poly == "quad"){
df.staticplot$col<-rep(bluespal["less05"],nrow(df.staticplot))
df.staticplot$greycol<-rep(greyspal["less05"],nrow(df.staticplot))
}
else{
df.staticplot$col[df.staticplot$p.val>.10]<-bluespal["greater10"]
df.staticplot$col[df.staticplot$p.val<=.10 & df.staticplot$p.val>.05]<-bluespal["less10"]
df.staticplot$col[df.staticplot$p.val<=.05 & df.staticplot$p.val>.01]<-bluespal["less05"]
df.staticplot$col[df.staticplot$p.val<=.01 & df.staticplot$p.val>.001]<-bluespal["less01"]
df.staticplot$col[df.staticplot$p.val<=.001]<-bluespal["less001"]
df.staticplot$greycol[df.staticplot$p.val>.10]<-greyspal["greater10"]
df.staticplot$greycol[df.staticplot$p.val<=.10 & df.staticplot$p.val>.05]<-greyspal["less10"]
df.staticplot$greycol[df.staticplot$p.val<=.05 & df.staticplot$p.val>.01]<-greyspal["less05"]
df.staticplot$greycol[df.staticplot$p.val<=.01 & df.staticplot$p.val>.001]<-greyspal["less01"]
df.staticplot$greycol[df.staticplot$p.val<=.001]<-greyspal["less001"]
}
values$dfplot<-plotelements$dfplot<-df.staticplot
# End Plot Data Creation Code ---------------------------------------------
# Creating a dataframe containing observed data for each small multiple---------------------------
modindex<- as.data.frame(unique(df.staticplot[c("modlevel","p.val","level")]))
values$modindex<-modindex
#Define a reduced data frame for display of observed data
dfpoints<-as.data.frame(cbind(data[,foc],glm(values$form,data, na.action = na.omit,family="gaussian")$y,m$fitted.values))
colnames(dfpoints)<-c("pred","y","fitted")
if(input$cat==FALSE){
modindex$scalelevel<-c(input$sm1,input$sm2,input$sm3,input$sm4,input$sm5)
divides<-c(((as.numeric(input$sm2) - as.numeric(input$sm1))/2),
((as.numeric(input$sm3) - as.numeric(input$sm2))/2),
((as.numeric(input$sm4) - as.numeric(input$sm3))/2),
((as.numeric(input$sm5) - as.numeric(input$sm4))/2))
divides<-abs(divides)
values$divides<-divides
#Compute percentile scores corresponding with each small multiple
perc.div1<-ecdf(scale(data[,mod]))(as.numeric(input$sm1)+divides[1])
perc.div2<-ecdf(scale(data[,mod]))(as.numeric(input$sm2)+divides[2])
perc.div3<-ecdf(scale(data[,mod]))(as.numeric(input$sm3)+divides[3])
perc.div4<-ecdf(scale(data[,mod]))(as.numeric(input$sm4)+divides[4])
dfpoints$p.val[(rank(data[,mod])/length(data[,mod]))<perc.div1]<-round(modindex$p.val[which(modindex$modlevel== sm1)],3)
dfpoints$p.val[(rank(data[,mod])/length(data[,mod]))<perc.div2 & (rank(data[,mod])/length(data[,mod]))>=perc.div1] <-round(modindex$p.val[which(modindex$modlevel== sm2)],3)
dfpoints$p.val[(rank(data[,mod])/length(data[,mod]))<perc.div3 & (rank(data[,mod])/length(data[,mod]))>=perc.div2]<-round(modindex$p.val[which(modindex$modlevel== sm3)],3)
dfpoints$p.val[(rank(data[,mod])/length(data[,mod]))<perc.div4 & (rank(data[,mod])/length(data[,mod]))>=perc.div3]<-round(modindex$p.val[which(modindex$modlevel== sm4)],3)
dfpoints$p.val[(rank(data[,mod])/length(data[,mod]))>perc.div4]<-round(modindex$p.val[which(modindex$modlevel== sm5)],3)
#SM1
dfpoints$level[(rank(data[,mod])/length(data[,mod]))<perc.div1]<-modindex$level[which(modindex$modlevel== sm1)]
#SM2
dfpoints$level[(rank(data[,mod])/length(data[,mod]))<perc.div2 & (rank(data[,mod])/length(data[,mod]))>=perc.div1]<-modindex$level[which(modindex$modlevel== sm2)]
#SM3
dfpoints$level[(rank(data[,mod])/length(data[,mod]))<perc.div3 & (rank(data[,mod])/length(data[,mod]))>=perc.div2]<-modindex$level[which(modindex$modlevel== sm3)]
#SM4
dfpoints$level[(rank(data[,mod])/length(data[,mod]))<perc.div4 & (rank(data[,mod])/length(data[,mod]))>=perc.div3]<-modindex$level[which(modindex$modlevel== sm4)]
#SM5
dfpoints$level[(rank(data[,mod])/length(data[,mod]))>perc.div4]<-modindex$level[which(modindex$modlevel== sm5)]
dfpoints<-na.omit(dfpoints)
dfpoints$level <- factor(dfpoints$level, levels = c(modindex$level[which(modindex$modlevel== sm1)],
modindex$level[which(modindex$modlevel== sm2)],
modindex$level[which(modindex$modlevel== sm3)],
modindex$level[which(modindex$modlevel== sm4)],
modindex$level[which(modindex$modlevel== sm5)]))
df.staticplot$level <- factor(df.staticplot$level, levels = c(modindex$level[which(modindex$modlevel== sm1)],
modindex$level[which(modindex$modlevel== sm2)],
modindex$level[which(modindex$modlevel== sm3)],
modindex$level[which(modindex$modlevel== sm4)],
modindex$level[which(modindex$modlevel== sm5)]))
}
else{
dfpoints$modlevel<-data[,mod]
modindex$scalelevel<-c(mod.seq[1],mod.seq[2])
dfpoints$level[dfpoints$modlevel==mod.seq[1]]<-modindex$level[which(modindex$scalelevel==mod.seq[1])]
dfpoints$level[dfpoints$modlevel==mod.seq[2]]<-modindex$level[which(modindex$scalelevel==mod.seq[2])]
dfpoints<-na.omit(dfpoints)
}
values$dfpoints<-plotelements$plotpoints<-dfpoints
df.colstemp<-df.staticplot[c("level","col","greycol")]
df.cols<-df.colstemp[!duplicated(df.colstemp$level), ]
df.cols$colslight<-sapply(df.cols$col, function(i) {df.cols$col<- lighten(i,pct=0.50)})
df.cols$greycolslight<-sapply(df.cols$greycol, function(i) {df.cols$greycol<- lighten(i,pct=0.50)})
df.cols<-as.data.frame(df.cols)
values$df.cols<-df.cols
# End data display code ---------------------------------------------------
#Specify a small multiples graphic based on the estimated point estimates and confidence regions (df.staticplot) as well as the observed data (dfpoints).
staticplot<-interactive.plot(data=data,dfpoints=dfpoints,plotdf = df.staticplot)
#Define a glyph of the crossover point if it exists within the observed data
if(values$co <= max(df.staticplot$focal.seq) & values$co >= min(df.staticplot$focal.seq))
{
staticplot <- staticplot +
annotate("point", x = values$co, y =
(df.staticplot[which(df.staticplot$focal.seq == values$co),"pe"]),
color= "black", fill = brewer.pal(9,"Greys")[2], size = 2, shape = 23)
}
values$plot<-plotelements$smplot<-staticplot; staticplot
# }#end of else statement for defining static plots
# }#End of else statement defining continuous moderator static plot
})#signifies end of output$modplot
#Read the above-defined elements into output in Shiny
output$results <- renderTable({
if (is.null(input$file1))
return(NULL)
values$resultstable
})
output$resultstitle<-reactive({
if (is.null(input$file1))
return("")
else (paste("Model Coefficients Summary"))
})
output$downloadPlot <- downloadHandler(
filename = 'ModerationPlot.png',
content = function(file) {
ggsave(file,values$plot, width = 7, height = 4, units = "in")
})
output$downloadPlot.final <- downloadHandler(
filename = 'ModerationPlot.final.png',
content = function(file) {
ggsave(file,values$plotfinal, width = 7, height = 4, units = "in")
})
output$rosplot.final <- downloadHandler(
filename = 'rosplot.png',
content = function(file) {
ggsave(file,values$plotfinal.ros,width = 7, height = 4, units = "in")
})
})