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covariatePlots.R
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`covariatePlots` <-
function (
data,
cont.cov=NULL,
cat.cov=NULL,
par.list=NULL,
eta.list=NULL,
...
)
{
plots <- list()
stopifnot("ID" %in% names(data))
if(length(cont.cov)) cont.cov <- intersect(cont.cov,names(data))
if(length(cat.cov)) cat.cov <- intersect(cat.cov,names(data))
if(length(par.list)) par.list <- intersect(par.list,names(data))
if(length(eta.list)) eta.list <- intersect(eta.list,names(data))
for(cov in cat.cov)data[[cov]] <- factor(data[[cov]])
data <- data[!duplicated(data$ID),]
#Covariate SPLOM
if (length(cont.cov) >= 2)plots$covSplom <- splom(
data[, cont.cov],
panel = function(x, y) {
panel.splom(x, y)
panel.lines(lowess(x,y))
},
main="Covariate Scatterplots",
xlab="",
pscales=0,
...
)
#Cont vs cat bwpots
if (length(cont.cov) & length(cat.cov)) {
molten <- melt(data,measure.var=cont.cov, id.var=cat.cov)
names(molten)[names(molten)=="variable"] <- "cont"
names(molten)[names(molten)=="value"] <- "y"
plasma <- melt(molten,measure.var=cat.cov)
names(plasma)[names(plasma)=="variable"] <- "cat"
names(plasma)[names(plasma)=="value"] <- "x"
plots$contCat <- bwplot(
y ~ factor(x) | cont + cat,
plasma,
as.table=TRUE,
layout=c(2,2),
horizontal=FALSE,
ylab="continuous covariate",
xlab="categorical covariate",
scales=list(relation="free"),
prepanel=function(x,y,...)prepanel.default.bwplot(factor(x),y,...),
panel=function(x,y,...)panel.bwplot(factor(x),y,...),
main="Continuous Covariates vs. Categorical Covariates",
...
)
}
#ETA SPLOM
if (length(eta.list) >= 2) {
plots$etaSplom <- splom(
data[, eta.list],
panel = function(x, y) {
panel.splom(x, y)
panel.lines(lowess(x,y))
},
main="ETA Scatterplots",
xlab="",
pscales=0,
...
)
}
#Parmater SPLOM
if (length(par.list) >= 2) {
plots$paramSplom <- splom(
data[, par.list],
panel = function(x, y) {
panel.splom(x, y)
panel.lines(lowess(x,y))
},
main="Parameter Scatterplots",
xlab="",
pscales=0,
...
)
}
#ETA Histograms
if(length(eta.list)){
etas <- melt(data,measure.var=eta.list)
plots$etaHist <- histogram(
~ value | variable,
etas,
as.table=TRUE,
layout=c(2,2),
main="Histograms of Etas",
breaks=NULL,
scales=list(relation="free"),
...
)
}
#ETA Densityplots
if(length(eta.list)){
etas <- melt(data,measure.var=eta.list)
plots$etaDens <- densityplot(
~ value | variable,
etas,
as.table=TRUE,
layout=c(2,2),
main="Density of Etas",
scales=list(relation="free"),
...
)
}
#ETA vs Categoricals
if(length(cat.cov) && length(eta.list)){
etas <- melt(data,measure.var=eta.list,id.var=cat.cov)
names(etas)[names(etas)=="variable"] <- "eta"
names(etas)[names(etas)=="value"] <- "delta"
condEtas <- melt(id.var=c("eta","delta"),etas)
plots$etaCat <- bwplot(
delta ~ factor(value) | variable + eta,
condEtas,
as.table=TRUE,
layout=c(2,2),
main="Boxplots of Etas by Categorical Covariate",
horizontal=FALSE,
scales=list(relation="free"),
prepanel=function(x,y,...)prepanel.default.bwplot(factor(x),y,...),
panel=function(x,y,...)panel.bwplot(factor(x),y,...),
ylab="ETA",
xlab="categorical covariate level",
...
)
}
#ETAS vs. Continuous
if (length(cont.cov) && length(eta.list)) {
etas <- melt(data,measure.var=eta.list,id.var=cont.cov)
names(etas)[names(etas)=="variable"] <- "eta"
names(etas)[names(etas)=="value"] <- "delta"
condEtas <- melt(id.var=c("eta","delta"),etas)
plots$etaCont <- xyplot(
delta ~ value | variable + eta,
condEtas,
as.table=TRUE,
layout=c(2,2),
main="Etas vs. Continuous Covariates",
ylab="ETA",
xlab="continuous covariate",
scales=list(relation="free"),
panel=function(x,y,...){
panel.xyplot(x,y,...)
panel.abline(h=0)
panel.lines(lowess(x,y),lty=2,col="red",...)
},
...
)
}
plots
}
`cwresPlots` <-
function (
data,
cont.cov=NULL,
cat.cov=NULL,
variant=NULL,
...
)
{
res <- .resvar()
if(is.null(variant)) variant <- res
variant <- intersect(variant, names(data))
plots <- list()
if(!length(variant))return(plots)
if(length(variant)>1)return( # recursive reduction to a single variant
c(
cwresPlots(
data=data,
cont.cov=cont.cov,
cat.cov=cat.cov,
variant=variant[-length(variant)],
...
),
cwresPlots(
data=data,
cont.cov=cont.cov,
cat.cov=cat.cov,
variant=variant[length(variant)],
...
)
)
)
#If we got here, we have only one variant.
if(length(cont.cov)) cont.cov <- intersect(cont.cov,names(data))
if(length(cat.cov)) cat.cov <- intersect(cat.cov,names(data))
for(cov in cat.cov)data[[cov]] <- factor(data[[cov]])
names(data)[names(data)==variant] <- '.res' # canonical name for easy melt formulas
#variant vs. Categoricals
if(length(cat.cov)){
res <- melt(data,id.var='.res',measure.var=cat.cov)
plots[[glue(variant,'Cat')]] <- bwplot(
.res ~ factor(value) | variable,
res,
as.table=TRUE,
layout=c(2,2),
main=paste(variant,"vs. Categorical Covariates"),
xlab="categorical Covariate",
ylab=variant,
scales=list(relation="free"),
prepanel=function(x,y,...)prepanel.default.bwplot(factor(x),y,...),
panel=function(x,y,...)panel.bwplot(factor(x),y,...),
...
)
}
#variant vs. Continuous
if(length(cont.cov)){
res <- melt(data,id.var='.res',measure.var=cont.cov)
plots[[glue(variant,'Cont')]] <- xyplot(
.res ~ value | variable,
res,
as.table=TRUE,
layout=c(2,2),
main=paste(variant,"vs. Continuous Covariates"),
xlab="continuous covariate",
ylab=variant,
scales=list(relation="free"),
panel=function(x,y,...){
panel.xyplot(x,y,...)
panel.abline(h=0)
panel.lines(lowess(x,y),lty=2,col="red",...)
},
...
)
}
plots
}