#'@title Plots design diagnostics
#'
#'@description Plots design diagnostics
#'
#'@param genoutput The output of either gen_design or eval_design/eval_design_mc
#'@param model Default `NULL`. Defaults to the model used in generating/evaluating
#'the design, augmented with 2-factor interactions. If specified, it will override the default
#'model used to generate/evaluate the design.
#'@param customcolors A vector of colors for customizing the appearance of the colormap
#'@param pow Default 2. The interaction level that the correlation map is showing.
#'@param custompar Default NULL. Custom parameters to pass to the `par` function for base R plotting.
#'@param standardize Default `TRUE`. Whether to standardize (scale to -1 and 1 and center) the continuous numeric columns. Not
#'standardizing the numeric columns can increase multi-collinearity (predictors that are correlated with other predictors in the model).
#'@param plot Default `TRUE`. If `FALSE`, this will return the correlation matrix.
#'@return Silently returns the correlation matrix with the proper row and column names.
#'@import graphics grDevices
#'@export
#'@examples
#'#We can pass either the output of gen_design or eval_design to plot_correlations
#'#in order to obtain the correlation map. Passing the output of eval_design is useful
#'#if you want to plot the correlation map from an externally generated design.
#'
#'#First generate the design:
#'
#'candidatelist = expand.grid(cost = c(15000, 20000), year = c("2001", "2002", "2003", "2004"),
#' type = c("SUV", "Sedan", "Hybrid"))
#'cardesign = gen_design(candidatelist, ~(cost+type+year)^2, 30)
#'plot_correlations(cardesign)
#'
#'#We can also increase the level of interactions that are shown by default.
#'
#'plot_correlations(cardesign, pow = 3)
#'
#'#You can also pass in a custom color map.
#'plot_correlations(cardesign, customcolors = c("blue", "grey", "red"))
#'plot_correlations(cardesign, customcolors = c("blue", "green", "yellow", "orange", "red"))
plot_correlations = function(
genoutput,
model = NULL,
customcolors = NULL,
pow = 2,
custompar = NULL,
standardize = TRUE,
plot = TRUE
) {
#Remove skpr-generated REML blocking indicators if present
if (!is.null(attr(genoutput, "splitanalyzable"))) {
if (attr(genoutput, "splitanalyzable")) {
allattr = attributes(genoutput)
remove_cols = which(colnames(genoutput) %in% allattr$splitcolumns)
if (length(remove_cols) > 0) {
genoutput = genoutput[, -remove_cols, drop = FALSE]
allattr$names = allattr$names[-remove_cols]
}
attributes(genoutput) = allattr
}
}
if (!is.null(attr(genoutput, "splitcolumns"))) {
allattr = attributes(genoutput)
genoutput = genoutput[,
!(colnames(genoutput) %in% attr(genoutput, "splitcolumns")),
drop = FALSE
]
allattr$names = allattr$names[
!allattr$names %in% attr(genoutput, "splitcolumns")
]
attributes(genoutput) = allattr
}
if (!is.null(attr(genoutput, "augmented"))) {
if (attr(genoutput, "augmented")) {
allattr = attributes(genoutput)
genoutput = genoutput[,
!(colnames(genoutput) %in% "Block1"),
drop = FALSE
]
allattr$names = allattr$names[!allattr$names %in% "Block1"]
attributes(genoutput) = allattr
}
}
if (is.null(attr(genoutput, "variance.matrix"))) {
genoutput = eval_design(genoutput, model, 0.2)
}
V = attr(genoutput, "variance.matrix")
if (is.null(model)) {
if (!is.null(attr(genoutput, "runmatrix"))) {
variables = paste0("`", colnames(attr(genoutput, "runmatrix")), "`")
} else {
variables = paste0("`", colnames(genoutput), "`")
}
linearterms = paste(variables, collapse = " + ")
linearmodel = paste0(c("~", linearterms), collapse = "")
model1 = as.formula(paste(
c(
linearmodel,
as.character(aliasmodel(as.formula(linearmodel), power = pow)[2])
),
collapse = " + "
))
if (!is.null(attr(genoutput, "generating.model"))) {
modelfactors = colnames(attr(
terms.formula(attr(genoutput, "generating.model")),
"factors"
))
quadmodelfactors = colnames(attr(
terms.formula(model1, "factors"),
"factors"
))
otherterms = modelfactors[!modelfactors %in% quadmodelfactors]
model = as.formula(paste(c(model1, otherterms), collapse = " + "))
} else {
model = model1
}
}
if (!is.null(attr(genoutput, "runmatrix"))) {
genoutput = attr(genoutput, "runmatrix")
}
factornames = colnames(genoutput)[
unlist(lapply(genoutput, class)) %in% c("factor", "character")
]
if (length(factornames) > 0) {
contrastlist = list()
for (name in 1:length(factornames)) {
contrastlist[[factornames[name]]] = contr.simplex
}
} else {
contrastlist = NULL
}
#------Normalize/Center numeric columns ------#
if (standardize) {
for (column in 1:ncol(genoutput)) {
if (is.numeric(genoutput[, column])) {
midvalue = mean(c(max(genoutput[, column]), min(genoutput[, column])))
genoutput[, column] = (genoutput[, column] - midvalue) /
(max(genoutput[, column]) - midvalue)
}
}
}
mm = model.matrix(model, genoutput, contrasts.arg = contrastlist)
#Generate pseudo inverse as it's likely the model matrix will be singular with extra terms
cormat = abs(cov2cor(getPseudoInverse(t(mm) %*% solve(V) %*% mm))[-1, -1])
if (!plot) {
return(cormat)
}
if (is.null(customcolors)) {
imagecolors = viridis::viridis(101)
} else {
imagecolors = colorRampPalette(customcolors)(101)
}
if (is.null(custompar)) {
par(mar = c(5, 3, 7, 0))
} else {
do.call(par, custompar)
}
image(
t(cormat[ncol(cormat):1, , drop = FALSE]),
x = 1:ncol(cormat),
y = 1:ncol(cormat),
zlim = c(0, 1),
asp = 1,
axes = F,
col = imagecolors,
xlab = "",
ylab = ""
)
axis(
3,
at = 1:ncol(cormat),
labels = colnames(mm)[-1],
pos = ncol(cormat) + 1,
las = 2,
hadj = 0,
cex.axis = 0.8
)
axis(
2,
at = ncol(cormat):1,
labels = colnames(mm)[-1],
pos = 0,
las = 2,
hadj = 1,
cex.axis = 0.8
)
legend(
length(colnames(mm)[-1]) + 1,
length(colnames(mm)[-1]),
c("0", "", "", "", "", "0.5", "", "", "", "", "1.0"),
title = "|r|\n",
fill = imagecolors[c(seq(1, 101, 10))],
xpd = TRUE,
bty = "n",
border = NA,
y.intersp = 0.3,
x.intersp = 0.1,
cex = 1
)
par(mar = c(5.1, 4.1, 4.1, 2.1))
results = t(cormat[ncol(cormat):1, , drop = FALSE])
colnames(results) = rev(colnames(mm)[-1])
rownames(results) = colnames(mm)[-1]
invisible(results)
}
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.