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crossvalidation.R
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crossvalidation.R
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# Set up environment -----------------------------------------------------------
## Libraries
"ggplot2" %>=>% libInstall %!=>% library(.., char = T)
"gridExtra" %=>% libInstall
"lme4" %>=>% libInstall %!=>% library(.., char = T)
"extrafont" %>=>% libInstall %!=>% library(.., char = T)
"pbapply" %>=>% libInstall %!=>% library(.., char = T)
## Import fonts for plots
if (!"Open Sans" %in% fonts()) {
font_import(
path = "FONT_PATH",
pattern = "OpenSans",
prompt = F
)
}
# Final model validation -------------------------------------------------------
## Final model with coraviates
final = function(.model, ...) {
targ = .model@resp$y
pred = fitted(.model)
accu = modelAccuracy(targ, pred, ...)
p = plotRoc(targ, pred, title = paste("Final model"), ...)
list(accu, p)
}
## Final model without covariates
finalNoCov = function(.model, ...) {
targ = .model@resp$y
pred = predict(.model, model.frame(.model), type = "resp", re.form = NA)
accu = modelAccuracy(targ, pred, ...)
p = plotRoc(targ, pred, title = paste("Fixed effects model"), ...)
list(accu, p)
}
# Cross validation models ------------------------------------------------------
## Leave-p-out internal cross validation
leavePOut = function(.model, p = 10, ...) {
data = randomize(model.frame(.model), ...)
ncv = split(rownames(data), seq_len(nrow(data)) %% ceiling(nrow(data) / p))
df = trainModel(.model, data, ncv[1:10], "leave-p-out")
accu = modelAccuracy(df$targ, df$pred)
p = plotRoc(df$targ, df$pred, paste0("Leave-", p, "-out CV"))
list(accu, p)
}
# k-fold internal cross validation
kFold = function(.model, k = 10, ...) {
data = randomize(model.frame(.model), ...)
ncv = split(rownames(data), seq_len(nrow(data)) %% k)
df = trainModel(.model, data, ncv, "k-fold")
accu = modelAccuracy(df$targ, df$pred)
p = plotRoc(df$targ, df$pred, title = paste0(k, "-fold CV"))
list(accu, p)
}
## external cross validation
extVal = function(.model, .data, ...) {
.data = .data[, colnames(.data) %in% colnames(model.frame(.model))]
.data = .data[complete.cases(.data), ]
targ = .data[, deparse(formula(.model)[[2]])]
pred = predict(.model, .data, type = "resp",
allow.new.levels = T, re.form = NA)
accu = modelAccuracy(targ, pred, ...)
p = plotRoc(targ, pred, title = paste("External CV"), ...)
assign("rocey", p, envir = globalenv())
list(accu, p)
}
# Accuracy and ROC functions ---------------------------------------------------
## Goodness of fit and model accuracy stats
modelAccuracy = function(targ, pred, thres = 0.5, ...) {
if ((n = length(targ)) != length(pred)) stop("targ and pred length vary.")
pos = targ == 1; neg = targ == 0
a = sum(pred >= thres & pos); c = sum(pos) - a
d = sum(pred < thres & neg); b = sum(neg) - d
pExp = ((a + c) * (a + b) + (b + d) * (c + d)) / n^2
mccDen = sqrt(as.numeric((a + b)) * (a + c) * (b + d) * (c + d))
data.frame(
"Sensitivity" = a / sum(pos),
"Specificity" = d / sum(neg),
"Correct class" = (a + d) / n,
"Cohen's kappa" = ((a + d) / n - pExp) / (1 - pExp),
"Matthew's Correlation Coefficient" = (a * d - b * c) / mccDen,
"True Skill Statistic" = (a * d - b * c) / ((a + c) * (b + d)),
check.names = FALSE
)
}
## Receiver Operator Characteristics Curve
plotRoc = function(targ, pred, title = "", bin = 20, ...) {
roc = data.frame(th = seq(0, 1, length.out = bin + 1))
pos = targ == 1; neg = targ == 0
roc %<=>% mutate(..,
tpr = sapply(th, x ->> sum(pred >= x & pos) / sum(pos)),
fpr = sapply(th, x ->> sum(pred >= x & neg) / sum(neg)),
type = title
)
}
## Actual plotting function for ROC
plotROCFunc = function(roc, thres = 0.5, ...) {
roc %=>%
split(.., ..$type) %=>>%
cbind(..,
hline = ..$tpr[..$th == thres],
vline = ..$fpr[..$th == thres],
auc = sum(sapply(2:nrow(..),
.(x, y) ->> (y$tpr[x] + y$tpr[x - 1]) / 2,
..) * -1 * diff(..$fpr)
)
) %=>%
do.call(rbind, ..) %->%
roc
.plot = ggplot(roc, aes(x = fpr, y = tpr, ymin = 0, ymax = tpr)) +
geom_ribbon(fill = "#f0ab7d", alpha = 0.3) +
geom_line(col = "#f55252", size = 1.5) +
geom_point(size = 2, alpha = 0.75) +
coord_fixed() +
geom_line(aes(th, th), col = "blue", size = 1) +
geom_hline(aes(yintercept = hline), linetype = 2) +
geom_vline(aes(xintercept = vline), linetype = 2) +
facet_wrap(. ~ type) +
scale_x_continuous(
"False Positive Rate",
breaks = seq(0, 1, 0.2),
limits = c(0, 1),
expand = expansion(add = 0.01)
) +
scale_y_continuous(
"True Positive Rate",
breaks = seq(0, 1, 0.2),
limits = c(0, 1),
expand = expansion(add = c(0, 0.01))
) +
geom_text(aes(label = paste("AUC =", round(auc, 2))),
size = 10, x = .65, y = .2) +
theme_classic() +
theme(
plot.title = element_text(hjust = 0.5, size = 0),
text = element_text(family = "Open Sans"),
axis.title = element_text(size = 20),
axis.text = element_text(size = 16),
strip.background = element_blank(),
panel.spacing.x = unit(4, "lines"),
panel.spacing.y = unit(2, "lines"),
strip.text = element_text(size = 20),
plot.margin = unit(c(1, 2, 1, 1), "lines"),
plot.tag = element_text(size = 22),
plot.background = element_rect(color = "black", size = 2)
)
.grob = ggplotGrob(.plot)
.grob[[1]][[12]] = .grob[[1]][[10]]
.grob[[1]][[13]] = .grob[[1]][[11]]
.grob[[1]][[14]] = .grob[[1]][[16]]
.grob[[1]][[15]] = .grob[[1]][[17]]
.grob
}
# Helper functions -------------------------------------------------------------
## Row randomization for cross validation
randomize = function(.data, seed = 10, ...) {
set.seed(seed)
.data = .data[sample(nrow(.data)), ]
row.names(.data) = NULL
.data
}
## Training for internal validation
trainModel = function(.model, .data, splitList, message = "training") {
message(paste("Processing", message, ":", length(splitList), "iterations."))
pblapply(splitList, x ->> {
glmer(formula(.model), .data[-as.numeric(x), ], family(.model)) %=>%
predict(.., .data[x, ], type = "resp", allow.new.levels = T) %=>%
data.frame(targ = .data[x, 1], pred = ..)
}) %=>%
do.call(rbind, ..) %=>%
..[complete.cases(..), ]
}
# Cross validation parent function ---------------------------------------------
xVal = function(.model, p = NULL, k = NULL, extdata = NULL, noCov = F, ...) {
if (! "glmerMod" %in% class(.model)) stop("Model should be class glmerMod.")
roc = list()
table = data.frame()
if (exists(".model")) {
out = final(.model, ...)
table = rbind(table, "Final model" = out[[1]])
roc[["Final model"]] = out[[2]]
}
if (noCov) {
out = finalNoCov(.model, ...)
table = rbind(table, "Fixed effect model" = out[[1]])
roc[["Fixed effect model"]] = out[[2]]
}
for (i in unique(p)) {
out = leavePOut(.model, i, ...)
table = rbind(table, "Leave-p-out CV" = out[[1]])
roc[[paste0("Leave-", i, "-out CV")]] = out[[2]]
}
for (i in unique(k)) {
out = kFold(.model, i, ...)
table = rbind(table, "k-fold CV" = out[[1]])
roc[[paste0(i, "-fold CV")]] = out[[2]]
}
if (!is.null(extdata)) {
out = extVal(.model, extdata, ...)
table = rbind(table, "External CV" = out[[1]])
roc[["External CV"]] = out[[2]]
}
round(table, 4) %=>%
as.data.frame(t(..)) %=>%
format(.., scientific = F, digits = 4) %=>%
print
do.call(rbind, roc) %=>%
mutate(.., type = factor(type, levels = names(roc))) %=>%
plotROCFunc
}