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Merged
ehrlinger merged 1 commit into from
Jun 25, 2025
Merged

remove cbind #56

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8 changes: 4 additions & 4 deletions R/gg_partial.R
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Original file line number Diff line number Diff line change
Expand Up @@ -8,24 +8,24 @@
#' multiple partial plot objects in figures.
#'
#' @export
gg_partial = function(part_dta,

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file=R/gg_partial.R,line=11,col=12,[assignment_linter] Use one of <-, <<- for assignment, not =.
nvars = NULL,
cat_limit = 10,
model = NULL) {
## Prepare the partial dependencies data for panel plots
if (is.null(nvars)) {
nvars = length(part_dta$plotthis)

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file=R/gg_partial.R,line=17,col=11,[assignment_linter] Use one of <-, <<- for assignment, not =.
}

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file=R/gg_partial.R,line=19,col=1,[trailing_whitespace_linter] Remove trailing whitespace.
cont_list = list()

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file=R/gg_partial.R,line=20,col=13,[assignment_linter] Use one of <-, <<- for assignment, not =.
cat_list = list()
for (feature in seq(nvars)) {
## Format any continuous features (those with fewer than cat_limit unique values)
if (length(unique(part_dta$plotthis[[feature]]$x)) > cat_limit) {
plt.df = as.data.frame(cbind(
plt.df = dplyr::bind_cols(
x = part_dta$plotthis[[feature]]$x,
yhat = part_dta$plotthis[[feature]]$yhat
))
)
plt.df$name = names(part_dta$plotthis)[[feature]]

cont_list[[feature]] <- plt.df
Expand All @@ -34,10 +34,10 @@

## Though VarPro works with logical or continuous only. Factors are
## one hot encoded internal to the varPro call.
plt.df = as.data.frame(cbind(
plt.df = dplyr::bind_cols(
x = factor(part_dta$plotthis[[feature]]$x),
yhat = part_dta$plotthis[[feature]]$yhat
))
)
plt.df$name = names(part_dta$plotthis)[[feature]]

cat_list[[feature]] <- plt.df
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29 changes: 13 additions & 16 deletions R/gg_partialpro.R
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Original file line number Diff line number Diff line change
Expand Up @@ -24,17 +24,16 @@ gg_partialpro = function(part_dta,
for (feature in seq(nvars)) {
## Format any continuous features (those with fewer than 10 unique values)
if (length(part_dta[[feature]]$xvirtual) > cat_limit) {
plt.df = as.data.frame(
cbind(
variable = part_dta[[feature]]$xvirtual,
parametric = colMeans(part_dta[[feature]]$yhat.par, na.rm =
TRUE),
nonparametric = colMeans(part_dta[[feature]]$yhat.nonpar, na.rm =
TRUE),
causal = colMeans(part_dta[[feature]]$yhat.causal, na.rm =
TRUE)
)
plt.df = dplyr::bind_cols(
variable = part_dta[[feature]]$xvirtual,
parametric = colMeans(part_dta[[feature]]$yhat.par, na.rm =
TRUE),
nonparametric = colMeans(part_dta[[feature]]$yhat.nonpar, na.rm =
TRUE),
causal = colMeans(part_dta[[feature]]$yhat.causal, na.rm =
TRUE)
)

plt.df$name = names(part_dta)[[feature]]

cont_list[[feature]] <- plt.df
Expand All @@ -46,12 +45,10 @@ gg_partialpro = function(part_dta,
cat_feat = list()
## Each yhat has at least 2 columns, for logical values...
for (ind in seq(length(unique(part_dta[[feature]]$xorg)))) {
cat_feat[[ind]] = as.data.frame(
cbind(
parametric = part_dta[[feature]]$yhat.par[, ind],
nonparametric = part_dta[[feature]]$yhat.nonpar[, ind],
causal = part_dta[[feature]]$yhat.causal[, ind]
)
cat_feat[[ind]] = dplyr::bind_cols(
parametric = part_dta[[feature]]$yhat.par[, ind],
nonparametric = part_dta[[feature]]$yhat.nonpar[, ind],
causal = part_dta[[feature]]$yhat.causal[, ind]
)
cat_feat[[ind]]$variable <-
unique(part_dta[[feature]]$xorg)[ind]
Expand Down
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