## Functions to extract effect information from fitted models
#' Calculate confidence intervals
#'
#' Given 2 column matrix or data frame, returns 3 column data.frame
#' with coefficient estimate plus lower and upper borders of the
#' 95% confidence intervals.
#'
#' @param ftab A table with two columns, containing coefficients in the first
#' column and standard-errors in the second column.
#' @importFrom tibble as_tibble
#' @keywords internal
calc_ci <- function(ftab) {
colnames(ftab) <- c("coef", "se")
rnames <- rownames(ftab)
ftab <- as_tibble(ftab)
ftab$variable <- rnames
ftab$lower <- ftab$coef - 2*ftab$se
ftab$upper <- ftab$coef + 2*ftab$se
ftab$se <- NULL
ftab[, c("variable", "coef", "lower", "upper")]
}
#' Extract fixed coefficient table from model object
#'
#' Given a model object, returns data frame with columns \code{variable},
#' \code{coef} (coefficient), \code{lower} (lower 95\% CI) and
#' \code{upper} (upper 95% CI).
#'
#' @param x A model object.
#' @param ... Currently not used.
#' @export
tidy_fixed <- function(x, ...) {
UseMethod("tidy_fixed", x)
}
#' @rdname tidy_fixed
#' @inheritParams tidy_fixed
#' @param intercept Should intercept also be returned?
#' @export
tidy_fixed.gam <- function(x, intercept=FALSE, ...) {
ftab <- summary(x)[["p.table"]][, 1:2]
if(!intercept) {
ftab <- ftab[!grepl("Intercept", rownames(ftab)), ]
}
calc_ci(ftab)
}
#' @rdname tidy_fixed
#' @inheritParams tidy_fixed
#' @importFrom tibble as_tibble
#' @keywords internal
#' @export
tidy_fixed.coxph <- function(x, ...) {
ftab <- summary(x)[["coefficients"]][, c(1, 3)]
calc_ci(ftab)
}
#' Extract 1d smooth objects in tidy data format.
#'
#' @rdname tidiers
#' @inheritParams get_plotinfo
#' @param keep A vector of variables to keep.
#' @param ci A logical value indicating whether confidence intervals should be
#' calculated and returned. Defaults to \code{TRUE}.
#' @importFrom dplyr bind_rows
#' @export
tidy_smooth <- function(
x,
keep = c("x", "fit", "se", "xlab", "ylab"),
ci = TRUE,
...) {
po <- get_plotinfo(x, ...)
# index of list elements that are 1d smooths and not random effects
ind1d <- vapply(
X = po,
FUN = function(z) !is.null(z[["x"]]) & is.null(z[["main"]]),
FUN.VALUE = logical(1))
# keep only variables of interest
po <- lapply(po[ind1d], "[", i=keep, drop=TRUE)
# transform to data.frame
po <- lapply(po, function(z) {
z[["fit"]] <- as.vector(z[["fit"]])
temp <- as_tibble(z)
if(ci) {
temp %<>% mutate(
low = fit - se,
high = fit + se)
}
temp
})
return(bind_rows(po))
}
#' Extract 2d smooth objects in tidy format.
#'
#' @inheritParams tidy_smooth
#' @importFrom purrr cross_df
#' @importFrom tibble as_tibble
#' @import dplyr
#' @export
tidy_smooth2d <- function(
x,
keep = c("x", "y", "fit","se", "xlab", "ylab", "main"),
ci = FALSE,
...) {
po <- get_plotinfo(x, ...)
ind2d <- vapply(
X = po,
FUN = function(z) !is.null(z[["x"]]) & !is.null(z[["y"]]),
FUN.VALUE = logical(1))
# keep only variables of interes
po <- lapply(po[ind2d], "[", i=keep, drop=TRUE)
# transform to data.frame
po <- lapply(po, function(z) {
z[["fit"]] <- as.vector(z[["fit"]])
p1 <- as_tibble(z[setdiff(keep, c("x", "y"))])
xy <- cross_df(z[c("x", "y")])
xy <- bind_cols(xy, p1)
if(ci) {
xy %<>% mutate(
low = fit - se,
high = fit + se)
}
xy
})
return(bind_rows(po))
}
#' Extract random effects objects in tidy data format.
#'
#' @inheritParams tidy_smooth
#' @importFrom dplyr bind_rows
#' @importFrom stats ppoints qnorm quantile
#' @rdname tidy_smooth
#' @seealso \code{\link[stats]{qqline}}
#' @export
tidy_re <- function(x, keep=c("fit", "main", "xlab", "ylab"), ...) {
po <- get_plotinfo(x, ...)
ind.re <- vapply(
X = po,
FUN = function(z) !is.null(z[["main"]]) & z[["xlab"]] == "Gaussian quantiles",
FUN.VALUE = logical(1))
po <- lapply(po[ind.re], "[", i=keep, drop=TRUE)
po <- lapply(po, function(z) {
re.df = do.call(cbind.data.frame, c(z, stringsAsFactors=FALSE))
re.df$x = qnorm(ppoints(length(re.df$fit))[order(order(re.df$fit))])
# code to calculate qqslope and qqintercept from ?stats::qqline
yl <- quantile(re.df$fit, probs=c(0.1, 0.9), type=7, names=FALSE)
xl <- qnorm(c(0.1, 0.9))
re.df$qqslope <- diff(yl)/diff(xl)
re.df$qqintercept <- yl[1L] - re.df$qqslope*xl[1L]
re.df
})
return(bind_rows(po))
}
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