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#' @importFrom nlme intervals
#' @export
nlme::intervals
#' Confidence intervals for parameters in saem.mmkin objects
#'
#' @param object The fitted saem.mmkin object
#' @param level The confidence level. Must be the default of 0.95 as this is what
#' is available in the saemix object
#' @param backtransform In case the model was fitted with mkin transformations,
#' should we backtransform the parameters where a one to one correlation
#' between transformed and backtransformed parameters exists?
#' @return An object with 'intervals.saem.mmkin' and 'intervals.lme' in the
#' class attribute
#' @export
intervals.saem.mmkin <- function(object, level = 0.95, backtransform = TRUE, ...)
{
if (!identical(level, 0.95)) {
stop("Confidence intervals are only available for a level of 95%")
}
mod_vars <- names(object$mkinmod$diffs)
pnames <- names(object$mean_dp_start)
# Confidence intervals are available in the SaemixObject, so
# we just need to extract them and put them into a list modelled
# after the result of nlme::intervals.lme
conf.int <- object$so@results@conf.int
rownames(conf.int) <- conf.int$name
colnames(conf.int)[2] <- "est."
confint_trans <- as.matrix(conf.int[pnames, c("lower", "est.", "upper")])
# Fixed effects
# In case objects were produced by earlier versions of saem
if (is.null(object$transformations)) object$transformations <- "mkin"
if (object$transformations == "mkin" & backtransform) {
bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod,
object$transform_rates, object$transform_fractions)
bpnames <- names(bp)
# Transform boundaries of CI for one parameter at a time,
# with the exception of sets of formation fractions (single fractions are OK).
f_names_skip <- character(0)
for (box in mod_vars) { # Figure out sets of fractions to skip
f_names <- grep(paste("^f", box, sep = "_"), pnames, value = TRUE)
n_paths <- length(f_names)
if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names)
}
confint_back <- matrix(NA, nrow = length(bp), ncol = 3,
dimnames = list(bpnames, colnames(confint_trans)))
confint_back[, "est."] <- bp
for (pname in pnames) {
if (!pname %in% f_names_skip) {
par.lower <- confint_trans[pname, "lower"]
par.upper <- confint_trans[pname, "upper"]
names(par.lower) <- names(par.upper) <- pname
bpl <- backtransform_odeparms(par.lower, object$mkinmod,
object$transform_rates,
object$transform_fractions)
bpu <- backtransform_odeparms(par.upper, object$mkinmod,
object$transform_rates,
object$transform_fractions)
confint_back[names(bpl), "lower"] <- bpl
confint_back[names(bpu), "upper"] <- bpu
}
}
confint_ret <- confint_back
} else {
confint_ret <- confint_trans
}
attr(confint_ret, "label") <- "Fixed effects:"
# Random effects
ranef_ret <- as.matrix(conf.int[paste0("SD.", pnames), c("lower", "est.", "upper")])
rownames(ranef_ret) <- paste0(gsub("SD\\.", "sd(", rownames(ranef_ret)), ")")
attr(ranef_ret, "label") <- "Random effects:"
# Error model
enames <- if (object$err_mod == "const") "a.1" else c("a.1", "b.1")
err_ret <- as.matrix(conf.int[enames, c("lower", "est.", "upper")])
res <- list(
fixed = confint_ret,
random = ranef_ret,
errmod = err_ret
)
class(res) <- c("intervals.saemix.mmkin", "intervals.lme")
attr(res, "level") <- level
return(res)
}
#' Confidence intervals for parameters in nlmixr.mmkin objects
#'
#' @param object The fitted saem.mmkin object
#' @param level The confidence level.
#' @param backtransform Should we backtransform the parameters where a one to
#' one correlation between transformed and backtransformed parameters exists?
#' @importFrom nlme intervals
#' @return An object with 'intervals.saem.mmkin' and 'intervals.lme' in the
#' class attribute
#' @export
intervals.nlmixr.mmkin <- function(object, level = 0.95, backtransform = TRUE, ...)
{
# Fixed effects
mod_vars <- names(object$mkinmod$diffs)
conf.int <- confint(object$nm)
dpnames <- setdiff(rownames(conf.int), names(object$mean_ep_start))
ndp <- length(dpnames)
confint_trans <- as.matrix(conf.int[dpnames, c(3, 1, 4)])
colnames(confint_trans) <- c("lower", "est.", "upper")
if (backtransform) {
bp <- backtransform_odeparms(confint_trans[, "est."], object$mkinmod,
object$transform_rates, object$transform_fractions)
bpnames <- names(bp)
# Transform boundaries of CI for one parameter at a time,
# with the exception of sets of formation fractions (single fractions are OK).
f_names_skip <- character(0)
for (box in mod_vars) { # Figure out sets of fractions to skip
f_names <- grep(paste("^f", box, sep = "_"), dpnames, value = TRUE)
n_paths <- length(f_names)
if (n_paths > 1) f_names_skip <- c(f_names_skip, f_names)
}
confint_back <- matrix(NA, nrow = length(bp), ncol = 3,
dimnames = list(bpnames, colnames(confint_trans)))
confint_back[, "est."] <- bp
for (pname in dpnames) {
if (!pname %in% f_names_skip) {
par.lower <- confint_trans[pname, "lower"]
par.upper <- confint_trans[pname, "upper"]
names(par.lower) <- names(par.upper) <- pname
bpl <- backtransform_odeparms(par.lower, object$mkinmod,
object$transform_rates,
object$transform_fractions)
bpu <- backtransform_odeparms(par.upper, object$mkinmod,
object$transform_rates,
object$transform_fractions)
confint_back[names(bpl), "lower"] <- bpl
confint_back[names(bpu), "upper"] <- bpu
}
}
confint_ret <- confint_back
} else {
confint_ret <- confint_trans
}
attr(confint_ret, "label") <- "Fixed effects:"
# Random effects
ranef_ret <- as.matrix(data.frame(lower = NA,
"est." = sqrt(diag(object$nm$omega)), upper = NA))
rownames(ranef_ret) <- paste0(gsub("eta\\.", "sd(", rownames(ranef_ret)), ")")
attr(ranef_ret, "label") <- "Random effects:"
# Error model
enames <- names(object$nm$sigma)
err_ret <- as.matrix(conf.int[enames, c(3, 1, 4)])
colnames(err_ret) <- c("lower", "est.", "upper")
res <- list(
fixed = confint_ret,
random = ranef_ret,
errmod = err_ret
)
class(res) <- c("intervals.nlmixr.mmkin", "intervals.lme")
attr(res, "level") <- level
return(res)
}
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