utils::globalVariables(c("predicted", "std", "ID", "TIME", "CMT", "DV", "IPRED", "IRES", "IWRES"))
#' @export
nlmixr::nlmixr
#' Fit nonlinear mixed models using nlmixr
#'
#' This function uses [nlmixr::nlmixr()] as a backend for fitting nonlinear mixed
#' effects models created from [mmkin] row objects using the Stochastic Approximation
#' Expectation Maximisation algorithm (SAEM) or First Order Conditional
#' Estimation with Interaction (FOCEI).
#'
#' An mmkin row object is essentially a list of mkinfit objects that have been
#' obtained by fitting the same model to a list of datasets using [mkinfit].
#'
#' @importFrom nlmixr nlmixr tableControl
#' @importFrom dplyr %>%
#' @param object An [mmkin] row object containing several fits of the same
#' [mkinmod] model to different datasets
#' @param data Not used, the data are extracted from the mmkin row object
#' @param est Estimation method passed to [nlmixr::nlmixr]
#' @param degparms_start Parameter values given as a named numeric vector will
#' be used to override the starting values obtained from the 'mmkin' object.
#' @param eta_start Standard deviations on the transformed scale given as a
#' named numeric vector will be used to override the starting values obtained
#' from the 'mmkin' object.
#' @param test_log_parms If TRUE, an attempt is made to use more robust starting
#' values for population parameters fitted as log parameters in mkin (like
#' rate constants) by only considering rate constants that pass the t-test
#' when calculating mean degradation parameters using [mean_degparms].
#' @param conf.level Possibility to adjust the required confidence level
#' for parameter that are tested if requested by 'test_log_parms'.
#' @param data Not used, as the data are extracted from the mmkin row object
#' @param table Passed to [nlmixr::nlmixr]
#' @param error_model Optional argument to override the error model which is
#' being set based on the error model used in the mmkin row object.
#' @param control Passed to [nlmixr::nlmixr]
#' @param \dots Passed to [nlmixr_model]
#' @param save Passed to [nlmixr::nlmixr]
#' @param envir Passed to [nlmixr::nlmixr]
#' @return An S3 object of class 'nlmixr.mmkin', containing the fitted
#' [nlmixr::nlmixr] object as a list component named 'nm'. The
#' object also inherits from 'mixed.mmkin'.
#' @seealso [summary.nlmixr.mmkin] [plot.mixed.mmkin]
#' @examples
#' \dontrun{
#' ds <- lapply(experimental_data_for_UBA_2019[6:10],
#' function(x) subset(x$data[c("name", "time", "value")]))
#' names(ds) <- paste("Dataset", 6:10)
#'
#' f_mmkin_parent <- mmkin(c("SFO", "FOMC", "DFOP", "HS"), ds, quiet = TRUE, cores = 1)
#' f_mmkin_parent_tc <- mmkin(c("SFO", "FOMC", "DFOP"), ds, error_model = "tc",
#' cores = 1, quiet = TRUE)
#'
#' library(nlmixr)
#' f_nlmixr_sfo_saem <- nlmixr(f_mmkin_parent["SFO", ], est = "saem",
#' control = saemControl(print = 0))
#' f_nlmixr_sfo_focei <- nlmixr(f_mmkin_parent["SFO", ], est = "focei",
#' control = foceiControl(print = 0))
#'
#' f_nlmixr_fomc_saem <- nlmixr(f_mmkin_parent["FOMC", ], est = "saem",
#' control = saemControl(print = 0))
#' f_nlmixr_fomc_focei <- nlmixr(f_mmkin_parent["FOMC", ], est = "focei",
#' control = foceiControl(print = 0))
#'
#' f_nlmixr_dfop_saem <- nlmixr(f_mmkin_parent["DFOP", ], est = "saem",
#' control = saemControl(print = 0))
#' f_nlmixr_dfop_focei <- nlmixr(f_mmkin_parent["DFOP", ], est = "focei",
#' control = foceiControl(print = 0))
#'
#' f_nlmixr_hs_saem <- nlmixr(f_mmkin_parent["HS", ], est = "saem",
#' control = saemControl(print = 0))
#' f_nlmixr_hs_focei <- nlmixr(f_mmkin_parent["HS", ], est = "focei",
#' control = foceiControl(print = 0))
#'
#' f_nlmixr_fomc_saem_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "saem",
#' control = saemControl(print = 0))
#' f_nlmixr_fomc_focei_tc <- nlmixr(f_mmkin_parent_tc["FOMC", ], est = "focei",
#' control = foceiControl(print = 0))
#'
#' AIC(
#' f_nlmixr_sfo_saem$nm, f_nlmixr_sfo_focei$nm,
#' f_nlmixr_fomc_saem$nm, f_nlmixr_fomc_focei$nm,
#' f_nlmixr_dfop_saem$nm, f_nlmixr_dfop_focei$nm,
#' f_nlmixr_hs_saem$nm, f_nlmixr_hs_focei$nm,
#' f_nlmixr_fomc_saem_tc$nm, f_nlmixr_fomc_focei_tc$nm)
#'
#' AIC(nlme(f_mmkin_parent["FOMC", ]))
#' AIC(nlme(f_mmkin_parent["HS", ]))
#'
#' # The FOCEI fit of FOMC with constant variance or the tc error model is best
#' plot(f_nlmixr_fomc_saem_tc)
#'
#' sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
#' A1 = mkinsub("SFO"), quiet = TRUE)
#' fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
#' A1 = mkinsub("SFO"), quiet = TRUE)
#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
#' A1 = mkinsub("SFO"), quiet = TRUE)
#'
#' f_mmkin_const <- mmkin(list(
#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
#' ds, quiet = TRUE, error_model = "const")
#' f_mmkin_obs <- mmkin(list(
#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
#' ds, quiet = TRUE, error_model = "obs")
#' f_mmkin_tc <- mmkin(list(
#' "SFO-SFO" = sfo_sfo, "FOMC-SFO" = fomc_sfo, "DFOP-SFO" = dfop_sfo),
#' ds, quiet = TRUE, error_model = "tc")
#'
#' nlmixr_model(f_mmkin_const["SFO-SFO", ])
#'
#' # A single constant variance is currently only possible with est = 'focei' in nlmixr
#' f_nlmixr_sfo_sfo_focei_const <- nlmixr(f_mmkin_const["SFO-SFO", ], est = "focei")
#' f_nlmixr_fomc_sfo_focei_const <- nlmixr(f_mmkin_const["FOMC-SFO", ], est = "focei")
#' f_nlmixr_dfop_sfo_focei_const <- nlmixr(f_mmkin_const["DFOP-SFO", ], est = "focei")
#'
#' # Variance by variable is supported by 'saem' and 'focei'
#' f_nlmixr_fomc_sfo_saem_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "saem")
#' f_nlmixr_fomc_sfo_focei_obs <- nlmixr(f_mmkin_obs["FOMC-SFO", ], est = "focei")
#' f_nlmixr_dfop_sfo_saem_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "saem")
#' f_nlmixr_dfop_sfo_focei_obs <- nlmixr(f_mmkin_obs["DFOP-SFO", ], est = "focei")
#'
#' # Identical two-component error for all variables is only possible with
#' # est = 'focei' in nlmixr
#' f_nlmixr_fomc_sfo_focei_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei")
#' f_nlmixr_dfop_sfo_focei_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei")
#'
#' # Two-component error by variable is possible with both estimation methods
#' # Variance by variable is supported by 'saem' and 'focei'
#' f_nlmixr_fomc_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "saem",
#' error_model = "obs_tc")
#' f_nlmixr_fomc_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["FOMC-SFO", ], est = "focei",
#' error_model = "obs_tc")
#' f_nlmixr_dfop_sfo_saem_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "saem",
#' error_model = "obs_tc")
#' f_nlmixr_dfop_sfo_focei_obs_tc <- nlmixr(f_mmkin_tc["DFOP-SFO", ], est = "focei",
#' error_model = "obs_tc")
#'
#' AIC(
#' f_nlmixr_sfo_sfo_focei_const$nm,
#' f_nlmixr_fomc_sfo_focei_const$nm,
#' f_nlmixr_dfop_sfo_focei_const$nm,
#' f_nlmixr_fomc_sfo_saem_obs$nm,
#' f_nlmixr_fomc_sfo_focei_obs$nm,
#' f_nlmixr_dfop_sfo_saem_obs$nm,
#' f_nlmixr_dfop_sfo_focei_obs$nm,
#' f_nlmixr_fomc_sfo_focei_tc$nm,
#' f_nlmixr_dfop_sfo_focei_tc$nm,
#' f_nlmixr_fomc_sfo_saem_obs_tc$nm,
#' f_nlmixr_fomc_sfo_focei_obs_tc$nm,
#' f_nlmixr_dfop_sfo_saem_obs_tc$nm,
#' f_nlmixr_dfop_sfo_focei_obs_tc$nm
#' )
#' # Currently, FOMC-SFO with two-component error by variable fitted by focei gives the
#' # lowest AIC
#' plot(f_nlmixr_fomc_sfo_focei_obs_tc)
#' summary(f_nlmixr_fomc_sfo_focei_obs_tc)
#' }
#' @export
nlmixr.mmkin <- function(object, data = NULL,
est = NULL, control = list(),
table = tableControl(),
error_model = object[[1]]$err_mod,
test_log_parms = TRUE,
conf.level = 0.6,
degparms_start = "auto",
eta_start = "auto",
...,
save = NULL,
envir = parent.frame()
)
{
m_nlmixr <- nlmixr_model(object, est = est,
error_model = error_model, add_attributes = TRUE,
test_log_parms = test_log_parms, conf.level = conf.level,
degparms_start = degparms_start, eta_start = eta_start
)
d_nlmixr <- nlmixr_data(object)
mean_dp_start <- attr(m_nlmixr, "mean_dp_start")
mean_ep_start <- attr(m_nlmixr, "mean_ep_start")
attributes(m_nlmixr) <- NULL
fit_time <- system.time({
f_nlmixr <- nlmixr(m_nlmixr, d_nlmixr, est = est, control = control)
})
if (is.null(f_nlmixr$CMT)) {
nlmixr_df <- as.data.frame(f_nlmixr[c("ID", "TIME", "DV", "IPRED", "IRES", "IWRES")])
nlmixr_df$CMT <- as.character(object[[1]]$data$variable[1])
} else {
nlmixr_df <- as.data.frame(f_nlmixr[c("ID", "TIME", "DV", "CMT", "IPRED", "IRES", "IWRES")])
}
return_data <- nlmixr_df %>%
dplyr::transmute(ds = ID, name = CMT, time = TIME, value = DV,
predicted = IPRED, residual = IRES,
std = IRES/IWRES, standardized = IWRES) %>%
dplyr::arrange(ds, name, time)
bparms_optim <- backtransform_odeparms(f_nlmixr$theta,
object[[1]]$mkinmod,
object[[1]]$transform_rates,
object[[1]]$transform_fractions)
result <- list(
mkinmod = object[[1]]$mkinmod,
mmkin = object,
transform_rates = object[[1]]$transform_rates,
transform_fractions = object[[1]]$transform_fractions,
nm = f_nlmixr,
est = est,
time = fit_time,
mean_dp_start = mean_dp_start,
mean_ep_start = mean_ep_start,
bparms.optim = bparms_optim,
bparms.fixed = object[[1]]$bparms.fixed,
data = return_data,
err_mod = error_model,
date.fit = date(),
nlmixrversion = as.character(utils::packageVersion("nlmixr")),
mkinversion = as.character(utils::packageVersion("mkin")),
Rversion = paste(R.version$major, R.version$minor, sep=".")
)
class(result) <- c("nlmixr.mmkin", "mixed.mmkin")
return(result)
}
#' @export
#' @rdname nlmixr.mmkin
#' @param x An nlmixr.mmkin object to print
#' @param digits Number of digits to use for printing
print.nlmixr.mmkin <- function(x, digits = max(3, getOption("digits") - 3), ...) {
cat("Kinetic nonlinear mixed-effects model fit by", x$est, "using nlmixr")
cat("\nStructural model:\n")
diffs <- x$mmkin[[1]]$mkinmod$diffs
nice_diffs <- gsub("^(d.*) =", "\\1/dt =", diffs)
writeLines(strwrap(nice_diffs, exdent = 11))
cat("\nData:\n")
cat(nrow(x$data), "observations of",
length(unique(x$data$name)), "variable(s) grouped in",
length(unique(x$data$ds)), "datasets\n")
cat("\nLikelihood:\n")
print(data.frame(
AIC = AIC(x$nm),
BIC = BIC(x$nm),
logLik = logLik(x$nm),
row.names = " "), digits = digits)
cat("\nFitted parameters:\n")
print(x$nm$parFixed, digits = digits)
invisible(x)
}
#' @rdname nlmixr.mmkin
#' @param add_attributes Should the starting values used for degradation model
#' parameters and their distribution and for the error model parameters
#' be returned as attributes?
#' @return An function defining a model suitable for fitting with [nlmixr::nlmixr].
#' @export
nlmixr_model <- function(object,
est = c("saem", "focei"),
degparms_start = "auto",
eta_start = "auto",
test_log_parms = TRUE, conf.level = 0.6,
error_model = object[[1]]$err_mod, add_attributes = FALSE)
{
if (nrow(object) > 1) stop("Only row objects allowed")
est = match.arg(est)
mkin_model <- object[[1]]$mkinmod
obs_vars <- names(mkin_model$spec)
if (error_model == object[[1]]$err_mod) {
if (length(object[[1]]$mkinmod$spec) > 1 & est == "saem") {
if (error_model == "const") {
message(
"Constant variance for more than one variable is not supported for est = 'saem'\n",
"Changing the error model to 'obs' (variance by observed variable)")
error_model <- "obs"
}
if (error_model =="tc") {
message(
"With est = 'saem', a different error model is required for each observed variable",
"Changing the error model to 'obs_tc' (Two-component error for each observed variable)")
error_model <- "obs_tc"
}
}
}
degparms_mmkin <- mean_degparms(object,
test_log_parms = test_log_parms,
conf.level = conf.level, random = TRUE)
degparms_optim <- degparms_mmkin$fixed
degparms_optim_ilr_names <- grep("^f_.*_ilr", names(degparms_optim), value = TRUE)
obs_vars_ilr <- unique(gsub("f_(.*)_ilr.*$", "\\1", degparms_optim_ilr_names))
degparms_optim_noilr <- degparms_optim[setdiff(names(degparms_optim),
degparms_optim_ilr_names)]
degparms_optim_back <- backtransform_odeparms(degparms_optim,
object[[1]]$mkinmod,
object[[1]]$transform_rates,
object[[1]]$transform_fractions)
if (degparms_start[1] == "auto") {
degparms_start <- degparms_optim_noilr
for (obs_var_ilr in obs_vars_ilr) {
ff_names <- grep(paste0("^f_", obs_var_ilr, "_"),
names(degparms_optim_back), value = TRUE)
f_tffm0 <- tffm0(degparms_optim_back[ff_names])
f_tffm0_qlogis <- qlogis(f_tffm0)
names(f_tffm0_qlogis) <- paste0("f_", obs_var_ilr,
"_tffm0_", 1:length(f_tffm0), "_qlogis")
degparms_start <- c(degparms_start, f_tffm0_qlogis)
}
}
if (eta_start[1] == "auto") {
eta_start <- degparms_mmkin$eta[setdiff(names(degparms_optim),
degparms_optim_ilr_names)]
for (obs_var_ilr in obs_vars_ilr) {
ff_n <- length(grep(paste0("^f_", obs_var_ilr, "_"),
names(degparms_optim_back), value = TRUE))
eta_start_ff <- rep(0.3, ff_n)
names(eta_start_ff) <- paste0("f_", obs_var_ilr,
"_tffm0_", 1:ff_n, "_qlogis")
eta_start <- c(eta_start, eta_start_ff)
}
}
degparms_fixed <- object[[1]]$bparms.fixed
odeini_optim_parm_names <- grep('_0$', names(degparms_optim), value = TRUE)
odeini_fixed_parm_names <- grep('_0$', names(degparms_fixed), value = TRUE)
odeparms_fixed_names <- setdiff(names(degparms_fixed), odeini_fixed_parm_names)
odeparms_fixed <- degparms_fixed[odeparms_fixed_names]
odeini_fixed <- degparms_fixed[odeini_fixed_parm_names]
names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names)
# Definition of the model function
f <- function(){}
ini_block <- "ini({"
# Initial values for all degradation parameters
for (parm_name in names(degparms_start)) {
# As initials for state variables are not transformed,
# we need to modify the name here as we want to
# use the original name in the model block
ini_block <- paste0(
ini_block,
parm_name, " = ",
as.character(degparms_start[parm_name]),
"\n",
"eta.", parm_name, " ~ ",
as.character(eta_start[parm_name]),
"\n"
)
}
# Error model parameters
error_model_mkin <- object[[1]]$err_mod
errparm_names_mkin <- names(object[[1]]$errparms)
errparms_mkin <- sapply(errparm_names_mkin, function(parm_name) {
mean(sapply(object, function(x) x$errparms[parm_name]))
})
sigma_tc_mkin <- errparms_ini <- errparms_mkin[1] +
mean(unlist(sapply(object, function(x) x$data$observed)), na.rm = TRUE) *
errparms_mkin[2]
if (error_model == "const") {
if (error_model_mkin == "tc") {
errparms_ini <- sigma_tc_mkin
} else {
errparms_ini <- mean(errparms_mkin)
}
names(errparms_ini) <- "sigma"
}
if (error_model == "obs") {
errparms_ini <- switch(error_model_mkin,
const = rep(errparms_mkin["sigma"], length(obs_vars)),
obs = errparms_mkin,
tc = sigma_tc_mkin)
names(errparms_ini) <- paste0("sigma_", obs_vars)
}
if (error_model == "tc") {
if (error_model_mkin != "tc") {
stop("Not supported")
} else {
errparms_ini <- errparms_mkin
}
}
if (error_model == "obs_tc") {
if (error_model_mkin != "tc") {
stop("Not supported")
} else {
errparms_ini <- rep(errparms_mkin, length(obs_vars))
names(errparms_ini) <- paste0(
rep(names(errparms_mkin), length(obs_vars)),
"_",
rep(obs_vars, each = 2))
}
}
for (parm_name in names(errparms_ini)) {
ini_block <- paste0(
ini_block,
parm_name, " = ",
as.character(errparms_ini[parm_name]),
"\n"
)
}
ini_block <- paste0(ini_block, "})")
body(f)[2] <- parse(text = ini_block)
model_block <- "model({"
# Population initial values for the ODE state variables
for (parm_name in odeini_optim_parm_names) {
model_block <- paste0(
model_block,
parm_name, "_model = ",
parm_name, " + eta.", parm_name, "\n",
gsub("(.*)_0", "\\1(0)", parm_name), " = ", parm_name, "_model\n")
}
# Population initial values for log rate constants
for (parm_name in grep("^log_", names(degparms_start), value = TRUE)) {
model_block <- paste0(
model_block,
gsub("^log_", "", parm_name), " = ",
"exp(", parm_name, " + eta.", parm_name, ")\n")
}
# Population initial values for logit transformed parameters
for (parm_name in grep("_qlogis$", names(degparms_start), value = TRUE)) {
parm_name_new <- names(
backtransform_odeparms(degparms_start[parm_name],
object[[1]]$mkinmod,
object[[1]]$transform_rates,
object[[1]]$transform_fractions))
model_block <- paste0(
model_block,
parm_name_new, " = ",
"expit(", parm_name, " + eta.", parm_name, ")\n")
}
# Calculate formation fractions from tffm0 transformed values
for (obs_var_ilr in obs_vars_ilr) {
ff_names <- grep(paste0("^f_", obs_var_ilr, "_"),
names(degparms_optim_back), value = TRUE)
pattern <- paste0("^f_", obs_var_ilr, "_to_(.*)$")
target_vars <- gsub(pattern, "\\1",
grep(paste0("^f_", obs_var_ilr, "_to_"), names(degparms_optim_back), value = TRUE))
for (i in 1:length(target_vars)) {
ff_name <- ff_names[i]
ff_line <- paste0(ff_name, " = f_", obs_var_ilr, "_tffm0_", i)
if (i > 1) {
for (j in (i - 1):1) {
ff_line <- paste0(ff_line, " * (1 - f_", obs_var_ilr, "_tffm0_", j , ")")
}
}
model_block <- paste0(
model_block,
ff_line,
"\n"
)
}
}
# Differential equations
model_block <- paste0(
model_block,
paste(
gsub("d_(.*) =", "d/dt(\\1) =", mkin_model$diffs),
collapse = "\n"),
"\n"
)
# Error model
if (error_model == "const") {
model_block <- paste0(model_block,
paste(paste0(obs_vars, " ~ add(sigma)"), collapse = "\n"))
}
if (error_model == "obs") {
model_block <- paste0(model_block,
paste(paste0(obs_vars, " ~ add(sigma_", obs_vars, ")"), collapse = "\n"),
"\n")
}
if (error_model == "tc") {
model_block <- paste0(model_block,
paste(paste0(obs_vars, " ~ add(sigma_low) + prop(rsd_high)"), collapse = "\n"),
"\n")
}
if (error_model == "obs_tc") {
model_block <- paste0(model_block,
paste(
paste0(obs_vars, " ~ add(sigma_low_", obs_vars, ") + ",
"prop(rsd_high_", obs_vars, ")"), collapse = "\n"),
"\n")
}
model_block <- paste0(model_block, "})")
body(f)[3] <- parse(text = model_block)
if (add_attributes) {
attr(f, "mean_dp_start") <- degparms_optim
attr(f, "eta_start") <- degparms_mmkin$eta
attr(f, "mean_ep_start") <- errparms_ini
}
return(f)
}
#' @rdname nlmixr.mmkin
#' @return An dataframe suitable for use with [nlmixr::nlmixr]
#' @export
nlmixr_data <- function(object, ...) {
if (nrow(object) > 1) stop("Only row objects allowed")
d <- lapply(object, function(x) x$data)
compartment_map <- 1:length(object[[1]]$mkinmod$spec)
names(compartment_map) <- names(object[[1]]$mkinmod$spec)
ds_names <- colnames(object)
ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
names(ds_list) <- ds_names
ds_nlmixr <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
ds_nlmixr$variable <- as.character(ds_nlmixr$variable)
ds_nlmixr_renamed <- data.frame(
ID = ds_nlmixr$ds,
TIME = ds_nlmixr$time,
AMT = 0, EVID = 0,
CMT = ds_nlmixr$variable,
DV = ds_nlmixr$observed,
stringsAsFactors = FALSE)
return(ds_nlmixr_renamed)
}
