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authorJohannes Ranke <jranke@uni-bremen.de>2020-04-10 08:26:44 +0200
committerJohannes Ranke <jranke@uni-bremen.de>2020-04-10 08:26:44 +0200
commit7777ff3b019e54364947ff393e2dab782d7cfe3c (patch)
treeb1a86eebd0722550e46d344b59afbba56660ca65 /R
parentc40f1a3d353b847582b7fb631698c31f1a2254e4 (diff)
Improve nlme function docs
Diffstat (limited to 'R')
-rw-r--r--R/nlme.R67
1 files changed, 33 insertions, 34 deletions
diff --git a/R/nlme.R b/R/nlme.R
index b17fe15a..79e4e9c1 100644
--- a/R/nlme.R
+++ b/R/nlme.R
@@ -1,14 +1,12 @@
#' Estimation of parameter distributions from mmkin row objects
#'
-#' This function sets up and attempts to fit a mixed effects model to
+#' These functions facilitate setting up a nonlinear mixed effects model for
#' an mmkin row object. 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.
#'
#' @param object An mmkin row object containing several fits of the same model to different datasets
#' @import nlme
-#' @importFrom purrr map_dfr
-#' @return A named vector containing mean values of the fitted degradation model parameters
#' @rdname nlme
#' @examples
#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
@@ -45,7 +43,7 @@
#' summary(m_nlme)
#'
#' \dontrun{
-#' Test on some real data
+#' # Test on some real data
#' ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
#' function(x) x$data[c("name", "time", "value")])
#' m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
@@ -113,36 +111,7 @@
#'
#' anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo)
#' }
-#' @export
-mean_degparms <- function(object) {
- if (nrow(object) > 1) stop("Only row objects allowed")
- p_mat_start_trans <- sapply(object, parms, transformed = TRUE)
- mean_degparm_names <- setdiff(rownames(p_mat_start_trans), names(object[[1]]$errparms))
- res <- apply(p_mat_start_trans[mean_degparm_names, ], 1, mean)
- return(res)
-}
-
-#' @rdname nlme
-#' @importFrom purrr map_dfr
-#' @return A groupedData data object
-#' @export
-nlme_data <- function(object) {
- if (nrow(object) > 1) stop("Only row objects allowed")
- ds_names <- colnames(object)
-
- ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
- names(ds_list) <- ds_names
- ds_nlme <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
- ds_nlme$variable <- as.character(ds_nlme$variable)
- ds_nlme_renamed <- data.frame(ds = ds_nlme$ds, name = ds_nlme$variable,
- time = ds_nlme$time, value = ds_nlme$observed,
- stringsAsFactors = FALSE)
- ds_nlme_grouped <- groupedData(value ~ time | ds, ds_nlme_renamed)
- return(ds_nlme_grouped)
-}
-
-#' @rdname nlme
-#' @return A function that can be used with nlme
+#' @return A function that can be used with \code{link{nlme}}
#' @export
nlme_function <- function(object) {
if (nrow(object) > 1) stop("Only row objects allowed")
@@ -211,3 +180,33 @@ nlme_function <- function(object) {
model_function <- as.function(c(model_function_alist, model_function_body))
return(model_function)
}
+
+#' @rdname nlme
+#' @return A named vector containing mean values of the fitted degradation model parameters
+#' @export
+mean_degparms <- function(object) {
+ if (nrow(object) > 1) stop("Only row objects allowed")
+ degparm_mat_trans <- sapply(object, parms, transformed = TRUE)
+ mean_degparm_names <- setdiff(rownames(degparm_mat_trans), names(object[[1]]$errparms))
+ res <- apply(degparm_mat_trans[mean_degparm_names, ], 1, mean)
+ return(res)
+}
+
+#' @rdname nlme
+#' @importFrom purrr map_dfr
+#' @return A \code{\link{groupedData}} object
+#' @export
+nlme_data <- function(object) {
+ if (nrow(object) > 1) stop("Only row objects allowed")
+ ds_names <- colnames(object)
+
+ ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
+ names(ds_list) <- ds_names
+ ds_nlme <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
+ ds_nlme$variable <- as.character(ds_nlme$variable)
+ ds_nlme_renamed <- data.frame(ds = ds_nlme$ds, name = ds_nlme$variable,
+ time = ds_nlme$time, value = ds_nlme$observed,
+ stringsAsFactors = FALSE)
+ ds_nlme_grouped <- groupedData(value ~ time | ds, ds_nlme_renamed)
+ return(ds_nlme_grouped)
+}

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