Add docs for options

R/ModelArray_Constructor.R

@@ -369,6 +369,9 @@ numElementsTotal <- function(modelarray, scalar_name = "FD") {
 #' If TRUE, it will return column names etc to be used for initiating data.frame;
 #' if FALSE, it will return the list of requested statistic values.
 #' @param ... Additional arguments for `stats::lm()`
+#' @param on_error Character: one of "stop", "skip", or "debug". When an error occurs while
+#' fitting one element, choose whether to stop, skip returning all-NaN values for that element,
+#' or drop into `browser()` (if interactive) then skip. Default: "stop".
 #'
 #' @return If flag_initiate==TRUE, returns column names, and list of term names of final results;
 #' if flag_initiate==FALSE, it will return the list of requested statistic values for a element.
@@ -623,6 +626,9 @@ analyseOneElement.lm <- function(i_element,
 #' @param flag_sse TRUE or FALSE, Whether to calculate SSE (sum of squared error) for the model (`model.sse`).
 #' SSE is needed for calculating partial R-squared.
 #' @param ... Additional arguments for `mgcv::gam()`
+#' @param on_error Character: one of "stop", "skip", or "debug". When an error occurs while
+#' fitting one element, choose whether to stop, skip returning all-NaN values for that element,
+#' or drop into `browser()` (if interactive) then skip. Default: "stop".
 #' @return If flag_initiate==TRUE, returns column names,
 #' list of term names of final results, and attr.name of sp.criterion;
 #' if flag_initiate==FALSE, it will return the list of requested statistic values for a element.

R/analyse.R

@@ -69,6 +69,9 @@
 #' @param verbose TRUE or FALSE, to print verbose message or not
 #' @param pbar TRUE or FALSE, to print progress bar or not
 #' @param n_cores Positive integer, The number of CPU cores to run with
+#' @param on_error Character: one of "stop", "skip", or "debug". When an error occurs
+#' while fitting an element, choose whether to stop, skip returning all-NaN values for
+#' that element, or drop into `browser()` (if interactive) then skip. Default: "stop".
 #' @param ... Additional arguments for `stats::lm()`
 #' @return Tibble with the summarized model statistics for all elements requested
 #' @importFrom dplyr %>%
@@ -651,6 +654,9 @@ ModelArray.lm <- function(formula, data, phenotypes, scalar, element.subset = NU
 #' @param verbose TRUE or FALSE, to print verbose messages or not
 #' @param pbar TRUE or FALSE, to print progress bar or not
 #' @param n_cores Positive integer, The number of CPU cores to run with
+#' @param on_error Character: one of "stop", "skip", or "debug". When an error occurs
+#' while fitting an element, choose whether to stop, skip returning all-NaN values for
+#' that element, or drop into `browser()` (if interactive) then skip. Default: "stop".
 #' @param ... Additional arguments for `mgcv::gam()`
 #' @return Tibble with the summarized model statistics for all elements requested
 #' @importFrom dplyr %>% mutate
@@ -1352,6 +1358,9 @@ ModelArray.gam <- function(formula, data, phenotypes, scalar, element.subset = N
 #' @param verbose TRUE/FALSE to print messages
 #' @param pbar TRUE/FALSE to show progress bar
 #' @param n_cores Positive integer number of CPU cores
+#' @param on_error Character: one of "stop", "skip", or "debug". When an error occurs in
+#' the user function for an element, choose whether to stop, skip returning all-NaN values
+#' for that element, or drop into `browser()` (if interactive) then skip. Default: "stop".
 #' @param ... Additional arguments forwarded to `FUN`
 #' @return Tibble/data.frame with one row per element and first column `element_id`
 #' @importFrom dplyr %>%

vignettes/wrap_function.Rmd

@@ -46,7 +46,6 @@ The next step is to use this HDF5 file and the CSV file we prepared to perform s
 If you installed RStudio (which we recommend), then you can simply launch RStudio.
 All the commands in this Step 2 section will be run in R.
 
-> Hint for voxel-wise data for Step 2: You can follow the same steps here. Note that each "element" is a *voxel* now, instead of a *fixel*. Make sure you replace the scalar name, covariates, file paths etc with yours. In addition, as voxel-wise data may have subject-specific masks, you can also tailor the lower threshold of number of subjects when applying `ModelArray.lm()` and `ModelArray.gam()`. See `vignette("voxel-wise_data")` page for more.
 
 ### Step 2.1. Load ModelArray package in R