removed robCompositions dependency

Also documentation updates

Author: tystan

DESCRIPTION

@@ -10,7 +10,6 @@ License: GPL-2
 Encoding: UTF-8
 LazyData: true
 Imports: compositions,
-    robCompositions,
-    testthat,
     ggplot2
+Suggests: testthat
 RoxygenNote: 7.1.1

NAMESPACE

@@ -1,6 +1,8 @@
 #exportPattern("^[[:alpha:]]+")
 exportPattern("predict_")
 exportPattern("plot_")
+exportPattern("fairclough")
+exportPattern("fat_data")
 importFrom("stats", "predict.lm", "as.formula", "coefficients", "df.residual", "lm", "median", "model.matrix", "qt", "quantile", "residuals")
 importFrom("utils", "head")
 importFrom("compositions", "gsi.buildilrBase", "ilr")

R/append_ilr_coords.R

@@ -6,23 +6,21 @@
 #' @param psi ilrBase passed to \code{compositions::ilr()}
 #' @export
 #' 
-#' @examples
-#' library(deltacomp)
-#' data(fat_data)
-#' head(fat_data)
-#' comp_vars <- c("sl", "sb", "lpa", "mvpa")
-#' # create sequential binary partition
-#' sbp <- create_seq_bin_part(length(comp_vars))
-#' # The orthonormal transformation matrix
-#' psi <- compositions::gsi.buildilrBase(sbp)
-#' head(append_ilr_coords(
-#'   fat_data,
-#'   comp_vars,
-#'   psi
-#' ))
-#' 
-#' 
-#' 
+# @examples
+# library(compositions)
+# data(fat_data)
+# head(fat_data)
+# comp_vars <- c("sl", "sb", "lpa", "mvpa")
+# # create sequential binary partition
+# sbp <- create_seq_bin_part(length(comp_vars))
+# # The orthonormal transformation matrix
+# psi <- compositions::gsi.buildilrBase(sbp)
+# head(append_ilr_coords(
+#   fat_data,
+#   comp_vars,
+#   psi
+# ))
+
 
 
 append_ilr_coords <- function(dataf, comps, psi) {

R/check_input_args.R

@@ -11,16 +11,16 @@
 #' @export
 #' @details 
 #' Throws errors for any problematic input. Returns \code{TRUE} invisibly if no issues found.
-#' @examples
-#' check_input_args(
-#'   dataf = fat_data,
-#'   y = "fat",
-#'   comps = c("sl", "sb", "lpa", "mvpa"),
-#'   covars = NULL,
-#'   deltas = seq(-60, 60, by = 5) / (24 * 60)
-#' )
-#' 
-#' 
+# @examples
+# check_input_args(
+#   dataf = fat_data,
+#   y = "fat",
+#   comps = c("sl", "sb", "lpa", "mvpa"),
+#   covars = NULL,
+#   deltas = seq(-60, 60, by = 5) / (24 * 60)
+# )
+# 
+# 
 
 
 

R/check_strictly_positive_vals.R

@@ -9,11 +9,11 @@
 #' Returns \code{TRUE} invisibly otherwise.
 #' @export
 #'
-#' @examples
-#' data(fat_data)
-#' check_strictly_positive_vals(fat_data, c("lpa", "sl"), tol = 1e-6) 
-#' # not run (throws error):
-#' # check_strictly_positive_vals(data.frame(a = rnorm(10)), "a", tol = 1e-6) 
+# @examples
+# data(fat_data)
+# check_strictly_positive_vals(fat_data, c("lpa", "sl"), tol = 1e-6) 
+# # not run (throws error):
+# # check_strictly_positive_vals(data.frame(a = rnorm(10)), "a", tol = 1e-6) 
 check_strictly_positive_vals <- function(dataf, comps, tol = 1e-6) {
 
   vals <- dataf[, comps]

R/cols_exist.R

@@ -8,11 +8,11 @@
 #' Returns \code{TRUE} invisibly otherwise.
 #' @export
 #'
-#' @examples
-#' data(fat_data)
-#' cols_exist(fat_data, c("lpa", "sl")) 
-#' # not run (throws error):
-#' # cols_exist(fat_data, "a") 
+# @examples
+# data(fat_data)
+# cols_exist(fat_data, c("lpa", "sl")) 
+# # not run (throws error):
+# # cols_exist(fat_data, "a") 
 cols_exist <- function(dataf, cols) {
 
   if (is_null_or_na(cols)) {

R/create_comparison_matrix.R

@@ -16,9 +16,9 @@
 #' Note that for both \code{comparisons} options the net change is 0 (each row sums to 0).
 #' @export
 #'
-#' @examples
-#' create_comparison_matrix("one-v-one", LETTERS[1:3], c(0.5, 0.3, 0.2))
-#' create_comparison_matrix("prop-realloc", LETTERS[1:3], c(0.5, 0.3, 0.2))
+# @examples
+# create_comparison_matrix("one-v-one", LETTERS[1:3], c(0.5, 0.3, 0.2))
+# create_comparison_matrix("prop-realloc", LETTERS[1:3], c(0.5, 0.3, 0.2))
 create_comparison_matrix <- function(comparisons, comps, mean_comps) {
 
   K <- poss_comps0 <- NULL

R/create_seq_bin_part.R

@@ -17,10 +17,10 @@
 #' etc
 #' @export
 #'
-#' @examples
-#' create_seq_bin_part(3)
-#' create_seq_bin_part(4)
-#' create_seq_bin_part(10)
+# @examples
+# create_seq_bin_part(3)
+# create_seq_bin_part(4)
+# create_seq_bin_part(10)
 create_seq_bin_part <- function(n_comp) {
 
 

R/extract_lm_quantities.R

@@ -4,17 +4,17 @@
 #' @param alpha level of significance. Defaults to 0.05.
 #'
 #' @return
-#' A list containing the \code{lm}'s model matrix (\code{dmX}), 
-#' the inverse of \code{t(dmX) %*% dmX} (\code{XtX_inv}), 
+#' A list containing the \code{lm} model matrix (\code{dmX}), 
+#' the inverse of \code{t(dmX) x dmX} (\code{XtX_inv}), 
 #' the standard error (\code{s_e}),
 #' the estimated single column beta matrix (\code{beta_hat}), and
 #' the critical value of the relevant degrees of freedom t-dist (\code{crit_val}).
 #' @export
 #'
-#' @examples
-#' data(fat_data)
-#' lm_fat <- lm(fat ~ sl, data = fat_data)
-#' extract_lm_quantities(lm_fat)
+# @examples
+# data(fat_data)
+# lm_fat <- lm(fat ~ sl, data = fat_data)
+# extract_lm_quantities(lm_fat)
 extract_lm_quantities <- function(lm_X, alpha = 0.05) {
 
   # get the design matrix from the LM

R/fit_lm.R

@@ -1,13 +1,18 @@
 
-#' Title
+#' fit linear model based on input data.frame
 #'
-#' @param y_str 
-#' @param X 
+#' @param y_str a string representation of the column in \code{X} that is the outcome
+#' @param X a data.frame or matrix that contains the predictor and outcome variables
 #'
-#' @return
+#' @return 
+#' A \code{lm} object where the \code{y_str} column has been regressed against the remaining
+#' columns of \code{X} (with an intercept term as well).
+#' 
 #' @export
 #'
-#' @examples
+# @examples
+# data(fairclough)
+# fit_lm("bmi", fairclough[, c("bmi", "sex", "decimal_age")])
 fit_lm <- function(y_str, X) {