Merge pull request #46 from arcaldwell49/docs-lifecycle

Docs lifecycle

R/agree_coef.R

@@ -1,5 +1,8 @@
-#' Agreeement Coefficents
-#' @description agree_coef produces inter-rater reliability or "agreement coefficients" as described by Gwet.
+#' @title Agreement Coefficents
+#' @description
+#' `r lifecycle::badge('maturing')`
+#'
+#' agree_coef produces inter-rater reliability or "agreement coefficients" as described by Gwet.
 #' @param measure Name of column containing the measurement of interest.
 #' @param item Name of column containing the items. If this is an inter-rater reliability study then this would indicate the rater (e.g., rater1, rater2, rater3, etc).
 #' @param id Column with subject identifier.

R/agree_nest.R

@@ -56,7 +56,7 @@ agree_nest <- function(x,
                        TOST = TRUE,
                        prop_bias = FALSE,
                        ccc = TRUE){
-
+  lifecycle::deprecate_soft("0.2.0", "agree_nest()", "agreement_limit()")
   agreeq = qnorm(1 - (1 - agree.level) / 2)
   agree_l = 1 - (1 - agree.level) / 2
   agree_u = (1 - agree.level) / 2

R/agree_np.R

@@ -1,4 +1,4 @@
-#' Nonparametric Test for Limits of Agreement
+#' @title Nonparametric Test for Limits of Agreement
 #'
 #' @description
 #'

R/agree_reps.R

@@ -48,6 +48,7 @@ agree_reps <- function(x,
                        prop_bias = FALSE,
                        TOST = TRUE,
                        ccc = TRUE){
+  lifecycle::deprecate_soft("0.2.0", "agree_reps()", "agreement_limit()")
 
   agreeq = qnorm(1 - (1 - agree.level) / 2)
   agree.l = 1 - (1 - agree.level) / 2

R/agree_test.R

@@ -53,6 +53,7 @@ agree_test <- function(x,
                        agree.level = .95,
                        TOST = TRUE,
                        prop_bias = FALSE) {
+  lifecycle::deprecate_soft("0.2.0", "agree_test()", "agreement_limit()")
   est <- lower.ci <- upper.ci <- NULL
   if (agree.level >= 1 || agree.level <= 0) {
 

R/agreement_limit.R

@@ -1,4 +1,4 @@
-#' Calculate the Limits of Agreement
+#' @title Limits of Agreement
 #'
 #' @description
 #' `r lifecycle::badge('maturing')`

R/blandPowerCurve.R

@@ -1,6 +1,7 @@
-#' Estimate power curve for Bland-Altman limits of agreement
+#' @title Power Curve for Bland-Altman Limits of Agreement
 #'
 #' @description
+#' `r lifecycle::badge('maturing')`
 #' This function calculates the power for the Bland-Altman method under varying parameter settings and for a range of sample sizes.
 #'
 #' @param samplesizes vector of samples sizes at which to estimate power.

R/dem_reg.R

@@ -1,26 +1,28 @@
-#' Deming Regression
+#' @title Deming Regression
+#'
 #' @description
+#'
 #' `r lifecycle::badge('stable')`
 #'
-#' A function for fitting a straight line to two-dimensional data (i.e., X and Y) are measured with error.
+#' A function for fitting a straight line to two-dimensional data (i.e., X and Y) that are measured with error.
 #'
-#' @param x Name of column with first measurement
+#' @param x Name of column with first measurement.
 #' @param y Name of other column with the other measurement to compare to the first.
-#' @param id Column with subject identifier
-#' @param data Data frame with all data
+#' @param id Column with subject identifier.
+#' @param data Data frame with all data.
 #' @param conf.level The confidence level required. Default is 95%.
 #' @param weighted Logical indicator (TRUE/FALSE) for whether to use weighted Deming regression. Default is FALSE.
 #' @param weights an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector.
 #' @param error.ratio Ratio of the two error variances. Default is 1. This argument is ignored if subject identifiers are provided.
-#' @param keep_data Logical indicator (TRUE/FALSE). If TRUE the jacknife samples are returned; default is FALSE.
+#' @param keep_data Logical indicator (TRUE/FALSE). If TRUE, the jacknife samples are returned; default is FALSE. Users may wish to set to FALSE if data is especially large.
 #' @details
 #'
 #' This function provides a Deming regression analysis wherein the sum of distances in both x and y direction is minimized.
-#' Deming is useful in situations where both X & Y are measured with error.
+#' Deming regression, also known as error-in-variable regression, is useful in situations where both X & Y are measured with error.
 #' The use of Deming regression is beneficial when comparing to methods for measuring the same continuous variable.
 #'
 #' Currently, the dem_reg function covers simple Deming regression and weighted Deming regression.
-#' Weighted Deming regression can be used by setting weighted to TRUE.
+#' Weighted Deming regression can be used by setting the weighted argument to TRUE.
 #' The weights can be provided by the user or can be calculated within function.
 #'
 #' If the data are measured in replicates, then the measurement error can be directly derived from the data.

R/loa_mixed.R

@@ -1,4 +1,4 @@
-#' Mixed Effects Limits of Agreement
+#' @title Mixed Effects Limits of Agreement
 #'
 #' @description
 #' `r lifecycle::badge('deprecated')`
@@ -50,6 +50,7 @@ loa_mixed = function(diff,
                      agree.level = .95,
                      replicates = 1999,
                      type = "bca"){
+  lifecycle::deprecate_warn("0.2.0", "loa_mixed()", "loa_lme()")
   if (conf.level >= 1 || conf.level <= 0) {
     stop("conf.level must be a value between 0 and 1")
   }

R/methods.simple_agree.R

@@ -11,7 +11,7 @@
 #' \describe{
 #'   \item{\code{print}}{Prints short summary of the Limits of Agreement}
 #'   \item{\code{plot}}{Returns a plot of the limits of agreement (type = 1) or concordance plot (type = 2)}
-#'   \item{\code{check}}{Returns 2 plots, p_norm and p_het, testing the assumptions of a Bland-Altman analysis. P-values for the normality and heteroskedascity tests are provided as captions to the plot.}
+#'   \item{\code{check}}{Returns 2 plots, p_norm and p_het, testing the assumptions of a Bland-Altman analysis. P-values for the normality and heteroskedasticity tests are provided as captions to the plot.}
 #' }
 #'
 #' @name simple_agree-methods
@@ -221,7 +221,7 @@ check.simple_agree <- function(x) {
   }
 
   dat = df
-  ## Heteroskedasticity -------
+  ## heteroskedasticity -------
   mod_check = if (as.character(x$call[1]) != "agree_test" && as.character(x$call[1]) != "SimplyAgree::agree_test") {
     lme4::lmer(data = dat,
                form_lmer1)

R/methods.tolerance_delta.R

@@ -12,7 +12,7 @@
 #' \describe{
 #'   \item{\code{print}}{Prints short summary of the tolerance limits.}
 #'   \item{\code{plot}}{Returns a plot of the tolerance limits.}
-#'   \item{\code{check}}{Returns plots testing the assumptions of the model. P-values for the normality and heteroskedascity tests are provided as captions to the plot.}
+#'   \item{\code{check}}{Returns plots testing the assumptions of the model. P-values for the normality and heteroskedasticity tests are provided as captions to the plot.}
 #' }
 #'
 #' @name tolerance_delta-methods
@@ -402,7 +402,7 @@ check.tolerance_delta <- function(x) {
 
   dat = model.frame(x$call$lm_mod)
   colnames(dat) = c("y","x","id","mean","delta","condition","time")
-  ## Heteroskedasticity -------
+  ## heteroskedasticity -------
 
   #stan_res = residuals(x$model, type = "pearson")
   #df_het = x$model$dims[["N"]] - x$model$dims[["p"]]

R/reli_stats.R

@@ -1,4 +1,4 @@
-#' Reliability Statistics
+#' @title Reliability Statistics
 #'
 #' @description
 #'
@@ -8,10 +8,10 @@
 #' This includes intraclass correlation coefficients, the coefficient of variation,
 #'  and the standard MSE of measurement.
 #'
-#' @param measure Name of column containing the measurement of interest
-#' @param item Name of column containing the items. If this is a test-retest reliability study then this would indicate the time point (e.g., time1,time2, time3, etc.)
-#' @param id Column with subject identifier
-#' @param data Data frame with all data
+#' @param measure Name of column containing the measurement of interest.
+#' @param item Name of column containing the items. If this is a test-retest reliability study then this would indicate the time point (e.g., time1,time2, time3, etc.).
+#' @param id Column with subject identifier.
+#' @param data Data frame with all data.
 #' @param wide Logical value (TRUE or FALSE) indicating if data is in a "wide" format. Default is TRUE.
 #' @param col.names If wide is equal to TRUE then col.names is a list of the column names containing the measurements for reliability analysis.
 #' @param conf.level the confidence level required. Default is 95%.
@@ -60,7 +60,7 @@
 #'   - `cv`: Coefficient of Variation
 #'   - `SEM`: List with Standard MSE of Measurement estimate (est)
 #'   - `SEE`: List with Standard MSE of the Estimate estimate (est)
-#'   - `SEP`: List with Standard MSE of Predicitions (est)
+#'   - `SEP`: List with Standard MSE of Predictions (est)
 #'   - `call`: the matched call
 
 #' @examples

R/tolerance_limit.R

@@ -1,10 +1,10 @@
-#' Tolerance Limits from an Agreement Study
+#' @title Tolerance Limits from an Agreement Study
 #'
 #' @description `r lifecycle::badge('maturing')`
 #'
 #' A function for calculating tolerance limits for the difference between two measurements (difference = x-y).
 #' This is a procedure that should produce results similar to the Bland-Altman limits of agreement.
-#' See `vignette("agreement_analysis")` for more details.
+#' See vignettes for more details.
 #'
 #' @param data A data frame containing the variables.
 #' @param x Name of the column for the first measurement.
@@ -19,7 +19,7 @@
 #' @param log_tf Calculate limits of agreement using log-transformed data.
 #' @param cor_type The type of correlation structure. "sym" is for Compound Symmetry, "car1" is for continuous autocorrelation structure of order 1, or "ar1" for autocorrelation structure of order 1.
 #' @param correlation an optional corStruct object describing the within-group correlation structure that overrides the default setting. See the documentation of corClasses for a description of the available corStruct classes. If a grouping variable is to be used, it must be specified in the form argument to the corStruct constructor. Defaults to NULL.
-#' @param weights an optional varFunc object or one-sided formula describing the within-group heteroscedasticity structure that overrides the default setting. If given as a formula, it is used as the argument to varFixed, corresponding to fixed variance weights. See the documentation on varClasses for a description of the available varFunc classes.
+#' @param weights an optional varFunc object or one-sided formula describing the within-group heteroskedasticity structure that overrides the default setting. If given as a formula, it is used as the argument to varFixed, corresponding to fixed variance weights. See the documentation on varClasses for a description of the available varFunc classes.
 #' @param keep_model Logical indicator to retain the GLS model. Useful when working with large data and the model is very large.
 #' @inheritParams loa_lme
 #' @details The tolerance limits calculated in this function are based on the papers by Francq & Govaerts (2016), Francq, et al. (2019), and Francq, et al. (2020).