version 0.15.0 on CRAN, update website

DESCRIPTION

@@ -2,9 +2,8 @@ Package: sjstats
 Type: Package
 Encoding: UTF-8
 Title: Collection of Convenient Functions for Common Statistical Computations
-Version: 0.14.3.9000
-Date: 2018-05-03
-Author: Daniel Lüdecke <[email protected]>
+Version: 0.15.0
+Date: 2018-06-06
 Authors@R: person("Daniel", "Lüdecke", role = c("aut", "cre"), email = "[email protected]", comment = c(ORCID = "0000-0002-8895-3206"))
 Maintainer: Daniel Lüdecke <[email protected]>
 Description: Collection of convenient functions for common statistical computations,

R/check_model_assumptions.R

@@ -56,7 +56,8 @@
 #'          < 0.05 indicates a significant deviation from normal distribution.
 #'          Note that this formal test almost always yields significant results
 #'          for the distribution of residuals and visual inspection (e.g. qqplots)
-#'          are preferable (see \code{\link[sjPlot]{sjp.lm}} with \code{type = "ma"}).
+#'          are preferable (see \code{\link[sjPlot]{plot_model}} with
+#'          \code{type = "diag"}).
 #'          \cr \cr
 #'          \code{multicollin()} wraps \code{\link[car]{vif}} and returns
 #'          the logical result as tibble. \code{TRUE}, if multicollinearity
@@ -74,7 +75,7 @@
 #' @note These formal tests are very strict and in most cases violation of model
 #'       assumptions are alerted, though the model is actually ok. It is
 #'       preferable to check model assumptions based on visual inspection
-#'       (see \code{\link[sjPlot]{sjp.lm}} with \code{type = "ma"}).
+#'       (see \code{\link[sjPlot]{plot_model}} with \code{type = "diag"}).
 #'
 #' @examples
 #' data(efc)

R/mean_n.R

@@ -1,21 +1,21 @@
 #' @title Row means with min amount of valid values
 #' @name mean_n
 #' @description This function is similar to the SPSS \code{MEAN.n} function and computes
-#'                row means from a \code{\link{data.frame}} or \code{\link{matrix}} if at least \code{n}
-#'                values of a row are valid (and not \code{\link{NA}}).
+#'                row means from a \code{data.frame} or \code{matrix} if at least \code{n}
+#'                values of a row are valid (and not \code{NA}).
 #'
 #' @param dat A data frame with at least two columns, where row means are applied.
 #' @param n May either be
 #'          \itemize{
 #'            \item a numeric value that indicates the amount of valid values per row to calculate the row mean;
 #'            \item or a value between 0 and 1, indicating a proportion of valid values per row to calculate the row mean (see 'Details').
 #'          }
-#'          If a row's sum of valid values is less than \code{n}, \code{\link{NA}} will be returned as row mean value.
+#'          If a row's sum of valid values is less than \code{n}, \code{NA} will be returned as row mean value.
 #' @param digits Numeric value indicating the number of decimal places to be used for rounding mean
 #'          value. Negative values are allowed (see ‘Details’).
 #'
 #' @return A vector with row mean values of \code{df} for those rows with at least \code{n}
-#'           valid values. Else, \code{\link{NA}} is returned.
+#'           valid values. Else, \code{NA} is returned.
 #'
 #' @details Rounding to a negative number of \code{digits} means rounding to a power of
 #'            ten, so for example mean_n(df, 3, digits = -2) rounds to the

R/merMod_p.R

@@ -10,7 +10,7 @@
 #'         conditional F-tests with Kenward-Roger approximation for the df (see
 #'         'Details').
 #'
-#' @return A \code{\link{tibble}} with the model coefficients' names (\code{term}),
+#' @return A \code{data.frame} with the model coefficients' names (\code{term}),
 #'         p-values (\code{p.value}) and standard errors (\code{std.error}).
 #'
 #' @details For linear mixed models (\code{lmerMod}-objects), the computation of
@@ -23,6 +23,10 @@
 #'         \cr \cr
 #'         If p-values already have been computed (e.g. for \code{merModLmerTest}-objects
 #'         from the \CRANpkg{lmerTest}-package), these will be returned.
+#'         \cr \cr
+#'         The \code{print()}-method has a \code{summary}-argument, that - in
+#'         case \code{p.kr = TRUE} - also prints information on the approximated
+#'         degrees of freedom (see 'Examples').
 #'
 #' @examples
 #' data(efc)
@@ -38,7 +42,8 @@
 #'
 #' # lme4-fit
 #' library(lme4)
-#' fit <- lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy)
+#' sleepstudy$mygrp <- sample(1:45, size = 180, replace = TRUE)
+#' fit <- lmer(Reaction ~ Days + (1 | mygrp) + (1 | Subject), sleepstudy)
 #' pv <- p_value(fit, p.kr = TRUE)
 #'
 #' # normal output

R/mwu.R

@@ -1,7 +1,7 @@
 #' @title Mann-Whitney-U-Test
 #' @name mwu
 #' @description This function performs a Mann-Whitney-U-Test (or Wilcoxon rank sum test,
-#'                see \code{\link{wilcox.test}} and \code{\link[coin]{wilcox_test}})
+#'                see \code{\link[stats]{wilcox.test}} and \code{\link[coin]{wilcox_test}})
 #'                for \code{x}, for each group indicated by \code{grp}. If \code{grp}
 #'                has more than two categories, a comparison between each combination of
 #'                two groups is performed. \cr \cr

R/pseudo_r2.R

@@ -6,7 +6,7 @@
 #'                an alternative to other Pseudo-R-squared values
 #'                like Nakelkerke's R2 or Cox-Snell R2.
 #'
-#' @param x Fitted \code{\link{glm}} or \code{\link[lme4]{glmer}} model.
+#' @param x Fitted \code{\link[stats]{glm}} or \code{\link[lme4]{glmer}} model.
 #'
 #' @return The \code{D} Coefficient of Discrimination, also known as
 #'           Tjur's R-squared value.

R/rmse.R

@@ -3,8 +3,8 @@
 #' @description Compute root mean squared error, residual standard error or
 #'              mean square error of fitted linear (mixed effects) models.
 #'
-#' @param fit Fitted linear model of class \code{\link{lm}},
-#'          \code{\link[lme4]{merMod}} (\pkg{lme4}) or \code{\link[nlme]{lme}} (\pkg{nlme}).
+#' @param fit Fitted linear model of class \code{lm}, \code{merMod} (\pkg{lme4})
+#'   or \code{lme} (\pkg{nlme}).
 #' @param normalized Logical, use \code{TRUE} if normalized rmse should be returned.
 #'
 #' @seealso \code{\link{r2}} for R-squared or pseude-R-squared values, and

R/sjStatistics.R

@@ -3,9 +3,10 @@
 #' @description This function calculates a table's cell, row and column percentages as
 #'                well as expected values and returns all results as lists of tables.
 #'
-#' @param tab Simple \code{\link{table}} or \code{\link{ftable}} of which cell, row and column percentages
-#'          as well as expected values are calculated. Tables of class \code{\link{xtabs}} and other will
-#'          be coerced to \code{\link{ftable}} objects.
+#' @param tab Simple \code{\link{table}} or \code{\link[stats]{ftable}} of which
+#'   cell, row and column percentages as well as expected values are calculated.
+#'   Tables of class \code{\link[stats]{xtabs}} and other will be coerced to
+#'   \code{ftable} objects.
 #' @param digits Amount of digits for the table percentage values.
 #'
 #' @return (Invisibly) returns a list with four tables:

R/std_b.R

@@ -3,8 +3,8 @@
 #' @description Returns the standardized beta coefficients, std. error and confidence intervals
 #'                of a fitted linear (mixed) models.
 #'
-#' @param fit Fitted linear (mixed) model of class \code{lm} or
-#'          \code{\link[lme4]{merMod}} (\CRANpkg{lme4} package).
+#' @param fit Fitted linear (mixed) model of class \code{lm} or \code{merMod}
+#'   (\CRANpkg{lme4} package).
 #' @param type If \code{fit} is of class \code{lm}, normal standardized coefficients
 #'          are computed by default. Use \code{type = "std2"} to follow
 #'          \href{http://www.stat.columbia.edu/~gelman/research/published/standardizing7.pdf}{Gelman's (2008)}
@@ -31,7 +31,7 @@
 #'         \cr \cr and \cr \cr
 #'         \code{head(model.matrix(nlme::gls(neg_c_7 ~ as.factor(e42dep), data = efc, na.action = na.omit)))}.
 #'         \cr \cr
-#'         In such cases, use \code{\link{to_dummy}} to create dummies from
+#'         In such cases, use \code{\link[sjmisc]{to_dummy}} to create dummies from
 #'         factors.
 #'
 #' @references \href{http://en.wikipedia.org/wiki/Standardized_coefficient}{Wikipedia: Standardized coefficient}

R/weight.R

@@ -15,7 +15,7 @@
 #'
 #' @details \code{weight2()} sums up all \code{weights} values of the associated
 #'            categories of \code{x}, whereas \code{weight()} uses a
-#'            \code{\link{xtabs}} formula to weight cases. Thus, \code{weight()}
+#'            \code{\link[stats]{xtabs}} formula to weight cases. Thus, \code{weight()}
 #'            may return a vector of different length than \code{x}.
 #'
 #' @note The values of the returned vector are in sorted order, whereas the values'

R/xtab_statistics.R

@@ -12,8 +12,8 @@
 #'          to be a data frame. If \code{x1} and \code{x2} are not specified,
 #'          the first two columns of the data frames are used as variables
 #'          to compute the crosstab.
-#' @param tab A \code{\link{table}} or \code{\link{ftable}}. Tables of class
-#'          \code{\link{xtabs}} and other will be coerced to \code{\link{ftable}}
+#' @param tab A \code{\link{table}} or \code{\link[stats]{ftable}}. Tables of class
+#'          \code{\link[stats]{xtabs}} and other will be coerced to \code{ftable}
 #'          objects.
 #' @param x1 Name of first variable that should be used to compute the
 #'           contingency table. If \code{data} is a table object, this argument