NEWS.md
equi_test()
to test if parameter values in Bayesian estimation should be accepted or rejected.mediation()
to print a summary of a mediation analysis from multivariate response models fitted with brms.link_inverse()
now also returns the link-inverse function for cumulative-family brms-models.model_family()
now also returns an is_ordinal
-element with information if the model is ordinal resp. a cumulative link model.model_family()
) now better support vglm
-models (package VGAM).r2()
now also calculates the standard error for brms or stanreg models.r2()
gets a loo
-argument to calculate LOO-adjusted rsquared values for brms or stanreg models. This measure comes conceptionally closer to an adjusted r-squared measure.anova_stats()
, eta_sq()
etc.) are now also computed for mixed models.n_eff()
now computes the number of effective samples, and no longer its ratio in relation to the total number of samples.tidy_stan()
is now named neff_ratio, to avoid confusion.mwu()
now requires a data frame as first argument, followed by the names of the two variables to perform the Mann-Whitney-U-Test on.tidy_stan()
was improved especially for more complex multilevel models.tidy_stan()
for large brmsfit
-objects (esp. with random effects) more efficient.print()
-method for tidy_stan()
, hdi()
, rope()
, icc()
and some other functions.link_inverse()
now also should return the link-inverse function for most (or some or all?) custom families of brms-models.weight.by
-arguments in grpmean()
and mwu()
now should be a variable name from a variable in x
, and no longer a separate vector.model_family()
to get model-information about family and link-functions. This function is intended to be “generic” and work with many different model objects, because not all packages provide a family()
function.omega_sq()
, eta_sq()
etc. when confidence intervals were computed with bootstrapping and the model-formula contained function calls like scale()
or as.factor()
.p_value()
for unconditional mixed models.xtab_statistics()
.r2()
.typical_value()
, when argument fun
for factors was set to mode
.hdi()
, tidy_stan()
etc. for brmsfit-objects.model_frame()
with spline-terms when missing values were removed due to casewise deletion.residuals.svyglm.nb()
as S3-generic residuals()
method for objects fitted with svyglm.nb()
.icc()
gets a posterior
-argument, to compute ICC-values from brmsfit
-objects, for the whole posterior distribution.icc()
now gives a warning when computed for random-slope-intercept models, to warn user about probably inappropriate inference.r2()
now computes Bayesian version of R-squared for stanreg
and brmsfit
objects.prob
in hdi()
now accepts a vector of scalars to compute HDIs for multiple probability tresholds at once.probs
in tidy_stan()
was renamed into prob
, to be consistent with hdi()
.mwu()
gets an out
-argument, to print output to console, or as HTML table in the viewer or web browser.scale_weights()
now also works if weights have missing values.hdi()
and rope()
get data.frame
-methods.omega_sq()
and eta_sq()
get a ci.lvl
-argument to compute confidence intervals for the effect size statistics.omega_sq()
, eta_sq()
and cohens_f()
now always return a data frame with at least two columns: term name and effect size. Confidence intervals are added as additional columns, if the ci.lvl
-argument is TRUE
.omega_sq()
gets a partial
-argument to compute partial omega-squared.omega_sq()
, eta_sq()
, cohens_f()
and anova_stats()
now support anova.rms
-objects from the rms-package.mic()
.model_frame()
does not return duplicated column names.tidy_stan()
with incorrect n_eff statistics for sigma parameter in mixed models.tidy_stan()
, which did not work when probs
was of length greater than 2.icc()
with brmsfit-models, which was broken probably due to internal changes in brms.dplyr::select_helpers
were updated to tidyselect::select_helpers
.var_names()
now also cleans variable names from variables modeled with the mi()
function (multiple imputation on the fly in brms).reliab_test()
gets an out
-argument, to print output to console, or as HTML table in the viewer or web browser.mcse()
, n_eff()
and tidy_stan()
with more complex brmsfit-models.typical_value()
to prevent error for R-oldrel-Windows.model_frame()
now returns response values from models, which are in matrix form (bound with cbind()
), as is.grpmean()
, where values instead of value labels were printed if some categories were not present in the data.grpmean()
now uses contrasts()
from package emmeans to compute p-values, which correclty indicate whether the sub-group mean is significantly different from the total mean.grpmean()
gets an out
-argument, to print output to console, or as HTML table in the viewer or web browser.tidy_stan()
now includes information on the Monte Carlo standard error.model_frame()
, p_value()
and link_inverse()
now support Zelig-relogit-models.typical_value()
gets an explicit weight.by
-argument.model_frame()
did not work properly for variables that were standardized with scale()
.weight.by
-argument did not work in grpmean()
.get_model_pval()
.overdisp()
.scale_weights()
to rescale design weights for multilevel models.pca()
and pca_rotate()
to create tidy summaries of principal component analyses or rotated loadings matrices from PCA.gmd()
to compute Gini’s mean difference.is_prime()
to check whether a number is a prime number or not.link_inverse()
now supports brmsfit
, multinom
and clm
-models.p_value()
now supports polr
and multinom
-models.zero_count()
gets a tolerance
-argument, to accept models with a ratio within a certain range of 1.var_names()
now also cleans variable names from variables modelled with the offset()
, lag()
or diff()
function.icc()
, re_var()
and get_re_var()
now support brmsfit
-objects (models fitted with the brms-package).fun = "weighted.mean"
, typical_value()
now checks if vector of weights is of same length as x
.grpmean()
now also prints the overall p-value from the model.resp_val()
, cv_error()
and pred_accuracy()
did not work for formulas with transforming function for response terms, e.g. log(response)
.p_value()
.model_frame()
to get the model frame from model objects, also of those models that don’t have a S3-generic model.frame-function.var_names()
to get cleaned variable names from model objects.link_inverse()
to get the inverse link function from model objects.fun
-argument in typical_value()
can now also be a named vector, to apply different functions for numeric and categorical variables.pred_vars()
.resp_val()
.re_var()
.tidy_stan()
to return a tidy summary of Stan-models.typical_value()
gets a “zero”-option for the fun
-argument.icc()
, which used stats::sigma()
and thus required R-version 3.3 or higher. Now should depend on R 3.2 again.se()
now also supports stanreg
and stanfit
objects.hdi()
now also supports stanfit
-objects.std_beta()
gets a ci.lvl
-argument, to specify the level of the calculated confidence interval for standardized coefficients.get_model_pval()
is now deprecated. Please use p_value()
instead.rope()
to calculate the region of practical equivalence for MCMC samples.grpmean()
to compute mean values by groups (One-way Anova).hdi()
to compute high density intervals (HDI) for MCMC samples.find_beta()
and find_beta2()
to find the shape parameters of a Beta distribution.find_normal()
and find_cauchy()
to find the parameters of a normal or cauchy distribution.typical_value()
, to return the typical value of a variable.eta_sq()
, cohens_f()
and omega_sq()
to compute (partial) eta-squared or omega-squared statistics, or Cohen’s F for anova tables.anova_stats()
to compute a complete model summary, including (partial) eta-squared, omega-squared and Cohen’s F statistics for anova tables, returned as tidy data frame.svy_md()
as convenient shortcut to compute the median for variables from survey designs.is_singular()
to check a model fit for singularity in case of post-fitting convergence warnings.r2()
for glm
-objects is now based on log-Likelihood methods and also accounts for count models.print()
-method for overdisp()
.print()
-method for svyglm.nb()
now also prints the dispersion parameter Theta.overdisp()
now supports glmmTMB
-objects.boot_ci()
also displays CI based on sample quantiles.std_beta()
did not work for models with only one predictor.icc()
, re_var()
and get_re_var()
now support glmmTMB
-objects.pred_accuracy()
now also reports the standard error of accuracy, and gets a print-method.pred_accuracy()
with cross-validation-method did not correctly account for the generated test data.smpsize_lmm()
and se_ybar()
.boot_est()
to return the estimate from bootstrap replicates.print()
-method for svyglm.nb()
-objects now also prints confidence intervals.cv_error()
and cv_compare()
to compute the root mean squared error for test and training data from cross-validation.props()
to calculate proportions in a vector, supporting multiple logical statements.or_to_rr()
to convert odds ratio estimates into risk ratio estimates.mn()
, md()
and sm()
to calculate mean, median or sum of a vector, but using na.rm = TRUE
as default.svyglm.nb
-models: family()
, print()
, formula()
, model.frame()
and predict()
.mse()
.std()
and center()
were removed and are now in the sjmisc-package.svyglm.nb()
to compute survey-weighted negative binomial regressions.xtab_statistics()
to compute various measures of assiciation for contingency tables.model.frame()
-function for gee
-models.se()
gets a type
-argument, which applies to generalized linear mixed models. You can now choose to compute either standard errors with delta-method approximation for fixed effects only, or standard errors for joint random and fixed effects.prop()
did not work for non-labelled data frames when used with grouped data frames.svy()
to compute robust standard errors for weighted models, adjusting the residual degrees of freedom to simulate sampling weights.zero_count()
to check whether a poisson-model is over- or underfitting zero-counts in the outcome.pred_accuracy()
to calculate accuracy of predictions from model fit.outliers()
to detect outliers in (generalized) linear models.heteroskedastic()
to check linear models for (non-)constant error variance.autocorrelation()
to check linear models for auto-correlated residuals.normality()
to check whether residuals in linear models are normally distributed or not.multicollin()
to check predictors in a model for multicollinearity.check_assumptions()
to run a set of model assumption checks.prop()
gets a digits
-argument to round the return value to a specific number of decimal places.split_half()
to compute the split-half-reliability of tests or questionnaires.sd_pop()
and var_pop()
to compute population variance and population standard deviation.se()
now also computes the standard error from estimates (regression coefficients) and p-values.print
-method for mwu()
-function.get_model_pval()
to return a tidy data frame (tibble) of model term names, p-values and standard errors from various regression model types.se_ybar()
to compute standard error of sample mean for mixed models, considering the effect of clustering on the standard error.std()
and center()
to standardize and center variables, supporting the pipe-operator.se()
now also computes the standard error for intraclass correlation coefficients, as returned by the icc()
-function.std_beta()
now always returns a tidy data frame (tibble) with model term names, standardized estimate, standard error and confidence intervals.r2()
now also computes alternative omega-squared-statistics, if null model is given.