NEWS.md
model_family()
, link_inverse()
or model_frame()
: MixMod
(package GLMMadaptive), MCMCglmm, mlogit
and gmnl
.cred_int()
, to compute uncertainty intervals of Bayesian models. Mimics the behaviour and style of hdi()
and is thus a convenient complement to functions like posterior_interval()
.equi_test()
now finds better defaults for models with binomial outcome (like logistic regression models).r2()
for mixed models now also should work properly for mixed models fitted with rstanarm.anova_stats()
and alike (e.g. eta_sq()
) now all preserve original term names.model_family()
now returns $is_count = TRUE
, when model is a count-model, and $is_beta = TRUE
for models with beta-family.pred_vars()
checks that return value has only unique values.pred_vars()
gets a zi
-argument to return the variables from a model’s zero-inflation-formula.wtd_sd()
and wtd_mean()
when weight was NULL
(which usually shoudln’t be the case anyway).deparse()
, cutting off very long formulas in various functions.dplyr::n()
, to meet forthcoming changes in dplyr 0.8.0.boot_ci()
gets a ci.lvl
-argument.rotation
-argument in pca_rotate()
now supports all rotations from psych::principal()
.pred_vars()
gets a fe.only
-argument to return only fixed effects terms from mixed models, and a disp
-argument to return the variables from a model’s dispersion-formula.icc()
for Bayesian models gets a adjusted
-argument, to calculate adjusted and conditional ICC (however, only for Gaussian models).icc()
for non-Gaussian Bayes-models, a message is printed that recommends setting argument ppd
to TRUE
.resp_val()
and resp_var()
now also work for brms-models with additional response information (like trial()
in formula).resp_var()
gets a combine
-argument, to return either the name of the matrix-column or the original variable names for matrix-columns.model_frame()
now also returns the original variables for matrix-column-variables.model_frame()
now also returns the variable from the dispersion-formula of glmmTMB-models.model_family()
and link_inverse()
now supports glmmPQL, felm and lm_robust-models.anova_stats()
and alike (omeqa_sq()
etc.) now support gam-models from package gam.p_value()
now supports objects of class svyolr
.se()
and get_re_var()
for objects returned by icc()
.icc()
for Stan-models.var_names()
did not clear terms with log-log transformation, e.g. log(log(y))
.model_frame()
for models with splines with only one column.re_grp_var()
to find group factors of random effects in mixed models.omega_sq()
and eta_sq()
give more informative messages when using non-supported objects.r2()
and icc()
give more informative warnings and messages.tidy_stan()
supports printing simplex parameters of monotonic effects of brms models.grpmean()
and mwu()
get a file
and encoding
argument, to save the HTML output as file.model_frame()
now correctly names the offset-columns for terms provided as offset
-argument (i.e. for models where the offset was not specified inside the formula).weights
-argument in grpmean()
when variable name was passed as character vector.r2()
for glmmTMB models with ar1
random effects structure.wtd_chisqtest()
to compute a weighted Chi-squared test.wtd_median()
to compute the weighted median of variables.wtd_cor()
to compute weighted correlation coefficients of variables.mediation()
can now cope with models from different families, e.g. if the moderator or outcome is binary, while the treatment-effect is continuous.model_frame()
, link_inverse()
, pred_vars()
, resp_var()
, resp_val()
, r2()
and model_family()
now support clm2
-objects from package ordinal.anova_stats()
gives a more informative message for non-supported models or ANOVA-options.model_family()
and link_inverse()
for models fitted with pscl::hurdle()
or pscl::zeroinfl()
.grpmean()
for grouped data frames, when grouping variable was an unlabelled factor.model_frame()
for coxph-models with polynomial or spline-terms.mediation()
for logical variables.wtd_ttest()
to compute a weighted t-test.wtd_mwu()
to compute a weighted Mann-Whitney-U or Kruskal-Wallis test.robust()
was revised, getting more arguments to specify different types of covariance-matrix estimation, and handling these more flexible.print()
-method for tidy_stan()
for brmsfit-objects with categorical-families.se()
now also computes standard errors for relative frequencies (proportions) of a vector.r2()
now also computes r-squared values for glmmTMB-models from genpois
-families.r2()
gives more precise warnings for non-supported model-families.xtab_statistics()
gets a weights
-argument, to compute measures of association for contingency tables for weighted data.statistics
-argument in xtab_statistics()
gets a "fisher"
-option, to force Fisher’s Exact Test to be used.icc()
for generalized linear mixed models with Poisson or negative binomial families.icc()
gets an adjusted
-argument, to calculate the adjusted and conditional ICC for mixed models.weight.by
is now deprecated and renamed into weights
.grpmean()
now also adjusts the n
-columm for weighted data.icc()
, re_var()
and get_re_var()
now correctly compute the random-effect-variances for models with multiple random slopes per random effect term (e.g., (1 + rs1 + rs2 | grp)
).tidy_stan()
, mcse()
, hdi()
and n_eff()
for stan_polr()
-models.equi_test()
did not work for intercept-only models.hdi()
, rope()
, equi_test()
etc. are now more generic, and function usage for each supported object is now included in the documentation.icc()
, r2()
, p_value()
, se()
, and std_beta()
.print()
-methods for some more functions, for a clearer output.r2()
for mixed models (packages lme4, glmmTMB). The r-squared value should be much more precise now, and reports the marginal and conditional r-squared values.stanmvreg
-models are now supported by many functions.binned_resid()
to plot binned residuals for logistic regression models.error_rate()
to compute model quality for logistic regression models.auto_prior()
to quickly create automatically adjusted priors for brms-models.difficulty()
to compute the item difficulty.icc()
gets a ppd
-argument for Stan-models (brmsfit and stanreg), which performs a variance decomposition based on the posterior predictive distribution. This is the recommended way for non-Gaussian models.icc()
now also computes the HDI for the ICC and random-effect variances. Use the prob
-argument to specify the limits of this interval.link_inverse()
and model_family()
now support clmm-models (package ordinal) and glmRob and lmRob-models (package robust).model_family()
gets a multi.resp
-argument, to return a list of family-informations for multivariate-response models (of class brmsfit
or stanmvreg
).link_inverse()
gets a multi.resp
-argument, to return a list of link-inverse-functions for multivariate-response models (of class brmsfit
or stanmvreg
).p_value()
now supports rlm-models (package MASS).check_assumptions()
for single models with as.logical = FALSE
now has a nice print-method.eta_sq()
and omega_sq()
now also work for repeated-measure Anovas, i.e. Anova with error term (requires broom > 0.4.5).model_frame()
and var_names()
now correctly cleans nested patterns like offset(log(x + 10))
from column names.model_frame()
now returns proper column names from gamm4 models.model_frame()
did not work when the model frame had spline-terms and weights.robust()
when exponentiate = TRUE
and conf.int = FALSE
.reliab_test()
returned an error when the provided data frame has less than three columns, instead of returning NULL
.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.