bayesx
(R2BayesX), bamlss
(bamlss) and flexsurvreg
(flexsurv). Note that support for these models is still somewhat experimental.get_data()
, get_parameters()
, find_parameters()
, clean_parameters()
, find_algorithm()
and get_priors()
(the two latter only for blavaan).get_statistic()
to return the test statistic of model estimates.get_varcov()
to return the variance-covariance matrix for models.supported_models()
to print a list of supported models.model_info()
now returns the element is_survival
for survival models.model_info()
now returns the element is_truncated
for truncated regression, or brmsfit models with trunc()
as additional response part.model_info()
now recognizes beta and beta inflated families from package gamlss.quantreg::nlrq()
).lme4::nlmer()
). Note that model-specification requires the random term to be written in parantheses, i.e. (slope | group)
.get_data()
, find_parameters()
and get_parameters()
for gamlss models.get_data()
for plm models, where the index
-argument was used in the plm()
-function call.get_data()
, find_predictors()
and find_variables()
for brmsfit multi-membership-models.is_model()
did not recognize objects of class anova
and manova
.model_info()
now correctly recognizes censored regression models from brmsfit.find_parameters()
and get_parameters()
with multinom models.clean_names()
for cases where variable transformations where made in specific patterns, like log(test/10)
.is_model()
function has been renamed to is_model_supported()
since it was unclear if the function checked the entered object was a model or a supported model in insight. The new is_model()
function checks if the entered object is a model object, while is_model_supported()
checks if a supported model object.find_statistic()
to return the test statistic of a regression model.format_value()
and format_table()
as utility-functions to format (model) output, especially for tabular output.color_if()
as utility-function to add color formatting to values, depending on certain conditions.find_parameters()
and get_parameters()
now also support objects of class sim
and sim.merMod
(from arm::sim()
).get_variance()
now also supports models of class clmm.find_predictors()
and find_variables()
now include the Euclidean distance matrix for spatial models from glmmTMB (returned as random effects element, or more precise, as random slope).find_formula()
now extracts group factors of random effects for gamlss models.find_parameters()
and get_parameters()
no longer show NA
coefficients from group factors of random effects for gamlss models.find_parameters()
and get_parameters()
did not work for multivariate response models of class brmsfit when argument parameters
was specified.get_data()
dropped value and variable label attributes, when model frame contained matrix variables (like splines).get_priors()
swapped column names location
and scale
for brmsfit -objects.get_parameters()
did not work for glmmTMB models without zero-inflation component.find_predictors()
did not remove parentheses from terms in multiple nested random effects.ziplss
or mvn
families.get_variance()
now supports models with Gamma-family.get_weights()
and find_weights()
now work for brms-models.betabin
and negbin
(aod), BBreg
and BBmm
(HRQoL), wbm
(panelr), survfit
(survival)clean_parameters()
, which returns a data frame with “decomposed” parameters, i.e. a data frame with information about the clean parameter name, whether it is a fixed or random effect, from conditional or zero-inflated component, and if it is a parameter related to specific grouping factors of random effects (#106).print_parameters()
, which can be called on top of clean_parameters()
to get a list of data frames that represent the different model components (fixed, random, zero-inflated, …) and which is in shape for printing summary statistics of complex models.find_interactions()
to return all low/high order interaction terms in a model.find_weights()
and get_weights()
to find / get model weights.find_parameters()
and get_parameters()
for objects of class aovlist
now return the elements $conditional
and $random
, to be in line with other supported objects.resp
in get_response()
was renamed to select
, to have a more clear verb (#114).find_variables()
and find_terms()
were flipped, because what previously was considered as “term” was actually a “variable”, and vice versa.find_parameters()
and get_parameters()
now allow to return a sigma
-element for multivariate-response models (brmsfit, stanmvreg).find_parameters()
and get_parameters()
now return the intercepts for polr models.find_parameters()
gets an effects
and a component
-argument, similar to many other functions in insight.get_priors()
now returns distribution = "uniform"
when model was fitted with flat priors.model_info()
now returns an element $is_hurdle
for hurdle models.find_parameters()
returned priors for brmsfit
-objects as $random
-element. Now, find_parameters()
returns a $priors
-element (#98).find_parameters()
did not remove smooth-parameters that used te()
or ti()
.find_formula()
(and hence, find_response()
or get_data()
) did not work for multi-column responses in null-models (#100).find_predictors()
did not split nested random effects when these were written as g1:g2
instead g2/g2
in the random part of the model formula.all_model_equal()
.get_data()
did not return (weights)
columns for some model objects.get_priors()
for stanreg-models returned the priors in sorted order, so sometimes parameter names and associated prior values did not match (#111).get_variance()
did not calculate random effect variances, when interaction terms in random slopes were also present in fixed effects, but the interaction was written in different order (e.g., a*b
and b*a
) (#112).get_data()
.get_priors()
for stanreg-models, when prior_summary()
returned NULL
for a prior (#116).biglm
and bigglm
(biglm), feis
(feisr), gbm
(gbm), BFBayesFactor
(BayesFactor), psm
(rms), LORgee
(multgee), censReg
(censReg), ols
(rms), speedlm
and speedglm
(speedglm), svyolr
(survey)is_nullmodel()
to check if model is a null-model (intercept-only), i.e. if the conditional part of the model has no predictors.has_intercept()
to check if model has an intercept.find_predictors()
or find_terms()
return NULL
for null-models (intercept-only models). Use is_nullmodel()
to check if your model only has an intercept-parameter (but no predictors).get_variance()
no longer stops if random effects variance cannot be calculated. Rather, the return-value for $var.random
will be NULL
.get_variance()
now computes the full variance for mixed models with zero-inflation component.get_priors()
now returns the default-prior that was defined for all parameters of a class, if certain parameters have no specific prior.find_parameters()
gets a flatten
-argument, to either return results as list or as simple vector.find_variables()
gets a flatten
-argument, to either return results as list or as simple vector.get_data()
did not work when model formula contained a function with namespace-prefix (like lm(Sepal.Length ~ splines::bs(Petal.Width, df=4)
) (#93).get_priors()
failed for stanreg-models, when one or more priors had no adjusted scales (#74).find_random()
failed for mixed models with multiple responses.get_random()
failed for brmsfit and stanreg models.get_parameters()
and find_parameters()
did not work for MixMod
-objects without zero-inflation component, when component = "all"
(the default).find_formula()
did not work for plm
-models without instrumental variables.find_formula()
returned random effects as conditional part of the formula for null-models (only intercept in fixed parts) (#87).felm
-models for R-devel on Linux.get_data()
for gee models, where incomplete cases were not removed from the data.get_data()
for null-models (only intercept in fixed parts) from models of class glmmTMB
, brmsfit
, MixMod
and rstanarm
(#91).find_variables()
no longer returns (multiple) "1"
for random effects.AsIs
-variables with division-operation as dependent variables, e.g. if outcome was defined as I(income/frequency)
, especially for find_response()
and get_data()
.felm
-models due to breaking changes in the lfe-package.iv_robust
(estimatr), crch
(crch), gamlss
(gamlss), lmrob
and glmrob
(robustbase, #64), rq
, rqss
and crq
(quantreg), rlmer
(robustlmm), mixed
(afex), tobit
(AER) and survreg
(survival).get_variance()
, to calculate the variance components from mixed models of class merMod
, glmmTMB
, MixMod
, rlmer
, mixed
, lme
and stanreg
(#52). Furthermore, convenient shortcuts to return the related components directly, like get_variance_random()
or get_variance_residual()
.find_algorithm()
, to get information about sampling algorithms and optimizers, and for Bayesian models also about chains and iterations (#38).find_random_slopes()
, which returns the names of the random slopes of mixed models.get_priors()
, to get a summary of priors used for a model (#39).is_model()
to check whether an object is a (supported) regression model (#69).all_models_equal()
to check whether objects are all (supported) regression models and of same class.print_color()
(resp. print_colour()
) to print coloured output to the console. Mainly implemented to reduce package dependencies.find_parameters()
and get_parameters()
get a parameters
-argument for brmsfit
and stanreg
models, to allow selection of specific parameters that should be returned (#55).find_parameters()
and get_parameters()
now also return simplex parameters of monotic effects (brms only) and smooth terms (e.g. for gam-models).find_terms()
and find_predictors()
no longer return constants, in particular pi
(#26).gls
and lme
objects, functions like find_formula()
etc. also return the correlation component (#19).model_info
now returns $is_tweedie
for models from tweedie-families.find_parameters()
and get_parameters()
did not preserve coefficients of monotonic category-specific effects from brmsfit-objects.find_predictors()
or get_parameters()
than requested.get_data()
for MixMod-objects when response variable was defined via cbind()
.get_response()
for models that used cbind()
with a substraction (e.g. cbind(success, total - success)
). In such cases, values for second column (in this example: total
) were the substracted values total - success
, not the original values from total
.