stanreg
tidier gains exponentiate
argument (wish of GH #122)tidy.brmsfit
gains optional rhat
and ess
columns (Alexey Stukalov)lqmm
models (David Luke Thiessen)glmmTMB
tidying with conf.int=TRUE
, random effects in multiple model components, subset of components requested in tidy output (GH #136, Daniel Sjoberg)tidy.brmsfit
works better for models with no random/group-level effects (Matthieu Bruneaux)as.data.frame.ranef.lme
now processes the optional argument (see ?as.data.frame)
, so that data.frame(ranef_object)
works
stanreg
tidier now checks for spurious values in ...
TMB
tidierslme
tidier gets functionality for information about variance models (use effects = "var_model"
) (Bill Denney)
support for models with fixed sigma values in lme
tidier (Bill Denney)
added tidy
and glance
methods for allFit
objects from the lme4
package
get_methods()
function returns a table of all available tidy
/glance
/augment
methods
improved lme tidying for random effects values
brms tidiers no longer use deprecated posterior_samples
glance.lme4
now returns nobs (Cory Brunson)
some tidiers are less permissive about unused arguments passed via ...
TMB
tidiers (the TMB package does not return an object of class TMB, so users should run class(fit) <- "TMB"
before tidying)term names are no longer “sanitized” in gamlss
tidiers (e.g. “(Intercept)” is not converted to “X.Intercept.”)
gamlss glance
method returns nobs
(GH #113)
Wald confidence intervals for lmerTest
models now respect ddf.method
tidy.glmmTMB(.,effects="ran_vals")
fixed for stringsAsFactors
changes in glmmTMB (GH #103)
tidy.gamlss
now works in a wider range of cases (GH #74)
tidy.brmsfit
works for models without group effects (GH #100)
dplyr
1.0.0; skip exampleslmer
tidier gets ddf.method
(applies only to lmerTest
fits)
glmmTMB
gets exponentiate
options
experimental GLMMadaptive
tidiers
tibble
packagegls
tidier gets confint
(GH #49)estimate.method
in MCMC tidiers goes away; use robust
to compute point estimates/uncertainty via median and MAD rather than mean and SEmisc fixes: lme4 tidiers (confint for ran_vals
, profile conf intervals fixed), R2jags, gamlss …
ran_vals
works for glmmTMB
don’t ignore conf.level
in tidy.(merMod|glmmTMB)
(GH #30,31: @strengejacke)
levels correct in tidy.brmsfit
(GH #36: @strengejacke)
component
argument works for random effects in glmmTMB
(GH #33: @strengejacke)
brmsfit
and rstanarm
methods allow conf.method="HPDinterval"
tidy.brmsfit
gets component column (GH #35: @strengejacke), response column for multi-response models (GH #34: @strengejacke)
component tags are stripped from tidied brmsfit
objects
“Intercept” terms in brms
fits are re-coded as “(Intercept)” by default, for dotwhisker/cross-model compatibility; for previous behaviour, specify fix.intercept=FALSE
more consistent term names in brmsfit
, rstanreg
tidiers
improved tidy.MCMCglmm
all methods return tibbles (tbl_df
) rather than data frames
the value of the group variable for fixed-effect parameters has changed from "fixed"
to NA
brmsfit
and rstanarm
tidiers are more consistent with other tidiers (e.g. the argument for setting confidence level is conf.level
rather than prob
)
"ran_vals"
extracts conditional modes/BLUPs/varying parameters (deviations from population-level estimates), while "ran_coefs"
extracts group-level estimatesimproved nlme
tidiers
improved glmmTMB
tidiers (can handle some zero-inflation parameters)
lme4
tidiers now optionally take a pre-computed profile argument when using conf.method="profile"
scales="sdcor"
[default]) or their variances and covariances (if scales = "varcov"
)effects = "ran_coefs"
for the group-level estimates (previously these effects were extracted with tidy(model, "random")
) or effects = "ran_vals"
for the conditional modes (deviations of the group-level parameters from the population-level estimates)effects
can take a vector of values (those listed above, plus “fixed” for fixed effects). The default value is effects = c(“ran_pars”, “fixed”) which extracts random effect variances/covariances and fixed effect estimates.group
specifier (at least for lme4 models
); use something like tidyr::unite(term,term,group,sep=".")
to collapse the two columns