model_info {insight} | R Documentation |
Access information from model objects
Description
Retrieve information from model objects.
Usage
model_info(x, ...)
## Default S3 method:
model_info(x, verbose = TRUE, ...)
Arguments
x |
A fitted model. |
... |
Currently not used. |
verbose |
Toggle off warnings. |
Details
model_info()
returns a list with information about the
model for many different model objects. Following information
is returned, where all values starting with is_
are logicals.
-
is_binomial
: family is binomial (but not negative binomial) -
is_bernoulli
: special case of binomial models: family is Bernoulli -
is_poisson
: family is poisson -
is_negbin
: family is negative binomial -
is_count
: model is a count model (i.e. family is either poisson or negative binomial) -
is_beta
: family is beta -
is_betabinomial
: family is beta-binomial -
is_orderedbeta
: family is ordered beta -
is_dirichlet
: family is dirichlet -
is_exponential
: family is exponential (e.g. Gamma or Weibull) -
is_logit
: model has logit link -
is_probit
: model has probit link -
is_linear
: family is gaussian -
is_tweedie
: family is tweedie -
is_ordinal
: family is ordinal or cumulative link -
is_cumulative
: family is ordinal or cumulative link -
is_multinomial
: family is multinomial or categorical link -
is_categorical
: family is categorical link -
is_censored
: model is a censored model (has a censored response, including survival models) -
is_truncated
: model is a truncated model (has a truncated response) -
is_survival
: model is a survival model -
is_zero_inflated
: model has zero-inflation component -
is_hurdle
: model has zero-inflation component and is a hurdle-model (truncated family distribution) -
is_dispersion
: model has dispersion component (not only dispersion parameter) -
is_mixed
: model is a mixed effects model (with random effects) -
is_multivariate
: model is a multivariate response model (currently only works for brmsfit and vglm/vgam objects) -
is_trial
: model response contains additional information about the trials -
is_bayesian
: model is a Bayesian model -
is_gam
: model is a generalized additive model -
is_anova
: model is an Anova object -
is_ttest
: model is an an object of classhtest
, returned byt.test()
-
is_correlation
: model is an an object of classhtest
, returned bycor.test()
-
is_ranktest
: model is an an object of classhtest
, returned bycor.test()
(if Spearman's rank correlation),wilcox.text()
orkruskal.test()
. -
is_variancetest
: model is an an object of classhtest
, returned bybartlett.test()
,shapiro.test()
orcar::leveneTest()
. -
is_levenetest
: model is an an object of classanova
, returned bycar::leveneTest()
. -
is_onewaytest
: model is an an object of classhtest
, returned byoneway.test()
-
is_proptest
: model is an an object of classhtest
, returned byprop.test()
-
is_binomtest
: model is an an object of classhtest
, returned bybinom.test()
-
is_chi2test
: model is an an object of classhtest
, returned bychisq.test()
-
is_xtab
: model is an an object of classhtest
orBFBayesFactor
, and test-statistic stems from a contingency table (i.e.chisq.test()
orBayesFactor::contingencyTableBF()
). -
link_function
: the link-function -
family
: name of the distributional family of the model. For some exceptions (like somehtest
objects), can also be the name of the test. -
n_obs
: number of observations -
n_grouplevels
: for mixed models, returns names and numbers of random effect groups
Value
A list with information about the model, like family, link-function etc. (see 'Details').
Examples
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20 - numdead)
dat <- data.frame(ldose, sex, SF, stringsAsFactors = FALSE)
m <- glm(SF ~ sex * ldose, family = binomial)
# logistic regression
model_info(m)
# t-test
m <- t.test(1:10, y = c(7:20))
model_info(m)