Several functions to retrieve information from model objects, like variable names, link-inverse function, model frame, model family etc., in a tidy and consistent way.
pred_vars(x)resp_var(x)
re_grp_var(x)
resp_val(x)
link_inverse(x, multi.resp = FALSE, mv = FALSE)
model_frame(x, fe.only = TRUE)
model_family(x, multi.resp = FALSE, mv = FALSE)
var_names(x)
A fitted model; for var_names(), x may also be a
character vector.
Logical, if TRUE and model is a multivariate response
model from a brmsfit object or of class stanmvreg, then a
list of values (one for each regression) is returned.
Logical, if TRUE (default) and x is a mixed effects
model, returns the model frame for fixed effects only.
For pred_vars() and resp_var(), the name(s) of the
response or predictor variables from x as character vector.
resp_val() returns the values from x's response vector.
re_grp_var() returns the group factor of random effects in
mixed models, or NULL if x has no such random effects term.
link_inverse() returns, if known, the inverse link function from
x; else NULL for those models where the inverse link function
can't be identified. model_frame() is similar to model.frame(),
but should also work for model objects that don't have a S3-generic for
model.frame(). var_names() returns the "cleaned" variable
names, i.e. things like s() for splines or log() are
removed. model_family() returns a list with information about the
model family (see 'Details').
model_family() returns a list with information about the
model family for many different model objects. Following information
is returned, where all values starting with is_ are logicals.
is_bin: family is binomial (but not negative binomial)
is_pois: family is either poisson or negative binomial
is_negbin: family is negative binomial
is_logit: model has logit link
is_linear: family is gaussian
is_ordinal: family is ordinal or cumulative link
is_categorical: family is categorical link
is_zeroinf: model has zero-inflation component
is_multivariate: model is a multivariate response model (currently only works for brmsfit objects)
link.fun: the link-function
family: the family-object
model_frame() slighty differs from model.frame(), especially
for spline terms. Where model.frame() returns a matrix for splines,
model_frame() returns the data of the original variable and uses
the same column name as in the data-argument from the model-function.
This makes it easier, for instance, to get data that should be used as new
data in predict(). See 'Examples'.
# NOT RUN {
data(efc)
fit <- lm(neg_c_7 ~ e42dep + c161sex, data = efc)
pred_vars(fit)
resp_var(fit)
resp_val(fit)
link_inverse(fit)(2.3)
# example from ?stats::glm
counts <- c(18, 17, 15, 20, 10, 20, 25, 13, 12)
outcome <- gl(3, 1, 9)
treatment <- gl(3, 3)
m <- glm(counts ~ outcome + treatment, family = poisson())
link_inverse(m)(.3)
# same as
exp(.3)
outcome <- as.numeric(outcome)
m <- glm(counts ~ log(outcome) + as.factor(treatment), family = poisson())
var_names(m)
# model.frame and model_frame behave slightly different
library(splines)
m <- lm(neg_c_7 ~ e42dep + ns(c160age), data = efc)
head(model.frame(m))
head(model_frame(m))
# get random effects grouping factor from mixed models
library(lme4)
data(sleepstudy)
m <- lmer(Reaction ~ Days + (1 + Days | Subject), data = sleepstudy)
re_grp_var(m)
# }
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