assert_dependency:
check if a package is available and return informative...
augment.ranef.mer:
Augmentation for random effects (for caterpillar plots etc.)
brms_tidiers:
Tidying methods for a brms model
compact:
Remove NULL items in a vector or list
extractEffects:
Internal function to extract the fixed or random effects from...
fixef.MCMCglmm:
Extract fixed effects from an 'MCMCglmm' object
gamlss_tidiers:
Tidying methods for gamlss objects
get_methods:
Retrieve all method/class combinations currently provided by...
glmmadmb_tidiers:
Tidying methods for glmmADMB models
glmmTMB_tidiers:
Tidying methods for glmmTMB models
insert_NAs:
insert a row of NAs into a data frame wherever another data...
lme4_tidiers:
Tidying methods for mixed effects models
mcmc_tidiers:
Tidying methods for MCMC (Stan, JAGS, etc.) fits
nlme_tidiers:
Tidying methods for mixed effects models
paramNamesMCMCglmm:
Extract the parameter names from an 'MCMCglmm' object
ranefLevels:
Extract the levels of factors used for random effects in...
ranef.MCMCglmm:
Extract random effects from an 'MCMCglmm' object
reexports:
Objects exported from other packages
rstanarm_tidiers:
Tidying methods for an rstanarm model
stdranef:
Extract standard deviation of "random" effects from an...
tidy.TMB:
Tidying methods for TMB models
tidy.varFunc:
Tidy variance structure for the 'nlme' package.
unrowname:
strip rownames from an object
Browse all...
Tidying methods for mixed effects models
Description
These methods tidy the coefficients of
lme4::lmer
and
lme4::glmer
models (i.e.,
merMod
objects). Methods are also provided for
allFit
objects.
Usage
## S3 method for class 'merMod'
tidy(
effects = c("ran_pars", "fixed"),
scales = NULL,
exponentiate = FALSE,
ran_prefix = NULL,
conf.int = FALSE,
conf.level = 0.95,
conf.method = "Wald",
ddf.method = NULL,
profile = NULL,
debug = FALSE,
## S3 method for class 'rlmerMod'
tidy(
effects = c("ran_pars", "fixed"),
scales = NULL,
exponentiate = FALSE,
ran_prefix = NULL,
conf.int = FALSE,
conf.level = 0.95,
conf.method = "Wald",
ddf.method = NULL,
profile = NULL,
debug = FALSE,
## S3 method for class 'merMod'
augment(x, data = stats::model.frame(x), newdata, ...)
## S3 method for class 'merMod'
glance(x, ...)
Arguments
An object of class
merMod
, such as those from
lmer
,
glmer
, or
nlmer
effects
A character vector including one or more of "fixed" (fixed-effect parameters); "ran_pars" (variances and covariances or standard deviations and correlations of random effect terms); "ran_vals" (conditional modes/BLUPs/latent variable estimates); or "ran_coefs" (predicted parameter values for each group, as returned by
coef.merMod
)
scales
scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if
scales
is
NULL
) or ‘"vcov"’ (variances and covariances).
NA
means no transformation, appropriate e.g. for fixed effects.
exponentiate
whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if
TRUE
, also scales the standard errors by the exponentiated coefficient, transforming them to the new scale
ran_prefix
a length-2 character vector specifying the strings to use as prefixes for self- (variance/standard deviation) and cross- (covariance/correlation) random effects terms
conf.int
whether to include a confidence interval
conf.level
confidence level for CI
conf.method
method for computing confidence intervals (see
lme4::confint.merMod
)
ddf.method
the method for computing the degrees of freedom and t-statistics (only applicable when using the
lmerTest
package: see
summary.lmerModLmerTest
profile
pre-computed profile object, for speed when using
conf.method="profile"
debug
print debugging output?
Additional arguments (passed to
confint.merMod
for
tidy
;
augment_columns
for
augment
; ignored for
glance
)
original data this was fitted on; if not given this will
attempt to be reconstructed
newdata
new data to be used for prediction; optional
Details
When the modeling was performed with
na.action = "na.omit"
(as is the typical default), rows with NA in the initial data are omitted
entirely from the augmented data frame. When the modeling was performed
with
na.action = "na.exclude"
, one should provide the original data
as a second argument, at which point the augmented data will contain those
rows (typically with NAs in place of the new columns). If the original data
is not provided to
augment
and
na.action = "na.exclude"
, a
warning is raised and the incomplete rows are dropped.
Value
All tidying methods return a
data.frame
without rownames.
The structure depends on the method chosen.
tidy
returns one row for each estimated effect, either
with groups depending on the
effects
parameter.
It contains the columns
group
the group within which the random effect is being estimated:
"fixed"
for fixed effects
level
level within group (
NA
except for modes)
term being estimated
estimate
estimated coefficient
std.error
standard error
statistic
t- or Z-statistic (
NA
for modes)
p.value
P-value computed from t-statistic (may be missing/NA)
augment
returns one row for each original observation,
with columns (each prepended by a .) added. Included are the columns
.fitted
predicted values
.resid
residuals
.fixed
predicted values with no random effects
Also added for "merMod" objects, but not for "mer" objects,
are values from the response object within the model (of type
lmResp
,
glmResp
,
nlsResp
, etc). These include
".mu",
".offset", ".sqrtXwt", ".sqrtrwt", ".eta"
.
glance
returns one row with the columns
the number of observations
sigma
the square root of the estimated residual variance
logLik
the data's log-likelihood under the model
the Akaike Information Criterion
the Bayesian Information Criterion
deviance
deviance
See Also
na.action
Examples
if (require("lme4")) {
## original model
## Not run:
lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
## End(Not run)
## load stored object
load(system.file("extdata", "lme4_example.rda", package="broom.mixed"))
(tt <- tidy(lmm1))
tidy(lmm1, effects = "fixed")
tidy(lmm1, effects = "fixed", conf.int=TRUE)
tidy(lmm1, effects = "fixed", conf.int=TRUE, conf.method="profile")
## lmm1_prof <- profile(lmm1) # generated by extdata/runexamples
tidy(lmm1, conf.int=TRUE, conf.method="profile", profile=lmm1_prof)
## conditional modes (group-level deviations from population-level estimate)
tidy(lmm1, effects = "ran_vals", conf.int=TRUE)
## coefficients (group-level estimates)
(rcoef1 <- tidy(lmm1, effects = "ran_coefs"))
if (require(tidyr) && require(dplyr)) {
## reconstitute standard coefficient-by-level table
spread(rcoef1,key=term,value=estimate)
## split ran_pars into type + term; sort fixed/sd/cor
(tt %>% separate(term,c("type","term"),sep="__",fill="left")
%>% arrange(!is.na(type),desc(type)))
head(augment(lmm1, sleepstudy))
glance(lmm1)
glmm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = binomial)
tidy(glmm1)
tidy(glmm1,exponentiate=TRUE)
tidy(glmm1, effects = "fixed")
## suppress warning about influence.merMod
head(suppressWarnings(augment(glmm1, cbpp)))
glance(glmm1)
startvec <- c(Asym = 200, xmid = 725, scal = 350)
nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree,
Orange, start = startvec)
## suppress warnings about var-cov matrix ...
op <- options(warn=-1)
tidy(nm1)
tidy(nm1, effects = "fixed")
options(op)
head(augment(nm1, Orange))
glance(nm1)
detach("package:lme4")
if (require("lmerTest")) {
lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
tidy(lmm1)
glance(lmm1)
detach("package:lmerTest") # clean up
