These methods tidy the coefficients of lme4::lmer and lme4::glmer
models (i.e., merMod objects). Methods are also provided for allFit
objects.
# S3 method for merMod
tidy(
x,
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 rlmerMod
tidy(
x,
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 merMod
augment(x, data = stats::model.frame(x), newdata, ...)
# S3 method for merMod
glance(x, ...)An object of class merMod, such as those from lmer,
glmer, or nlmer
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 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.
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
a length-2 character vector specifying the strings to use as prefixes for self- (variance/standard deviation) and cross- (covariance/correlation) random effects terms
whether to include a confidence interval
confidence level for CI
method for computing confidence intervals (see lme4::confint.merMod)
the method for computing the degrees of freedom and t-statistics (only applicable when using the lmerTest package: see summary.lmerModLmerTest
pre-computed profile object, for speed when using conf.method="profile"
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
new data to be used for prediction; optional
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
the group within which the random effect is being estimated: "fixed" for fixed effects
level within group (NA except for modes)
term being estimated
estimated coefficient
standard error
t- or Z-statistic (NA for modes)
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
predicted values
residuals
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
the square root of the estimated residual variance
the data's log-likelihood under the model
the Akaike Information Criterion
the Bayesian Information Criterion
deviance
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.
if (require("lme4")) {
## original model
if (FALSE) {
lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
}
## 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")
}
#> Computing profile confidence intervals ...
#> Loading required package: tidyr
#> Warning: package ‘tidyr’ was built under R version 4.1.2
#>
#> Attaching package: ‘tidyr’
#> The following objects are masked from ‘package:Matrix’:
#>
#> expand, pack, unpack
if (require("lmerTest")) {
lmm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
tidy(lmm1)
glance(lmm1)
detach("package:lmerTest") # clean up
}
#> Loading required package: lmerTest
#> Loading required package: lme4
#>
#> Attaching package: ‘lmerTest’
#> The following object is masked from ‘package:lme4’:
#>
#> lmer
#> The following object is masked from ‘package:stats’:
#>
#> step