lme4_tidiers | R Documentation |
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 class 'merMod'
tidy(
x,
effects = c("ran_pars", "fixed"),
scales = NULL,
exponentiate = FALSE,
exponentiate_ran_coefs = 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(
x,
effects = c("ran_pars", "fixed"),
scales = NULL,
exponentiate = FALSE,
exponentiate_ran_coefs = 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, ...)
x |
An object of class |
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 |
scales |
scales on which to report the variables: for random effects, the choices are ‘"sdcor"’ (standard deviations and correlations: the default if |
exponentiate |
whether to exponentiate the fixed-effect coefficient estimates and confidence intervals (common for logistic regression); if |
exponentiate_ran_coefs |
whether to exponentiate the predicted paramater values for each group |
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 |
ddf.method |
the method for computing the degrees of freedom and t-statistics (only applicable when using the lmerTest package: see |
profile |
pre-computed profile object, for speed when using |
debug |
print debugging output? |
... |
Additional arguments (passed to |
data |
original data this was fitted on; if not given this will attempt to be reconstructed |
newdata |
new data to be used for prediction; optional |
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.
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: |
level |
level within group ( |
term |
term being estimated |
estimate |
estimated coefficient |
std.error |
standard error |
statistic |
t- or Z-statistic ( |
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
nobs |
the number of observations |
sigma |
the square root of the estimated residual variance |
logLik |
the data's log-likelihood under the model |
AIC |
the Akaike Information Criterion |
BIC |
the Bayesian Information Criterion |
deviance |
deviance |
na.action
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
}
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