These methods tidy the coefficients of glmmADMB models
# S3 method for glmmadmb
tidy(
x,
effects = c("fixed", "ran_pars"),
component = "cond",
scales = NULL,
ran_prefix = NULL,
conf.int = FALSE,
conf.level = 0.95,
conf.method = "Wald",
...
)
# S3 method for glmmadmb
augment(x, data = stats::model.frame(x), newdata, ...)
# S3 method for glmmadmb
glance(x, ...)An object of class glmmadmb
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) or "ran_vals" (conditional modes/BLUPs/latent variable estimates)
Which component(s) to report for (e.g., conditional, zero-inflation, dispersion: at present only works for "cond")
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 ‘"varcov"’ (variances and covariances). NA means no transformation, appropriate e.g. for fixed effects; inverse-link transformations (exponentiation
or logistic) are not yet implemented, but may be in the future.
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 confint.merMod)
extra arguments (not used)
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 tbl_df 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: NA 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 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("glmmADMB") && require("lme4")) {
## original model
if (FALSE) {
data("sleepstudy", package="lme4")
lmm1 <- glmmadmb(Reaction ~ Days + (Days | Subject), sleepstudy,
family="gaussian")
}
## load stored object
load(system.file("extdata","glmmADMB_example.rda",package="broom.mixed"))
tidy(lmm1, effects = "fixed")
tidy(lmm1, effects = "fixed", conf.int=TRUE)
## tidy(lmm1, effects = "fixed", conf.int=TRUE, conf.method="profile")
## tidy(lmm1, effects = "ran_vals", conf.int=TRUE)
head(augment(lmm1, sleepstudy))
glance(lmm1)
glmm1 <- glmmadmb(cbind(incidence, size - incidence) ~ period + (1 | herd),
data = cbpp, family = "binomial")
tidy(glmm1)
tidy(glmm1, effects = "fixed")
head(augment(glmm1, cbpp))
glance(glmm1)
}
#> Loading required package: glmmADMB
#> Warning: there is no package called ‘glmmADMB’