glmmTMB_tidiers: Tidying methods for glmmTMB models

In broom.mixed: Tidying Methods for Mixed Models

Tidying methods for glmmTMB models

Description

These methods tidy the coefficients of mixed effects models, particularly responses of the merMod class

Usage

## S3 method for class 'glmmTMB'
tidy(
  x,
  effects = c("ran_pars", "fixed"),
  component = c("cond", "zi"),
  scales = NULL,
  ran_prefix = NULL,
  conf.int = FALSE,
  conf.level = 0.95,
  conf.method = "Wald",
  exponentiate = FALSE,
  ...
)

## S3 method for class 'glmmTMB'
augment(x, data = stats::model.frame(x), newdata = NULL, ...)

## S3 method for class 'glmmTMB'
glance(x, ...)

Arguments

x	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) or "ran_vals" (conditional modes/BLUPs/latent variable estimates)
component	which component to extract (e.g. cond for conditional effects (i.e., traditional fixed effects); zi for zero-inflation model; disp for dispersion model
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 ‘"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.
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 confint.merMod)
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
...	extra arguments (not used)
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

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 tibble. 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: NA for fixed effects
level	level within group (NA except for modes)
term	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

glance returns one row with the columns

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

Note

zero-inflation parameters (including the intercept) are reported on the logit scale

See Also

na.action

Examples

if (require("glmmTMB") && require("lme4")
    ## &&
    ## make sure package versions are OK
    ## checkDepPackageVersion(dep_pkg = "TMB",
    ##                       this_pkg = "glmmTMB",
    ##                        warn = FALSE) &&
    ## checkDepPackageVersion(dep_pkg = "Matrix",
    ##                       this_pkg = "TMB",
    ##                      warn = FALSE)
)
{
    data("sleepstudy",package="lme4")
    ## original model:
    ## Not run: 
        lmm1 <- glmmTMB(Reaction ~ Days + (Days | Subject), sleepstudy)
    
## End(Not run)
    ## load stored object
    L <- load(system.file("extdata","glmmTMB_example.rda",package="broom.mixed"))
    for (obj in L) {
       assign(obj, glmmTMB::up2date(get(obj)))
    }
    tidy(lmm1)
    tidy(lmm1, effects = "fixed")
    tidy(lmm1, effects = "fixed", conf.int=TRUE)
    tidy(lmm1, effects = "fixed", conf.int=TRUE, conf.method="uniroot")
    ## FIX: tidy(lmm1, effects = "ran_vals", conf.int=TRUE)
    head(augment(lmm1, sleepstudy))
    glance(lmm1)

    ## original model:
    ##  glmm1 <- glmmTMB(incidence/size ~ period + (1 | herd),
    ##                  data = cbpp, family = binomial, weights=size)
    tidy(glmm1)
    tidy(glmm1, effects = "fixed")
    tidy(glmm1, effects = "fixed", exponentiate=TRUE)
    tidy(glmm1, effects = "fixed", conf.int=TRUE, exponentiate=TRUE)
    head(augment(glmm1, cbpp))
    head(augment(glmm1, cbpp, type.residuals="pearson"))
    glance(glmm1)
## Not run: 
    ## profile CIs - a little bit slower but more accurate
    tidy(glmm1, effects = "fixed", conf.int=TRUE, exponentiate=TRUE, conf.method="profile")

## End(Not run)
}

broom.mixed documentation built on Oct. 16, 2024, 1:06 a.m.