panelr provides methods to access wbm data in a tidy format

tidy.wbm(x, conf.int = FALSE, conf.level = 0.95, effects = c("fixed",
  "ran_pars"), conf.method = "Wald", ran_prefix = NULL, ...)

glance.wbm(x, ...)

Arguments

x

An object of class merMod, such as those from lmer, glmer, or nlmer

conf.int

whether to include a confidence interval

conf.level

confidence level for CI

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_modes" (conditional modes/BLUPs/latent variable estimates)

conf.method

method for computing confidence intervals (see lme4::confint.merMod)

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

...

extra arguments (not used)

Examples

data("WageData") wages <- panel_data(WageData, id = id, wave = t) model <- wbm(lwage ~ lag(union) + wks, data = wages) if (requireNamespace("broom")) { broom::tidy(model) }
#> # A tibble: 7 x 6 #> group estimate std.error statistic p.value term #> <chr> <dbl> <dbl> <dbl> <dbl> <chr> #> 1 within 0.0528 0.0250 2.11 3.50e- 2 lag(union) #> 2 within -0.00166 0.00108 -1.54 1.25e- 1 wks #> 3 between 6.14 0.247 24.8 9.32e-94 (Intercept) #> 4 between 0.0168 0.0374 0.449 6.53e- 1 imean(lag(union)) #> 5 between 0.0125 0.00518 2.41 1.62e- 2 imean(wks) #> 6 id 0.388 NA NA NA sd_(Intercept).id #> 7 Residual 0.233 NA NA NA sd_Observation.Residual