Tidy methods for wbm models

panelr provides methods to access wbm data in a tidy format

Usage

# S3 method for wbm
tidy(
  x,
  conf.int = FALSE,
  conf.level = 0.95,
  effects = c("fixed", "ran_pars"),
  conf.method = "Wald",
  ran_prefix = NULL,
  ...
)

# S3 method for wbm
glance(x, ...)

# S3 method for summ.wbm
glance(x, ...)

# S3 method for summ.wbm
tidy(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); "ran_vals" (conditional modes/BLUPs/latent variable estimates); or "ran_coefs" (predicted parameter values for each group, as returned by coef.merMod.

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

...

Additional arguments (passed to confint.merMod for tidy; augment_columns for augment; ignored for glance)

Examples

data("WageData")
wages <- panel_data(WageData, id = id, wave = t)
model <- wbm(lwage ~ lag(union) + wks, data = wages)
if (requireNamespace("broom.mixed")) {
  broom.mixed::tidy(model)
}
#> Loading required namespace: broom.mixed
#> # A tibble: 7 × 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)  
#> 7 Residual  0.233    NA          NA     NA        sd__Observation