`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
```