`wbm`

models`wbm_tidiers.Rd`

`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, ...)

x | An object of class |
---|---|

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 |

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) |

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