Fit "within-between" and several other regression variants for panel data via generalized estimating equations.

asym_gee( formula, data, id = NULL, wave = NULL, cor.str = c("ar1", "exchangeable", "unstructured"), use.wave = FALSE, wave.factor = FALSE, min.waves = 1, family = gaussian, weights = NULL, offset = NULL, ... )

formula | Model formula. See details for crucial
info on |
---|---|

data | The data, either a |

id | If |

wave | If |

cor.str | Any correlation structure accepted by |

use.wave | Should the wave be included as a predictor? Default is FALSE. |

wave.factor | Should the wave variable be treated as an unordered factor instead of continuous? Default is FALSE. |

min.waves | What is the minimum number of waves an individual must
have participated in to be included in the analysis? Default is |

family | Use this to specify GLM link families. Default is |

weights | If using weights, either the name of the column in the data that contains the weights or a vector of the weights. |

offset | this can be used to specify an |

... | Additional arguments provided to |

An `asym_gee`

object, which inherits from `wbgee`

and `geeglm`

.

See the documentation for `wbm()`

for many details on formula syntax and
other arguments.

Allison, P. D. (2019). Asymmetric fixed-effects models for panel data.
*Socius*, *5*, 1-12. https://doi.org/10.1177/2378023119826441

McNeish, D. (2019). Effect partitioning in cross-sectionally clustered data
without multilevel models. *Multivariate Behavioral Research*,
Advance online publication. https://doi.org/10.1080/00273171.2019.1602504

McNeish, D., Stapleton, L. M., & Silverman, R. D. (2016). On the unnecessary
ubiquity of hierarchical linear modeling. *Psychological Methods*, *22*,
114-140. https://doi.org/10.1037/met0000078

data("WageData") wages <- panel_data(WageData, id = id, wave = t) model <- asym_gee(lwage ~ lag(union) + wks, data = wages)#>#>#> #> #>#>#>#> #> #> #> #>summary(model)#> MODEL INFO: #> Entities: 595 #> Time periods: 3-7 #> Dependent variable: lwage #> Model family: Linear #> Variance: ar1 (alpha = -0.3) #> Specification: Asymmetric effects (via GEE) #> #> MODEL FIT: #> QIC = 121.63, QICu = 117.85, CIC = 6.89 #> #> ------------------------------------------------ #> Est. S.E. z val. p #> ----------------- ------- ------ -------- ------ #> (Intercept) 0.10 0.00 39.46 0.00 #> +lag(union) 0.02 0.02 1.13 0.26 #> -lag(union) -0.00 0.02 -0.08 0.94 #> +wks -0.00 0.00 -0.45 0.65 #> -wks -0.00 0.00 -0.49 0.63 #> ------------------------------------------------ #> #> Tests of asymmetric effects: #> ------------------------------- #> chi^2 p #> ---------------- ------- ------ #> lag(union) 0.66 0.42 #> wks 1.10 0.29 #> -------------------------------