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,
  ...
)An asym_gee object, which inherits from wbgee and geeglm.
Model formula. See details for crucial
info on panelr's formula syntax.
The data, either a panel_data object or data.frame.
If data is not a panel_data object, then the name of the
individual id column as a string. Otherwise, leave as NULL, the default.
If data is not a panel_data object, then the name of the
panel wave column as a string. Otherwise, leave as NULL, the default.
Any correlation structure accepted by geepack::geeglm().
Default is "ar1", most useful alternative is "exchangeable". "unstructured"
may cause problems due to its computational complexity.
Should the wave be included as a predictor? Default is FALSE.
Should the wave variable be treated as an unordered factor instead of continuous? Default is FALSE.
What is the minimum number of waves an individual must
have participated in to be included in the analysis? Default is 2 and
any valid number is accepted. "all" is also acceptable if you want to
include only complete panelists.
Use this to specify GLM link families. Default is gaussian,
the linear model.
If using weights, either the name of the column in the data that contains the weights or a vector of the weights.
this can be used to specify an a priori known
    component to be included in the linear predictor during
    fitting. This should be NULL or a numeric vector of length
    equal to the number of cases.  One or more offset
    terms can be included in the formula instead or as well, and if more
    than one is specified their sum is used.  See model.offset.
Additional arguments provided to geepack::geeglm().
Jacob A. Long
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
if (requireNamespace("geepack")) {
  data("WageData")
  wages <- panel_data(WageData, id = id, wave = t)
  model <- asym_gee(lwage ~ lag(union) + wks, data = wages)
  summary(model)
}
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