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, ...)

## Arguments

formula 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().

## Value

An asym_gee object, which inherits from wbgee and geeglm.

## Details

See the documentation for wbm() for many details on formula syntax and other arguments.

## References

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

## Examples

data("WageData")
wages <- panel_data(WageData, id = id, wave = t)
model <- asym_gee(lwage ~ lag(union) + wks, data = wages)#> Loading required namespace: geepacksummary(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
#> -------------------------------