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

## Usage

```
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.- data
The data, either a

`panel_data`

object or`data.frame`

.- id
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.- wave
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.- cor.str
Any correlation structure accepted by

`geepack::geeglm()`

. Default is "ar1", most useful alternative is "exchangeable". "unstructured" may cause problems due to its computational complexity.- 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

`2`

and any valid number is accepted.`"all"`

is also acceptable if you want to include only complete panelists.- family
Use this to specify GLM link families. Default is

`gaussian`

, the linear model.- 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

*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()`

.

## 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: geepack
#> Registered S3 methods overwritten by 'broom':
#> method from
#> tidy.glht jtools
#> tidy.summary.glht jtools
#> Loading required namespace: car
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
#> -------------------------------
```