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


  id = NULL,
  wave = NULL,
  model = "w-b",
  cor.str = c("ar1", "exchangeable", "unstructured"),
  detrend = FALSE,
  use.wave = FALSE,
  wave.factor = FALSE,
  min.waves = 2,
  family = gaussian,
  balance.correction = FALSE,
  dt.random = TRUE,
  dt.order = 1,
  weights = NULL,
  offset = NULL, = c("double-demean", "demean", "raw"),
  scale = FALSE,
  scale.response = FALSE, = 1, = TRUE,



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.


One of "w-b", "within", "between", "contextual". See details for more on these options.


Any correlation structure accepted by geepack::geeglm(). Default is "ar1", most useful alternative is "exchangeable". "unstructured" may cause problems due to its computational complexity.


Adjust within-subject effects for trends in the predictors? Default is FALSE, but some research suggests this is a better idea (see Curran and Bauer (2011) reference).


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.


Correct between-subject effects for unbalanced panels following the procedure in Curran and Bauer (2011)? Default is FALSE.


Should the detrending procedure be performed with a random slope for each entity? Default is TRUE but for short panels FALSE may be better, fitting a trend for all entities.


If detrending using detrend, what order polynomial would you like to specify for the relationship between time and the predictors? Default is 1, a 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.

The best way to calculate interactions in within models is in some dispute. The conventional way ("demean") is to first calculate the product of the variables involved in the interaction before those variables have their means subtracted and then subtract the mean of the product from the product term (see Schunk and Perales (2017)). Giesselmann and Schmidt-Catran (2020) show this method carries between-entity differences that within models are designed to model out. They suggest an alternate method ("double-demean") in which the product term is first calculated using the de-meaned lower-order variables and then the subject means are subtracted from this product term. Another option is to simply use the product term of the de-meaned variables ("raw"), but Giesselmann and Schmidt-Catran (2020) show this method biases the results towards zero effect. The default is "double-demean" but if emulating other software is the goal, "demean" might be preferred.


If TRUE, reports standardized regression coefficients by scaling and mean-centering input data (the latter can be changed via the scale.only argument). Default is FALSE.


Should the response variable also be rescaled? Default is FALSE.

How many standard deviations should you divide by for standardization? Default is 1, though some prefer 2.

Calculate fit statistics? Default is TRUE, but occasionally poor-fitting models might trip up here.


Additional arguments provided to geepack::geeglm().


A wbgee object, which inherits from geeglm.


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


Allison, P. (2009). Fixed effects regression models. Thousand Oaks, CA: SAGE Publications.

Bell, A., & Jones, K. (2015). Explaining fixed effects: Random effects modeling of time-series cross-sectional and panel data. Political Science Research and Methods, 3, 133–153.

Curran, P. J., & Bauer, D. J. (2011). The disaggregation of within-person and between-person effects in longitudinal models of change. Annual Review of Psychology, 62, 583–619.

Giesselmann, M., & Schmidt-Catran, A. W. (2020). Interactions in fixed effects regression models. Sociological Methods & Research, 1–28.

McNeish, D. (2019). Effect partitioning in cross-sectionally clustered data without multilevel models. Multivariate Behavioral Research, Advance online publication.

McNeish, D., Stapleton, L. M., & Silverman, R. D. (2016). On the unnecessary ubiquity of hierarchical linear modeling. Psychological Methods, 22, 114-140.

Schunck, R., & Perales, F. (2017). Within- and between-cluster effects in generalized linear mixed models: A discussion of approaches and the xthybrid command. The Stata Journal, 17, 89–115.


Jacob A. Long


if (requireNamespace("geepack")) {
  wages <- panel_data(WageData, id = id, wave = t)
  model <- wbgee(lwage ~ lag(union) + wks | blk + fem | blk * lag(union),
           data = wages)
#> Entities: 595
#> Time periods: 2-7
#> Dependent variable: lwage
#> Model type: Linear GEE
#> Variance: ar1 (alpha = 0.85)
#> Specification: within-between
#> QIC = 655.54, QICu = 653.36, CIC = 9.09
#> -----------------------------------------------
#>                     Est.   S.E.   z val.      p
#> ---------------- ------- ------ -------- ------
#> lag(union)          0.02   0.02     0.98   0.33
#> wks                -0.00   0.00    -0.82   0.41
#> -----------------------------------------------
#> ------------------------------------------------------
#>                            Est.   S.E.   z val.      p
#> ----------------------- ------- ------ -------- ------
#> (Intercept)                6.61   0.24    27.12   0.00
#> imean(lag(union))         -0.01   0.03    -0.40   0.69
#> imean(wks)                 0.00   0.01     0.75   0.45
#> blk                       -0.23   0.06    -3.86   0.00
#> fem                       -0.43   0.05    -8.94   0.00
#> ------------------------------------------------------
#> ---------------------------------------------------
#>                         Est.   S.E.   z val.      p
#> -------------------- ------- ------ -------- ------
#> lag(union):blk         -0.11   0.05    -2.22   0.03
#> ---------------------------------------------------