Fit "within-between" and several other regression variants for panel data via generalized estimating equations.
wbgee(
formula,
data,
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,
interaction.style = c("double-demean", "demean", "raw"),
scale = FALSE,
scale.response = FALSE,
n.sd = 1,
calc.fit.stats = TRUE,
...
)A wbgee object, which inherits from 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.
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().
Jacob A. Long
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. https://doi.org/10.4135/9781412993869.d33
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. https://doi.org/10.1017/psrm.2014.7
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. https://doi.org/10.1146/annurev.psych.093008.100356
Giesselmann, M., & Schmidt-Catran, A. W. (2020). Interactions in fixed effects regression models. Sociological Methods & Research, 1–28. https://doi.org/10.1177/0049124120914934
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
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.
https://doi.org/10.1177/1536867X1701700106
if (requireNamespace("geepack")) {
data("WageData")
wages <- panel_data(WageData, id = id, wave = t)
model <- wbgee(lwage ~ lag(union) + wks | blk + fem | blk * lag(union),
data = wages)
summary(model)
}
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