This is an R package designed to aid in the analysis of panel data, designs in which the same group of respondents/entities are contacted/measured multiple times. `panelr`

provides some useful infrastructure, like a `panel_data`

object class, as well as automating some emerging methods for analyses of these data.

It automates the “within-between” (also known as “between-within” and “hybrid”) specification that combines the desirable aspects of both fixed effects and random effects econometric models and fits them using the lme4 package in the backend. Bayesian estimation of these models is supported by interfacing with the brms package.

At the moment, `panelr`

is only available through Github. A submission to CRAN is coming soon.

```
install.packages("devtools")
devtools::install_github("jacob-long/panelr")
```

Note the several dependencies: `dplyr`

, `tidyr`

, `lme4`

, `pbkrtest`

, `jtools`

, `magrittr`

, `stringr`

, and `rlang`

. You will need `brms`

(and its dependencies, like `rstan`

) to do Bayesian estimation.

`panel_data`

framesWhile not strictly required, the best way to start is to declare your data as panel data. I’ll load the example data `WageData`

to demonstrate.

```
#> [1] "exp" "wks" "occ" "ind" "south" "smsa" "ms" "fem"
#> [9] "union" "ed" "blk" "lwage" "t" "id"
```

The two key variables here are `t`

and `id`

. `t`

is the wave of the survey the row of the data refers to while `id`

is the survey respondent. This is a perfectly balanced data set, so there are 7 observations for each of the 595 respondents. We will use those two pieces of information to create a `panel_data`

object.

`wages <- panel_data(WageData, id = id, wave = t)`

We have to tell `panel_data()`

which column refers to the unique identifiers for respondents/entities (the latter when you have something like countries or companies instead of people) and which column refers to the period/wave of data collection. If the waves are not numeric and indexed starting at 1, the function will attempt to coerce them to that kind of numbering scheme.

Note that the resulting `panel_data`

object will always use the column names `id`

and `wave`

, so it will overwrite those columns if they already exist in the source data. `panel_data`

frames are modified tibbles (`tibble`

package) that are grouped by entity.

`wbm`

— the within-between modelAnyone can fit a within-between model without the use of this package as it is just a particular specification of a multilevel model. With that said, it’s something that will require some programming and could be rather prone to error. In the best case, it is cumbersome and inefficient to create the necessary variables.

`wbm`

is the primary function that you’ll use from this package and it fits within-between models for you, utilizing `lme4`

as a backend.

A three-part model syntax is used that goes like this:

`dv ~ varying_variables | invariant_variables | cross_level_interactions`

It works like a typical formula otherwise. The bars just tell `panelr`

how to treat the variables. Note also that you can specify random slopes using `lme4`

-style syntax in the third part of the formula as well.

Lagged variables are supported as well through the `lag`

function. Unlike base R, `panelr`

lags the variables correctly — wave 1 observations will have NA values for the lagged variable rather than taking the final wave value of the previous entity.

Here we will specify a model using the `wages`

data. We will predict logged wages (`lwage`

) using two time-varying variables — lagged union membership (`union`

) and contemporaneous weeks worked (`wks`

) — along with a time-invariant predictor, a binary indicator for black race (`blk`

). For demonstrative purposes, we’ll fit a random slope for `wks`

and an interaction between `blk`

and `lag(union)`

.

```
model <- wbm(lwage ~ lag(union) + wks | blk | blk * lag(union) + (wks | id),
data = wages)
summary(model)
```

```
#> MODEL INFO:
#> Entities: 595
#> Time periods: 2-7
#> Dependent variable: lwage
#> Model type: Linear mixed effects
#> Specification: within-between
#>
#> MODEL FIT:
#> AIC = 1426.48, BIC = 1494.47
#> Pseudo-R² (fixed effects) = 0.05
#> Pseudo-R² (total) = 0.75
#>
#> WITHIN EFFECTS:
#> Est. S.E. t val. d.f. p
#> lag(union) 0.05 0.03 2.01 2966 0.04 *
#> wks -0.00 0.00 -2.93 116 0.00 **
#>
#> Within-entity ICC = 0.73
#>
#> BETWEEN EFFECTS:
#> Est. S.E. t val. d.f. p
#> (Intercept) 6.25 0.24 25.93 591 0.00 ***
#> imean(union) 0.03 0.04 0.85 589 0.40
#> imean(wks) 0.01 0.01 2.06 591 0.04 *
#> blk -0.35 0.06 -5.61 587 0.00 ***
#>
#> INTERACTIONS:
#> Est. S.E. t val. d.f. p
#> lag(union):blk -0.12 0.12 -0.98 2934 0.33
#>
#> p values calculated using Satterthwaite d.f.
#>
#> RANDOM EFFECTS:
#> Group Parameter Std. Dev.
#> id (Intercept) 0.38
#> id wks 0.01
#> Residual 0.23
```

Note that `imean`

is an internal function that calculates the individual-level mean, which represents the between-subjects effects of the time-varying predictors. The within effects are the time-varying predictors at the occasion level with the individal-level mean subtracted. If you want the model specified such that the occasion level predictors do not have the mean subtracted, use the `model = "contextual"`

argument. The “contextual” label refers to the way these terms are normally interpreted when it is specified that way.

`widen_panel`

and `long_panel`

Two functions that should cover your bases for the tricky business of **reshaping** panel data are included. Sometimes, like for doing SEM-based analyses, you need your data in wide format — i.e., one row per entity. `widen_panel`

makes that easy and should require minimal trial and error or thinking.

Perhaps more often, your raw data are already in wide format and you need to get it into long format to do cool stuff like use `wbm`

. That can be very tricky, but `long_panel`

(I didn’t think `lengthen_panel`

or `longen_panel`

quite worked as names) should cover most situations. You tell it what the labels for periods are (e.g., does it range from `1`

to `5`

, `"A"`

to `"E"`

, or something else?), where they are located (before or after the variable’s name?), and what kinds of formatting go before/after it. Unbalanced data are perfectly fine, unlike when trying to use the already confusing `reshape`

function.

I’m happy to receive bug reports, suggestions, questions, and (most of all) contributions to fix problems and add features. I prefer you use the Github issues system over trying to reach out to me in other ways. Pull requests for contributions are encouraged.

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.

The source code of this package is licensed under the MIT License.