Is it possible in some way to use a fit object, specifically the regression object I get form a plm() model, to flag observations, in the data used for the regression, if they were in fact used in the regression. I realize this could be done my looking for complete observations in my original data, but I am curious if there's a way to use the fit/reg object to flag the data.
Let me illustrate my issue with a minimal working example,
First some packages needed,
# install.packages(c("stargazer", "plm", "tidyverse"), dependencies = TRUE)
library(plm); library(stargazer); library(tidyverse)
Second some data, this example is drawing heavily on Baltagi (2013), table 3.1, found in ?plm,
data("Grunfeld", package = "plm")
dta <- Grunfeld
now I create some semi-random missing values in my data object, dta
dta[c(3:13),3] <- NA; dta[c(22:28),4] <- NA; dta[c(30:33),5] <- NA
final step in the data preparation is to create a data frame with an index attribute that describes its individual and time dimensions, using tidyverse,
dta.p <- dta %>% group_by(firm, year)
Now to the regression
plm.reg <- plm(inv ~ value + capital, data = dta.p, model = "pooling")
the results, using stargazer,
stargazer(plm.reg, type="text") # stargazer(dta, type="text")
#> ============================================
#> Dependent variable:
#> ---------------------------
#> inv
#> ----------------------------------------
#> value 0.114***
#> (0.008)
#>
#> capital 0.237***
#> (0.028)
#>
#> Constant -47.962***
#> (9.252)
#>
#> ----------------------------------------
#> Observations 178
#> R2 0.799
#> Adjusted R2 0.797
#> F Statistic 348.176*** (df = 2; 175)
#> ===========================================
#> Note: *p<0.1; **p<0.05; ***p<0.01
Say I know my data has 200 observations, and I want to find the 178 that was used in the regression.
I am speculating if there's some vector in the plm.reg I can (easily) use to crate a flag i my original data, dta, if this observation was used/not used, i.e. the semi-random missing values I created above. Maybe some broom like tool.
I imagine something like,
dta <- dta %>% valid_reg_obs(plm.reg)
The desired outcome would look something like this, the new element is the vector plm.reg at the end, i.e.,
dta %>% as_tibble()
#> # A tibble: 200 x 6
#> firm year inv value capital plm.reg
#> * <int> <int> <dbl> <dbl> <dbl> <lgl>
#> 1 1 1935 318 3078 2.80 T
#> 2 1 1936 392 4662 52.6 T
#> 3 1 1937 NA 5387 157 F
#> 4 1 1938 NA 2792 209 F
#> 5 1 1939 NA 4313 203 F
#> 6 1 1940 NA 4644 207 F
#> 7 1 1941 NA 4551 255 F
#> 8 1 1942 NA 3244 304 F
#> 9 1 1943 NA 4054 264 F
#> 10 1 1944 NA 4379 202 F
#> # ... with 190 more rows
Update, I tried to use broom's augment(), but unforunatly it gave me the error message I had hoped would create some flag,
# install.packages(c("broom"), dependencies = TRUE)
library(broom)
augment(plm.reg, dta)
#> Error in data.frame(..., check.names = FALSE) :
#> arguments imply differing number of rows: 200, 178
The vector is plm.reg$residuals. Not sure of a nice broom solution, but this seems to work:
library(tidyverse)
dta.p %>%
as.data.frame %>%
rowid_to_column %>%
mutate(plm.reg = rowid %in% names(plm.reg$residuals))
for people who use the class pdata.frame() to create an index attribute that describes its individual and time dimensions, you can us the following code, this is from another Baltagi in the ?plm,
# == Baltagi (2013), pp. 204-205
data("Produc", package = "plm")
pProduc <- pdata.frame(Produc, index = c("state", "year", "region"))
form <- log(gsp) ~ log(pc) + log(emp) + log(hwy) + log(water) + log(util) + unemp
Baltagi_reg_204_5 <- plm(form, data = pProduc, model = "random", effect = "nested")
pProduc %>% mutate(reg.re = rownames(pProduc) %in% names(Baltagi_reg_204_5$residuals)) %>%
as_tibble() %>% select(state, year, region, reg.re)
#> # A tibble: 816 x 4
#> state year region reg.re
#> <fct> <fct> <fct> <lgl>
#> 1 CONNECTICUT 1970 1 T
#> 2 CONNECTICUT 1971 1 T
#> 3 CONNECTICUT 1972 1 T
#> 4 CONNECTICUT 1973 1 T
#> 5 CONNECTICUT 1974 1 T
#> 6 CONNECTICUT 1975 1 T
#> 7 CONNECTICUT 1976 1 T
#> 8 CONNECTICUT 1977 1 T
#> 9 CONNECTICUT 1978 1 T
#> 10 CONNECTICUT 1979 1 T
#> # ... with 806 more rows
finally, if you are running the first Baltagi without index attributes, i.e. unmodified example from the help file, the code should be,
Grunfeld %>% rowid_to_column %>%
mutate(plm.reg = rowid %in% names(p$residuals)) %>% as_tibble()