As in title, I'm trying to locate Stata equivalent to R's complete.cases function.
The closest i got so far was to use
generate sample = e(sample)
after running regression and either deleting cases or using if clause on this newl generated variable (solution stolen from here).
Is there any better solution?
I'm not sure how exactly you're accustomed to using complete.cases in R, but here is an example with application both in R and a Stata equivalent (rmiss2):
First, let's make up some data in R for demonstration. We'll save it as a dta file that we can use in Stata later on.
library(foreign)
set.seed(1)
dat <- data.frame(one = rnorm(15),
two = sample(LETTERS, 15),
three = rnorm(15),
four = runif(15))
dat <- data.frame(lapply(dat, function(x) { x[sample(15, 5)] <- NA; x }))
write.dta(dat, file="completeCases.dta")
The data look like this:
dat
# one two three four
# 1 NA M 0.80418951 0.8921983
# 2 0.1836433 O -0.05710677 NA
# 3 -0.8356286 L 0.50360797 0.3899895
# 4 NA E NA NA
# 5 0.3295078 S NA 0.9606180
# 6 -0.8204684 <NA> -1.28459935 0.4346595
# 7 0.4874291 <NA> NA NA
# 8 0.7383247 C -0.23570656 0.3999944
# 9 NA N -0.54288826 0.3253522
# 10 -0.3053884 <NA> NA 0.7570871
# 11 NA R -0.64947165 0.2026923
# 12 0.3898432 <NA> NA NA
# 13 NA K 1.15191175 NA
# 14 -2.2146999 <NA> 0.99216037 0.2454885
# 15 1.1249309 Q -0.42951311 0.1433044
Running complete.cases on the data simply gives us a vector of TRUEs and FALSEs telling us if each row represents a complete case.
complete.cases(dat)
# [1] FALSE FALSE TRUE FALSE FALSE FALSE FALSE TRUE
# [9] FALSE FALSE FALSE FALSE FALSE FALSE TRUE
More often, complete.cases is useful for subsetting our data, as in the following:
dat[complete.cases(dat), ]
# one two three four
# 3 -0.8356286 L 0.5036080 0.3899895
# 8 0.7383247 C -0.2357066 0.3999944
# 15 1.1249309 Q -0.4295131 0.1433044
Or, here, subsetting just based on whether the first three columns are complete.
dat[complete.cases(dat[, 1:3]), ]
# one two three four
# 2 0.1836433 O -0.05710677 NA
# 3 -0.8356286 L 0.50360797 0.3899895
# 8 0.7383247 C -0.23570656 0.3999944
# 15 1.1249309 Q -0.42951311 0.1433044
Now, let's switch to Stata.
First, install rmiss2 if you don't already have it.
. findit rmiss2
Second, load the dta file that we created in R.
. use "path\to\completeCases.dta", clear
Third, we'll use rmiss2 to generate a new column named "nmis" that tells us how many variables are missing for each case.
. egen nmis = rmiss2(one two three four)
. list
+-----------------------------------------------+
| one two three four nmis |
|-----------------------------------------------|
1. | . M .8041895 .8921983 1 |
2. | .1836433 O -.0571068 . 1 |
3. | -.8356286 L .503608 .3899895 0 |
4. | . E . . 3 |
5. | .3295078 S . .960618 1 |
|-----------------------------------------------|
6. | -.8204684 . -1.284599 .4346595 1 |
7. | .4874291 . . . 3 |
8. | .7383247 C -.2357066 .3999944 0 |
9. | . N -.5428883 .3253522 1 |
10. | -.3053884 . . .7570871 2 |
|-----------------------------------------------|
11. | . R -.6494716 .2026923 1 |
12. | .3898432 . . . 3 |
13. | . K 1.151912 . 2 |
14. | -2.2147 . .9921604 .2454885 1 |
15. | 1.124931 Q -.4295131 .1433044 0 |
+-----------------------------------------------+
Finally, we can use keep if... to drop cases with missing data.
. keep if (nmis == 0)
(12 observations deleted)
. list
+-----------------------------------------------+
| one two three four nmis |
|-----------------------------------------------|
1. | -.8356286 L .503608 .3899895 0 |
2. | .7383247 C -.2357066 .3999944 0 |
3. | 1.124931 Q -.4295131 .1433044 0 |
+-----------------------------------------------+
As with complete.cases, you can also specify which columns to check for completeness.
. use "path\to\completeCases.dta", clear
(Written by R. )
. egen nmis = rmiss2(one two three)
. keep if (nmis == 0)
(11 observations deleted)
. list
+-----------------------------------------------+
| one two three four nmis |
|-----------------------------------------------|
1. | .1836433 O -.0571068 . 0 |
2. | -.8356286 L .503608 .3899895 0 |
3. | .7383247 C -.2357066 .3999944 0 |
4. | 1.124931 Q -.4295131 .1433044 0 |
+-----------------------------------------------+
It should be noted that keep if... is "destructive"--you can't get back to your original dataset without reloading your dat file. As such it is safer to use if as follows:
. summarize one two three four if nmis == 0
Variable | Obs Mean Std. Dev. Min Max
-------------+--------------------------------------------------------
one | 3 .3425423 1.038475 -.8356286 1.124931
two | 3 6.666667 5.507571 1 12
three | 3 -.0538706 .4924195 -.4295131 .503608
four | 3 .3110961 .145398 .1433044 .3999944
