Propensity Score Method
Bambangpe
1.Load data
csch <- read.csv("d:/UPWORK-COX/csch.csv")
dim(csch)## [1] 1000 7head(csch)## X race_white wkincome p1numpla wkmomed_1 catholic c5r2mtsc_std
## 1 1 1 -0.58432647 2 0 0 -1.1348567
## 2 2 0 0.06801235 1 1 0 0.6970083
## 3 3 0 -0.10415629 5 1 0 -0.3932214
## 4 4 0 1.18582186 3 0 0 -0.7499141
## 5 5 1 0.22493278 3 1 0 0.5998077
## 6 6 0 1.16179262 6 1 0 1.02043482.Check outcome
We will then examine the difference of outcome variable and covariants using difference and t-test.
mn <- with(csch, tapply(c5r2mtsc_std, catholic, mean)) mn mn[2] - mn[1]
with(csch, t.test(c5r2mtsc_std ~ catholic)) ```
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unioncsch %>%
group_by(catholic) %>%
summarise_each(funs(mean))## Warning: `summarise_each_()` was deprecated in dplyr 0.7.0.
## Please use `across()` instead.## Warning: `funs()` was deprecated in dplyr 0.8.0.
## Please use a list of either functions or lambdas:
##
## # Simple named list:
## list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`:
## tibble::lst(mean, median)
##
## # Using lambdas
## list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))## # A tibble: 2 x 7
## catholic X race_white wkincome p1numpla wkmomed_1 c5r2mtsc_std
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 0 500. 0.384 0.0436 3.02 0.678 -0.0141
## 2 1 503. 0.360 -0.118 3.11 0.702 0.0455csch## X race_white wkincome p1numpla wkmomed_1 catholic c5r2mtsc_std
## 1 1 1 -0.584326475 2 0 0 -1.134856722
## 2 2 0 0.068012350 1 1 0 0.697008294
## 3 3 0 -0.104156285 5 1 0 -0.393221365
## 4 4 0 1.185821857 3 0 0 -0.749914093
## 5 5 1 0.224932779 3 1 0 0.599807737
## 6 6 0 1.161792624 6 1 0 1.020434809
## 7 7 1 -0.796917107 2 0 1 0.540556436
## 8 8 0 0.600496477 3 1 1 -0.509539621
## 9 9 0 1.143189141 3 1 1 1.743432079
## 10 10 1 1.475496555 3 0 1 0.693887513
## 11 11 1 -1.268353770 2 0 1 0.520761006
## 12 12 1 0.183263210 7 0 0 1.271697545
## 13 13 0 0.223990593 2 0 0 0.701223289
## 14 14 0 -0.107785908 2 0 0 2.045055739
## 15 15 1 -0.299892343 7 1 0 0.254752674
## 16 16 0 -1.090727103 1 1 0 -0.130383491
## 17 17 1 1.214580107 3 1 1 1.007210767
## 18 18 0 0.063296168 3 1 0 0.433091220
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## 21 21 0 0.818234483 2 1 0 0.494552963
## 22 22 0 1.325294648 3 0 1 2.139872026
## 23 23 0 0.385765505 4 1 0 -0.772575139
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## 999 999 0 1.069002500 1 0 0 -0.089364858
## 1000 1000 1 0.886102455 4 0 0 0.4473041473.Propensity score estimation
m_ps <- glm(catholic ~ race_white + wkincome + p1numpla + wkmomed_1,
family=binomial(), data=csch)
library(arm)## Warning: package 'arm' was built under R version 4.0.5## Loading required package: MASS##
## Attaching package: 'MASS'## The following object is masked from 'package:dplyr':
##
## select## Loading required package: Matrix## Loading required package: lme4##
## arm (Version 1.11-2, built: 2020-7-27)## Working directory is D:/UPWORK-COXdisplay(m_ps, digits = 4)#0o## glm(formula = catholic ~ race_white + wkincome + p1numpla + wkmomed_1,
## family = binomial(), data = csch)
## coef.est coef.se
## (Intercept) -1.7164 0.2411
## race_white -0.1052 0.1724
## wkincome -0.1722 0.0848
## p1numpla 0.0434 0.0576
## wkmomed_1 0.1250 0.1809
## ---
## n = 1000, k = 5
## residual deviance = 931.3, null deviance = 936.7 (difference = 5.4)prs_df <- data.frame( pr_score = predict(m_ps, type = "response"),
catholic = csch$catholic )
head(prs_df)## pr_score catholic
## 1 0.1632454 0
## 2 0.1736823 0
## 3 0.2047904 0
## 4 0.1430035 0
## 5 0.1672494 0
## 6 0.1778064 04. PLOT Probability of going to Catholic school
library(ggplot2)
## Loading required package: ggplot2
levels(prs_df$catholic) <- paste("Actual school type attended:", c("Public", "Catholic"))
p1<- ggplot(prs_df, aes(x = pr_score)) +
geom_histogram(color = "white") +
facet_wrap(~catholic) +
xlab("Probability of going to Catholic school") +
theme_bw()
p1## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.