CODEWITHSUNDEEP
routputregressionmodelsummary
dilly
dilly

Reputation: 63

Print tables with 3 regression output models from rdrobust

I have run 3 regressions with rdrobust, and would like to print some of the values (not all of them in a table, so that each model has a column and the results can be compared side by side.

I tried with stargazer but with no success, same thing goes for modelsummary.

This is what my regression code looks like:

model <- rdrobust::rdrobust(x, 
                            y,
                            c = cutoffvalue,
                            kernel = "tri", #default
                            bwselect = "mserd"

And I'd like to show only the regression estimate, values, bandwidth and kernel in the table.

This is what I tried, but it doesn't give me the values that I want, and also i'ts for one model only. I'd like to have all 3 in the same table.

tidy.rdrobust <- function(model, ...){
ret <- data.frame(term = row.names(model$coef), 
estimate = model$coef[, 1], 
std.error = model$se[, 1], 
p.value = model$pv[, 1])
    row.names(ret) <- NULL
ret
}

glance.rdrobust <- function(model, ...){
ret <- data.frame(nobs.left = model$N[1],
kernel = model$kernel,
bwselect = model$bwselect)
ret
}
x <- runif(1000, -1, 1)
y <- 5 + 3 * x + 2 * (x >= 0) + rnorm(1000)
fit <- rdrobust(y, x)
modelsummary(fit)

thanks!

Upvotes: 1

Views: 1659

Answers (1)

Vincent
Vincent

Reputation: 17725

I asked for clarification in a comment, but here is my best attempt at guessing what you want in the table:

  1. Multiple models side-by-side
  2. Model estimates
  3. p values in parentheses below estimates
  4. Kernel type at the bottom of hte table
  5. Bandwidth selection at the bottom of the table

To achieve this, I only modified your glance.rdrobust method, and I used the statistic argument of the modelsummary function.

Load libraries and define custom tidy and glance methods to extract information (see documentation) from rdrobust objects:

library(rdrobust)
library(modelsummary)

tidy.rdrobust <- function(model, ...) {
  ret <- data.frame(
    term = row.names(model$coef),
    estimate = model$coef[, 1],
    std.error = model$se[, 1],
    p.value = model$pv[, 1]
  )
  row.names(ret) <- NULL
  ret
}

glance.rdrobust <- function(model, ...) {
  ret <- data.frame(
    Kernel = model$kernel,
    Bandwidth = model$bwselect
  )
  ret
}

Simulate data, estimate 3 models, and store them in a list:

x1 <- runif(1000, -1, 1)
x2 <- runif(1000, -1, 1)
x3 <- runif(1000, -1, 1)
y1 <- 5 + 3 * x1 + 2 * (x1 >= 0) + rnorm(1000)
y2 <- 5 + 3 * x2 + 2 * (x2 >= 0) + rnorm(1000)
y3 <- 5 + 3 * x3 + 2 * (x3 >= 0) + rnorm(1000)

fit1 <- rdrobust(y1, x1)
fit2 <- rdrobust(y2, x2)
fit3 <- rdrobust(y3, x3)
models <- list(fit1, fit2, fit3)

Create a table:

modelsummary(models, statistic = "p.value")
Model 1 Model 2 Model 3
Conventional 2.155 2.085 2.050
(0.000) (0.000) (0.000)
Bias-Corrected 2.110 2.110 1.984
(0.000) (0.000) (0.000)
Robust 2.110 2.110 1.984
(0.000) (0.000) (0.000)
Bandwidth mserd mserd mserd
Kernel Triangular Triangular Triangular

Upvotes: 2

Related Questions