library(readxl)
library(janitor)
library(tidyverse)
library(gt)
library(infer)
library(psych)
library(skimr)
library(broom)
library(sandwich)
library(lmtest)
library(rlm)
library(reprex)
data <- read_csv("data_2/ohie_assignment2.csv") %>%
na.omit() %>%
clean_names()
data %>%
rlm(ed_visits ~ medicaid, data = data)
The error message I get reads "Error in rlm(., ed_visits ~ medicaid, data = data) : unused argument (data = data)"
I'm sure I'm doing something really stupid wrong, but I can't seem to figure out what it is... any ideas?
My code works when I run the standard lm, but I want to use the robust method... Would something like this accomplish the same thing as rlm?
model <- lm(ed_visits ~ medicaid, data = data)
# This takes what I have done and saves the heteroscedastic robust standard error
robust <-
coeftest(model, vcov = vcovHC(model, "HC1"))
Thank you!
Created on 2020-03-16 by the reprex package (v0.3.0)