Linear regression in R with if statement [duplicate]

Question

This question already has an answer here:

• How to run linear model in R with certain data range? 1 answer

I have a dummy variable black where black==0 is White and black==1 is Black. I am trying to fit a linear model lm for the black==1 category only, however running the code below gives me the incorrect coefficients. Is there a way in R to run a model with the if statement, similar to Stata?

library(foreign)
df<-read.dta("hw4.dta")
attach(df)
black[black==0]<-NA
model3<-lm(rent~I(income^2)+income+black)

Answer 1

If looks like there are a few issues here. First, you've stored all your data in separate vectors rent, income and black. You should instead store it in a data frame:

data <- data.frame(rent, income, black)

To limit a data frame based on a logical expression, you can use the subset function:

data.limited <- subset(data, black == 1)

Finally, you can run your analysis on your limited data frame (presumably without the black variable):

model3 <- lm(rent~I(income^2)+income, data=data.limited)

Answer 2

Why not subset the data before running the model? I personally prefer using a dataframe rather than separate vectors which will make the subsetting easier.

df <- data.frame(rent, income, black)

Then subset the dataframe, o create another one

df <- df[df$black==1,]

And run the model

model3 <- lm(rent ~ I(income^2) , data=df)

Answer 3

The code written below should do it.

model3 <- lm(rent~I(income^2)+income+black, data=df, subset=df$black==1))