I am looking for an equivalent in R of Stata's * function that I can use when running regressions.
For example, if I have a dataframe like the following:
outcome var1 var2 var3 new
3 2 3 4 3
2 3 2 4 2
4 3 2 1 4
I would like to be able to select all variable names that begin with "var" without typing each one out separately in order to run the following regression more efficiently:
lm(outcome ~ var1 + var2 + var3 + new, data = df)
This question explains how I can select the necessary columns. How can I cleanly incorporate these into a regression?
One technique is to subset the data to the required columns, and then to use the . operator for the formula object to represent the independent variables in lm(). The . operator is interpreted as "all columns not otherwise in the formula".
data <- as.data.frame(matrix(runif(1000),nrow = 100)*100)
colnames(data) <- c("outcome", "x1","x2","x3","x4", "x5","x6", "x7", "var8", "var9")
# select outcome plus vars beginning with var
desiredCols <- grepl("var",colnames(data)) | grepl("outcome",colnames(data))
# use desiredCols to subset data frame argument in lm()
summary(lm(outcome ~ .,data = data[desiredCols]))
...and the output:
> summary(lm(outcome ~ .,data = data[desiredCols]))
Call:
lm(formula = outcome ~ ., data = data[desiredCols])
Residuals:
Min 1Q Median 3Q Max
-57.902 -25.359 2.296 26.213 52.871
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 58.712722 7.334937 8.005 2.62e-12 ***
var8 0.008617 0.101298 0.085 0.932
var9 -0.154073 0.103438 -1.490 0.140
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 29.86 on 97 degrees of freedom
Multiple R-squared: 0.02249, Adjusted R-squared: 0.002331
F-statistic: 1.116 on 2 and 97 DF, p-value: 0.3319
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