Linear regression of the same result, the same number of covariates, and one unique covariate in each model
I want to run linear regression for the same result and number of covariances minus one covariate in each model. I looked at an example on this page , but could it provide not what I wanted.
Sample data
a <- data.frame(y = c(30,12,18), x1 = c(7,6,9), x2 = c(6,8,5),
x3 = c(4,-2,-3), x4 = c(8,3,-3), x5 = c(4,-4,-2))
m1 <- lm(y ~ x1 + x4 + x5, data = a)
m2 <- lm(y ~ x2 + x4 + x5, data = a)
m3 <- lm(y ~ x3 + x4 + x5, data = a)
How could I run these models in a short time without repeating the same covariates over and over?
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1 answer
Following this example , you can do this:
lapply(1:3, function(i){
lm(as.formula(sprintf("y ~ x%i + x4 + x5", i)), a)
})
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