All possible Regression in R: Saving coefficients in a matrix

Question

I am running code for all possible models of a phylogenetic generalised linear model. The issue I am having is extracting and saving the beta coefficients for each model. <

Answer 1

for(i in 1:length(formula)){
    fit = lm(formula(formula), data)
     beta[i, 1:length(fit$coefficients)] <- fit$coefficients
}

Update

Idea: name your columns after coefficients, and assign values to columns by name.

It is just a dummy example but should help you: Create your output matrix:

beta <- matrix(NA,  nrow=7, ncol=4)
colnames(beta) <- c("(Intercept)", 'A', 'B', 'C')

Create some dummy data:

 A <- rnorm(10)
 B <- rpois(10, 1)
 C <- rnorm(10, 2)
 Y <- rnorm(10, -1)

Now you can do something like that:

fit <- lm(Y ~ A + B + C)
beta[1, names(fit$coefficients)] <- fit$coefficients

fit <- lm(Y ~ A + B)
beta[2, names(fit$coefficients)] <- fit$coefficients

fit <- lm(Y ~ A + C)
beta[3, names(fit$coefficients)] <- fit$coefficients

fit <- lm(Y ~ B + C)
beta[4, names(fit$coefficients)] <- fit$coefficients

fit <- lm(Y ~ A)
beta[5, names(fit$coefficients)] <- fit$coefficients

fit <- lm(Y ~ B)
beta[6, names(fit$coefficients)] <- fit$coefficients

fit <- lm(Y ~ C)
beta[7, names(fit$coefficients)] <- fit$coefficients