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All possible regression in R: storing coefficients in a matrix

I run the code for all possible phylogenetic generalized linear model models. The problem I am facing is retrieving and maintaining beta ratios for each model.

I want to store the coefficients in a matrix where the columns correspond to a specific variable and the rows correspond to the formula. The problem arises because the variables are different for each model. Thus, one cannot simply compare the coefficients with the matrix.

The example below shows what the problem is:

y = rnorm(10)
inpdv = matrix(c(rnorm(10), runif(10), rpois(10, 1)), ncol = 3)
colnames(inpdv) = c("A", "B", "C")
data = cbind(y, inpdv)

model.mat = expand.grid(c(TRUE,FALSE), c(TRUE,FALSE), c(TRUE,FALSE))
names(model.mat) = colnames(inpdv)

formula = apply(model.mat, 1, function(x)
                       paste(colnames(model.mat)[x], collapse=" + "))
formula = paste("y", formula, sep = " ~ ")
formula[8] = paste(formula[8], 1, sep = "")

beta = matrix(NA, nrow = length(formula), ncol = 3)

for(i in 1:length(formula)){
   fit = lm(formula(formula), data)
   ## Here I want to extract the beta coeffecients here into a n * k matrix
   ## However, I cannot find a way to assign the value to the right cell in the matrix

}

Therefore, I suppose that each coefficient will need to be placed in the appropriate cell, but I can’t come up with a quick and effective way to do this.

30 000 , .

: , , y ~ a + c

a NA b

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names %in% . coef. :

beta = matrix(NA, nrow = length(formula), ncol = 3)
colnames(beta) <- colnames(inpdv)

for(i in 1:length(formula)){
   fit = lm(formula(formula[i]), data)
    coefs <- coef(fit)
    beta[ i , colnames(beta) %in% names( coefs ) ] <- coefs[ names( coefs ) %in% colnames( beta ) ]
}
#              A          B         C
#[1,] -0.4229837 -0.0519900 0.3787666
#[2,]         NA  0.7015679 0.0555350
#[3,] -0.4165834         NA 0.3692974
#[4,]         NA         NA 0.1346726
#[5,] -0.2035173  0.7049951        NA
#[6,]         NA  0.7978726        NA
#[7,] -0.2229959         NA        NA
#[8,]         NA         NA        NA

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for - , , , , .

+4
for(i in 1:length(formula)){
    fit = lm(formula(formula), data)
     beta[i, 1:length(fit$coefficients)] <- fit$coefficients
}

: .

, : :

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

:

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

- :

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
+1

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