Extracting Coefficients, Std Errors, R2 etc from multiple regressions - r

Question

I have the following regression model;
models <- lapply(1:25, function(x) lm(Y_df[,x] ~ X1))
Which runs 25 regressions on 25 columns in the Y_df dataframe.
One of the outputs can be shown as;
models[15] # Gives me the coefficients for model 15
Call:
lm(formula = Y_df[, x] ~ X1)
Coefficients:
(Intercept) X1
0.1296812 1.0585835
Which I can store in a separate df. The problem I am running into is regarding Std. Error, R2, residules etc.
I would like to store these also into a separate dataframe.
I can run individual regressions and extract the summaries as a normal R regression output would look like.
ls_1 <- summary(models[[1]])
ls_1
ls_1$sigma
However I am hoping to take the values directly from the line of code which runs the 25 regressions.
This code works
> (models[[15]]$coefficients)
(Intercept) X1
-0.3643446787 1.0789369642
However; this code does not.
> (models[[15]]$sigma)
NULL
I have tried a variety of different combinations to try and extract these results with no luck.
The following did exactly what I wanted perfectly. I had hoped there was a way to replace the word coef with Std Error or R2 etc. but this does not work.
models <- lapply(1:25, function(x) lm(Y_df[,x] ~ X1))
# extract just coefficients
coefficients <- sapply(Y_df, coef)
Ideally I would like to store the Std Error from the above model

Answer 1

If a model is named mod, you can get to all of the residuals in the same way as the coefficients:
mod$residuals
There are also functions that extract the coefficients and residuals:
coef(mod)
resid(mod)
The other outputs, you can extract via summary:
summary(mod)$coef[,"Std. Error"] # standard errors
summary(mod)$r.squared # r squared
summary(mod)$adj.r.squared # adjusted r squared
So you can either create a list containing each of these results for each model:
outputList <- lapply(models, function(x){
coefs <- coef(mod)
stdErr <- summary(mod)$coef[,"Std. Error"]
rsq <- summary(mod)$r.squared
rsq_adj <- summary(mod)$adj.r.squared
rsd <- resid(mod)
list(coefs = coefs,
stdErr = stdErr,
rsq = rsq,
rsq_adj = rsq_adj,
rsd = rsd)
})
You can then get to the rsq for mod1 via outputList$mod1$rsq, for example.
Or you can create separate dataframes for each:
library(tidyverse)
# coefficients
coefs <- lapply(models, coef) %>%
do.call(rbind, .) %>%
as.data.frame() %>% # convert from matrix to dataframe
rownames_to_column("model") # add original model name as a column in the dataframe
# standard errors
stdErr <- lapply(models, function(x){
summary(mod)$coef[,"Std. Error"]
}) %>%
do.call(rbind, .) %>%
as.data.frame() %>%
rownames_to_column("model")
# r squareds
rsq <- sapply(models, function(x){
summary(mod)$r.squared
}) %>%
as.data.frame() %>%
rownames_to_column("model")
# adjusted r squareds
rsq_adj <- sapply(models, function(x){
summary(mod)$adj.r.squared
})%>%
as.data.frame() %>%
rownames_to_column("model")
# residuals
rsd <- lapply(models, resid) %>%
do.call(rbind, .) %>%
as.data.frame() %>%
rownames_to_column("model")
Worth noting that, if you're in RStudio and you assign the summary to something (ie temp <- summary(mod)), you can type the name of the object, then "$" and a dropdown of all the other objects that can be extracted from the summary appears.