How to Loop/Repeat a Linear Regression in R - r

Question

I have figured out how to make a table in R with 4 variables, which I am using for multiple linear regressions. The dependent variable (Lung) for each regression is taken from one column of a csv table of 22,000 columns. One of the independent variables (Blood) is taken from a corresponding column of a similar table.
Each column represents the levels of a particular gene, which is why there are so many of them. There are also two additional variables (Age and Gender of each patient). When I enter in the linear regression equation, I use lm(Lung[,1] ~ Blood[,1] + Age + Gender), which works for one gene.
I am looking for a way to input this equation and have R calculate all of the remaining columns for Lung and Blood, and hopefully output the coefficients into a table.
Any help would be appreciated!

Answer 1

You want to run 22,000 linear regressions and extract the coefficients? That's simple to do from a coding standpoint.
set.seed(1)
# number of columns in the Lung and Blood data.frames. 22,000 for you?
n <- 5
# dummy data
obs <- 50 # observations
Lung <- data.frame(matrix(rnorm(obs*n), ncol=n))
Blood <- data.frame(matrix(rnorm(obs*n), ncol=n))
Age <- sample(20:80, obs)
Gender <- factor(rbinom(obs, 1, .5))
# run n regressions
my_lms <- lapply(1:n, function(x) lm(Lung[,x] ~ Blood[,x] + Age + Gender))
# extract just coefficients
sapply(my_lms, coef)
# if you need more info, get full summary call. now you can get whatever, like:
summaries <- lapply(my_lms, summary)
# ...coefficents with p values:
lapply(summaries, function(x) x$coefficients[, c(1,4)])
# ...or r-squared values
sapply(summaries, function(x) c(r_sq = x$r.squared,
adj_r_sq = x$adj.r.squared))
The models are stored in a list, where model 3 (with DV Lung[, 3] and IVs Blood[,3] + Age + Gender) is in my_lms[[3]] and so on. You can use apply functions on the list to perform summaries, from which you can extract the numbers you want.

Answer 2

The question seems to be about how to call regression functions with formulas which are modified inside a loop.
Here is how you can do it in (using diamonds dataset):
attach(ggplot2::diamonds)
strCols = names(ggplot2::diamonds)
formula <- list(); model <- list()
for (i in 1:1) {
formula[[i]] = paste0(strCols[7], " ~ ", strCols[7+i])
model[[i]] = glm(formula[[i]])
#then you can plot or do anything else with the result ...
png(filename = sprintf("diamonds_price=glm(%s).png", strCols[7+i]))
par(mfrow = c(2, 2))
plot(model[[i]])
dev.off()
}

Answer 3

Sensible or not, to make the loop at least somehow work you need:
y<- c(1,5,6,2,5,10) # response
x1<- c(2,12,8,1,16,17) # predictor
x2<- c(2,14,5,1,17,17)
predictorlist<- list("x1","x2")
for (i in predictorlist){
model <- lm(paste("y ~", i[[1]]), data=df)
print(summary(model))
}
The paste function will solve the problem.

Answer 4

A tidyverse addition - with map()
Another way - using map2() from the purrr package:
library(purrr)
xs <- anscombe[,1:3] # Select variables of interest
ys <- anscombe[,5:7]
map2_df(ys, xs,
function(i,j){
m <- lm(i ~j + x4 , data = anscombe)
coef(m)
})
The output is a dataframe (tibble) of all coefficients:
`(Intercept)` j x4
1 4.33 0.451 -0.0987
2 6.42 0.373 -0.253
3 2.30 0.526 0.0518
If more variables are changing this can be done using the pmap() functions