How to Loop/Repeat a Linear Regression in 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.