Loop through various data subsets in lm() in R

I would like to loop over various regressions referencing different data subsets, however I'm unable to appropriately call different subsets. For example:

dat <- data.frame(y = rnorm(10), x1 = rnorm(10), x2 = rnorm(10), x3 = rnorm(10) ) 
x.list <- list(dat$x1,dat$x2,dat$x3)  
dat1 <- dat[-9,] 

fit <- list()
for(i in 1:length(x.list)){ fit[[i]] <- summary(lm(y ~ x.list[[i]], data = dat))}         
for(i in 1:length(x.list)){ fit[[i]] <- summary(lm(y ~ x.list[[i]], data = dat1))}

Is there a way to call in "dat1" such that it subsets the other variables accordingly? Thanks for any recs.

Asked By: coding_heart

Source

Answer #1:

I'm not sure it makes sense to copy your covariates into a new list like that. Here's a way to loop over columns and to dynamically build formulas

dat <- data.frame(y = rnorm(10), x1 = rnorm(10), x2 = rnorm(10), x3 = rnorm(10) ) 
dat1 <- dat[-9,] 
#x.list not used

fit <- list()
for(i in c("x1","x2","x3")){ fit[[i]] <- summary(lm(reformulate(i,"y"), data = dat))}   
for(i in c("x1","x2","x3")){ fit[[i]] <- summary(lm(reformulate(i,"y"), data = dat1))}

Answered By: MrFlick

Answer #2:

How about this?

dat <- data.frame(y = rnorm(10), x1 = rnorm(10), x2 = rnorm(10), x3 = rnorm(10) ) 
mods <- lapply(list(y ~ x1, y ~ x2, y ~ x3), lm, data = dat1)

If you have lots of predictors, create the formulas something like this:

lapply(paste('y ~ ', 'x', 1:10, sep = ''), as.formula)

If your data was in long format, it would be similarly simple to do by using lapply on a split data.frame.

dat <- data.frame(y = rnorm(30), x = rnorm(30), f = rep(1:3, each = 10))
lapply(split(dat, dat$f), function(x) lm(y ~ x, data = x))

Answered By: oropendola

Answer #3:

Sorry being late - but have you tried to apply the data.table solution similar to yours in:

R data.table loop subset by factor and do lm()

I have just applied the links solution by altering your data which should illustrate how I understood your question:

set.seed(1)

df <- data.frame(x1 = letters[1:3], 
                 x2 = sample(c("a","b","c"), 30, replace = TRUE),
                 x3 = sample(c(20:50), 30, replace = TRUE),   
                 y = sample(c(20:50), 30, replace = TRUE))
dt <- data.table(df,key="x1")

fits <- lapply(unique(dt$x1),
               function(z)lm(y~x2+x3, data=dt[J(z),], y=T))

fit <-  dt[, lm(y ~ x2 + x3)]

# Using id as a "by" variable you get a model per id
coef_tbl <- dt[, as.list(coef(lm(y ~ x2 + x3))), by=x1]
# coefficients
sapply(fits,coef)

anova_tbl = dt[, as.list(anova(lm(y ~ x2 + x3))), by=x1]
row_names = dt[, row.names(anova(lm(y ~ x2 + x3))), by=x1]
anova_tbl[, variable := row_names$V1]

It extends your solution.

Answered By: chrischi

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