I have an object (S3; lm) that contains the linear regression outputs of 471 different models. I am trying to extract the standard error of a specific variable in each model but I'm unsure how to do so, can anyone help? Specifically, I want to extract the standard error for the variable "p" for EACH of the 471 models saved in the "fit" object.
varnames = names(merged1)[2036:2507]
fit <- lapply(varnames,
FUN=function(p) lm(formula(paste("Dx ~ x + y + z + q +", p)),data=merged1))
names(fit) <- varnames
Thank you so much!
Note Edited to reflect the anonymous function p, rather than x, as stated previously.
Using fit shown reproducibly in the Note at the end invoke map_dfr on that with tidy which will give a data frame containing coefficients and associated statistics. We filter out the rows we want.
library(broom) # tidy
library(dplyr)
library(purrr) # map_dfr
fit %>%
map_dfr(tidy, .id = "variable") %>%
filter(term == variable)
giving:
# A tibble: 8 x 6
variable term estimate std.error statistic p.value
<chr> <chr> <dbl> <dbl> <dbl> <dbl>
1 hp hp -0.0147 0.0147 -1.00 0.325
2 drat drat 1.21 1.50 0.812 0.424
3 wt wt -3.64 1.04 -3.50 0.00160
4 qsec qsec -0.243 0.402 -0.604 0.551
5 vs vs -0.634 1.90 -0.334 0.741
6 am am 1.93 1.34 1.44 0.161
7 gear gear 0.158 0.910 0.174 0.863
8 carb carb -0.737 0.393 -1.88 0.0711
We compute fit reproducibly using mtcars which is built into R.
data <- mtcars
resp <- "mpg" # response
fixed <- c("cyl", "disp") # always include these
varnames <- setdiff(names(data), c(resp, fixed)) # incl one at a time
fit <- Map(function(v) {
fo <- reformulate(c(fixed, v), resp)
lm(fo, data)
}, varnames)
Significantly revised.