Getting summary statistics from model using permutation values in R - r

Question

I am trying to obtain the summary statistics (summary()) for the linear model (below), which uses 1000 permutations of the original dataset to create a 1000 random dataset (large matrix).
random_model <- rep(NA,1000)
for (i in c(1:1000)) {
random_data <- final_data
random_data$weighted_degree <- rowSums(node.perm_1000[i,,],na.rm=T)
random_model[i] <- coef(lm(weighted_degree ~ age + sex + age*sex, data=random_data))
}
I am not simply trying to compare the models to get an overall p-value but I want to get a t-value for each of the variables in the model that uses the random permutations as well.

Answer 1

Try with tidy() from broom package. It returns the expected values like this (example):
# A tibble: 2 x 5
term estimate std.error statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl>
1 (Intercept) 6.53 0.479 13.6 6.47e-28
2 iris$Sepal.Width -0.223 0.155 -1.44 1.52e- 1
In your case, the previous output will be stored for each element in the list of the loop according to your definition:
library(broom)
#Data
random_model <- rep(NA,1000)
#Loop
for (i in c(1:1000)) {
random_data <- final_data
random_data$weighted_degree <- rowSums(node.perm_1000[i,,],na.rm=T)
random_model[i] <- broom::tidy(lm(weighted_degree ~ age + sex + age*sex, data=random_data))
}

Answer 2

You should store the results of interest (estimated coefficients and t-values) in a list.
Here is a reproducible example using 10 replications on the mtcars dataset which is sampled at 50% rate for each replication.
The results of interest are retrieved using the $coefficients attribute of the summary() output on the lm object.
# The data
data(mtcars)
# Define sample size of each replication
N <- nrow(mtcars)
sample_size <- floor(N/2)
# Number of replications (model fits) and initialization of the list to store the results
set.seed(1717)
replications <- 10
random_model <- vector( "list", length=replications )
for (i in seq_along(random_model)) {
shuffle = sample(N, sample_size)
mtcars_shuffle = mtcars[shuffle, ]
random_model[[i]] <- summary(lm(mpg ~ cyl + disp + cyl*disp, data=mtcars_shuffle))$coefficients
}
For example, the model fitted for replications 1 and 10 are:
> random_model[[1]]
Estimate Std. Error t value Pr(>|t|)
(Intercept) 48.26285335 8.219065181 5.872061 7.573836e-05
cyl -3.33999161 1.366231326 -2.444675 3.089262e-02
disp -0.12941685 0.063269362 -2.045490 6.337414e-02
cyl:disp 0.01394436 0.007877833 1.770076 1.020931e-01
> random_model[[10]]
Estimate Std. Error t value Pr(>|t|)
(Intercept) 54.27312267 7.662593317 7.082866 1.277746e-05
cyl -4.40545653 1.586392001 -2.777029 1.674235e-02
disp -0.15330770 0.047932153 -3.198431 7.654790e-03
cyl:disp 0.01792561 0.006707396 2.672514 2.031615e-02