Extract covariance from data frame in a loop - r

Question

sample_size <- 200
sample_meanvector <- c(3, 4)
sample_covariance_matrix <- matrix(c(2, 1, 1, 2),
ncol = 2)
# create bivariate normal distribution
sample_distribution <- mvrnorm(n = sample_size,
mu = sample_meanvector,
Sigma = sample_covariance_matrix)
#Convert the datatype
df_sample_distribution <- as.data.frame(sample_distribution)
df_sample_distribution$Y <- (1 + df_sample_distribution$V1*2 + df_sample_distribution$V2 + rnorm(200,0,1))
colnames(df_sample_distribution)[1] <- "X1"
colnames(df_sample_distribution)[2] <- "X2"
Code above is the one I use to generate a bivariate normal distribution vectors and code below is the code to run regression over the generated data.
Test2 <- lm( Y ~ X1, data = df_sample_distribution)
#to extract only specific coefficients
summary(Test)$coefficients[2,1]
My question is whether there is a way such that I can regenerate data and run regression over it for 200 times and save all the outputs in a list. Here is the pseudo code in my head.
for (){
#generate data
for ()
{
#extract coeffiients and insert them in a list
}
}
In simple terms,
step 1: create data
step 2: run regression over it
step 3: get the coefficient (hopefully save them in a list)
I am looking for code that can loop through step 1 to 3 for 200 times and save everything results. Any ideas or inspirations are welcomed. Thank you guys in advance.

Answer 1

Just wrap your code into a for-loop like your pseudo code:
library(MASS)
iterations <- 10 # In your example this should be 200
sample_size <- 200
sample_meanvector <- c(3, 4)
sample_covariance_matrix <- matrix(c(2, 1, 1, 2),
ncol = 2)
# create output data.frame
df_output <- data.frame(iteration = integer(0), coeff = double(0))
# loop over data generation and regression
for (i in seq_len(iterations)) {
sample_distribution <- mvrnorm(n = sample_size,
mu = sample_meanvector,
Sigma = sample_covariance_matrix)
#Convert the datatype
df_sample_distribution <- as.data.frame(sample_distribution)
df_sample_distribution$Y <- (1 + df_sample_distribution$V1*2 + df_sample_distribution$V2 + rnorm(200,0,1))
colnames(df_sample_distribution)[1] <- "X1"
colnames(df_sample_distribution)[2] <- "X2"
df_output[i, 1] <- i
df_output[i, 2] <- summary(lm( Y ~ X1, data = df_sample_distribution))$coefficients[2,1]
}
This returns df_output containing coefficients for each iteration:
iteration coeff
1 1 2.647886
2 2 2.274654
3 3 2.447453
4 4 2.451471
5 5 2.568877
6 6 2.428295
7 7 2.440396
8 8 2.478357
9 9 2.477211
10 10 2.367012