I am aware of several posts on this topic, see here Print and export loop from simple linear regression and here How to Loop/Repeat a Linear Regression in R.
However, I am interested in looping through not only a set of predictors but also a set of outcomes. Please see my code attempt below.
set.seed(42)
n <- 100
age <- runif(n, min=45, max=85)
sex <- factor(sample(c('Male','Female'), n, rep=TRUE, prob=c(.6, .4)))
smoke <- factor(sample(c("Never", 'Former', 'Current'), n, rep=TRUE, prob=c(.25, .6, .15)))
bmi <- runif(n, min=16, max=45)
outcome1 <- rbinom(n = 100, size = 1, prob = 0.3)
outcome2 <- rbinom(n = 100, size = 1, prob = 0.13)
predictorlist<- list("age","sex", "bmi", "smoke")
outcomelist <- list("outcome1","outcome2")
for (i in predictorlist){
for (j in outcomelist){
model <- lm(paste("j ~", i[[1]]))
print(summary(model))
}
}
In doing so I receive the following error message:
Error in model.frame.default(formula = paste("j ~", i[[1]]), drop.unused.levels = TRUE) :
variable lengths differ (found for 'age')
A quick check of their lengths reveals everything to be in order
> length(age)
[1] 100
> length(sex)
[1] 100
> length(bmi)
[1] 100
> length(smoke)
[1] 100
> length(outcome1)
[1] 100
> length(outcome2)
[1] 100
Any assistance is, naturally, tremendously appreciated.
my_models <- list()
n <- 1
for (i in 1:length(predictorlist)){
for (j in 1:length(outcomelist)){
formula <- paste(outcomelist[[j]], "~", predictorlist[[i]])
out <- coefficients(summary(model))
Coeff <- rownames(out)
rownames(out) <- NULL
model <- lm(formula)
my_models[[n]]<-data.frame(Model=formula ,Coeff = Coeff,out )
n <- n+1
}
}
do.call(rbind,my_models)
gives,
Model Coeff Estimate Std..Error t.value Pr...t..
1 outcome1 ~ age (Intercept) 5.551115e-16 0.085820144 6.468313e-15 1.0000000000
2 outcome1 ~ age smokeFormer 1.756757e-01 0.094117066 1.866566e+00 0.0649830089
3 outcome1 ~ age smokeNever -1.638898e-16 0.131940938 -1.242145e-15 1.0000000000
4 outcome2 ~ age (Intercept) 4.997536e-01 0.239278415 2.088586e+00 0.0393369696
5 outcome2 ~ age age -3.936905e-03 0.003567878 -1.103430e+00 0.2725424813
6 outcome1 ~ sex (Intercept) 3.600995e-01 0.188106966 1.914334e+00 0.0584945208
7 outcome1 ~ sex age -3.487458e-03 0.002804861 -1.243362e+00 0.2167006313
8 outcome2 ~ sex (Intercept) 2.173913e-01 0.063533308 3.421690e+00 0.0009091606
9 outcome2 ~ sex sexMale 4.186795e-02 0.086457881 4.842584e-01 0.6292829854
10 outcome1 ~ bmi (Intercept) 1.304348e-01 0.050088617 2.604080e+00 0.0106444776
11 outcome1 ~ bmi sexMale -8.051530e-04 0.068161974 -1.181235e-02 0.9905993425
12 outcome2 ~ bmi (Intercept) 3.919620e-01 0.159188779 2.462246e+00 0.0155528798
13 outcome2 ~ bmi bmi -4.967139e-03 0.005010602 -9.913259e-01 0.3239678274
14 outcome1 ~ smoke (Intercept) 1.085051e-01 0.125958776 8.614332e-01 0.3911023372
15 outcome1 ~ smoke bmi 7.025988e-04 0.003964659 1.772154e-01 0.8597049249
16 outcome2 ~ smoke (Intercept) 3.333333e-01 0.111424058 2.991574e+00 0.0035189594
17 outcome2 ~ smoke smokeFormer -1.036036e-01 0.122196316 -8.478456e-01 0.3986112406
18 outcome2 ~ smoke smokeNever -1.515152e-01 0.171304709 -8.844774e-01 0.3786256699