[英]Nested loop with simple linear regression
我知道有關此主題的幾篇文章,請參閱此處從簡單線性回歸打印和導出循環和此處如何在 R 中循環/重復線性回歸。
但是,我感興趣的不僅是一組預測變量,還有一組結果。 請在下面查看我的代碼嘗試。
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))
}
}
在這樣做時,我收到以下錯誤消息:
Error in model.frame.default(formula = paste("j ~", i[[1]]), drop.unused.levels = TRUE) :
variable lengths differ (found for 'age')
快速檢查它們的長度會發現一切都井井有條
> length(age)
[1] 100
> length(sex)
[1] 100
> length(bmi)
[1] 100
> length(smoke)
[1] 100
> length(outcome1)
[1] 100
> length(outcome2)
[1] 100
任何幫助自然都會受到極大的贊賞。
my_models <- list()
n <- 1
for (i in 1:length(predictorlist)){
for (j in 1:length(outcomelist)){
model <- lm(paste(outcomelist[[j]], "~", predictorlist[[i]]))
my_models[[n]]<-data.frame(ModelNo=n,coefficients(summary(model)))
n <- n+1
}
}
do.call(rbind,my_models)
給,
ModelNo Estimate Std..Error t.value Pr...t..
# (Intercept) 1 4.997536e-01 0.239278415 2.088586e+00 0.0393369696
# age 1 -3.936905e-03 0.003567878 -1.103430e+00 0.2725424813
# (Intercept)1 2 3.600995e-01 0.188106966 1.914334e+00 0.0584945208
# age1 2 -3.487458e-03 0.002804861 -1.243362e+00 0.2167006313
# (Intercept)2 3 2.173913e-01 0.063533308 3.421690e+00 0.0009091606
# sexMale 3 4.186795e-02 0.086457881 4.842584e-01 0.6292829854
# (Intercept)3 4 1.304348e-01 0.050088617 2.604080e+00 0.0106444776
# sexMale1 4 -8.051530e-04 0.068161974 -1.181235e-02 0.9905993425
# (Intercept)4 5 3.919620e-01 0.159188779 2.462246e+00 0.0155528798
# bmi 5 -4.967139e-03 0.005010602 -9.913259e-01 0.3239678274
# (Intercept)5 6 1.085051e-01 0.125958776 8.614332e-01 0.3911023372
# bmi1 6 7.025988e-04 0.003964659 1.772154e-01 0.8597049249
# (Intercept)6 7 3.333333e-01 0.111424058 2.991574e+00 0.0035189594
# smokeFormer 7 -1.036036e-01 0.122196316 -8.478456e-01 0.3986112406
# smokeNever 7 -1.515152e-01 0.171304709 -8.844774e-01 0.3786256699
# (Intercept)7 8 5.551115e-16 0.085820144 6.468313e-15 1.0000000000
# smokeFormer1 8 1.756757e-01 0.094117066 1.866566e+00 0.0649830089
# smokeNever1 8 -1.638898e-16 0.131940938 -1.242145e-15 1.0000000000
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