R:对所有变量重复线性回归并将结果保存在新的数据框中 [英] R: repeat linear regression for all variables and save results in a new data frame
问题描述
我有一个名为"dat"的数据框，其中包含10个数字变量(var1，var2，var3，var4，var5，…var 10)，每个变量都有若干观察结果……
I have a data frame named "dat" with 10 numeric variables (var1, var2,var3,var4 , var5,…var 10), each with several observations…
dat
var1 var2 var3 var4 var5 var6 var7 var8 var9 var10
1 12 5 18 19 12 17 11 16 18 10
2 3 2 10 6 13 17 11 16 18 10
3 13 15 14 13 1 17 11 16 18 10
4 17 11 16 18 10 17 11 16 18 10
5 9 13 8 8 7 17 11 16 18 10
6 15 6 20 17 3 17 11 16 18 10
7 12 5 18 19 12 17 11 16 18 10
8 3 2 10 6 13 17 11 16 18 10
9 13 15 14 13 1 17 11 16 18 10
...
我想编写一个代码，对数据帧中的所有变量(第一个变量除外)重复相同的功能. 该函数应使用lm()函数同时分析var 1和所有其他变量(var2，var3，var4，var5)之间的线性回归.
I would like to write a code to repeat the same function for all the variables (except the first) in a data frame. The function should analyse the linear regression between var 1 and all the other variables (var2, var3, var4, var5) each at time, using the lm() function
例如 周期1:var 1和var 2之间的线性回归
e.g. cycle 1: linear regression between var 1 and var 2
lm(var1~var2, data=dat)
周期2:var 1和var 3之间的线性回归，
cycle 2: linear regression between var 1 and var 3,
lm(var1~var3, data=dat)
周期3:变量1和变量4之间的线性回归
cycle 3: linear regression between var 1 and var 4
lm(var1~var4, data=dat)
以此类推...
我还希望每个循环的结果将保存在名为结果"的新数据框中，其结构如下:
I would also like that the results from each cycle will be saved in a new data frame named "results", having the following structure
Var_tested Correlation_coefficient P_value_correlation R_squared
Var2 corr_coeff_var2 p_value_var2 R_sq_var2
Var3 corr_coeff_var3 p_value_var3 R_sq_var3
Var4 corr_coeff_var4 p_value_var4 R_sq_var4
每行报告数据的每个相关结果. 有可能吗?
With each rows reporting data the results of each correlation. Is it possible?
非常感谢您的帮助！
推荐答案
您可以尝试以下代码来获得所需的输出
You can try the following code to have the desired output
data <- structure(list(var1 = c(12L, 3L, 13L, 17L, 9L, 15L, 12L, 3L,
13L), var2 = c(5L, 2L, 15L, 11L, 13L, 6L, 5L, 2L, 15L), var3 = c(18L,
10L, 14L, 16L, 8L, 20L, 18L, 10L, 14L), var4 = c(19L, 6L, 13L,
18L, 8L, 17L, 19L, 6L, 13L), var5 = c(12L, 13L, 1L, 10L, 7L,
3L, 12L, 13L, 1L), var6 = c(17L, 17L, 17L, 17L, 17L, 17L, 17L,
17L, 17L), var7 = c(11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L, 11L
), var8 = c(16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L), var9 = c(18L,
18L, 18L, 18L, 18L, 18L, 18L, 18L, 18L), var10 = c(10L, 10L,
10L, 10L, 10L, 10L, 10L, 10L, 10L)), class = "data.frame", row.names = c(NA,
-9L))
head(data,2)
#> var1 var2 var3 var4 var5 var6 var7 var8 var9 var10
#> 1 12 5 18 19 12 17 11 16 18 10
#> 2 3 2 10 6 13 17 11 16 18 10
x = names(data[,-1])
out <- unlist(lapply(1, function(n) combn(x, 1, FUN=function(row) paste0("var1 ~ ", paste0(row, collapse = "+")))))
out
#> [1] "var1 ~ var2" "var1 ~ var3" "var1 ~ var4" "var1 ~ var5"
#> [5] "var1 ~ var6" "var1 ~ var7" "var1 ~ var8" "var1 ~ var9"
#> [9] "var1 ~ var10"
library(broom)
#> Warning: package 'broom' was built under R version 3.5.3
library(dplyr)
#> Warning: package 'dplyr' was built under R version 3.5.3
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
#To have the regression coefficients
tmp1 = bind_rows(lapply(out, function(frml) {
a = tidy(lm(frml, data=data))
a$frml = frml
return(a)
}))
head(tmp1)
#> # A tibble: 6 x 6
#> term estimate std.error statistic p.value frml
#> <chr> <dbl> <dbl> <dbl> <dbl> <chr>
#> 1 (Intercept) 6.46 2.78 2.33 0.0529 var1 ~ var2
#> 2 var2 0.525 0.288 1.82 0.111 var1 ~ var2
#> 3 (Intercept) -1.50 4.47 -0.335 0.748 var1 ~ var3
#> 4 var3 0.863 0.303 2.85 0.0247 var1 ~ var3
#> 5 (Intercept) 0.649 2.60 0.250 0.810 var1 ~ var4
#> 6 var4 0.766 0.183 4.18 0.00413 var1 ~ var4
#To have the regression results i.e. R2, AIC, BIC
tmp2 = bind_rows(lapply(out, function(frml) {
a = glance(lm(frml, data=data))
a$frml = frml
return(a)
}))
head(tmp2)
#> # A tibble: 6 x 12
#> r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC
#> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <dbl> <dbl> <dbl>
#> 1 0.321 0.224 4.33 3.31 0.111 2 -24.8 55.7 56.3
#> 2 0.537 0.471 3.58 8.12 0.0247 2 -23.1 52.2 52.8
#> 3 0.714 0.673 2.81 17.5 0.00413 2 -20.9 47.9 48.5
#> 4 0.276 0.173 4.47 2.67 0.146 2 -25.1 56.2 56.8
#> 5 0 0 4.92 NA NA 1 -26.6 57.2 57.6
#> 6 0 0 4.92 NA NA 1 -26.6 57.2 57.6
#> # ... with 3 more variables: deviance <dbl>, df.residual <int>, frml <chr>
write.csv(tmp1, "Try_lm_coefficients.csv")
write.csv(tmp2, "Try_lm_results.csv")
由 reprex软件包(v0.3.0)于2019-11-20创建 sup>
Created on 2019-11-20 by the reprex package (v0.3.0)
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