提取R中多个不变自变量的系数和p值列表 [英] Extract lists of coefficients and p-values for multiple invariant independent variables in R
问题描述
我尝试使用相同的模型进行1104线性回归.我的自变量不变.但是，我的因变量确实如此.确实，我有1104个因变量.我不知道如何提取所有系数(包括截距)和p值，以计算每一个的均值(系数和p值).如何轻松做到这一点?这是我的模型:
I try to do 1104 linear regressions with the same model. My independent variables do not change. However, my dependent variable does. Indeed, I have 1104 dependent variables. I do not know how to extract all the coefficients (intercepts included) and p-values in order to compute means of each (coefficients & p-values). How to do that with an easy way ? This is my model :
testMCFG1 <- lapply(101:1204, function(i) lm(recexp[,i]~recexp[,"rm"] + recexp[,"zdy"] + recexp[,"ztbl"] + recexp[,"ztms"] + recexp[,"zdfy"] + recexp[,"rm_zdy"] + recexp[,"rm_ztbl"] + recexp[,"rm_ztms"] + recexp[,"rm_zdfy"] + recexp[,"contexte"] + recexp[,"rm_contexte"]))
但是，这里的某个人已经向我展示了如何仅使用一个不变的自变量来完成该操作.这样可行.在下面找到这种情况的代码:
However, someone here has already showed me how to do that with only one invariant independent variable. That works. Find below the codes for this case:
y <- 'rm'
x <- names(recexp[101:1204])
models <- map(setNames(x, x),
~ lm(as.formula(paste(.x, y, sep="~")),
data=recexp))
pvalues <-
data.frame(rsquared = unlist(map(models, ~ summary(.)$r.squared)),
RSE = unlist(map(models, ~ summary(.)$sigma))) %>%
rownames_to_column(var = "which_dependent")
results <- full_join(basic_information, pvalues)
results %>% group_by(term) %>% summarise(mean_estimate = mean(estimate))
results %>% group_by(term) %>% summarise(mean_p = mean(p.value))
推荐答案
这里是使用多个tidyverse软件包的解决方案.您没有提供数据，因此我将使用 mtcars 作为示例.将您的自变量放入称为 independents 的固定字符串中，我们将像使用代码生成字符向量一样使用切片来获取您的依赖项
Here is a solution using several tidyverse packages. You don't provide your data so I'll use mtcars as an example. Put your independent variables into a fixed string called independents and we'll grab your dependents using a slice as you did with your code producing a character vector
#####
independents <- 'mpg + vs + am + gear'
dependent <- names(mtcars[2:7])
加载库
library(dplyr)
library(purrr)
library(broom)
library(tidyr)
library(tibble)
使用 purrr :: map
models <- map(setNames(dependent, dependent),
~ lm(as.formula(paste(.x, independents, sep="~")),
data=mtcars))
获取 lm 模型的列表，并将其输入到 broom :: tidy 中，以提取有关beta估计值和p值等的基本信息.列表项的名称(这是因变量)，并将其添加为列.从拦截中删除括号，并添加零，因此它始终是第一个，并且您知道它是beta0
Take that list of lm models and feed it to broom::tidy to extract the basic information about beta estimates, and p values etc. To keep it neat use the name of the list item (which is the dependent variable) and add it as a column. Remove the parens from intercept and add a zero so it is always first and you know it's beta0
basics <-
map(models, ~ broom::tidy(.)) %>%
map2_df(.,
names(.),
~ mutate(.x, which_dependent = .y)) %>%
select(which_dependent, everything()) %>%
mutate(term = gsub("\\(Intercept\\)", "0Intercept", term))
这次再次输入列表，提取出r平方和sigma，即残差标准误差"
Feed the list in again this time extract r squared and sigma a.k.a. "Residual standard error"
model_summary <-
data.frame(rsquared = unlist(map(models, ~ summary(.)$r.squared)),
RSE = unlist(map(models, ~ summary(.)$sigma))) %>%
rownames_to_column(var = "which_dependent")
根据哪个因变量加入两者
Join the two based on which dependent variable
results <- full_join(basics, model_summary)
#> Joining, by = "which_dependent"
results
#> # A tibble: 30 x 8
#> which_dependent term estimate std.error statistic p.value rsquared RSE
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl 0Inter… 10.4 1.14 9.13 9.58e-10 0.861 0.714
#> 2 cyl mpg -0.117 0.0382 -3.06 4.98e- 3 0.861 0.714
#> 3 cyl vs -1.80 0.374 -4.81 5.09e- 5 0.861 0.714
#> 4 cyl am -0.414 0.502 -0.826 4.16e- 1 0.861 0.714
#> 5 cyl gear -0.258 0.290 -0.891 3.81e- 1 0.861 0.714
#> 6 disp 0Inter… 571. 94.1 6.07 1.76e- 6 0.804 58.8
#> 7 disp mpg -9.50 3.14 -3.02 5.47e- 3 0.804 58.8
#> 8 disp vs -85.9 30.8 -2.79 9.49e- 3 0.804 58.8
#> 9 disp am -31.9 41.3 -0.774 4.45e- 1 0.804 58.8
#> 10 disp gear -26.8 23.9 -1.12 2.71e- 1 0.804 58.8
#> # … with 20 more rows
它采用长格式，因此您可以执行按 term
It's in long format so you can do things like summarise grouped by term
results %>%
group_by(term) %>%
summarise(mean_p = mean(p.value)) %>%
arrange(term)
#> `summarise()` ungrouping output (override with `.groups` argument)
#> # A tibble: 5 x 2
#> term mean_p
#> <chr> <dbl>
#> 1 0Intercept 0.000168
#> 2 am 0.359
#> 3 gear 0.287
#> 4 mpg 0.0538
#> 5 vs 0.159
或者，如果您愿意，也可以将其扩大...
Or you can make it wider if you prefer...
wide_results <-
results %>%
pivot_wider(names_from = term,
values_from = estimate:p.value)
wide_results
#> # A tibble: 6 x 23
#> which_dependent rsquared RSE estimate_0Inter… estimate_mpg estimate_vs
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 cyl 0.861 0.714 10.4 -0.117 -1.80
#> 2 disp 0.804 58.8 571. -9.50 -85.9
#> 3 hp 0.736 37.7 241. -8.17 -41.4
#> 4 drat 0.667 0.331 2.07 0.0228 0.166
#> 5 wt 0.804 0.464 5.90 -0.104 -0.146
#> 6 qsec 0.734 0.988 17.5 0.0894 2.29
#> # … with 17 more variables: estimate_am <dbl>, estimate_gear <dbl>,
#> # std.error_0Intercept <dbl>, std.error_mpg <dbl>, std.error_vs <dbl>,
#> # std.error_am <dbl>, std.error_gear <dbl>, statistic_0Intercept <dbl>,
#> # statistic_mpg <dbl>, statistic_vs <dbl>, statistic_am <dbl>,
#> # statistic_gear <dbl>, p.value_0Intercept <dbl>, p.value_mpg <dbl>,
#> # p.value_vs <dbl>, p.value_am <dbl>, p.value_gear <dbl>
names(wide_results)
#> [1] "which_dependent" "rsquared" "RSE"
#> [4] "estimate_0Intercept" "estimate_mpg" "estimate_vs"
#> [7] "estimate_am" "estimate_gear" "std.error_0Intercept"
#> [10] "std.error_mpg" "std.error_vs" "std.error_am"
#> [13] "std.error_gear" "statistic_0Intercept" "statistic_mpg"
#> [16] "statistic_vs" "statistic_am" "statistic_gear"
#> [19] "p.value_0Intercept" "p.value_mpg" "p.value_vs"
#> [22] "p.value_am" "p.value_gear"
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