获得针对"mlm"的回归系数的标准误差. lm()返回的对象 [英] Obtain standard errors of regression coefficients for an "mlm" object returned by `lm()`

问题描述

我想针对同一个回归变量运行10个回归，然后不使用循环提取所有标准错误.

I'd like to run 10 regressions against the same regressor, then pull all the standard errors without using a loop.

depVars <- as.matrix(data[,1:10]) # multiple dependent variables
regressor <- as.matrix([,11]) # independent variable
allModels <- lm(depVars ~ regressor) # multiple, single variable regressions

summary(allModels)[1] # Can "view" the standard error for 1st regression, but can't extract...

allModels存储为"mlm"对象，这确实很难使用.如果我可以存储一个lm对象列表或一个包含感兴趣的统计信息的矩阵，那就太好了.

allModels is stored as an "mlm" object, which is really tough to work with. It'd be great if I could store a list of lm objects or a matrix with statistics of interest.

同样，目标是不使用循环.这是等效的循环:

Again, the objective is to NOT use a loop. Here is a loop equivalent:

regressor <- as.matrix([,11]) # independent variable
for(i in 1:10) { 
  tempObject <- lm(data[,i] ~ regressor) # single regressions
  table1Data[i,1] <- summary(tempObject)$coefficients[2,2] # assign std error
  rm(tempObject)
  }

推荐答案

如果以长格式存储数据，则可以很容易地使用lmList从nlme或lme4包中获得大量回归结果.输出是回归结果的列表，摘要可以为您提供一个系数矩阵，就像您想要的一样.

If you put your data in long format it's very easy to get a bunch of regression results using lmList from the nlme or lme4 packages. The output is a list of regression results and the summary can give you a matrix of coefficients, just like you wanted.

library(lme4)

m <- lmList( y ~ x | group, data = dat)
summary(m)$coefficients

这些系数位于简单的3维数组中，因此标准误差在[,2,2]处.

Those coefficients are in a simple 3 dimensional array so the standard errors are at [,2,2].

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