Obtain standard errors of regression coefficients for an “mlm” object returned by `lm()`

Question

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

depVars <- as.matrix(         


        

Answer 1

Given an "mlm" model object model, you can use the below function written by me to get standard errors of coefficients. This is very efficient: no loop, and no access to summary.mlm().

std_mlm <- function (model) {
  Rinv <- with(model$qr, backsolve(qr, diag(rank)))
  ## unscaled standard error
  std_unscaled <- sqrt(rowSums(Rinv ^ 2)[order(model$qr$pivot)])
  ## residual standard error
  sigma <- sqrt(colSums(model$residuals ^ 2) / model$df.residual)
  ## return final standard error
  ## each column corresponds to a model
  "dimnames<-"(outer(std_unscaled, sigma), list = dimnames(model$coefficients))
  }

A simple, reproducible example

set.seed(0)
Y <- matrix(rnorm(50 * 5), 50)    ## assume there are 5 responses
X <- rnorm(50)    ## covariate

fit <- lm(Y ~ X)

We all know that it is simple to extract estimated coefficients via:

fit$coefficients    ## or `coef(fit)`
#                   [,1]       [,2]        [,3]        [,4]        [,5]
#(Intercept) -0.21013925  0.1162145  0.04470235  0.08785647  0.02146662
#X            0.04110489 -0.1954611 -0.07979964 -0.02325163 -0.17854525

Now let's apply our std_mlm:

std_mlm(fit)
#                 [,1]      [,2]      [,3]      [,4]      [,5]
#(Intercept) 0.1297150 0.1400600 0.1558927 0.1456127 0.1186233
#X           0.1259283 0.1359712 0.1513418 0.1413618 0.1151603

We can of course, call summary.mlm just to check our result is correct:

coef(summary(fit))
#Response Y1 :
#               Estimate Std. Error    t value  Pr(>|t|)
#(Intercept) -0.21013925  0.1297150 -1.6200072 0.1117830
#X            0.04110489  0.1259283  0.3264151 0.7455293
#
#Response Y2 :
#              Estimate Std. Error    t value  Pr(>|t|)
#(Intercept)  0.1162145  0.1400600  0.8297485 0.4107887
#X           -0.1954611  0.1359712 -1.4375183 0.1570583
#
#Response Y3 :
#               Estimate Std. Error    t value  Pr(>|t|)
#(Intercept)  0.04470235  0.1558927  0.2867508 0.7755373
#X           -0.07979964  0.1513418 -0.5272811 0.6004272
#
#Response Y4 :
#               Estimate Std. Error    t value  Pr(>|t|)
#(Intercept)  0.08785647  0.1456127  0.6033574 0.5491116
#X           -0.02325163  0.1413618 -0.1644831 0.8700415
#
#Response Y5 :
#               Estimate Std. Error    t value  Pr(>|t|)
#(Intercept)  0.02146662  0.1186233  0.1809646 0.8571573
#X           -0.17854525  0.1151603 -1.5504057 0.1276132

Yes, all correct!