How to store the standard errors with the lm() function in R? [closed]

All is in the title... I know how to store the estimates but I don't know how to store their standard errors... Thanks

> x <- runif(100)
> y <- 5 + 3 * x + rnorm(100, 0, 0.15)
> reg <- lm(y~x)
>
> summary(reg)

Call:
lm(formula = y ~ x)

Residuals:
Min       1Q   Median       3Q      Max
-0.32198 -0.12626  0.02584  0.10873  0.31411

Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept)  5.00931    0.03087  162.25   <2e-16 ***
x            2.98162    0.05359   55.64   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 0.1499 on 98 degrees of freedom
Multiple R-squared: 0.9693,     Adjusted R-squared: 0.969
F-statistic:  3096 on 1 and 98 DF,  p-value: < 2.2e-16

>
> reg$coef (Intercept) x 5.009309 2.981617 • it is reg$resid Oct 26 '11 at 12:56
• @user603: Thank you... but I am interested in the standard errors... not in the residuals... Oct 26 '11 at 12:58

Check the object that summary(reg) returns. You find then that

> str(summary(reg)$coef) ... > X <- summary(reg)$coef
> X[,2]
(Intercept)           x
0.03325738  0.05558073

gives you what you want. Or, if you calculate them yourself (as @caracal showed in the comments) :

sqrt(diag(vcov(reg)))
• Another way, based on the lm() fit alone: sqrt(diag(vcov(reg))) Oct 26 '11 at 13:20

Somewhere Doug Bates once mentioned that accessor functions are preferable, so I'd do

R> example(lm)   ## to create lm.D9 object
[...]
R> coef(summary(lm.D9))
Estimate Std. Error  t value    Pr(>|t|)
(Intercept)    5.032   0.220218 22.85012 9.54713e-15
groupTrt      -0.371   0.311435 -1.19126 2.49023e-01
R> str(coef(summary(lm.D9)))
num [1:2, 1:4] 5.032 -0.371 0.22 0.311 22.85 ...
- attr(*, "dimnames")=List of 2
..$: chr [1:2] "(Intercept)" "groupTrt" ..$ : chr [1:4] "Estimate" "Std. Error" "t value" "Pr(>|t|)"
R> coef(summary(lm.D9))[,"Std. Error"]
(Intercept)    groupTrt
0.220218    0.311435
R>

and the key is the coef() accessor for the summary object.

• Very true, accessors should be used preferably. I just always forget their names... Oct 26 '11 at 16:59
• Why is this preferable if it gives the same result as the method given by Joris? Oct 26 '11 at 19:17
• Better object-oriented behaviour---there are lots of modelling function returning differently organized objects. By providing coef(), you abstract that inner layer away. Oct 26 '11 at 20:20

Following on @Joris Meys answer for how to calculate std. error manually.

#Std. Error = residual variance / variable variance =
sqrt(diag(vcov(reg)))

#where vcov(reg) =
summary(reg)$$cov.unscaled * summary(reg)$$sigma^2

#where summary(reg)\$cov.unscaled = 1/(variable variance) (diagonal of precision matrix) =
solve(t(x) %*% x)

#where summary(reg)$$sigma = residual variance = sqrt(sum(reg$$residuals^2) / (nrow(x)-ncol(x)))

> x <- matrix(runif(200),nrow=100)
> y <- 5 + 3 * x[,1] + rnorm(100, 0, 0.15)
> reg <- lm(y~x)
> summary(reg)$$coefficient[,'Std. Error'] (Intercept) x1 x2 0.03842706 0.05494507 0.05243990 > sqrt(diag(vcov(reg))) (Intercept) x1 x2 0.03842706 0.05494507 0.05243990 > sqrt(diag( summary(reg)$$sigma^2*summary(reg)$$cov.unscaled )) (Intercept) x1 x2 0.03842706 0.05494507 0.05243990 > x_ = cbind(rep(1,nrow(x)),x) > sqrt(diag( sum(reg$$residuals^2)/(nrow(x)-ncol(x)-1) * solve(t(x_) %*% x_) ))
 0.03842706 0.05494507 0.05243990
• Although implementation is often mixed with substantive content in questions, we are supposed to be a site for providing information about statistics, machine learning, etc., not code. It can be good to provide code as well, but please elaborate your substantive answer in text for people who don't read this language well enough to recognize & extract the answer from the code. Jan 29 '19 at 13:09