I did a glm
and I just want to extract the standard errors of each coefficient. I saw on the internet the function se.coef()
but it doesn't work, it returns "Error: could not find function "se.coef""
.
The formula for standard error of mean is the standard deviation divided by the square root of the length of the data. It is relatively simple in R to calculate the standard error of the mean. We can either use the std. error() function provided by the plotrix package, or we can easily create a function for the same.
The information you're after is stored in the coefficients
object returned by summary()
. You can extract it thusly: summary(glm.D93)$coefficients[, 2]
#Example from ?glm
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
print(d.AD <- data.frame(treatment, outcome, counts))
glm.D93 <- glm(counts ~ outcome + treatment, family=poisson())
#coefficients has the data of interest
> summary(glm.D93)$coefficients
Estimate Std. Error z value Pr(>|z|)
(Intercept) 3.044522e+00 0.1708987 1.781478e+01 5.426767e-71
outcome2 -4.542553e-01 0.2021708 -2.246889e+00 2.464711e-02
outcome3 -2.929871e-01 0.1927423 -1.520097e+00 1.284865e-01
treatment2 1.337909e-15 0.2000000 6.689547e-15 1.000000e+00
treatment3 1.421085e-15 0.2000000 7.105427e-15 1.000000e+00
#So extract the second column
> summary(glm.D93)$coefficients[, 2]
(Intercept) outcome2 outcome3 treatment2 treatment3
0.1708987 0.2021708 0.1927423 0.2000000 0.2000000
Take a look at names(summary(glm.D93))
for a quick review of everything that is returned. More details can be found by checking out summary.glm
if you want to see the specific calculations that are going on, though that level of detail probably is not needed every time, unless you <3 statistics.
Another way:
sqrt(diag(vcov(glm.D93)))
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