 #### R: standard error output from lm object

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### Question

We got a lm object from and want to extract the standard error

``````lm_aaa<- lm(aaa~x+y+z)
``````

I know the function summary, names and coefficients. However, summary seems to be the only way to manually access the standard error. Have you any idea how I can just output se?

thanks!

### Answer 1:

The output of from the `summary` function is just an R list. So you can use all the standard list operations. For example:

``````#some data (taken from Roland's example)
x = c(1,2,3,4)
y = c(2.1,3.9,6.3,7.8)

#fitting a linear model
fit = lm(y~x)
m = summary(fit)
``````

The `m` object or list has a number of attributes. You can access them using the bracket or named approach:

``````m\$sigma
m[]
``````

A handy function to know about is, `str`. This function provides a summary of the objects attributes, i.e.

``````str(m)
``````

### Answer 2:

To get a list of the standard errors for all the parameters, you can use

``````summary(lm_aaa)\$coefficients[, 2]
``````

As others have pointed out, `str(lm_aaa)` will tell you pretty much all the information that can be extracted from your model.

### Answer 3:

``````#some data
x<-c(1,2,3,4)
y<-c(2.1,3.9,6.3,7.8)

#fitting a linear model
fit<-lm(y~x)

#look at the statistics summary
summary(fit)

#get the standard error of the slope
se_slope<-summary(fit)\$coef[]
#the index depends on the model and which se you want to extract

#get the residual standard error
rse<-summary(fit)\$sigma
``````

### Answer 4:

If you don't want to get the standard error/deviation of the model, but instead the standard error/deviation of the individual coefficients, use

``````# some data (taken from Roland's example)
x = c(1, 2, 3, 4)
y = c(2.1, 3.9, 6.3, 7.8)

# fitting a linear model
fit = lm(y ~ x)

# get vector of all standard errors of the coefficients
coef(summary(fit))[, "Std. Error"]
``````

For more information on the standard error/deviation of the model, see here. For more information on the standard error/deviation of the coefficients, see here.

### Answer 5:

I think that the following lines can also provide you with a quick answer:

``````lm_aaa<- lm(aaa~x+y+z)
se <- sqrt(diag(vcov(lm_aaa)))
``````

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