I want to compare estimate with standard error in function of a continuous variable and a categorial variable . Here an example of what my data look like.

y   stdy   ConVar  CatVar
1.3    0.1    1    Bob
2.4   0.4     1    Bob
1.5    0.3    2    Bob
3.6    0.2    3    Henri

I would like to perform a regression of my y estimate in function of the ConVar in first place. Then I would like to compare the estimate in function of the categorial variable.

I want to rectify my slope and average comparaison with the known standard error (stdy).

Is it possible .

I know orthogonal regression to compare two variables with known error but I don't known of a regression in which I can input standard error only on the y value.

Is that would do it if I do a mean of the standard error. mean of the standard error is sqrt(sum(std^2)/numberofobs^2)

Deming(ConVar,y,stdy, boot=FALSE, keep.boot=FALSE, alpha=0.05)


  • $\begingroup$ I would use weighted regression. E..g, the varFixed or varIdent structures from package nlme (which can be passed to the gls function) might be useful. $\endgroup$
    – Roland
    Commented Dec 2, 2015 at 15:29

1 Answer 1


Thanks to Roland,

I have solve this problem.

If someone is looking to adjust the slope of a regression in function of a known error on the dependent variable, the function varIdent is very useful.

Here is the reference for the package nlme in which you find this function !

Pinheiro J, Bates D, DebRoy S, Sarkar D and R Core Team (2015). nlme: Linear and Nonlinear Mixed Effects Models. R package version 3.1-122, http://CRAN.R-project.org/package=nlme>.

I also provide an example of my solution :

mod<-gls(y~x, weight=varIdent(Var), data = data)

The varIdent is allow us the input a known variance value.

If someone has a better solution or think I have a problem with this code , please contact me but I think it's all good !

Thanks again to Roland


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.