[R] Regression performance when using summary() twice

[Michael Dewey]

Dear Christian

Without knowing how big your datset is it is hard to be sure but 
confint() can take some time.

Have you thought of calling summary once
summ <- summary(model)
and then replace all subsequent calls to summary with summ

Michael

On 21/06/2024 15:38, c.buhtz using posteo.jp wrote:
> Hello,
> 
> I am not a regular R user but coming from Python. But I use R for 
> several special task.
> 
> Doing a regression analysis does cost some compute time. But I wonder 
> when this big time consuming algorithm is executed and if it is done 
> twice in my sepcial case.
> 
> It seems that calling "glm()" or similar does not execute the time 
> consuming part of the regression code.
> It seems it is done when calling "summary(model)".
> Am I right so far?
> 
> If this is correct I would say that in my case the regression is down 
> twice with the identical formula and data. Which of course is 
> inefficient. See this code:
> 
> my_function <- function(formula_string, data) {
>              formula <- as.formula(formula_string)
>              model <- glm.nb(formula, data = data)
> 
>              result = cbind(summary(model)$coefficients, confint(model))
>              result = as.data.frame(result)
> 
>              string_result = capture.output(summary(model))
> 
>              return(list(result, string_result))
>          }
> 
> I do call summary() once to get the "$coefficents" and a second time 
> when capturing its output as a string.
> 
> If this really result in computing the regression twice I ask myself if 
> there is a R-way to make this more efficent?
> 
> Best regards,
> Christian Buhtz
> 
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-- 
Michael