Dear Christian,
You're apparently using the glm.nb() function in the MASS package.
Your function is peculiar in several respects. For example, you specify
the model formula as a character string and then convert it into a
formula, but you could just pass the formula to the function -- the
conversion seems unnecessary. Similarly, you compute the summary for the
model twice rather than just saving it in a local variable in your
function. And the form of the function output is a bit strange, but I
suppose you have reasons for that.
The primary reason that your function is slow, however, is that the
confidence intervals computed by confint() profile the likelihood, which
requires refitting the model a number of times. If you're willing to use
possibly less accurate Wald-based rather than likelihood-based
confidence intervals, computed, e.g., by the Confint() function in the
car package, then you could speed up the computation considerably,
Using a model fit by example(glm.nb),
library(MASS)
example(glm.nb)
microbenchmark::microbenchmark(
Wald = car::Confint(quine.nb1, vcov.=vcov(quine.nb1),
estimate=FALSE),
LR = confint(quine.nb1)
)
which produces
Unit: microseconds
expr min lq mean median uq max
Wald 136.366 161.13 222.0872 184.541 283.72 386.466
LR 87223.031 88757.09 95162.8733 95761.568 97672.23 182734.048
neval
100
100
I hope this helps,
Johm
--
John Fox, Professor Emeritus
McMaster University
Hamilton, Ontario, Canada
web: https://www.john-fox.ca/
--
On 2024-06-21 10:38 a.m., c.bu...@posteo.jp wrote:
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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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