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View Code? Open in Web Editor NEWGoodness-of-fit tests for categorical response models
Home Page: https://ejikeugba.github.io/gofcat/
License: GNU General Public License v2.0
Goodness-of-fit tests for categorical response models
Home Page: https://ejikeugba.github.io/gofcat/
License: GNU General Public License v2.0
Hi,
Thanks for this package, I have a question while conducting some tests for the review ( openjournals/joss-reviews#4382 ).
What does happen if you have a variable called 'grp' in the data ? I suspect some results depending on the variable, see the snippet bellow, adapted from the README.
dt <- data.frame(y = ordered(rbinom(n,2,0.5)), x1 = factor(rbinom(n,2,0.7)), x2 = runif(n))
dt$grp = dt$x1
sp <- serp(y ~ x1 +x2, slope="parallel", link = "logit", reverse= TRUE, data = dt)
sp_grp <- serp(y ~ grp +x2, slope="parallel", link = "logit", reverse= TRUE, data = dt)
#Same results
hosmerlem(sp,tables = TRUE)
hosmerlem(sp_grp,tables = TRUE)
#different results
lipsitz(sp)
lipsitz(sp_grp)
Hello,
I am still conducting tests for the JOSS review (openjournals/joss-reviews#4382).
I was testing the use of a nnet
fitted model. I just took the example from the help of the function, which is a binary response model but I think it should work ?
library(nnet)
library(MASS)
example(birthwt)
bwt.mu <- multinom(low ~ ., bwt)
erroR(bwt.mu)
Rsquared(bwt.mu)
I obtain the following error:
Error: more than one column of fitted values for a binary model is not allowed
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