Hi Bianca,
I hope you’ve solved your problem with SPSS and R probit analysis, but if you
haven’t, I have your solution:
Based on the output you’ve given, I see that your residual deviance is
under-dispersed (that the ratio of residual deviance to residual deviance df
does is less than 1). However, you’ve told R to treat your dispersion parameter
as 1 (you did this by using the ‘family = binomial’ argument). Instead, if you
use ‘family=quasibinomial’ you allow the dispersion parameter to be estimated.
This changes how the variance, SE, etc are calculated. Modeling it this way is
akin to the SPSS method, and thus produces nearly-identical results. You may
still see very, very minor differences in chi square goodness of fit, and 95%
CI of the doses/concentrations, etc. but this is due to differences in rounding
under the hood of the software.
Hope this helps!
Edwin R. Burgess IV, Ph.D.
[[alternative HTML version deleted]]
______________________________________________
R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
