search for: multicolinear

...e model, but insignificant regarding F2 as fixed factor. How is it possible? I have ran many linear models and those two values correspond (or are the same). Anyway, how can it be to have insignificant effect that is significant in the model? Some strange property of that factor, like distribution? Multicolinearity? Please, help me on that. Sincerely, Petar

...lized GLM, you might be better off > investigating the sources of quasi-perfect separation and simplifying > the model to avoid or reduce it. In your data set you have several > factors with large number of levels, making the data sparse for all > their combinations. > > Like multicolinearity, near perfect separation is a data problem, and > is often better solved by careful thought about the model, rather than > wrapping the data in a computationally intensive band aid. > > -Michael > > On 7/26/2017 10:14 AM, john polo wrote: >> UseRs, >> >> I h...

Rather than go to a penalized GLM, you might be better off investigating the sources of quasi-perfect separation and simplifying the model to avoid or reduce it. In your data set you have several factors with large number of levels, making the data sparse for all their combinations. Like multicolinearity, near perfect separation is a data problem, and is often better solved by careful thought about the model, rather than wrapping the data in a computationally intensive band aid. -Michael On 7/26/2017 10:14 AM, john polo wrote: > UseRs, > > I have a dataframe with 2547 rows and seve...

...an go to a penalized GLM, you might be better off investigating the sources of quasi-perfect separation and simplifying the model to avoid or reduce it. In your data set you have several factors with large number of levels, making the data sparse for all their combinations. >> >> Like multicolinearity, near perfect separation is a data problem, and is often better solved by careful thought about the model, rather than wrapping the data in a computationally intensive band aid. >> >> -Michael >> >> On 7/26/2017 10:14 AM, john polo wrote: >>> UseRs, >>&gt...

...o a penalized GLM, you might be better off investigating the sources of quasi-perfect separation and simplifying the model to avoid or reduce it. In your data set you have several factors with large number of levels, making the data sparse for all their combinations. >>> >>> Like multicolinearity, near perfect separation is a data problem, and is often better solved by careful thought about the model, rather than wrapping the data in a computationally intensive band aid. >>> >>> -Michael >>> >>> On 7/26/2017 10:14 AM, john polo wrote: >>>>...

I'm statistician I need help with tobit regression Is there assumption in tobit regression ? if any, what kind of that ? please help me !!

UseRs, I have a dataframe with 2547 rows and several hundred columns in R 3.1.3. I am trying to run a small logistic regression with a subset of the data. know_fin ~ comp_grp2+age+gender+education+employment+income+ideol+home_lot+home+county > str(knowf3) 'data.frame': 2033 obs. of 18 variables: $ userid : Factor w/ 2542 levels

I have a linear model y~x1+x2 of some data where the coefficient for x1 is higher than I would have expected from theory (0.7 vs 0.88) I wondered whether this would be an artifact due to x1 and x2 being correlated despite that the variance inflation factor is not too high (1.065): I used perturbation analysis to evaluate collinearity library(perturb)