About npandis

npandis · ‎01-27-2020

Thank Rick. I have seen this paper and my understanding is that the goal is to convert the categorical variables to dummy 0/1 vars. This especially important when categorical vars have more than 2 levels. My 2 categorical vars have only two levels and are already coded 0/1. However, I am not sure if I am ok. Best, Nikos

npandis · ‎01-27-2020

Hi, I have two categorical variables coded 0/1 Can I add their interaction like var1*var2 in proc nlmixed? Would the procedure correctly recognise it and treat it correctly or do I need to create dummy variables? Thank you, Nikos

npandis · ‎11-19-2019

I have found a solution by applying 3 restrict statements: restrict X2 1 , X2 -1; restrict X2 1, X2 0, X2 -1; restrict X2 0, X2 0, X2 1, X2 -1; Thank you, Nikos

npandis · ‎11-19-2019

Dear Rick, Thank you for the response which I tried to implement. The model converged but provided no estimates for the predictor and only values for the intercepts of the 4 components. The Pearson statistic is also strange 4.12E-15! I have about 400 observations. For the above model I subtracted the continuous predictor X2 from the Y and X1 was binary. Also, what if all my predictors are all categorical? Best, Nikos

npandis · ‎11-19-2019

Hi, I am fitting a finite mixture model using the following code: proc fmm data=mydataset; class x1 ; model y = x1 x2 / dist=poisson k=4; restrict x2 1, x2 -1,x2 1,x2 -1; run; I would like to restrict x2 to be the same across the 4 classes. I have seen examples for k=2 and in fact the code above makes the x2 coeffs equal in the first 2 components but the coefs in the 3rd and 4rth components are not the same. I have tried different combinations but they have failed. It would be great if you could help me correctly add the restrict statement. Thank you, Nikos SAS 9.4

npandis · ‎06-21-2018

Thank you and best wishes, nikos

npandis · ‎06-21-2018

Hi, I have a dataset with approximately 1300 observations and I am interested in fitting a series of models using: proc logistic proc surveylogistic proc genmod Some of the included covariates have missing values and the missing values of the covariates belong to different observations. What I am trying to say is that for a single observations not all covariates included in the model are missing. When I fit the models I get about 15% of the observations not used. Is there an option to tell SAS to include the observations in the analysis which have at least one covariate with no missing data for the observation (to use available case analysis)? Thank you, Nikos

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Re: proc nlmixed interaction

proc nlmixed interaction

Re: How to apply the RESTRICT statement in proc FMM with k=4

Re: How to apply the RESTRICT statement in proc FMM with k=4

How to apply the RESTRICT statement in proc FMM with k=4

Re: How to use available case analysis

How to use available case analysis

Re: How to apply the RESTRICT statement in proc FMM with k=4

Re: proc nlmixed interaction

proc nlmixed interaction

Re: How to apply the RESTRICT statement in proc FMM with k=4

Re: How to apply the RESTRICT statement in proc FMM with k=4

How to apply the RESTRICT statement in proc FMM with k=4

Re: How to use available case analysis

How to use available case analysis