To Whom It May Concern; I use SAS 9.4 and am having difficulty running a medium to large analysis. I would like to undertake a hazard analysis of some historical commercial, not experimental, animal data. Hazard is whether a hazard was observed, this is a binary variable with a mean of 0.93 and standard dev of 0.29. I have just under 1E6 observations on 1E5 animals. I have 10 fixed factors 43 continuous variables 3 random factors Having fitted a fixed effects model in Proc GLM there are many interactions and quadratic effects that are significant at P<0.0001 I have chosen to fit fixed effects and interactions, including quadratic effects only if they are significant at P<=0.0001. After correction for Nelder's marginality requirements (1994) this gives me a fixed effects model with >4,000 df. The data is unbalanced commercial data, similar to that used for animal genetic analyses, but with a much more complicated fixed effects model. I would like to fit animal, handler and year as random effects without any interactions. Using HPL Mixed I receive the following error NOTE: The HPLMIXED procedure is executing in single-machine mode. ERROR: Insufficient resources to proceed. Terminating the mixed modeling task. Using HP Mixed I receive the following error NOTE: 56969 observations are excluded because of: missing response values (n=377), missing fixed effects (n=56969), missing random effects (n=6546). ERROR: The MMEq matrix is not positive semidefinite. My computer is a PC running Windows7 Professional with 64Gb RAM running at 3.6GHz. It has 2 by 1 Tb hard disc drives that back up to one another. I have a solid state 500Gb Hard Drive that has been included to use as virtual memory. I am not sure what resources I need to continue with HPLMixed. It is possible that I am not directing the working directory to the solid state hard drive correctly. I do not know what to do to ensure I get a positive semidefinite MMEq matrix. I would be most obliged if someone could give me some direction. See code below TIA Dave proc hplmixed data=train0; *proc hpmixed data=train0; *P<=0.0001; class Month doweek notwrk Site Observn L14 T6 T7 T31 T32 Handl Year Anml; model hazd= doweek ... T5*T5 T9*T9 W16*W16 / S; random Anml Handl Year / Solution; run;
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