comp.soft-sys.sas - The SAS statistics package.
Dear All, Consider a model Y=b0+b1*F+b2*X+b3*Z+b4*F*X F f0 the reference category, f1 the remaining category X x0 the reference category,x1 the the remaining category F,X,Z are dummy variables I want to compute the odds ratios of being F=f1 versus F=f0 with X held constant at X=x1 , I use exp(b1+b4) I would like to be sure about this, because this is a subject of a real confusion in the logistic model. Our friend Dale and others have already produced a way to do this in SAS in some previous messages. Many thanks in advance Adel --------------------------------- Douvrez le nouveau Yahoo! Mail : 250 Mo d'espace de stockage pour vos mails ! Crz votre Yahoo! Mail
Below is part of Analysis Of Parameter from my Proc Genmod procedure. I am trying to model insurance premium against interaction of Group (grp), longevity (long) and Number of Claims (NumC). However, I find the results repeated twice (N, Y) for some combination of Grp, Long and NumC. The classification of variables are exclusive and exhaustive. What does the result mean? How to get rid of the repetitions. Your advice is greatly appreciated. Maryann Parameter ? Grp Long NumC Estimate P-value Grou(GrpF*Long*NumC) N NGH-4 0-2 B1 0.0473 0.4062 Grou(GrpF*Long*NumC) Y NGH-4 0-2 B1 0.3662 0.0008 Grou(GrpF*Long*NumC) N NGH-4 0-2 Z0 0.0557 0.1216 Grou(GrpF*Long*NumC) Y NGH-4 0-2 Z0 0.181 <.0001 Grou(GrpF*Long*NumC) N NGH-4 3-5 B1 0.1279 0.007 Grou(GrpF*Long*NumC) Y NGH-4 3-5 B1 0.4069 0.0019 Grou(GrpF*Long*NumC) N NGH-4 3-5 D3 0.52 0.0042 Grou(GrpF*Long*NumC) N NGH-4 3-5 Z0 0.0363 0.3109 Grou(GrpF*Long*NumC) Y NGH-4 3-5 Z0 0.1416 0.0003 Grou(GrpF*Long*NumC) N NGH-4 6-8 B1 0.1042 0.058 Grou(GrpF*Long*NumC) Y NGH-4 6-8 B1 0.2639 0.0153 Grou(GrpF*Long*NumC) N NGH-4 6-8 C2 0.1596 0.3795 Grou(GrpF*Long*NumC) N NGH-4 6-8 Z0 -0.0164 0.6489 Grou(GrpF*Long*NumC) Y NGH-4 6-8 Z0 0.1326 0.0025
I have a data set in which I recorded cricket calling songs of 13 populations that were divided into two zones, allopatric zone and sympatric zone. In addition to calling song characters, I also noted temperature to adjust for calling song characters. My goal of these analyses is to see whether calling song characters differ between two zones after adjusting temperature. The followings are the structure of my data set: dependent variables: PRO, CRO, PDO, CFO (calling song characters) fixed factors: ZONE, POP (POP is nested within zone) covariates: TEMPERATURE So I ran nested analyses of variance with TEMPERATURE as covariate. My question is whether I should include the interaction term (ZONE*TEMPERATURE) for the analyses. Specific Question 1: It turns out that the interaction term was not significant in three dependent variables: CRO, PDO, CFO. Even though the interaction term was not significant, it did influence the significane of other terms such as ZONE or ZONE(POP). Then should I report the results of nested ANOVA without the interaction term? That is, the analysis was done without the interaction term from the beginning. Specific Question 2: The interaction term (ZONE*TEMPERATURE) was significant in PRO (P = 0.025). This means that ZONE or TEMPERATURE were important for PRO regardless of significance of ZONE or TEMPERATURE separately. Is this understanding right? Thanks for the help in advance.
Dear SAS-L, I have a hard time in interpreting interaction term in a proportional cox model. Basically I have two variables, group (treatment=1; control=0) and period (post=1; pre=0) and an interaction term consisting of those two (grouppost). The output I got is as follow: beta S.E Chi-Square Pr>ChiSq Hazard Ratio group 1 0.20744 0.02129 94.9269 <.0001 1.231 post 1 0.01789 0.02106 0.7219 0.3955 1.018 grouppost 1 -0.06118 0.02924 4.3785 0.0364 0.941 At Pre, the beta of the group is 0.20744. At post, what is the beta of the group? Is it multiplicative so that it is (0.20744 * -0.06118) or additive (0.20744 + -0.06118) or anything else? Thank you very much in advance. Duckhye Yang