This is not a technical/code related question (more of a general question related to proc GLM). I had some codes written for a project a couple of years back (2 factor anova model). I used  Proc GLM. A simple version of the code is below:   proc glm data = mydata; class Independent_Var1 Independent_Var2; model Dependent_Var=Independent_Var1|Independent_Var2/ss3 solution; lsmeans Independent_Var1|Independent_Var2; run; I recently dug out this long forgotten project and started working on it again. I tried to replicate my previous findings (dataset is exactly the same). However, I am not getting my previous results back. There are slight changes in the F statistic and p-values. For example for Independent_Var1, the original p-value was 0.08 and I now get 0.12. Again, with the same dataset, I would expect to get my previous results.   Can anyone help me understand why I am getting somewhat different results. Have there been any changes or updates to Proc GLM over the last couple of years or so? Is there a resource to track changes to the proc's? I do keep my data and codes organized so I am confident that I am using my original data as well as the code.    Thanks in advance.   EDIT: During this time, I have gone from using SAS 9.2 to SAS 9.4.  ... View more

I have a dataset in this format:   Factor A is a between subject factor (with 2 levels - High and Low). Factor B is a within subject factor (with 3 levels - High , Moderate and Low). I want to run a mixed model with nested random effects factor.   The code that I am using is: proc mixed data=data.mydata; class FactorA FactorB; model DV = FactorA|FactorB; random FactorB(FactorA) FactorB*FactorA(FactorA); lsmeans FactorA|FactorB; run; The log states: **Estimated G matrix is not positive definite.** I also do not get any of the p-values (only a '.' is displayed). Furthermore in the output tables, I see that DF = 0. I have a hunch that this is what is symptomatic of the error. But I have been unable to figure out why this is happening. Any leads will be appreciated. Thanks. ... View more

Reposting this from here: https://communities.sas.com/t5/SAS-Statistical-Procedures/Anova-Analyzing-experiment-with-within-and-between-subjects/m-p/472707   Hi I have an experiment with one between subjects factor (Lets say FactorA) with two levels: Big and Small and one within subjects factor (FactorB - Quality) with three levels: 1, 2 and 3 - corresponding to High, Medium and Low.   I want to examine the effect of both the factors on the Dependent Variable measured after each of the participant was put into one of the two conditions (Big or small) and evaluate some things (High, medium and low) : Dep_Var1, Dep_Var2 and Dep_Var3.   I am using the following code:    proc glm data = Dataset.data; class FactorA FactorB SubjectID; model  Dep_Var1, Dep_Var2 Dep_Var3= FactorA|FactorB /ss3; repeated SubjectID, FactorB; lsmeans Condition/pdiff stderr; run;   However I keep on getting the following statement in the Log:   NOTE: The CLASS variable FactorB has only one level: '1'. ERROR: Ambiguous level information in REPEATED statement   Also if I change this to the following:   proc glm data = Dataset.data; class FactorA FactorB SubjectID; model  Dep_Var1, Dep_Var2 Dep_Var3= FactorA|FactorB /ss3; repeated SubjectID; lsmeans Condition/pdiff stderr; run;   I get the following error:  NOTE: The CLASS variable FactorB has only one level: '1'. NOTE: H Matrix for SubjectID*FactorB has zero d.f. NOTE: H Matrix for SubjectID*FactorA*FactorB has zero d.f.   Here's a snippet of the data for easy reference:   SubjectID FactorA Dep_Var1 Factor_B Dep_Var2 Factor_B Dep_Var3 Factor_B 1 Big 9 1 2 2 29 3 2 Small 9 1 5 2 28 3 3 Small 12 1 2 2 27 3 4 Small 9 1 2 2 28 3 5 Big 11 1 5 2 29 3 6 Big 9 1 5 2 21 3 7 Small 9 1 2 2 26 3 8 Big 10 1 2 2 26 3 9 Big 13 1 5 2 25 3 10 Small 10 1 4 2 25 3   I realize that I am making a mistake in how I have input FactorB, but cannot figure out what to do here. Any help is appreciated.   Thanks. ... View more

Hi I have an experiment with one between subjects factor (Lets say FactorA) with two levels: Big and Small and one within subjects factor (FactorB - Quality) with three levels: 1, 2 and 3 - corresponding to High, Medium and Low.   I want to examine the effect of both the factors on the Dependent Variable measured after each of the participant was put into one of the two conditions (Big or small) and evaluate some things (High, medium and low) : Dep_Var1, Dep_Var2 and Dep_Var3.   I am using the following code:    proc glm data = Dataset.data; class FactorA FactorB SubjectID; model  Dep_Var1, Dep_Var2 Dep_Var3= FactorA|FactorB /ss3; repeated SubjectID, FactorB; lsmeans Condition/pdiff stderr; run;   However I keep on getting the following statement in the Log:   NOTE: The CLASS variable FactorB has only one level: '1'. ERROR: Ambiguous level information in REPEATED statement   Here's a snippet of the data for easy reference:   SubjectID FactorA Dep_Var1 Factor_B Dep_Var2 Factor_B Dep_Var3 Factor_B 1 Big 9 1 2 2 29 3 2 Small 9 1 5 2 28 3 3 Small 12 1 2 2 27 3 4 Small 9 1 2 2 28 3 5 Big 11 1 5 2 29 3 6 Big 9 1 5 2 21 3 7 Small 9 1 2 2 26 3 8 Big 10 1 2 2 26 3 9 Big 13 1 5 2 25 3 10 Small 10 1 4 2 25 3   I realize that I am making a mistake in how I have input FactorB, but cannot figure out what to do here. Any help is appreciated.   Thanks.   ... View more

Hi,   Could someone please tell me how to assign a set of variables, values from a table. I have a table of parameter estimates of a non linear regression and I want to pass the values of the estimates to the variables for a differetn set of calculation.   Debjit ... View more

own votefavorite I have a tobit regression model in hand and I want to calculate the marginal effects at means for the dummy variables in the reg equation. There are two dummy variables in the regression equation and three continuous variables. For SAS, this link has some information for the marginal effects for a tobit model : (http://support.sas.com/rnd/app/examples/ets/margeff/). However this tells me how to compute the marginal effects for continuous variables and not for a dummy variable in the case of a tobit regression. This is what I have done following the SAS link. Note that f and g are dummy variables which takes the value of 1 or 0. proc qlim data=tobit1; model a = b c d f g; endogenous a~censored(lb=0 ub=1); run; Here I input the means of the variables. Note that for the marginal effect of dummy variable g, I cannot set the value of f to its mean value as it is a dummy variable and set it at 0. data tobit2; input b c d e f g a; datalines; 0.48 0.34 5.59 5.57 0 1 . 0.48 0.34 5.59 5.57 0 0 . run; data tobit; set tobit1 tobit2; run; proc qlim data=tobit noprint; model a = b c d f g; endogenous a~censored(lb=0 ub=1); output out = tob2 marginal; run; proc print data = man2;run; After inputting the extra values and running the tobit regression, I get the marginal values for g 'Meff_g' for the last two added observations. In order to get the marginal effect of g at means I use: Meff_g(at g =1) - Meff_g(at g=0) i.e. substracting the Meff_g for the last two observations. Is this correct? Could somone help please? ... View more