It may be so. Also, when I try to run genmod with the factors Bact Host and their interaction, the warning about the Hessian matrix not being positive definite may be caused by that many zeroes. The model runs ok with no interaction. ... View more

BactA is successfully introduced into other hosts (HostB, HostC, HostD, HostE) with various proportions of success. ... View more

All bacteria (except BactA) "Fail" to be introduced into HostA. The proportion of success for the introduction of BactB, BactC, BactD and BactE into HostA is zero. The natural bacteria of HostA, named BactA in the table, is the only one that it is "introduced" successfully (The symbol "#" represents a proportion different from Zero). ... View more

The measures of pairwise relatedness between bacteria are represented by the matrix below:   BactA BactB BactC BactD BactE BactA 0 # # # # BactB # 0 # 0 # BactC # # 0 # # BactD # 0 # 0 # BactE # # # # 0   There are some zeroes outside the diagonal       The measures of pairwise relatedness between hosts are represented by the matrix below:   HostA HostB HostC HostD HostE HostA 0 # # # # HostB # 0 # # # HostC # # 0 # # HostD # # # 0 # HostE # # # # 0     No zeroes outside the diagonal.   These measures of relatedness were used as continuous variables, but the logistic stepwise selection kept them out form the final model.   ... View more

This is the frequency of response values per each bacteria and host:   ... View more

I am using HostA and BactA as the Reference categories, with GLM coding. ... View more

Below is a condensed matrix of the data. Each host has a natural bacteria. The bacteria were introduced into new hosts as well as into their native host ("self-introduced"). The diagonal shows the results of "self-introduction"     HostA HostB HostC HostD HostE BactA # # 0.01 # # BactB 0.00 # # # 0.10 BactC 0.00 # # 0.10 0.00 BactD 0.00 # # # # BactE 0.00 # 0.01 # 0.00   The symbol "#" represents proportion (of infected  individuals) that are different from each other in the whole matrix. There are two cells with value 0.01 that are equal. There are two cells with value 0.10 that are equal. The Zeroes are real data.   I tried to run a model with: PropInf = Bact + Host +Bact*Host   in Genmod but I get the warning:   "WARNING: Negative of Hessian not positive definite"   I think he many zeroes from HostA may be causing such a problem. The model PropInf = Bact + Host ran ok in genmod.     ... View more

Thank you for your comments. I just checked the logistic with continuous variables ONLY. It does not solve the problem. With categorical variable only, I had a quasi separation the warnings: Quasi-complete separation of data points detected. Warning: The maximum likelihood estimate may not exist. Warning: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable. The Firth correction made the warning go away. ... View more

Hi Reeza, Just one more thing. I have continuous variables that I originally included in my logistic regression, but removed it after model selection. Even when I put back the continuous variables in the model the problem persists. Any other suggestions are welcome ... View more

Thanks Reeza, I will check it out right now. ... View more

Thank you for your answer. Here is the situation. When I use SAS, as explained in my post (and the attachment to my post), the variable TotFec IS NOT proprotional. When I run the analysis in R, Tot Fec is proportional, as seen below:   > print(cox.zph(CoxModel.15))                               rho                  chisq               p TotFec                  0.056822        1.58e-01         0.691 InfStaStr[T.UN]      0.000708        3.49e-05         0.995 GLOBAL                NA                  1.71e-01         0.918   I did test the factor by itself, and agin, its seems to be proportional.   > cox.zph(fit, transform = 'identity')                            rho         chisq         p TotFec               0.14        0.924       0.336 > cox.zph(fit, transform='log')                             rho         chisq        p TotFec                0.181      1.55        0.214   As shown above, my variable is proportional in all test performed in R. Additionaly, in the analysis with SAS, only the inclusion of TotFec*LOG(Time) in the cox model produces non-proprotionality for this variable. Another test, using Assess with ph, shows that TotFec is proportional (Supremum test, p=0.0930).    A book on Cox Model, published in 2000, authored by Therneau and Rambsch, "Modeling Survival Data: Extending The Cox Model" (page 144-145) refers to a question by a user that found a somehow similar situation when analyzing his data (SAS shows a variable to be non-proportional, but R otherwise). The authors show that when testing in R, the "identity" tansformation produces a p=0.69 (proportional) and the log transformation produces a p=0.00694 (non-proportional). They point out that after examining the data and transformation graphs, it is clear that the non-proportionality was caused by ONLY two early data points that were outliers in a log plot (hence, the log test has a p=0.00694, as shown obove). They argue that the non-proprotionality for the variable in question should be ignored.   I was using an analog rationale for my data set. The points that seem to be causing the non-proprotiuonality are "early" data points.   In a separate point, it must be really scary living in a world where everyone who asks a question like this is a "data dredger" (paraphrasing your words). Is not more logical to assume that if someone asks this question is sincerely looking for some advise? How is it logical to assume that someone who wants to cheat in his data analysis would bring this question to light in a public forum? Perhaps I am naive to think that this forum is a place to candidly address doubts on data analysis.   Anyway, Thank you for your answer. If you feel that you do not want to answer me back, I understand it. Have a good day.      ... View more

Thank you sas users for their input. I just wish that you would have paid such much attention to other questions I posted in the past. I have several posts that many not even cared to comment.   I understand that no one is under the obligation to answer any of my questions. But, at least if you want to provide an answer, I expect it to be a respectful one. Respect goes both ways.   I want to thank a couple of you that did pay attention to my questions in the past and gave answers that were helpful.   It was not my intention to offend anyone with my answer. But I see that now some of you feel the need to continue providing sarcastic observations to my question. Please, do not answer my post if you are going to be sarcastic. Show some respect to the person who posted the question too. You cannot judge anyone of you do not have a full knowledge of the facts. Porbably some of you did not even see the attachment I provided with my question.   Again, please, do not waste your time to post sarcasm. You also waste my time if you do. ... View more

Please, do not tire yourself. ... View more

Is that why you are a super user? You promptly repond with made up phrases?   ... View more