Hello all I am trying to fit a model using JMP 10, and the results confuse me, I thought perhaps you could assist. I have an ordinal dependent variable, with 3 categories. My main independent variable of interest is discrete, with not less than 5 categories ! In addition to this IV, I have several other IV's to examine, some are discrete and some continuous. I ran the model using the Fit model module. First, I tested only the main IV of interest, and something weird has happened. JMP have created dummy variables, leaving the last category as control, fine. The model was not significant, with P=0.1134 (whole model test), however, one dummy variable was actually significant (P=0.036). The generalized R square was 0.01, the entropy R square was 0.009, and the training misclassification rate was 35% (so any testing set would be poorer). Every indication show that the results are week, but how can I explain a whole model being non-significant while one IV (in this case a level of dummy), being significant ? There isn't a correlation, since all IV's are levels of the same dummy ! Then I tried adding a couple of continuous IV's. Both were very significant (P=0.0015, P=0.0126). However, the generalized R square is still around 5% only. The odds ratios are 1.16 and 0.9. When I have changed the roll play, and created a couple of box-plots of these IV's group by my DV, it didn't take an expert to say that there is not an interesting difference. My sample size is 411, can it be that the large sample size caused the low P-values, or is it a type I error ? How can I explain very significant results when I get the sense that they have no meaning ? Thank you ! ... View more

Hi all, I will need very soon to perform a big number of hypothesis testing. Since it's well known that in such a case relying on p values is a wrong thing to do, due to type I errors, I would like to use the false discovery rate, and more preciecly the positive false discovery rate (Storey), and to compute q-values as my "adjusted p-values". My question is divided in two: 1. Is there anywhere a code to calculate the pFDR and the corresponding q-values in JMP 9 ? I saw something on the Internet from which I assume that such functions already exist in JMP Clinical. I only have a licence for JMP 9, and I have no idea how much JMP clinical cost or if I will have any budget. So if there is a simple JSL code somewhere, it can be very usefull for me. 2. In case there is such a code, most likely it will require a vector of p-values as output. Consider that I have a JMP file with 1 categorical variable, and 500 continuous ones, and I want to run a t-test for each one of the 500, and to save in a new file, a variable of the p-values, containing 500 of them. How do I do that in JSL ? Is it possible at all ? That JMP will run automatically 500 tests and save only the p-values ? Thank you in advance.... ... View more

Hello, I have a small question, probably a silly one... When using the distribution function, I am creating a histogram of some data. I would like to add the counts axis to the histogram, but when I do, it adds it to the right side of the histogram, and I want it on the traditional left side. I coulnd't manage to change the default. Does anyone knows how to add the count axis on the left side of a histogram ? cheers ! ... View more

Hello, I saw that people connect R and JMP, and build graphical interfaces for R via JMP, in other words, for some R procedure, they build a GUI like interface that allow to drage variables in the JMP superb style. How do I do it ? I read a bit about how to connect R to JMP from JSL, but how do I create this GUI, let's say for a very simple procedure, for example, if I have a variables X and Y, and I want to perform: mean(x), how to I build a GUI that does that ? thanks... ... View more