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Hello, Is it possible to generate simulated data that look like the attached figure? The curve in the figure can be obtained from this equation respecting some conditions: if(Temperature > T_min & Temperature < T_max){ a*( Temperature -T_min)*( Temperature -T_max) } else 0 T_min ~ Uniform(0, 24) T_max ~ Uniform(25, 45) -a ~ Gamma(1, 1) Temperature <- seq(0, 45, by = 1) Thank you very much for your help. Have a nice day Marine -------------- next part -------------- A non-text attachment was s...

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...response variable it leads me to the gamm option… I start the model building procedure by finding firstly the best fixed structure and after the best random effect structure and finally the best model.   I my case my first attempt final model was obtained by:   Model <- gamm4(TREE_01 ~ s(Day) + T_MAX + SHRUB_CV + D_AV5RS_KM + D_RIP_KM + FOREST_PP, random = ~ (1 | fAnimal), method = "REML", control = lmc, family = binomial, data = RSTEMP_01_X2) summary(Model$gam)   Family: binomial Link function: logit Formula: TREE_01 ~ s(Day) + T_MAX + SHRUB_CV + D_AV5RS_KM + D_RIP_KM +     FORES...

Hi, if I got it right then the survival-time we expect for a subject is the integral over the specific survival-function of the subject from 0 to t_max. If I have a trained cox-model and want to make a prediction of the survival-time for a new subject I could use survfit(coxmodel, newdata=newSubject) to estimate a new survival-function which I have to integrate thereafter. Actually I thought predict(coxmodel, newSubject) would do this for me,...