I have a question concerning the statistical validity of methods. I have data about the survival probabilities of vegetative and reproductive structures of a plant along the flowering time. I am trying to model the survival probability of these structures between the different sexes (females, males), types (flowers and cladodes), and treatments (control and exclusion treatment). The response variable was binomial, 1= alive, and 0= death. I did two methods to analyze this data, but I don't know if it is statistically acceptable.

First, I performed a generalized linear mixed model (GLMM) with binomial error distribution with "probit" link function to estimate the survival probability of flowers between sexes, treatments and types. Using the next model:

m.a <- glmmTMB(survival ~ sex * treatment *type +(1|ID),family=binomial(link="probit"),data=df)

With this model, I used the same data for sex, treatments, and types to PREDICT the probabilities of each row of data used:

dPred <- data.frame(ID = df$ID, sex= df$sex, treatment = df$treatment, type = df$type)

then I used the new data to predict the probabilities of each row used in binomial model:

df.pred<- cbind(dPred, predicted = predict(m.a, type="response", newdata=dPred))

Then, I performed a Linear Mixed-Effects Model using these predicted data. I used the nlme package to fit the model:

model<- lme(predicted ~ sex * treatment *type, random= ~1|ID, data = df.pred)

I performed pairwise comparisons among the different independent variables using emmeans package. My questions are:

Are these methods valid to infer the survival probabilities of these structures? Is there a problem to use these new predicted data to fit this model and to make inferences about the differences among sexes, treatments, and types of structures?

Thank you All the best


Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.