I have a dataset that is constructed like this: ID Replicate Y X1 X2 A 1 0 .78 .33 A 2 0.25 .44 .09 B 1 0.25 .76 .66 B 2 1 .31 .39 C 1 0 .11 .48 C 2 0 .07 .22 The beta binomail distribution seems appropriate because my response variable (Y) is porportional. Thus, I am interested in looking at the relationship of the two X variables to Y. In order to use a beta binomial regression in jmp, you need to: The beta binomial distribution requires a sample size greater than one for each observation. Thus, the user must specify a sample size column. To insert a sample size column, specify two continuous columns as Y in this order: the count of the number of successes, and the count of the number of trials. The link function for p is the logit. Has anyone ever used this type of analysis and understands how to arrange the dataset to perform it correctly? Thank you for your help!
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I have a porportional response variable (herbivore damage to a leaf) that is between 0 and 1 with many zeros. I am interested in fitting a model of several different traits (continuous variables) to this response variable. These observations are on many different genotypes, each having several replicates. I am not entirely certain how to transform the dataset in order to properly use this model. A part of creating a ZI beta regression model is that you need to "insert a sample size column, specify two continuous columns as Y in this order: the count of the number of successes, and the count of the number of trials." I don't know what the sample size variable looks like. Any help is greatly appreciated!
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