Outcome~predictor model evaluation, repeated measurements
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rad mac ▴ 10
Last seen 7.7 years ago
Dear all, I have simple question regarding how to fit a model (i.e. linear) to the data. Say I have 10 subjects with different phenotypes (dependent var Y, identical for a particular subject) and one predictor variable measured 3 times for each subject (X). By other words: Y Subj X 1 1 1.2 1 1 1.3 1 1 0.7 3 2 2.1 3 2 2.5 3 2 4 5 3 3 5 3 4 5 3 4 ... 20 10 12 20 10 13 20 10 12.5 Subj is a grouping variable. I would like know the correlation of Y with X (Y~X) and the effect of within subject variance on this correlation. And thus, overall significance and correlation. Will it be valid to fit lm to all combinations of x and y and take an average values of p and R-squared? Usually, I estmate the correlation using simple lm between outcome and averaged predictor (1-to-1, i.e. 20 outcomes versus 20 predictors). However, I would like to take in account variations associated with replicated measurements (i.e. the same 20 outcomes versus 20 predictors replicated say 3 times), and, therefore, evaluate slope and intercept variabilities. Do mixed model regression analysis suitable for such an analysis for example using lme function from nlme package? If not, what kind of analysis is most appropriate? Thank you. [[alternative HTML version deleted]]
Regression Regression • 531 views

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