phico2max is a slope derived from linear regression of a Y variable to a X variable. To simplify the model, neither this Y nor this X variable are included, but instead I am running my statistical analysis on the values for phico2max. Therefore each value of phico2max only appears once and has a unique identifier, and there are no repeated measures. The model is then simply phico2max=μ + canopy_position + species + canopy_position*species + year + ε there is replication within each canopy_position, each species, and each year. The weight statement is used because each value of phico2max was obtained from a linear regression, and so is associated with a standard error: I want to give more weight to slopes associated with a small standard error, hence the (1/individual_variance)/(1/total_variance). The unstructured variance/covariance matrix is used because residual variance is unequal between canopy_positions. The random statement is used because year is considered random. Using 1/individual_variance as the weighting factor actually allowed the code to run correctly, but I believe that for the weighting factor to be correct the sum of all weighting factors must be equal to 1. This isn't the case if the weighting factor is 1/individual_variance. Does my issue lie in the math, or in the code?
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