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The estimated regression coefficients may be biased because of imprecision of the fish variable.
Similarly, the other outcomes showed stronger mercury effects when the imprecision in the fish variable was increased.
When the imprecision of the fish variable increased, the adverse mercury effects became stronger and more significant.
In a separate structural equation model, the fish variable was assumed to be a sum of true nutrient intake and a random measurement error.
Make your fish variable, don't color them all the same.
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These food groups were subdivided into blocks of variables for which we had the random subset of filled-in data and those for which we did not (e.g., fish variables that were included in those later filled in versus fish variables not so included) for purposes of imputation.
For instance, for fish protein, each of the 6 fish variables (fried fish in batter, fish fingers/fish cakes, other white fish, oily fish, shellfish, and fish roe) were multiplied by their corresponding protein content (protein/per gram of fish) from food codes in UK food-composition tables and were summed to obtain total fish protein.
First, we examined plots of the martingale residuals against the single HER1 HER4 FISH variables using the PROC LOESS option in SAS and chose DIRECT SMOOTH with a smoothing parameter of 2/3.
Log likelihood ratio statistics was used to evaluate whether the fish variables contribute significantly to the model fit, either for the linear or the restricted cubic spline model, and whether the restricted cubic spline model parameters add significantly to the model fit compared to the linear model.
The initial freshness of fish is variable and taken into account in the model in the predictions of the freshness status of the packed fish.
Thus, fish had variable numbers of frames for analysis, depending on how much time each individual spent near the school.
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