I am analyzing data from cohort of 500 calves investigating the impact of disease on growth.
My outcome variables are normally distributed, continuous data. I am using hierarchical models with calf nested within farms and testing for the longer term impacts of disease.
The problem I am having is with how to include disease data. I have variables for the number of weeks a calf had disease and the total score over a validated threshold for diagnosis
As I am inexperienced in uploading images, here are the tabulated results of the data above:
Disease Duration (weeks) 0 1 2 3 4 5 6 Frequency 266 128 50 33 8 5 2
Total Score 0 1 2 3 4 5 6 7 8 9 10 13 14 15 Frequency 266 88 51 30 20 13 2 6 4 5 3 1 2 1
Obviously, this data is far from normal. But there a lot of levels to use a dummy coded categorical variable, and I think an ordinal scale better represents the data. What do you think it the best way to include this data as an independent variable in my LME models? (n.b. I don't include both in the same model just one or the other)
The models do return results without convergence errors or other warnings when I include these variables but it doesn't feel like very good practice and I am unsure of what sort of transformation I could do to make this data better (e.g. log transformation leaves the data looking very odd and plots of the raw data make it look like a linear relationship is the most likely)
Here is an example of what I would like to improve:
(adj_w_63 - calf weight, weeks_brd - weeks with disease (as described above), rid - a normally distributed continuous variable, milksolids_total - a normally distributed continuous variable)
library(lme4) model1<-lmer(adj_w_63 ~ weeks_brd + rid + milksolids_total + (1|farm_ac), data=comp) summary(model1) Linear mixed model fit by REML ['lmerMod'] Formula: adj_w_63 ~ weeks_brd + rid + milksolids_total + (1 | farm_ac) Data: comp REML criterion at convergence: 3247 Scaled residuals: Min 1Q Median 3Q Max -3.5180 -0.5525 -0.0458 0.5945 6.1674 Random effects: Groups Name Variance Std.Dev. farm_ac (Intercept) 30.10 5.487 Residual 83.37 9.131 Number of obs: 443, groups: farm_ac, 11 Fixed effects: Estimate Std. Error t value (Intercept) 68.06279 3.30996 20.563 weeks_brd -1.00200 0.42089 -2.381 rid 0.11010 0.04981 2.210 milksolids_total 0.19904 0.07679 2.592 Correlation of Fixed Effects: (Intr) wks_br rid weeks_brd -0.174 rid -0.285 0.141 mlkslds_ttl -0.795 0.038 -0.016
Thank you so much for your help.