hello all, I wonder if anyone could give me a hint on which statistical technique I should use and how to carry it out in R in my case. Thanks in advance.
My data is composed of two columns, the same numerical variable (continuous) from actual measurement and model prediction. My objective is to compare the data agreement (if there is significant difference) and make conclusions about the model efficiency. Since the measured and predicted variable was based on the same unit, the first test came into my mind was paired t-test. However, the paired difference is not normal (p-value = 0.0048 from SAS proc univariate). In this case, I can either do a wilcoxon signed-rank test or do transformations about the data. I was told that wilcoxon signed-rank test is not as widely recognized as paired t-test in the literature, so I prefer to do transformation. My question is: do I need to do transformations on both columns of original data, or just the paired difference? What transformation is appropriate? I thought about log transformation, but if I find significant (or no significant) difference between the logged data (measured and predicted), can I say there is significant (or no significant) difference between the original data? After this step of analysis, I will convert the continuous numerical data into qualitative categorical ranking (value=1, 2, 3 and 4). Which statistical test and R command should I use to compare the ranking agreement between the actual measurement and prediction? Thank you very much for helping me out. I haven't slept since a long time ago and this is kind of emergency. If there is any confusion about my description, please let me know. Regards, XY ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.