Hi there,
I am a beginner in statistics and also with JMP, so I hope someone can help me with my question below. I am trying to use JMPs PCA co-variance module on a relatively small environmental data set of 80 rows x 10 columns (800 values). My goal is to reduce the data set so I can predict whether the 10 variables (in the columns) co-vary across 80 locations (80 rows). The variables have all the same units and I log10-transformed the data.
My problem is that I have missing values for some cells (i.e. locations where the variable was not detected at the LOD). When I use the default estimation method (REML), I get about 80% of the variance explained by PC1. But when I use the exact data with the Row-Wise method, this value drops to 60%. I would like to understand why. I understand that REML is using all my data and the Row-Wise method apparently omits the missing values. But what does omit mean in this respect, using zero instead and thus decreasing the calculated variance? The overall question for me is, which method is most conservative in interpreting the data when the missing values are due to non-detects in the data set?
Also, I tried to add +1 to the log10-transformed data set to avoid the missing value problem (tip from another forum), but JMP does not allow me to add +1 to the empty log10-transformed cells. I am using JMPPro 12.
Thanks a lot for your help! Sascha