After all data were collected, statistical analyses were performed to test for correlations between TI and D_d, RI, RV, and MRP. Due to the high amount of uncertainty introduced when comparing TI estimates between Groups, typical regression analyses cannot be conducted on these data. A typical regression model requires that the data are independent of each other. However, the collected data within a group are dependent on the error associated with that individual group. Therefore, a typical regression model would produce inaccurate results. Consequently, I have to use a set of multilevel regression models, which is appropriate for nested data.
In this case, estimated TI values are nested within Group, and using a multilevel model allowed us to compare TI both within and between scenes. This comparison was viable even when the data exhibit different slopes and y-intercepts caused by variations in uncertainty between group. I have to use the multilevel and lme4 packages with the R statistical language to run multilevel regression models on all sets of data. After the models were run, the statistical significance of each model result was determined using a likelihood ratio test to attain p-values.
Since I am new to R and statistics, if anyone could help me in understanding the design it will be of great help to me. I am attaching a screenshot of my dataframe. I really need help. ID = 138 Group = 18
I have to account for nesting by both random intercept and random slopes, for a single crater ID, multiple values, i.e., TI,RI,RV,D_d and MRP is measured. example: for ID 103, TI, RI, RV, D_d and MRP is measured, similarly for each crater these parameters were measured.