I am analysing data by means of multiple regression. My goal is to find out about the relative importance of the independent variables, using hierarchical partitioning (package hier.part in R). However, I assume that the interaction of some of the independent variables is important, too. Now I am wondering if it is valid to have interactions when doing hierarchical partitioning? I could not find anything in the literature (Mac Nally (2002) - Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables)

My model would look something like this: DV ~ IV1 + IV2 + IV1:IV2 + IV3 + IV4 + IV5

Result of hierarchical partioning with interaction:

           I
IV1       20.8255247
IV2        4.3218387
IV1:IV2   70.7574155
IV3        1.6795456
IV4        0.8780111
IV5        1.5376644

Result of hierarchical partioning without interaction:

           I
IV1       74.132474
IV2       14.690646
IV3        6.872467
IV4        1.311382
IV5        2.993031

It appears that interactions always have (much) greater values than their IVs have seperately. So is that a "true" effect and a sign to include the interaction in the model or should this be treated with caution?

I am analysing data by means of multiple regression. My goal is to find out about the relative importance of the independent variables, using hierarchical partitioning (package hier.part in R). However, I assume that the interaction of some of the independent variables is important, too. Now I am wondering if it is valid to have interactions when doing hierarchical partitioning? I could not find anything in the literature (Mac Nally (2002) - Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables, Chevan & Sutherland (1991) - Hierarchical Partioning)

My model would look something like this: DV ~ IV1 + IV2 + IV1:IV2 + IV3 + IV4 + IV5

Result of hierarchical partioning with interaction:

           I
IV1       20.8255247
IV2        4.3218387
IV1:IV2   70.7574155
IV3        1.6795456
IV4        0.8780111
IV5        1.5376644

Result of hierarchical partioning without interaction:

           I
IV1       74.132474
IV2       14.690646
IV3        6.872467
IV4        1.311382
IV5        2.993031

It appears that interactions always have (much) greater values than their IVs have seperately. So is that a "true" effect and a sign to include the interaction in the model or should this be treated with caution?

I am analysing data by means of multiple regression. My goal is to find out about the relative importance of the independent variables, using hierarchical partitioning (package hier.part in R). However, I assume that the interaction of some of the independent variables is important, too. Now I am wondering if it is valid to have interactions when doing hierarchical partitioning? I could not find anything in the literature (Mac Nally (2002) - Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables)

My model would look something like this: DV ~ IV1 + IV2 + IV1:IV2 + IV3 + IV4 + IV5

Result of hierarchical partioning with interaction:

           I
IV1       20.8255247
IV2        4.3218387
IV1:IV2   70.7574155
IV3        1.6795456
IV4        0.8780111
IV5        1.5376644

Result of hierarchical partioning without interaction:

           I
IV1       74.132474
IV2       14.690646
IV3        6.872467
IV4        1.311382
IV5        2.993031

I am analysing data by means of multiple regression. My goal is to find out about the relative importance of the independent variables, using hierarchical partitioning (package hier.part in R). However, I assume that the interaction of some of the independent variables is important, too. Now I am wondering if it is valid to have interactions when doing hierarchical partitioning? I could not find anything in the literature (Mac Nally (2002) - Multiple regression and inference in ecology and conservation biology: further comments on identifying important predictor variables)

My model would look something like this: DV ~ IV1 + IV2 + IV1:IV2 + IV3 + IV4 + IV5

Result of hierarchical partioning with interaction:

           I
IV1       20.8255247
IV2        4.3218387
IV1:IV2   70.7574155
IV3        1.6795456
IV4        0.8780111
IV5        1.5376644

Result of hierarchical partioning without interaction:

           I
IV1       74.132474
IV2       14.690646
IV3        6.872467
IV4        1.311382
IV5        2.993031

It appears that interactions always have (much) greater values than their IVs have seperately. So is that a "true" effect and a sign to include the interaction in the model or should this be treated with caution?