# Interpreting outputs of calc.relimp

I am using the package "relaimpo" to calculate the relative importance of variables that I have fitted to a linear model. Code is:

``````calc.relimp(model,rela=TRUE)
``````

It is returning me outputs like these:

``````> calc.relimp(model,rela=TRUE)
Response variable: Sales
Total response variance: 3505.987
Analysis based on 20 observations

2 Regressors:
Proportion of variance explained by model: 53.51%
Metrics are normalized to sum to 100% (rela=TRUE).

Relative importance metrics:

lmg
Price       0.2246168

Average coefficients for different model sizes:

1X       2Xs
Price       -16.92037 -21.71100
``````

I understand that ultimately I just need to look at the relative importance metrics for both variables (ie 22.5% for Price and 77.5% for Advertising). But what do the numbers in "Average coefficients for different model sizes" mean? Thanks.

Can you provide a reproducible example? FAQ: What's a reproducible example (`reprex`) and how do I do one?

I ran reprex and got the following:

``````calc.relimp(model,rela=TRUE)
#> Error in calc.relimp(model, rela = TRUE): could not find function "calc.relimp"
``````

Created on 2020-08-05 by the reprex package (v0.3.0)

Not sure if this is helpful.

Basically, I'd just like to get an understanding of the numbers under "1X" and "2Xs" in the calc.relimp output.

Not really, but this is a description:

average coefficients for variables not not requested by always only for models of different sizes;

note that coefficients refer to modeling residuals after adjusting out variables listed in always (both from response and other explanatory variables)

And a really detailed description of the function here:

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