I have a linear model with both continuous and categorical (>2 categories) variables. I am aware of other statistics (e.g., AIC sum of weights and lmg from R package relaimpo) that can be used to compare relative influence of predictor variables, but for various reasons cannot be used for my situation. I calculated eta squared using R package heplots (function etasq). Below is a simplified version of my slightly more complex model:
SPS_Jn5$REGION <- as.factor(SPS_Jn5$REGION)
SPST1x <- lm(PINDX ~ FChange * PLAND_42 + REGION, data = SPS_Jn5)
I get the following output for eta squared:
etasq(SPST1x) Partial eta^2 FChange 0.007043574 PLAND_42 0.158118552 REGION 0.148318074 FChange:PLAND_42 0.013788113 Residuals NA
However, I am not sure whether it is valid to use eta squared to compare categorical and continuous variables present in the same model, particularly because a categorical variable (with >2 categories) involves more than 1 degree of freedom. I searched online but could not find any documentation. This post states that using eta is not appropriate when ordinal and nominal data are present, but does not specifically answer my question. The R documentation for heplots does not talk about categorical variables.
I tried using eta squared by reassigning my categorical variable (REGION) as a continuous variable (the observations were region numbers so could be used as a continuous data). I got a different output:
etasq(SPST2x) Partial eta^2 FChange 0.01788473 PLAND_42 0.18224738 REGION 0.07655448 FChange:PLAND_42 0.01962085 Residuals NA
Here the influence of my categorical variable is obviously low. Presumably, the function was able to recognize between a categorical and a continuous variable. However, this does not either support or reject the use of eta squared for models that have both categorical (specifically >2 categories) and continuous variables.
I tried to study the codes for eta squared using
but my knowledge is not sufficient to understand the details of the code. Thank you.