# Understanding Spikeslab Output

I'm using spikeslab for the first time, but don't understand what the output means. It was suggested to me that I use it to tell which variables my dependent variable is most correlated to, in a ranked order. Particuarly, what is "bma" bma.scale" "gnet" and "gnet.scale"? I also don't understand how to read the corresponding plot to the model.Thanks for any help!

For example, this is one of the models I created using spikeslab, with its output:

model2_ss <-spikeslab(Risk_Pct ~ Race
+                    +hispanic
+                    +Born_In_US
+                    +Highest_Education
+                    +Marital_Status
+                    , na.rm = TRUE, data = LabeledData)
> model2_ss
-------------------------------------------------------------------
Variable selection method     : AIC
Big p small n                 : FALSE
Screen variables              : FALSE
Fast processing               : TRUE
Sample size                   : 26
No. predictors                : 5
No. burn-in values            : 500
No. sampled values            : 500
Estimated mse                 : 0.4299
Model size                    : 3

---> Top variables:
bma      gnet     bma.scale     gnet.scale
Marital_Status  0.516     0.516     0.319         0.319
Born_In_US      -0.469    -0.447    -0.440       -0.419
Race            0.458     0.421     0.926         0.851


Have you tried reading the Ishwaran and Rao papers as mentioned in the documentation for spikeslab? There's an article in the R Journal as well that might be worth reading too: https://journal.r-project.org/archive/2010-2/RJournal_2010-2_Ishwaran~et~al.pdf - no sense duplicating it all here.