I am conducting model selection on my dataset using the package MASS and the function stepAIC. This is the current code I am using:
mod <- lm(Distance~DiffAge + DiffR + DiffSize + DiffRep + DiffSeason +
Diff.Bkp + Diff.Fzp + Diff.AO + Diff.Aow +
Diff.Lag.NAOw + Diff.Lag.NAO + Diff.Lag.AO + Diff.Lag.Aow, data=data,
na.action="na.exclude")
library(MASS)
step.model<-stepAIC(mod, direction = "both",
trace = FALSE)
summary(step.model)
this gives me the following output:
Call:
lm(formula = Distance ~ Diff.Lag.NAOw + Diff.Lag.AO + DiffSeason,
data = data, na.action = "na.exclude")
Residuals:
Min 1Q Median 3Q Max
-146.984 -48.397 -9.533 42.169 194.950
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 77.944 20.247 3.850 0.000184 ***
Diff.Lag.NAOw 11.868 6.261 1.896 0.060209 .
Diff.Lag.AO 24.696 17.475 1.413 0.159947
DiffSeasonEW-LW 41.891 18.607 2.251 0.026014 *
DiffSeasonLW-LW 22.863 20.791 1.100 0.273465
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 67.2 on 132 degrees of freedom
Multiple R-squared: 0.06031, Adjusted R-squared: 0.03183
F-statistic: 2.118 on 4 and 132 DF, p-value: 0.08209
If I am reading this right, the output only shows me the top model (Let me know if this is incorrect!). I would like to see the other, lower-ranked models as well, with their accompanying AIC scores.
Any suggestions on how I can achieve this? Should I modify my code in any way?
