Multiple Regression
#############################
#BLOCK #1
library('BayesFactor')
#load data
#dat=read.table('bailey.dat',head=T)
dat=read.table(url("http://pcl.missouri.edu/jeff/sites/pcl.missouri.edu.jeff/files/bailey.dat"),head=T)
#############################
#############################
#BLOCK #2, Conventional Analysis
cc=dat$CC
climate=dat$diftemp
pop=dat$dpop_30
para=dat$tpar
o2=dat$Isosd
#make a new DataFrame, needed for BF
myDat=data.frame(cc,climate,pop,para,o2)
summary(lm(cc~.,data=myDat))
round(cor(myDat),2)
#######################################
#BLOCK 3
bfTop=regressionBF(cc~climate+para+o2+pop,data=myDat,whichModels="top")
bfTop
plot(bfTop)
#######################################
#BLOCK 4 Bayes Factor, All Models, Go Slow
bf=regressionBF(cc~climate+para+o2+pop,data=myDat)
length(bf)
head(bf)
sort(bf)
tail(bf)
plot(bf)
bf[11]
bf[11:15]
#renormalizng BF
bf
bf.normalized=bf/bf[15]
plot(bf.normalized)
bf[10]/bf[14] #better than the addition of para
bf[10]/bf[13] #better than the addition of climate
bf[10]/bf[3] #better than the omission of pop
bf[10]/bf[4] #better than the omission of 02
Attachment | Size |
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bailey.dat | 9.06 KB |