################################
#Calculate AIC from logL or RSS.  
#Example from Burnham and Anderson 2002, page 102, "cement hardening data"
################################

#Name models
model=c("m12", "m124", "m123", "m14", "m134", "m234", "m1234", "m34", "m23", "m4", "m2", "m24", "m1", "m13", "m3")
#Enter sample size
n=13
#Enter logL
logL=c(-9.704, -8.478, -8.504, -11.370, -8.863, -11.290, -8.469, -16.927, -22.519, -27.426, -27.586, -27.315, -29.760, -29.558, -32.533)
 #Or calculate logL from least squares theory "regression analysis"
 #RSS=c( , , , , , , , , , , )   #Enter RSS values here.
 #sig2=RSS/n
      #sig2=c(4.45, 3.69, 3.70, 5.75, etc.) #Or if you have sigma2, you can enter it here.
 #logL=(-n/2)*log(sig2)
logL
#Enter number of parameters
k=c(4, 5, 5, 4, 5, 5, 6, 4, 4, 3, 3, 4, 3, 4, 3)
k
#Calculate AICc
aicc=-2*logL+2*k+(2*k*(k+1))/(n-k-1)  #aic=-2*logL+2*k
aicc
#Calculate delta
delta=NULL
for (i in 1:length(aicc)){
     delta[[i]]<-aicc[[i]]-min(aicc)
     i+1
     }
aicc
#Calculate w
w=NULL
for (i in 1:length(delta)){
     w[[i]]<-((exp(-0.5*delta[[i]]))/sum(exp(-0.5*delta)))
     i+1
     }
w
AIC.table.unsorted=data.frame(model, logL, k, n, aicc, delta, w)
AIC.table.unsorted
#Sort the AIC table
o <- order(aicc)
AIC.table=data.frame(model[o], logL[o], k[o], aicc[o], delta[o], w[o])
colnames(AIC.table)=c("Model", "logL", "k", "AICc", "dAICc", "w")
AIC.table
#Write a .csv table, which can be opened in Excel and then copied to Word/Powerpoint.
write.csv(AIC.table, file = "AIC.table.csv", row.names = FALSE)
#The .csv file will be written to the following folder:
getwd()