1) What could cause the AICc in the individual model results to differ from the AICc in the model.table?
I am running a multi-scale occupancy analysis. When I compare the AICc values reported in the model table
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results$model.table$AICc
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results$Psi.1.Theta.1.p.1$results$AICc
2) What could cause extreme (20 AICc or more) differences between identical model runs?
If I immediately rerun models, I often get wildly different reported AICc in the results$model$results$AICc output between runs.
I have not been able to demonstrate these drastic fluctuations in simple example code so I am posting unreproducible code below with the output commented out.
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#1a. Process Data
proc.data <- process.data(run.data, model = "MultScalOcc", begin.time = 1, mixtures = 12)
#1b. Create the data design
ddl.run = make.design.data(proc.data)
#1c. Build a formula list within a MARK function
do.Species=function()
{
p.1=list(formula=~TempC + RHPer + WindMS + RainMm + RecSpce + RecLnHr + YDay)
Theta.1 = list(formula=~DstRoadM + DstWatrM)
Psi.1 = list(formula=~FrstConct + FrstClump + FrstCorHa + FrstNDCA + DevAreaHa + WtrAreaHa + SumTempC + SumPrecMm + CliffHA)
Psi.2 = list(formula=~FrstConct + FrstClump + FrstCorHa + FrstNDCA + DevAreaHa + WtrAreaHa + SumTempC + SumPrecMm + CliffHA + Lat + Long)
Psi.3 = list(formula=~FrstConct + FrstClump + FrstCorHa + FrstNDCA + DevAreaHa + WtrAreaHa + SumTempC + SumPrecMm + CliffHA + Lat * Long)
cml=create.model.list("MultScalOcc")
return(mark.wrapper(cml,data=proc.data,ddl=ddl.run,adjust=FALSE,realvcv=TRUE))
}
#1d. Run the global model
a <- do.Species()
b <- do.Species()
c <- do.Species()
d <- do.Species()
e <- do.Species()
f <- do.Species()
g <- do.Species()
h <- do.Species()
i <- do.Species()
j <- do.Species()
k <- do.Species()
l <- do.Species()
a.1 <- c(a$Psi.1.Theta.1.p.1$results$AICc, a$Psi.2.Theta.1.p.1$results$AICc, a$Psi.3.Theta.1.p.1$results$AICc)
b.1 <- c(b$Psi.1.Theta.1.p.1$results$AICc, b$Psi.2.Theta.1.p.1$results$AICc, b$Psi.3.Theta.1.p.1$results$AICc)
c.1 <- c(c$Psi.1.Theta.1.p.1$results$AICc, c$Psi.2.Theta.1.p.1$results$AICc, c$Psi.3.Theta.1.p.1$results$AICc)
d.1 <- c(d$Psi.1.Theta.1.p.1$results$AICc, d$Psi.2.Theta.1.p.1$results$AICc, d$Psi.3.Theta.1.p.1$results$AICc)
e.1 <- c(e$Psi.1.Theta.1.p.1$results$AICc, e$Psi.2.Theta.1.p.1$results$AICc, e$Psi.3.Theta.1.p.1$results$AICc)
f.1 <- c(f$Psi.1.Theta.1.p.1$results$AICc, f$Psi.2.Theta.1.p.1$results$AICc, f$Psi.3.Theta.1.p.1$results$AICc)
g.1 <- c(g$Psi.1.Theta.1.p.1$results$AICc, g$Psi.2.Theta.1.p.1$results$AICc, g$Psi.3.Theta.1.p.1$results$AICc)
h.1 <- c(h$Psi.1.Theta.1.p.1$results$AICc, h$Psi.2.Theta.1.p.1$results$AICc, h$Psi.3.Theta.1.p.1$results$AICc)
i.1 <- c(i$Psi.1.Theta.1.p.1$results$AICc, i$Psi.2.Theta.1.p.1$results$AICc, i$Psi.3.Theta.1.p.1$results$AICc)
j.1 <- c(j$Psi.1.Theta.1.p.1$results$AICc, j$Psi.2.Theta.1.p.1$results$AICc, j$Psi.3.Theta.1.p.1$results$AICc)
k.1 <- c(k$Psi.1.Theta.1.p.1$results$AICc, k$Psi.2.Theta.1.p.1$results$AICc, k$Psi.3.Theta.1.p.1$results$AICc)
l.1 <- c(l$Psi.1.Theta.1.p.1$results$AICc, l$Psi.2.Theta.1.p.1$results$AICc, l$Psi.3.Theta.1.p.1$results$AICc)
data.frame(a = a.1,
b = b.1,
c = c.1,
d = d.1,
e = e.1,
f = f.1,
g = g.1,
h = h.1,
i = i.1,
j = j.1,
k = k.1,
l = l.1)
# a b c d e f g h i j k l
# 1 656.8418 656.8418 656.8418 656.8418 656.8418 656.8418 656.8418 656.8418 656.8418 656.8418 656.8418 656.8418
# 2 639.4293 639.4293 639.4293 642.6255 639.4293 639.4293 639.4293 642.6255 642.6255 639.4293 642.6255 642.6255
# 3 624.0836 648.7748 621.3204 645.4115 652.2266 626.9131 652.2266 652.2266 652.2266 652.2265 652.2266 652.2266
I'd be happy to share my data off list to see if the issues could be reproduced.
Thanks