InfDim (version 1.0)

IDM.bootCI: Infinite dimensional model (IDM) with bootstrapping

Description

A function to calculate infinite-dimensional model (IDM) elements, as implemented by Kirkpatrick et al. (1990), and to estimate error ranges for the model outputs via bootstrapping.

Usage

IDM.bootCI(data, age, nboot)

Arguments

data

A matrix containing growth trajectories of individuals (rows) measured at fixed times (columns). Mssing values are not allowed.

age

A numeric vector of ages at which sizes were measured. Ages must be positive valuies given in an ascending order.

nboot

number of bootstrap iterations.

Value

Details

For calculation of IDM elements, IDM.bootCI calls IDM function. Bootstrapping is performed by resampling the growth trajectory data with replacing, and IDM elements are calculated for every resampled dataset. Confidence intervals are then obtained as 0.25 and 0.975 quantiles.

References

Kirkpatrick M, Lofsvold D, Bulmer M (1990) Analysis of the inheritance, selection and evolution of growth trajectories. Genetics 124:979-993.

Kuparinen A, Bjrklund M (2011) Theory put into practice: an R implementation of the infinite-dimensional model. Ecological Modelling (in press).

See Also

IDM.

Examples

#a built-in dataset fish is used. This data gives sizes of 53 female stickleback measured at the age of 17, 45, 80, and 115 days.
age=c(17,45,80,115)
P=cov(fish)

out=IDM.bootCI(data=fish,age=age,nboot=1000)

#Growth patterns (i.e. trajectories)
out$Trajectories

#Percantages of variation accounted for by each growth trajectory
out$Percent.trajectory

#figure showing first two trajectories and percentages of variations accounted for by each trajectory.
par(mfrow=c(3,1),mar=c(5,4,0.5,0.5),oma=c(0.5,0.5,0.5,0.5))
barplot(out$Percent.trajectory,ylim=c(0,105),las=1,names.arg=c("1","2","3","4"), ylab="Percentage of variation",cex.lab=1.35,cex.axis=1.25)
lines(c(0.65,0.65),c(out$Percent.trajectory2.5CI[1],out$Percent.trajectory97.5CI[1]))
lines(c(1.9,1.9),c(out$Percent.trajectory2.5CI[2],out$Percent.trajectory97.5CI[2]))
lines(c(3.15,3.15),c(out$Percent.trajectory2.5CI[3],out$Percent.trajectory97.5CI[3]))
lines(c(4.4,4.4),c(out$Percent.trajectory2.5CI[4],out$Percent.trajectory97.5CI[4]))
text(0.18,100,"a",cex=1.4)
box(which="plot")

plot(age,out$Trajectories97.5CI[,1],type="l",lty=2,ylim=c(-1,1),las=1,ylab="Trajectory value",xlab="Age (days)",cex.lab=1.35,cex.axis=1.25)
lines(age,out$Trajectories2.5CI[,1],lty=2)
lines(age,out$Trajectories[,1],lty=1)
abline(h=0,lty=1)
text(17,0.9,"b",cex=1.4)

plot(age,out$Trajectories97.5CI[,2],type="l",lty=2,ylim=c(-1,1),las=1,ylab="Trajectory value",xlab="Age (days)",cex.lab=1.35,cex.axis=1.25)
lines(age,out$Trajectories2.5CI[,2],lty=2)
lines(age,out$Trajectories[,2],lty=1)
abline(h=0,lty=1)
text(17,0.9,"c",cex=1.4)