IDM.bootCI: Infinite dimensional model (IDM) with bootstrapping
In InfDim: Infine-dimensional model (IDM) to analyse phenotypic variation in growth trajectories.

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/IDM.bootCI.R

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.

1	IDM.bootCI(data, age, nboot)

`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.

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.

The function returns as a list eigenvalues, growth trajectores (each trajectory is given as a column in the trajectory matrix), and percentages of variation accounted for by each growth trajectory, as well as boundaries for 95 percentage confidence intervals (CI) for each output element.

Anna Kuparinen and Mats Bj<f6>rklund

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

Kuparinen A, Bj<f6>rklund M (2011) Theory put into practice: an R implementation of the infinite-dimensional model. Ecological Modelling (in press).

IDM.

#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)