IPMpack (version 2.1)

sampleIPM: Builds list of IPMs or P matrices from list growth, survival, fecundity and discreteTrans objects. It is helpful when building multiple IPMs for study of parameter uncertainty or stochastic dynamics.

Description

Uses lists of vital rate objects to create a list of IPM or P matrices.

Usage

sampleIPM( growObjList=NULL,survObjList=NULL,fecObjList=NULL, offspringObjList=NULL, discreteTransList=1,  nBigMatrix,minSize,maxSize,  covariates=FALSE,envMat=NULL, integrateType="midpoint",correction="none",warn=TRUE)

Arguments

growObjList

list of growth objects.

survObjList

list of survival objects.

fecObjList

list of fecundity objects.

offspringObjList

list of survival objects.

discreteTransList

list of survival objects.

nBigMatrix

number of meshpoints.

minSize

minimum size.

maxSize

maximum size.

covariates

level of the covariate.

envMat

environmental matrix - defaults to NULL.

integrateType

integration type, defaults to "midpoint" (which uses probability density function); other option is "cumul" (which uses the cumulative density function).

correction

correction type, defaults to "none"; option is "constant" which will multiply every column of the IPM by a constant sufficient to adjust values to those predicted for survival at that size.

warn

turn warning messages on/off.

See Also

sampleVitalRateObj,sampleIPMOutput,sampleSequentialIPMs

Examples

# ===========================================================================
# Sample Vital Rate Objects
	# Parametric bootstrap sample for a growth object 
		dff <- generateData(type='discrete')
		gr1 <- makeGrowthObj(dff)
		gr1List=sampleVitalRateObj(gr1,nSamp=9)

	# Parametric bootstrap sample for a survival object 
		sv1 <- makeSurvObj(dff)
		sv1List=sampleVitalRateObj(sv1,nSamp=9)

	# Parametric bootstrap sample for a fecundity object 
		fv1 <- makeFecObj(dff)
		fv1List=sampleVitalRateObj(
			fv1,nSamp=9,
			nDiscreteOffspringTransitions =100,
			nOffspring=100)

	# Parametric bootstrap sample for a discrete transition object 
		dt1 <- makeDiscreteTrans(dff)
		dt1List=sampleVitalRateObj(
			dt1,nSamp=9,
			nDiscreteGrowthTransitions=100)
# ===========================================================================
	# Make a list of growth/survival (P) matrices (omitting fecundity)
		Pmatrixlist=sampleIPM(
			growObjList=gr1List,
			survObjList=sv1List,
			fecObjList =NULL,
			nBigMatrix = 20, minSize = -5, maxSize = 20)
		# plot results
		par(mfrow=c(3,3))
		lapply(Pmatrixlist,image)

	# Combine the list of fecundity objects with a single survival 
	# and growth object in a list of IPMs to look at just the impact 
	# of uncertainty in fecundity parameter estimates on population growth rate
		IPMlist2=sampleIPM(
			growObjList=list(gr1),
			survObjList=list(sv1),
			fecObjList =fv1List,
			discreteTransList=list(dt1), 
			nBigMatrix = 20, minSize = -5, maxSize = 20)
		# plot results
		lapply(IPMlist2,image) 

	# Combine the lists of all vital rate objects in a list of IPMs to 
	# look at the impact of uncertainty in all parameters on 
	# population growth rate
		IPMlist3=sampleIPM(
			growObjList=gr1List,
			survObjList=sv1List,
			fecObjList =fv1List,
			discreteTransList=list(dt1),
			nBigMatrix = 20, minSize = -5, maxSize = 20)
		# plot results
		lapply(IPMlist3,image) 

# ===========================================================================
# Summarize the outputs	
	# Get uncertainty in passage time from the list of growth/survival matrices
		IPMout1=sampleIPMOutput(PMatrixList=Pmatrixlist)
		qLE=apply(IPMout1[['LE']],2,quantile,probs=c(.025,.5,.975))
		plot(IPMout1$meshpoints,qLE[2,],type='l',ylim=c(0,max(qLE)))
		lines(IPMout1$meshpoints,qLE[1,],type='l',lty=3)
		lines(IPMout1$meshpoints,qLE[3,],type='l',lty=3)

	# Get uncertainty in lambda from the list of IPMs where only fecundity 
	# varied
		IPMout2=sampleIPMOutput(IPMList=IPMlist2)
		qlambda=quantile(IPMout2[['lambda']],probs=c(.025,.5,.975))
		boxplot(IPMout2[['lambda']])
	
	# Get uncertainty in lambda and passage time from size 5 
	# to a series of size from the list of IPMs where all vital rates varied
		IPMout3=sampleIPMOutput(
			IPMList=IPMlist3,
			passageTimeTargetSize=c(10),
			sizeToAgeStartSize=c(5),
			sizeToAgeTargetSize=c(6,7,8,9,10))
		qlambda=quantile(IPMout3[['lambda']],probs=c(.025,.5,.975))
		boxplot(IPMout3[['resAge']])