Last data update: 2014.03.03

 R: Builds list of IPMs or P matrices from list growth, survival,...
sampleIPMR Documentation

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.

Author(s)

Cory Merow, C. Jessica E. Metcalf, Sean M. McMahon, Roberto Salguero-Gomez, Eelke Jongejans.

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']])

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
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Type 'license()' or 'licence()' for distribution details.

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Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(IPMpack)
Loading required package: Matrix
Loading required package: MASS
Loading required package: nlme
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/IPMpack/sampleIPM.Rd_%03d_medium.png", width=480, height=480)
> ### Name: sampleIPM
> ### Title: 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.
> ### Aliases: sampleIPM '.makeListPmatrix '
> 
> ### ** Examples
> 
> # ===========================================================================
> # Sample Vital Rate Objects
> 	# Parametric bootstrap sample for a growth object 
> 		dff <- generateData(type='discrete')
> 		gr1 <- makeGrowthObj(dff)
> 		gr1List=sampleVitalRateObj(gr1,nSamp=9)
Loading required package: mvtnorm
Loading required package: MCMCpack
Loading required package: coda
##
## Markov Chain Monte Carlo Package (MCMCpack)
## Copyright (C) 2003-2016 Andrew D. Martin, Kevin M. Quinn, and Jong Hee Park
##
## Support provided by the U.S. National Science Foundation
## (Grants SES-0350646 and SES-0350613)
##
> 
> 	# 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)
[1] "Warning - offspring splitter indicates more than just continuous stages. No fecundity by the discrete stages supplied in fecByDiscrete; assumed that is 0"
> 		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)
[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

[[4]]
NULL

[[5]]
NULL

[[6]]
NULL

[[7]]
NULL

[[8]]
NULL

[[9]]
NULL

> 
> 	# 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)
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
Warning messages:
1: In sampleIPM(growObjList = list(gr1), survObjList = list(sv1), fecObjList = fv1List,  :
  Length of growth object list is less than the length of another vital rate object list, so some members of the growth object list have been repeated.
2: In sampleIPM(growObjList = list(gr1), survObjList = list(sv1), fecObjList = fv1List,  :
  Length of survival object list is less than the length of another vital rate object list, so some members of the survival object list have been repeated.
> 		# plot results
> 		lapply(IPMlist2,image) 
[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

[[4]]
NULL

[[5]]
NULL

[[6]]
NULL

[[7]]
NULL

[[8]]
NULL

[[9]]
NULL

> 
> 	# 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)
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
[1] "Warning: fertility values < 0 exist in matrix, consider transforms. Negative values set to zero"
> 		# plot results
> 		lapply(IPMlist3,image) 
[[1]]
NULL

[[2]]
NULL

[[3]]
NULL

[[4]]
NULL

[[5]]
NULL

[[6]]
NULL

[[7]]
NULL

[[8]]
NULL

[[9]]
NULL

> 
> # ===========================================================================
> # Summarize the outputs	
> 	# Get uncertainty in passage time from the list of growth/survival matrices
> 		IPMout1=sampleIPMOutput(PMatrixList=Pmatrixlist)
[1] "no target size for passage time provided; taking meshpoint median"
[1] "no starting size for size to age provided; taking minimum size"
[1] "no target size for size to age provided; taking meshpoint values"
> 		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)
[1] "no target size for passage time provided; taking meshpoint median"
[1] "no starting size for size to age provided; taking minimum size"
[1] "no target size for size to age provided; taking meshpoint values"
> 		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']])
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


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