makeIPMPmatrix: Builds P matrices.

Description

Uses growth and survival objects to construct a matrix defining probabilities for transitions between sizes due to growth and survival. Extensions for transition to discrete classes are possible. NOTE - old createIPMPmatrix is being deprecated; use makeIPMPmatrix instead.

Usage

makeIPMPmatrix(nEnvClass = 1, nBigMatrix = 50,
minSize = -1, maxSize = 50, chosenCov = data.frame(covariate=1), 
growObj, survObj, discreteTrans=1, 
integrateType = "midpoint", correction="none")

Arguments

nEnvClass

numeric, number of environmental classes, always = 1 for non-compound matrices.

nBigMatrix

numeric, number of size bins in the P matrix, defaults to 50.

minSize

numeric, minimum size of the P matrix, defaults to -1.

maxSize

numeric, maximum size of the P matrix, defaults to 50.

chosenCov

data-frame indicating level of the discrete covariate, or range of values where multiple covariates are modeled.

growObj

growth object.

survObj

survival object.

discreteTrans

object of class discreteTrans, or numeric.

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. The first 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. The second option is discre

Value

an object of class IPMmatrix with dimensions nBigMatrix*nBigMatrix, or nbig.matrix+nrow(discreteTrans).

Details

The number of bins (nBigMatrix) is combined with the minimum and maximum size to define the meshpoints of the IPM. Bins should be sufficient and the size range should encompass the size range of the data. If a "discreteTrans" exists, then discrete stages will be added to the IPM structure. If multiple discrete covariate levels are available, chosenCov identifies the covariate value for which an IPM is required; if a series of covariates are being modeled, chosenCov is a vector of these covariates, and growth will reflect these values.

References

Easterling, Ellner & Dixon. 2000. Size-specific sensitivity: a new structured population model. Ecology 81, p694-708.

Ellner & Rees. 2006. Integral projection models for species with complex demography. The American Naturalist 167, p410-428.

For information on P matrix: Caswell. 2001. Matrix population models: construction, analysis, and interpretation. 2nd ed. Sinauer. p110-112.

For information on unintentional eviction from IPMs (which the various corrections try and account for) see Williams et al. 2012 Avoiding unintentional eviction from integral projection models. Ecology.

Examples

Run this code

dff <- generateData()
Pmatrix <- makeIPMPmatrix(minSize = min(dff$size, na.rm = TRUE), 
	maxSize=max(dff$size, na.rm=TRUE), growObj = makeGrowthObj(dff), 
		survObj = makeSurvObj(dff))

image(Pmatrix@meshpoints, Pmatrix@meshpoints, t(Pmatrix), 
	xlab = "Continuous (e.g. size) stage at t", 
		ylab = "Continuous (e.g. size) stage at t+1")

Pmatrix <- makeIPMPmatrix(minSize = min(dff$size, na.rm = TRUE), 
	maxSize=max(dff$size, na.rm=TRUE), 
	   growObj = makeGrowthObj(dff,regType="changingVar"), 
		survObj = makeSurvObj(dff))

image(Pmatrix@meshpoints, Pmatrix@meshpoints, t(Pmatrix), 
	xlab = "Continuous (e.g. size) stage at t", 
		ylab = "Continuous (e.g. size) stage at t+1")

#example with discrete transition matrix
dff <- generateData(type="discrete")
Pmatrix <- makeIPMPmatrix(minSize = min(dff$size, na.rm = TRUE), 
	maxSize=max(dff$size, na.rm=TRUE), discreteTrans=makeDiscreteTrans(dff),
	growObj = makeGrowthObj(dff), 
		survObj = makeSurvObj(dff))

#discrete stages not plotted 
image(Pmatrix@meshpoints, Pmatrix@meshpoints, 
	t(log(Pmatrix[2:length(Pmatrix@meshpoints),2:length(Pmatrix@meshpoints)])), 
	xlab = "Continuous (e.g. size) stage at t", 
		ylab = "Continuous (e.g. size) stage at t+1")

Run the code above in your browser using DataLab

Last chance! 50% off unlimited learning