stochGrowthRateManyCov: Estimates stochastic population growth rates (lambda_s) or...
In IPMpack: Builds and analyses Integral Projection Models (IPMs).

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

Iterates a population vector through a time series of covariates according to growth, survival and fecundity objects, and calculates the stochastic population rate of increase if no density-dependence is specified, or the rate of invasion if density dependence is specified.

stochGrowthRateManyCov(covariate, nRunIn, tMax,
growthObj, survObj, fecObj, nBigMatrix, minSize, maxSize, 
nMicrosites,integrateType = "midpoint", correction = "none",
trackStruct=FALSE, plot=FALSE, ...)

`covariate`	matrix with tMax rows, and as many columns as there are relevant covariates.
`nRunIn`	numeric, number of initial samples to discard.
`tMax`	numeric, total number of time-steps to run (same as ncol(covariate)).
`growthObj`	a growth object, defined to correspond to covariate definition (indexing used to make the growth object must match up).
`survObj`	a survival object, defined to correspond to covariate definition in covariate.
`fecObj`	a fecundity object, defined to correspond to covariate definition in covariate.
`nBigMatrix`	numeric, number of size bins in the IPM.
`minSize`	numeric, minimum size in the IPM.
`maxSize`	numeric, maximum size in the IPM.
`nMicrosites`	vector, if sum(nMicrosites)> 0 then density dependence is assumed to operate on seedling establishment, and if length(nMicrosites)>1, then the number of microsites available for establishment at time t is nMicrosites[min(t,length(nMicrosites))].
`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 continuous stage value.
`trackStruct`	Boolean indicating whether you want to track the population structure (beyond simply estimating the growth rate)
`plot`	Boolean indicating whether a plot of the population structure is desired.
`...`	extra arguments relating to plotting if trackStruct and plot are TRUE.

Forms of density dependence beyond density dependence in seedling establishment not yet defined.

`Rt`	If trackStruct is TRUE, numeric, converging on log lambda_s, or invasion rate (density dependence) for large enough tMax, and if covariate distribution is stationary.
`rc`	matrix of the numbers of individuals in each size and seed class (row) over time (columns).
`IPM.here`	IPM constructed corresponding to pop structure and covariates at tMax.

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

Ellner & Rees. 2007. Stochastic stable population growth in integral projection models: theory and application. Journal of Mathematical Biology 54, p227-256.

Rees & Ellner. 2009. Integral projection models for populations in temporally varying environments. Ecological Monographs 79, p575-594.

stochGrowthRateSampleList,

### NOT RUN - this is hashed out because compiles too slowly ###

# Generate relevant data, build objects
#dff <- generateData(type="stochastic")
#print(head(dff))
#gr1 <- makeGrowthObj(dff, Formula = sizeNext~size+size2+covariate1)
#sv1 <- makeSurvObj(dff, Formula = surv~size+size2+covariate2)
#fv1 <- makeFecObj(dff, Formula = fec~size+size2,Transform="log")

# Generate time series of covariates for which population growth rate 
#is required. Here set to be seasonal environment. 
#Names of covariates must be same as in dff
#tVals <- seq(1,100,by = 1/12)
#covTest <- (1 + 0.5*sin(2*pi*tVals))
#covMatTest <- data.frame(covariate1 = rnorm(length(covTest),covTest,0.5) - 1, 
#covariate2 = rnorm(length(covTest), covTest,0.5) - 1, 
#covariate3 = rnorm(length(covTest), covTest,0.5) - 1, row.names = NULL)

# Calculate

#r <- stochGrowthRateManyCov(covariate = covMatTest, nRunIn = 5*10, 
#tMax = length(tVals), growthObj = gr1, survObj = sv1, fecObj = fv1, 
#nBigMatrix = 100, 
#minSize = 1.1*min(dff$size, na.rm = TRUE), 
#maxSize = 1.1*max(dff$size, na.rm = TRUE), nMicrosites = 0)

#r

# Track population strucuture instead
#st <- stochGrowthRateManyCov(covariate = covMatTest, nRunIn = 5*10, 
#tMax = length(tVals), growthObj = gr1, survObj = sv1, fecObj = fv1, 
#nBigMatrix = 100, 
#minSize = 1.1*min(dff$size, na.rm = TRUE), 
#maxSize = 1.1*max(dff$size, na.rm = TRUE), nMicrosites = 0, 
#trackStruct=TRUE,plot=TRUE)