Projects how long it takes to get from a starting distribution to a target continuous stage value.

Description

Provided with a starting vector reflecting starting individual sizes, this function projects forward via the provided IPM until a defined proportion of the population has reach the chosen endSize. Only works for single environment or compound matrices (not time-varying covariates apart from a single discrete one).

Usage

timeToSize(startingSizes, IPM, endSize, startingEnv = 1, maxT = 100, propReach = 0.01)

Arguments

Details

Plots and returned values of survivorship from preliminary runs will give a notion of how low this has to be.

Value

Author(s)

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

References

Caswell, 2001. Matrix population models: analysis, construction and interpretation. 2nd ed. Sinauer.

Examples

#note that with the "fake data" essentially either takes 
#forever or is immediate...
dff <- generateData()
startSizes <- rnorm(1000, 2.5, 1)
Pmatrix <- makeIPMPmatrix(minSize = 1.2*min(dff$size, na.rm=TRUE),
maxSize = 1.2*max(dff$size, na.rm=TRUE), 
growObj = makeGrowthObj(dff), 
survObj = makeSurvObj(dff))

rc <- timeToSize(startingSizes = startSizes, IPM = Pmatrix, endSize = 6, 
startingEnv = 1, maxT = 1000, propReach = 0.001)

names(rc)

par(mfrow=c(2,2), bty = "l")
## Make picture with lines for distribution of 
## population on different time points
matplot(Pmatrix@meshpoints, rc$ts.dist, type = "l", xlab = "size", 
ylab = "Number of individuals")

## Examine time elapsed for propReach to attain the chosen endSize
rc$time.reach

## Plot out the survivorship
plot(rc$survivorship, type = "l", #log = "y", 
xlab = "time step", ylab = "Probability original population survival", 
ylim = c(0,1), col = "gray")

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/timeToSize.Rd_%03d_medium.png", width=480, height=480)
> ### Name: timeToSize
> ### Title: Projects how long it takes to get from a starting distribution
> ###   to a target continuous stage value.
> ### Aliases: timeToSize
> ### Keywords: ~kwd1 ~kwd2
> 
> ### ** Examples
> 
> #note that with the "fake data" essentially either takes 
> #forever or is immediate...
> dff <- generateData()
> startSizes <- rnorm(1000, 2.5, 1)
> Pmatrix <- makeIPMPmatrix(minSize = 1.2*min(dff$size, na.rm=TRUE),
+ maxSize = 1.2*max(dff$size, na.rm=TRUE), 
+ growObj = makeGrowthObj(dff), 
+ survObj = makeSurvObj(dff))
> 
> rc <- timeToSize(startingSizes = startSizes, IPM = Pmatrix, endSize = 6, 
+ startingEnv = 1, maxT = 1000, propReach = 0.001)
[1] "Time to reach: 1"
> 
> names(rc)
[1] "ts.dist"      "time.reach"   "survivorship"
> 
> par(mfrow=c(2,2), bty = "l")
> ## Make picture with lines for distribution of 
> ## population on different time points
> matplot(Pmatrix@meshpoints, rc$ts.dist, type = "l", xlab = "size", 
+ ylab = "Number of individuals")
> 
> ## Examine time elapsed for propReach to attain the chosen endSize
> rc$time.reach
[1] 1
> 
> ## Plot out the survivorship
> plot(rc$survivorship, type = "l", #log = "y", 
+ xlab = "time step", ylab = "Probability original population survival", 
+ ylim = c(0,1), col = "gray")
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>