lefko3 (version 3.3.2)

sensitivity3.lefkoMat: Estimate Sensitivity of Population Growth Rate of a lefkoMat Object

Description

sensitivity3.lefkoMat() returns the sensitivities of population growth rate to elements of all $A matrices in an object of class lefkoMat. If deterministic, then \(\lambda\) is taken as the population growth rate. If stochastic, then the log of stochastic \(\lambda\), or the log stochastic growth rate, is taken as the population growth rate. This function can handle large and sparse matrices, and so can be used with large historical matrices, IPMs, age x stage matrices, as well as smaller ahistorical matrices.

Usage

# S3 method for lefkoMat
sensitivity3(mats, stochastic = FALSE, steps = 10000, time_weights = NA, ...)

Arguments

mats

An object of class lefkoMat.

stochastic

A logical value determining whether to conduct a deterministic (FALSE) or stochastic (TRUE) sensitivity analysis. Defaults to FALSE.

steps

The number of times to project forward in stochastic simulation. Defaults to 10,000.

time_weights

Numeric vector denoting the probabilistic weightings of annual matrices. Defaults to equal weighting among times.

...

Other parameters.

Value

This function returns an object of class lefkoSens, which is a list of 7 elements. The first, h_sensmats, is a list of historical sensitivity matrices (NULL if an ahMPM is used as input). The second, ah_elasmats, is a list of either ahistorical sensitivity matrices if an ahMPM is used as input, or, if an hMPM is used as input, then the result is a list of ahistorical matrices based on the equivalent historical dependencies assumed in the input historical matrices. The third element, h_stages, is a data frame showing historical stage pairs (NULL if ahMPM used as input). The fourth element, ah_stages, is a data frame showing the order of ahistorical stages. The last 3 elements are the A, U, and F portions of the input.

Notes

Deterministic sensitivities are estimated as eqn. 9.14 in Caswell (2001, Matrix Population Models). Stochastic sensitivities are estimated as eqn. 14.97 in Caswell (2001). Note that stochastic sensitivities are of the log of the stochastic \(\lambda\).

Currently, this function does not estimate equivalent ahistorical stochastic sensitivities for input historical matrices.

See Also

sensitivity3()

sensitivity3.matrix()

sensitivity3.list()

Examples

# NOT RUN {
data(lathyrus)

sizevector <- c(0, 100, 13, 127, 3730, 3800, 0)
stagevector <- c("Sd", "Sdl", "VSm", "Sm", "VLa", "Flo", "Dorm")
repvector <- c(0, 0, 0, 0, 0, 1, 0)
obsvector <- c(0, 1, 1, 1, 1, 1, 0)
matvector <- c(0, 0, 1, 1, 1, 1, 1)
immvector <- c(1, 1, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 1, 1, 1, 1, 1, 1)
binvec <- c(0, 100, 11, 103, 3500, 3800, 0.5)

lathframe <- sf_create(sizes = sizevector, stagenames = stagevector,
  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
  immstatus = immvector, indataset = indataset, binhalfwidth = binvec,
  propstatus = propvector)

lathvert <- verticalize3(lathyrus, noyears = 4, firstyear = 1988,
  patchidcol = "SUBPLOT", individcol = "GENET", blocksize = 9,
  juvcol = "Seedling1988", sizeacol = "Volume88", repstracol = "FCODE88",
  fecacol = "Intactseed88", deadacol = "Dead1988",
  nonobsacol = "Dormant1988", stageassign = lathframe, stagesize = "sizea",
  censorcol = "Missing1988", censorkeep = NA, censor = TRUE)

lathrepm <- matrix(0, 7, 7)
lathrepm[1, 6] <- 0.345
lathrepm[2, 6] <- 0.054

lathover3 <- overwrite(stage3 = c("Sd", "Sd", "Sdl"),
  stage2 = c("Sd", "Sd", "Sd"), stage1 = c("Sd", "rep", "rep"),
  givenrate = c(0.345, 0.345, 0.054))

ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe,
  year = c(1989, 1990), stages = c("stage3", "stage2", "stage1"),
  repmatrix = lathrepm, overwrite = lathover3, yearcol = "year2",
  indivcol = "individ")

sensitivity3(ehrlen3, stochastic = TRUE)

# }