sensitivity3.lefkoMat {lefko3}

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 class 'lefkoMat'
sensitivity3(
  mats,
  stochastic = FALSE,
  times = 10000,
  tweights = NA,
  seed = NA,
  sparse = "auto",
  append_mats = FALSE,
  ...
)

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.

times

The number of occasions to project forward in stochastic simulation. Defaults to 10000.

tweights

An optional numeric vector or matrix denoting the probabilities of choosing each matrix in a stochastic projection. If a matrix is input, then a first-order Markovian environment is assumed, in which the probability of choosing a specific annual matrix depends on which annual matrix is currently chosen. If a vector is input, then the choice of annual matrix is assumed to be independent of the current matrix. Defaults to equal weighting among matrices.

seed

A number to use as a random number seed in stochastic projection.

sparse

A text string indicating whether to use sparse matrix encoding ("yes") or dense matrix encoding ("no"). Defaults to "auto", in which case sparse matrix encoding is used with square matrices with at least 50 rows and no more than 50% of elements with values greater than zero.

append_mats

A logical value indicating whether to include the original A, U, and F matrices in the output lefkoSens object.

...

Other parameters.

Value

This function returns an object of class lefkoSens, which is a list of 8 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, hstages, is a data frame showing historical stage pairs (NULL if an ahMPM used as input). The fourth element, agestages, show the order of age-stage combinations, if age-by-stage MPMs have been supplied. The fifth element, ahstages, 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

All sensitivity matrix outputs from this function are in standard matrix format.

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.

Speed can sometimes be increased by shifting from automatic sparse matrix determination to forced dense or sparse matrix projection. This will most likely occur when matrices have between 30 and 300 rows and columns. Defaults work best when matrices are very small and dense, or very large and sparse.

The time_weights and steps arguments are now deprecated. Instead, please use the tweights and times arguments.

See Also

sensitivity3()

sensitivity3.matrix()

sensitivity3.dgCMatrix()

sensitivity3.list()

Examples

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)

lathsupp3 <- supplemental(stage3 = c("Sd", "Sd", "Sdl", "Sdl", "Sd", "Sdl", "mat"),
  stage2 = c("Sd", "Sd", "Sd", "Sd", "rep", "rep", "Sdl"),
  stage1 = c("Sd", "rep", "Sd", "rep", "npr", "npr", "Sd"),
  eststage3 = c(NA, NA, NA, NA, NA, NA, "mat"),
  eststage2 = c(NA, NA, NA, NA, NA, NA, "Sdl"),
  eststage1 = c(NA, NA, NA, NA, NA, NA, "NotAlive"),
  givenrate = c(0.345, 0.345, 0.054, 0.054, NA, NA, NA),
  multiplier = c(NA, NA, NA, NA, 0.345, 0.054, NA),
  type = c(1, 1, 1, 1, 3, 3, 1), type_t12 = c(1, 2, 1, 2, 1, 1, 1),
  stageframe = lathframe, historical = TRUE)

ehrlen3 <- rlefko3(data = lathvert, stageframe = lathframe, year = "all", 
  stages = c("stage3", "stage2", "stage1"), supplement = lathsupp3,
  yearcol = "year2", indivcol = "individ")

sensitivity3(ehrlen3, stochastic = TRUE)