R: Estimate Sensitivity of Population Growth Rate of a List of...

sensitivity3.list

R Documentation

Estimate Sensitivity of Population Growth Rate of a List of Matrices

Description

sensitivity3.list() returns the sensitivities of population growth rate to elements of matrices supplied in a list. The sensitivity analysis can be deterministic or stochastic, but if the latter then at least two A matrices must be included in the list. 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 'list'
sensitivity3(
  mats,
  stochastic = FALSE,
  times = 10000,
  tweights = NA,
  historical = FALSE,
  seed = NA,
  sparse = "auto",
  append_mats = FALSE,
  ...
)

Arguments

`mats`	An object of class `matrix`.
`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 10,000.
`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.
`historical`	A logical value indicating whether matrices are historical. Defaults to `FALSE`.
`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 matrices input as object `mats` in the output `lefkoSense` object. Defaults to FALSE.
`...`	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 ahistorical sensitivity matrices if an ahMPM is used as input (NULL if an hMPM is used as input). The third element, hstages, the fourth element, agestages, and the fifth element, ahstages, are NULL. The last 3 elements include the original A matrices supplied (as the A element), followed by NULLs for the U and F elements.

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 with regard to the log of the stochastic λ.

Currently, this function does not estimate equivalent ahistorical stochastic sensitivities for input historical matrices, due to the lack of guidance input on the order of stages (guidance is provided within lefkoMat objects).

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.

Examples

# Lathyrus example
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$A)

# Cypripedium example
data(cypdata)

sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)
stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",
  "XLg")
repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)

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

cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
  patchidcol = "patch", individcol = "plantid", blocksize = 4,
  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,
  NRasRep = TRUE)

cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D", 
    "XSm", "Sm", "SD", "P1"),
  stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",
    "rep"),
  eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),
  eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
  givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),
  multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),
  type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),
  stageframe = cypframe_raw, historical = FALSE)

cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw, 
  year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
  size = c("size3added", "size2added"), supplement = cypsupp2r,
  yearcol = "year2", patchcol = "patchid", indivcol = "individ")

sensitivity3(cypmatrix2r$A)