elasticity3.list: Estimate Elasticity of Population Growth Rate of a List of...
In lefko3: Historical and Ahistorical Population Projection Matrix Analysis
elasticity3.list R Documentation
Estimate Elasticity of Population Growth Rate of a List of Matrices
Description
elasticity3.list() returns the elasticities of lambda to elements
of a single matrix. 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'
elasticity3(
mats,
stochastic = FALSE,
times = 10000,
tweights = NA,
historical = FALSE,
seed = NA,
sparse = "auto",
append_mats = FALSE,
...
)
Arguments
mats
A list of objects of class matrix or dgCMatrix.
stochastic
A logical value determining whether to conduct a
deterministic (FALSE) or stochastic (TRUE) elasticity 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 denoting whether the input 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 lefkoElas object.
...
Other parameters.
Value
This function returns an object of class lefkoElas, which is a
list with 8 elements. The first, h_elasmats, is a list of historical
elasticity matrices, though in the standard list case it returns a NULL
value. The second, ah_elasmats, is a list of ahistorical elasticity
matrices. The third element, hstages, the fourth element,
agestages, and the fifth element, ahstages, are set to NULL.
The last 3 elements are the original A matrices in element A, followed by
NULL values for the U and F elements.
Notes
Deterministic elasticities are estimated as eqn. 9.72 in Caswell (2001,
Matrix Population Models). Stochastic elasticities are estimated as eqn.
14.99 in Caswell (2001). Note that stochastic elasticities are of stochastic
\lambda, while stochastic sensitivities are with regard to 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, steps, and force_sparse arguments are
now deprecated. Instead, please use the tweights, times, and
sparse arguments.
See Also
elasticity3()
elasticity3.lefkoMat()
elasticity3.matrix()
elasticity3.dgCMatrix()
summary.lefkoElas()
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")
elasticity3(ehrlen3$A, stochastic = TRUE)
# Cypripedium example
data(cypdata)
sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)
stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",
"XLg")
repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)
cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
propstatus = propvector, immstatus = immvector, indataset = indataset,
binhalfwidth = binvec)
cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,
NRasRep = TRUE)
cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",
"XSm", "Sm", "SD", "P1"),
stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",
"rep"),
eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),
eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),
multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),
type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),
stageframe = cypframe_raw, historical = FALSE)
cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,
year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
size = c("size3added", "size2added"), supplement = cypsupp2r,
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
elasticity3(cypmatrix2r$A)
lefko3 documentation built on April 4, 2025, 2:53 a.m.
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| elasticity3.list | R Documentation |
Estimate Elasticity of Population Growth Rate of a List of Matrices
Description
elasticity3.list() returns the elasticities of lambda to elements
of a single matrix. 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'
elasticity3(
mats,
stochastic = FALSE,
times = 10000,
tweights = NA,
historical = FALSE,
seed = NA,
sparse = "auto",
append_mats = FALSE,
...
)
Arguments
mats |
A list of objects of class |
stochastic |
A logical value determining whether to conduct a deterministic (FALSE) or stochastic (TRUE) elasticity 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 denoting whether the input 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
( |
append_mats |
A logical value indicating whether to include the original
matrices input as object |
... |
Other parameters. |
Value
This function returns an object of class lefkoElas, which is a
list with 8 elements. The first, h_elasmats, is a list of historical
elasticity matrices, though in the standard list case it returns a NULL
value. The second, ah_elasmats, is a list of ahistorical elasticity
matrices. The third element, hstages, the fourth element,
agestages, and the fifth element, ahstages, are set to NULL.
The last 3 elements are the original A matrices in element A, followed by
NULL values for the U and F elements.
Notes
Deterministic elasticities are estimated as eqn. 9.72 in Caswell (2001,
Matrix Population Models). Stochastic elasticities are estimated as eqn.
14.99 in Caswell (2001). Note that stochastic elasticities are of stochastic
\lambda, while stochastic sensitivities are with regard to 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, steps, and force_sparse arguments are
now deprecated. Instead, please use the tweights, times, and
sparse arguments.
See Also
elasticity3()
elasticity3.lefkoMat()
elasticity3.matrix()
elasticity3.dgCMatrix()
summary.lefkoElas()
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")
elasticity3(ehrlen3$A, stochastic = TRUE)
# Cypripedium example
data(cypdata)
sizevector <- c(0, 0, 0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)
stagevector <- c("SD", "P1", "P2", "P3", "SL", "D", "XSm", "Sm", "Md", "Lg",
"XLg")
repvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
obsvector <- c(0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1)
matvector <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
immvector <- c(0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1)
binvec <- c(0, 0, 0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)
cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
propstatus = propvector, immstatus = immvector, indataset = indataset,
binhalfwidth = binvec)
cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
patchidcol = "patch", individcol = "plantid", blocksize = 4,
sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,
NRasRep = TRUE)
cypsupp2r <- supplemental(stage3 = c("SD", "P1", "P2", "P3", "SL", "D",
"XSm", "Sm", "SD", "P1"),
stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "rep",
"rep"),
eststage3 = c(NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),
eststage2 = c(NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, NA, NA, NA, NA, NA),
multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),
type =c(1, 1, 1, 1, 1, 1, 1, 1, 3, 3),
stageframe = cypframe_raw, historical = FALSE)
cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw,
year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
size = c("size3added", "size2added"), supplement = cypsupp2r,
yearcol = "year2", patchcol = "patchid", indivcol = "individ")
elasticity3(cypmatrix2r$A)
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