ltre3: Conduct a Life Table Response Experiment
In lefko3: Historical and Ahistorical Population Projection Matrix Analysis
ltre3 R Documentation
Conduct a Life Table Response Experiment
Description
ltre3() returns a set of matrices of one-way LTRE (life table response
experiment), stochastic LTRE (sLTRE) matrices, or small noise approximation
LTRE (sna-LTRE) contributions.
Usage
ltre3(
mats,
refmats = NA,
ref = NA,
stochastic = FALSE,
times = 10000,
burnin = 3000,
tweights = NA,
sparse = "auto",
seed = NA,
append_mats = FALSE,
sna_ltre = FALSE,
tol = 1e-30,
...
)
Arguments
mats
An object of class lefkoMat.
refmats
A reference lefkoMat object, or matrix, for use as the
control. Default is NA, which sets to the same object as mats.
ref
A numeric value indicating which matrix or matrices in
refmats to use as the control. The numbers used must correspond to the
number of the matrices in the labels element of the associated
lefkoMat object. The default setting, NA, uses all entries in
refmats.
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 10000.
burnin
The number of initial steps to ignore in stochastic projection
when calculating stochastic elasticities. Must be smaller than steps.
Defaults to 3000.
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. Note that SNA-LTRE analysis cannot take matrix
input.
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.
seed
Optional numeric value corresponding to the random seed for
stochastic simulation.
append_mats
A logical value denoting whether to include the original
A, U, and F matrices in the returned lefkoLTRE
object. Defaults to FALSE.
sna_ltre
A logical value indicating whether to treat stochastic LTRE
via the sna-LTRE approach from Davison et al. (2019) (TRUE), or the
stochastic LTRE approximation from Davison et al. (2010) (FALSE).
Defaults to FALSE.
tol
A numeric value indicating a lower positive limit to matrix
element values when applied to stochastic and small noise approximation LTRE
estimation protocols. Matrix element values lower than this will be treated
as 0.0 values. Defaults to 1e-30.
...
Other parameters.
Value
This function returns an object of class lefkoLTRE. This
includes a list of LTRE matrices as object cont_mean if a
deterministic LTRE is called for, or a list of mean-value LTRE matrices as
object cont_mean and a list of SD-value LTRE matrices as object
cont_sd if a stochastic LTRE is called for. If a small-noise
approximation LTRE (SNA-LTRE) is performed, then the output includes six
objects: cont_mean, which provides the contributions of shifts in mean
matrix elements; cont_elas, which provides the contributions of shifts
in the elasticities of matrix elements; cont_cv, which provides the
contributions of temporal variation in matrix elements; cont_corr,
which provides the contributions of temporal correlations in matrix elements;
r_values_m, which provides a vector of log deterministic lambda values
for treatment populations; and r_values_ref, which provides the log
deterministic lambda of the mean reference matrix.This is followed by the
stageframe as object ahstages, the order of historical stages as
object hstages, the age-by-stage order as object agestages, the
order of matrices as object labels, and, if requested, the original A,
U, and F matrices.
Notes
Deterministic LTRE is one-way, fixed, and based on the sensitivities of the
matrix midway between each input matrix and the reference matrix, per Caswell
(2001, Matrix Population Models, Sinauer Associates, MA, USA). Stochastic
LTRE is performed via two methods. The stochastic LTRE approximation is
simulated per Davison et al. (2010) Journal of Ecology 98:255-267
(doi: 10.1111/j.1365-2745.2009.01611.x). The small noise approximation
(sna-LTRE) is analyzed per Davison et al. (2019) Ecological Modelling 408:
108760 (doi: 10.1016/j.ecolmodel.2019.108760).
All stochastic and small noise approximation LTREs conducted without
reference matrices are performed as spatial tests of the population dynamics
among patches.
Default behavior for stochastic LTRE uses the full population provided in
mats as the reference if no refmats and ref is provided.
If no refmats is provided but ref is, then the matrices noted
in ref are used as the reference matrix set. Year and patch order is
utilized from object mats, but not from object refmats, in
which each matrix is assumed to represent a different year from one
population. This function cannot currently handle multiple populations within
the same mats object (although such analysis is possible if these
populations are designated as patches instead).
If sparse = "auto", the default, then sparse matrix encoding
will be used if the size of the input matrices is at least 50 columns by 50
rows for deterministic and stochastic LTREs and 10 columns by 10 rows for
small noise approximation LTREs, in all cases as long as 50% of the elements
in the first matrix are non-zero.
Stochastic LTREs do not test for the impact of temporal change in vital
rates. An MPM with a single population, a single patch, and only annual
matrices will produce contributions of 0 to 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 10 and 300 rows and columns.
Defaults work best when matrices are very small and dense, or very large and
sparse.
SNA-LTRE analysis cannot test the impact of first-order Markovian
environments. However, different random weightings of annual matrices are
allowed if given in vector format.
The time_weights, steps, force_sparse, and rseed
arguments are now deprecated. Instead, please use the tweights,
times, sparse, and seed arguments.
See Also
summary.lefkoLTRE()
Examples
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")
ltre3(cypmatrix2r, sna_ltre = TRUE)
lefko3 documentation built on Sept. 11, 2024, 9:35 p.m.
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| ltre3 | R Documentation |
Conduct a Life Table Response Experiment
Description
ltre3() returns a set of matrices of one-way LTRE (life table response
experiment), stochastic LTRE (sLTRE) matrices, or small noise approximation
LTRE (sna-LTRE) contributions.
Usage
ltre3(
mats,
refmats = NA,
ref = NA,
stochastic = FALSE,
times = 10000,
burnin = 3000,
tweights = NA,
sparse = "auto",
seed = NA,
append_mats = FALSE,
sna_ltre = FALSE,
tol = 1e-30,
...
)
Arguments
mats |
An object of class |
refmats |
A reference lefkoMat object, or matrix, for use as the
control. Default is |
ref |
A numeric value indicating which matrix or matrices in
|
stochastic |
A logical value determining whether to conduct a
deterministic ( |
times |
The number of occasions to project forward in stochastic
simulation. Defaults to |
burnin |
The number of initial steps to ignore in stochastic projection
when calculating stochastic elasticities. Must be smaller than |
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. Note that SNA-LTRE analysis cannot take matrix input. |
sparse |
A text string indicating whether to use sparse matrix encoding
( |
seed |
Optional numeric value corresponding to the random seed for stochastic simulation. |
append_mats |
A logical value denoting whether to include the original
|
sna_ltre |
A logical value indicating whether to treat stochastic LTRE
via the sna-LTRE approach from Davison et al. (2019) ( |
tol |
A numeric value indicating a lower positive limit to matrix
element values when applied to stochastic and small noise approximation LTRE
estimation protocols. Matrix element values lower than this will be treated
as |
... |
Other parameters. |
Value
This function returns an object of class lefkoLTRE. This
includes a list of LTRE matrices as object cont_mean if a
deterministic LTRE is called for, or a list of mean-value LTRE matrices as
object cont_mean and a list of SD-value LTRE matrices as object
cont_sd if a stochastic LTRE is called for. If a small-noise
approximation LTRE (SNA-LTRE) is performed, then the output includes six
objects: cont_mean, which provides the contributions of shifts in mean
matrix elements; cont_elas, which provides the contributions of shifts
in the elasticities of matrix elements; cont_cv, which provides the
contributions of temporal variation in matrix elements; cont_corr,
which provides the contributions of temporal correlations in matrix elements;
r_values_m, which provides a vector of log deterministic lambda values
for treatment populations; and r_values_ref, which provides the log
deterministic lambda of the mean reference matrix.This is followed by the
stageframe as object ahstages, the order of historical stages as
object hstages, the age-by-stage order as object agestages, the
order of matrices as object labels, and, if requested, the original A,
U, and F matrices.
Notes
Deterministic LTRE is one-way, fixed, and based on the sensitivities of the matrix midway between each input matrix and the reference matrix, per Caswell (2001, Matrix Population Models, Sinauer Associates, MA, USA). Stochastic LTRE is performed via two methods. The stochastic LTRE approximation is simulated per Davison et al. (2010) Journal of Ecology 98:255-267 (doi: 10.1111/j.1365-2745.2009.01611.x). The small noise approximation (sna-LTRE) is analyzed per Davison et al. (2019) Ecological Modelling 408: 108760 (doi: 10.1016/j.ecolmodel.2019.108760).
All stochastic and small noise approximation LTREs conducted without reference matrices are performed as spatial tests of the population dynamics among patches.
Default behavior for stochastic LTRE uses the full population provided in
mats as the reference if no refmats and ref is provided.
If no refmats is provided but ref is, then the matrices noted
in ref are used as the reference matrix set. Year and patch order is
utilized from object mats, but not from object refmats, in
which each matrix is assumed to represent a different year from one
population. This function cannot currently handle multiple populations within
the same mats object (although such analysis is possible if these
populations are designated as patches instead).
If sparse = "auto", the default, then sparse matrix encoding
will be used if the size of the input matrices is at least 50 columns by 50
rows for deterministic and stochastic LTREs and 10 columns by 10 rows for
small noise approximation LTREs, in all cases as long as 50% of the elements
in the first matrix are non-zero.
Stochastic LTREs do not test for the impact of temporal change in vital
rates. An MPM with a single population, a single patch, and only annual
matrices will produce contributions of 0 to 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 10 and 300 rows and columns. Defaults work best when matrices are very small and dense, or very large and sparse.
SNA-LTRE analysis cannot test the impact of first-order Markovian environments. However, different random weightings of annual matrices are allowed if given in vector format.
The time_weights, steps, force_sparse, and rseed
arguments are now deprecated. Instead, please use the tweights,
times, sparse, and seed arguments.
See Also
summary.lefkoLTRE()
Examples
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")
ltre3(cypmatrix2r, sna_ltre = TRUE)
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