ltre3  R Documentation 
ltre3()
returns a set of matrices of oneway LTRE (life table response
experiment), stochastic LTRE (sLTRE) matrices, or small noise approximation
LTRE (snaLTRE) contributions.
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 = 1e30,
...
)
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 firstorder 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 SNALTRE 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 snaLTRE 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. 
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 meanvalue LTRE matrices as
object cont_mean
and a list of SDvalue LTRE matrices as object
cont_sd
if a stochastic LTRE is called for. If a smallnoise
approximation LTRE (SNALTRE) 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 agebystage order as object agestages
, the
order of matrices as object labels
, and, if requested, the original A,
U, and F matrices.
Deterministic LTRE is oneway, 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 98:255267 (doi: 10.1111/j.13652745.2009.01611.x). The small noise approximation (snaLTRE) 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 nonzero.
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.
SNALTRE analysis cannot test the impact of firstorder 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.
summary.lefkoLTRE()
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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