repvalue3.lefkoMat: Estimate Reproductive Value Vectors of Matrices in a lefkoMat...
In lefko3: Historical and Ahistorical Population Projection Matrix Analysis
repvalue3.lefkoMat R Documentation
Estimate Reproductive Value Vectors of Matrices in a lefkoMat Object
Description
repvalue3.lefkoMat() returns the reproductive values for stages in a
set of population projection matrices provided as a lefkoMat object.
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 ahistorical
matrices.
Usage
## S3 method for class 'lefkoMat'
repvalue3(
mats,
stochastic = FALSE,
times = 10000,
tweights = NA,
seed = NA,
force_sparse = "auto",
...
)
Arguments
mats
An object of class lefkoMat object.
stochastic
A logical value indicating whether to use deterministic
(FALSE) or stochastic (TRUE) analysis. Defaults to
FALSE.
times
An integer variable indicating number of occasions to project if
using stochastic analysis. Defaults to 10000.
tweights
An optional vector indicating the probability weighting to
use for each matrix in stochastic simulations. If not given, then defaults to
equal weighting.
seed
A number to use as a random number seed.
force_sparse
A text string indicating whether to use sparse matrix
encoding ("yes") when supplied with standard matrices. 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.
...
Other parameters.
Value
This function returns the asymptotic reproductive value vectors if
deterministic analysis is chosen, and long-run mean reproductive value
vectors if stochastic analysis is chosen.
The output depends on whether the lefkoMat object used as input is
ahistorical or historical, and whether the analysis is deterministic or
stochastic. If deterministic and ahistorical, then a single data frame is
output, which includes the number of the matrix within the A element
of the input lefkoMat object, followed by the stage id (numeric and
assigned through sf_create()), the stage name, and the
estimated proportion of the reproductive value vector (rep_value). If
stochastic and ahistorical, then a single data frame is output starting with
the number of the population-patch (matrix_set), a string
concatenating the names of the population and the patch (poppatch),
the assigned stage id number (stage_id), and the stage name
(stage), and the long-run mean reproductive value vector
(rep_value).
If a historical matrix is used as input, then two data frames are output
into a list object. The hist element describes the historical
stage-pair reproductive values, while the ahist element describes the
stage reproductive values. If deterministic, then hist contains a data
frame including the matrix number (matrix), the numeric stage
designations for stages in occasions t and t-1,
(stage_id_2 and stage_id_1, respectively), followed by the
respective stage names (stage_2 and stage_1), and ending with
the estimated reproductive values (rep_value). The associated
ahist element is as before. If stochastic, then the hist
element contains a single data frame with the number of the population-patch
(matrix_set), a string concatenating the names of the population and
the patch (poppatch), the assigned stage id numbers in times t
and t-1 (stage_id_2 and stage_id_2, respectively), and
the associated stage names (stage_2 and stage_1, respectively),
and the long-run mean reproductive values (rep_value). The associated
ahist element is as before in the ahistorical, stochastic case.
In addition to the data frames noted above, stochastic analysis will result
in the additional output of a list of matrices containing the actual
projected reproductive value vectors across all projected occasions, in the
order of population-patch combinations in the lefkoMat input.
Notes
In stochastic analysis, the projected mean reproductive value vector is the
arithmetic mean across the final projected 1000 occasions if the simulation
is at least 2000 projected occasions long. If between 500 and 2000 projected
occasions long, then only the final 200 are used, and if fewer than 500
occasions are used, then all are used. Note that because reproductive values
in stochastic simulations can change greatly in the initial portion of the
run, we encourage a minimum 2000 projected occasions per simulation, with
10000 preferred.
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 several hundred rows and columns. Defaults
work best when matrices are very small and dense, or very large and sparse.
See Also
repvalue3()
repvalue3.matrix()
repvalue3.dgCMatrix()
repvalue3.list()
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)
# Here we use supplemental() to provide overwrite and reproductive info
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")
repvalue3(cypmatrix2r, stochastic = TRUE)
lefko3 documentation built on Oct. 14, 2023, 1:07 a.m.
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| repvalue3.lefkoMat | R Documentation |
Estimate Reproductive Value Vectors of Matrices in a lefkoMat Object
Description
repvalue3.lefkoMat() returns the reproductive values for stages in a
set of population projection matrices provided as a lefkoMat object.
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 ahistorical
matrices.
Usage
## S3 method for class 'lefkoMat'
repvalue3(
mats,
stochastic = FALSE,
times = 10000,
tweights = NA,
seed = NA,
force_sparse = "auto",
...
)
Arguments
mats |
An object of class |
stochastic |
A logical value indicating whether to use deterministic
( |
times |
An integer variable indicating number of occasions to project if using stochastic analysis. Defaults to 10000. |
tweights |
An optional vector indicating the probability weighting to use for each matrix in stochastic simulations. If not given, then defaults to equal weighting. |
seed |
A number to use as a random number seed. |
force_sparse |
A text string indicating whether to use sparse matrix
encoding ( |
... |
Other parameters. |
Value
This function returns the asymptotic reproductive value vectors if deterministic analysis is chosen, and long-run mean reproductive value vectors if stochastic analysis is chosen.
The output depends on whether the lefkoMat object used as input is
ahistorical or historical, and whether the analysis is deterministic or
stochastic. If deterministic and ahistorical, then a single data frame is
output, which includes the number of the matrix within the A element
of the input lefkoMat object, followed by the stage id (numeric and
assigned through sf_create()), the stage name, and the
estimated proportion of the reproductive value vector (rep_value). If
stochastic and ahistorical, then a single data frame is output starting with
the number of the population-patch (matrix_set), a string
concatenating the names of the population and the patch (poppatch),
the assigned stage id number (stage_id), and the stage name
(stage), and the long-run mean reproductive value vector
(rep_value).
If a historical matrix is used as input, then two data frames are output
into a list object. The hist element describes the historical
stage-pair reproductive values, while the ahist element describes the
stage reproductive values. If deterministic, then hist contains a data
frame including the matrix number (matrix), the numeric stage
designations for stages in occasions t and t-1,
(stage_id_2 and stage_id_1, respectively), followed by the
respective stage names (stage_2 and stage_1), and ending with
the estimated reproductive values (rep_value). The associated
ahist element is as before. If stochastic, then the hist
element contains a single data frame with the number of the population-patch
(matrix_set), a string concatenating the names of the population and
the patch (poppatch), the assigned stage id numbers in times t
and t-1 (stage_id_2 and stage_id_2, respectively), and
the associated stage names (stage_2 and stage_1, respectively),
and the long-run mean reproductive values (rep_value). The associated
ahist element is as before in the ahistorical, stochastic case.
In addition to the data frames noted above, stochastic analysis will result
in the additional output of a list of matrices containing the actual
projected reproductive value vectors across all projected occasions, in the
order of population-patch combinations in the lefkoMat input.
Notes
In stochastic analysis, the projected mean reproductive value vector is the arithmetic mean across the final projected 1000 occasions if the simulation is at least 2000 projected occasions long. If between 500 and 2000 projected occasions long, then only the final 200 are used, and if fewer than 500 occasions are used, then all are used. Note that because reproductive values in stochastic simulations can change greatly in the initial portion of the run, we encourage a minimum 2000 projected occasions per simulation, with 10000 preferred.
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 several hundred rows and columns. Defaults work best when matrices are very small and dense, or very large and sparse.
See Also
repvalue3()
repvalue3.matrix()
repvalue3.dgCMatrix()
repvalue3.list()
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)
# Here we use supplemental() to provide overwrite and reproductive info
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")
repvalue3(cypmatrix2r, stochastic = TRUE)
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