repvalue3.lefkoMat | R Documentation |

`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.

```
## S3 method for class 'lefkoMat'
repvalue3(
mats,
stochastic = FALSE,
times = 10000,
tweights = NA,
seed = NA,
force_sparse = "auto",
...
)
```

`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. |

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.

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.

`repvalue3()`

`repvalue3.matrix()`

`repvalue3.dgCMatrix()`

`repvalue3.list()`

```
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 May 3, 2023, 9:12 a.m.

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