Description Usage Arguments Value Notes See Also Examples

`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 smaller
ahistorical matrices.

1 2 3 4 5 6 7 8 9 10 |

`mats` |
An object of class |

`stochastic` |
A logical value indicating whether to use deterministic
( |

`times` |
An integer variable indicating number of times 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. |

`sparse` |
A text string indicating whether to use sparse matrix encoding
( |

`...` |
Other parameters. |

This function returns the asymptotic reproductive value vectors if deterministic analysis chosen, and long-run mean reproductive value vectors if stochastic analysis was 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 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
reproductive value (`rep_value`

). Reproductive values are scaled by the
first non-zero value.

If a historical matrix is used as input, then two data frames are output
into a list object. The `$hist`

element contains a data frame in which
the stable stage distribution is given in terms of across-year stage pairs.
The structure includes the matrix number, the numeric stage designations for
stages in times *t* and *t*-1, respectively, followed by the
respective stage names, and ending with the estimated reproductive value for
that stage within its matrix (`rep_value`

). The `$ahist`

element is
a data frame showing the reproductive values of the basic stages in the
associated stageframe. The reproductive values in this second data frame are
estimated via the approach developed in Ehrlen (2000), in which each
ahistorical stage's reproductive value is the average of the RVs summed by
stage at time *t* weighted by the proportion of that stage pair within
the historical stable stage distribution associated with the matrix. Both
historical and ahistorical reproductive values are scaled to the first non-
zero reproductive value in each 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 times, 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 times if the simulation is at least 2000 projected times long. If between 500 and 2000 projected times long, then only the final 200 are used, and if fewer than 500 times 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 times per simulation, with 10000 preferred.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 | ```
# Lathyrus deterministic 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")
ehrlen3mean <- lmean(ehrlen3)
repvalue3(ehrlen3mean)
# Cypripedium stochastic example
rm(list=ls(all=TRUE))
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", "SL", "D",
"XSm", "Sm", "SD", "P1"),
stage2 = c("SD", "SD", "P1", "P2", "P3", "SL", "SL", "SL", "SL", "rep",
"rep"),
eststage3 = c(NA, NA, NA, NA, NA, NA, "D", "XSm", "Sm", NA, NA),
eststage2 = c(NA, NA, NA, NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
givenrate = c(0.10, 0.20, 0.20, 0.20, 0.25, 0.40, NA, NA, NA, NA, NA),
multiplier = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.5, 0.5),
type =c(1, 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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