Description Usage Arguments Value Notes See Also Examples

`stablestage3.lefkoMat()`

returns the deterministic stable stage
distributions of all `$A`

matrices in an object of class
`lefkoMat`

, as well as the long-run projected mean stage distribution in
stochastic analysis. 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 | ```
## S3 method for class 'lefkoMat'
stablestage3(
mats,
stochastic = FALSE,
times = 10000,
tweights = NA,
seed = NA,
sparse = "auto",
...
)
``` |

`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 stable stage distributions (and long-run
mean stage distributions in stochastic analysis) corresponding to the
matrices in a `lefkoMat`

object.

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
proportion of the stable stage distribution (`ss_prop`

).

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 proportion of the
stable stage distribution for that stage within its matrix (`ss_prop`

).
The `$ahist`

element contains the stable stage distribution in stages
as given in the original stageframe. It includes a data frame with the matrix
of origin, the numeric stage designation, stage name, and the stable stage
distribution estimated as the sum of distribution elements from `$hist`

corresponding to the equivalent stage in time *t*, irrespective of stage
in time *t*-1.

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 stage distributions across all projected times, in the order of
population-patch combinations in the `lefkoMat`

input.

In stochastic analysis, the projected mean distribution is the arithmetic mean across the final 1000 projected 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 stage distributions in stochastic simulations can change greatly in the initial portion of the run, we encourage a minimum of 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)
stablestage3(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")
stablestage3(cypmatrix2r, stochastic = TRUE)
``` |

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