adapt3 (version 2.0.7)

summary.adaptProjBatch: Summarize adaptProjBatch Objects

Description

Function summary.adaptProjBatch() summarizes adaptProjBatch objects.

Usage

# S3 method for adaptProjBatch
summary(
  object,
  finalN_mean = FALSE,
  finalN_used = 100,
  threshold = 1,
  inf_alive = TRUE,
  ext_time = FALSE,
  print_output = FALSE,
  ...
)

Value

Apart from a statement of the results, this function outputs a data frame with the following elements:

projection

The identity of the current projection in the original adaptProjBatch object.

target_mpm

The identity of the MPM targeted for alteration in the batch projection.

stage3

Stage at occasion t+1 in the transition replaced.

stage2

Stage at occasion t in the transition replaced.

stage1

Stage at occasion t-1 in the transition replaced.

age2

Age at occasion t in the transition replaced.

eststage3

Stage at occasion t+1 in the transition to replace the transition designated by stage3, stage2, and stage1.

eststage2

Stage at occasion t in the transition to replace the transition designated by stage3, stage2, and stage1.

eststage1

Stage at occasion t-1 in the transition to replace the transition designated by stage3, stage2, and stage1.

estage2

Age at occasion t in the transition to replace the transition designated by age2.

givenrate

A constant to be used as the value of the transition.

offset

A constant value to be added to the transition or proxy transition.

multiplier

A multiplier for proxy transitions or for fecundity.

convtype

Designates whether the transition from occasion t to occasion t+1 is a survival transition probability (1), a fecundity rate (2), or a fecundity multiplier (3).

convtype_t12

Designates whether the transition from occasion t-1 to occasion t is a survival transition probability (1), or a fecundity rate (2).

rep

The identity of the replicate being summarized, within the current projection.

mpm

The identity of the MPM for which the population summary corresponding to the row in question is being given.

final_N

The final population size, meaning the population size given for the current MPM in the current replicate in the current projection, in the final time recorded.

extinct_by

The first time by which the population size goes below the extinction threshold, or hits 0.

final_N_mean

The mean population size during the final finalN_used times for the current MPM in the current replicate in the current projection.

extinction_times

A dataframe showing the numbers of replicates going extinct (ext_reps) and mean extinction time (ext_time) per population-patch. If ext_time = FALSE, then only outputs NA.

Arguments

object

An adaptProjBatch object.

finalN_mean

A logical value indicating whether to take the arithmetic mean of the final population sizes for each MPM in each projection. Defaults to FALSE, in which case only the final population sizes are reported.

finalN_used

An integer value indicating the number of final population sizes in the arithmetic mean noted in argument finalN_mean. Defaults to 100, unless the projections are for fewer time steps, in which case defaults to 10.

threshold

A threshold population size to be searched for in projections. Defaults to 1.

inf_alive

A logical value indicating whether to treat infinitely large population size as indicating that the population is still extant. If FALSE, then the population is considered extinct. Defaults to TRUE.

ext_time

A logical value indicating whether to output extinction times per population-patch. Defaults to FALSE.

print_output

A logical value indicating whether to print the output data frame to the screen. Defaults to FALSE.

...

Other parameters currently not utilized.

Notes

The inf_alive and ext_time options both assess whether replicates have reached a value of NaN or Inf. If inf_alive = TRUE or ext_time = TRUE and one of these values is found, then the replicate is counted in the milepost_sums object if the last numeric value in the replicate is above the threshold value, and is counted as extant and not extinct if the last numeric value in the replicate is above the extinction threshold of a single individual.

Extinction time is calculated on the basis of whether the replicate ever falls below a single individual. A replicate with a positive population size below 0.0 that manages to rise above 1.0 individual is still considered to have gone extinct the first time it crossed below 1.0.

If the input lefkoProj object is a mixture of two or more other lefkoProj objects, then mileposts will be given relative to the maximum number of time steps noted.

Examples

library(lefko3)
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)

sizevector <- c(0, 0, 3.0, 15)
stagevector <- c("P1", "D", "Sm", "Lg")
repvector <- c(0, 0, 1, 1)
obsvector <- c(0, 0, 1, 1)
matvector <- c(0, 1, 1, 1)
immvector <- c(1, 0, 0, 0)
indataset <- c(0, 1, 1, 1)
binvec <- c(0, 0.5, 2.5, 9.5)

cypframe_small_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
  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)

cypraw_v2 <- 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_small_raw, stagesize = "sizeadded", NAas0 = TRUE,
  NRasRep = TRUE)

cypraw_v3 <- 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",
  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, 1500, 500),
  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")
cypmean <- lmean(cypmatrix2r)

cypsupp2r_small <- supplemental(stage3 = c("D", "Sm", "Lg", "P1"),
  stage2 = c("P1", "P1", "P1", "rep"), eststage3 = c(NA, "Sm", "Lg", NA),
  eststage2 = c(NA, "D", "D", NA), givenrate = c(0.05, NA, NA, NA),
  offset = c(NA, NA, -0.1, NA), multiplier = c(NA, NA, NA, 0.5),
  type =c(1, 1, 1, 3), stageframe = cypframe_small_raw, historical = FALSE)
cypmatrix2r_small <- rlefko2(data = cypraw_v2, stageframe = cypframe_small_raw, 
  year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
  size = c("size3added", "size2added"), supplement = cypsupp2r_small,
  yearcol = "year2", patchcol = "patchid", indivcol = "individ")
cypmean_small <- lmean(cypmatrix2r_small)

cypmatrixL_small <- rleslie(data = cypraw_v3, start_age = 1, last_age = 4,
  continue = TRUE, fecage_min = 3, year = "all", pop = NA, patch = "all",
  yearcol = "year2", patchcol = "patchid", indivcol = "individ")

cyp_mpms1 <- list(cypmatrix2r, cypmatrix2r_small, cypmatrixL_small)

c2d_4 <- density_input(cypmean, stage3 = c("P1", "P1"), stage2= c("SD", "rep"),
  style = 1, time_delay = 1, alpha = 1, beta = 0.0005, type = c(2, 2))
c2d_4a <- density_input(cypmean_small, stage3 = c("P1", "P1"), stage2= c("P1", "rep"),
  style = 1, time_delay = 1, alpha = 1, beta = 0.0005, type = c(2, 2))
cypL_dv <- density_input(cypmatrixL_small, stage3 = c("Age1"), stage2 = c("rep"),
  style = c(1), alpha = c(0.5), beta = c(1.0), type = c(2))
cyp_density <- list(c2d_4, c2d_4a, cypL_dv)

cyp_start1 <- start_input(cypmatrix2r, stage2 = c("SD", "P1", "D"),
  value = c(100, 200, 4))
cyp_start2 <- start_input(cypmatrix2r_small, stage2 = c("P1", "D"),
  value = c(10, 2000))
cypL_start_1 <- start_input(cypmatrixL_small, stage2 = c("Age1"),
  value = c(200))
cyp_start <- list(cyp_start1, cyp_start2, cypL_start_1)

new_supplement_cyp2_small <- sup_skeleton(2)
new_supplement_cyp2_small$stage3 <- c("D", "Sm")
new_supplement_cyp2_small$stage2 <- c("Lg", "Lg")
new_supplement_cyp2_small$convtype <- c(1, 1)
used_supplements <- list(new_supplement_cyp2_small,
  new_supplement_cyp2_small, NULL)

aaa1_prj_batch2 <- batch_project3(used_mpms = "all", all_elems = FALSE,
  mpms =  cyp_mpms1, entry_time = c(0, 5, 8), times = 15, nreps = 3,
  supplement = used_supplements, integeronly = TRUE, density = cyp_density)
  
summary(aaa1_prj_batch2)