lefko3 (version 3.1.0)

aflefko2: Create Function-based Ahistorical Age x Stage Matrix Projection Model

Description

Function aflefko2() returns ahistorical age x stage MPMs corresponding to the patches and years given, including the associated component transition and fecundity matrices, a data frame detailing the characteristics of ahistorical stages, and a data frame characterizing the patch and year combinations corresponding to these matrices. Unlike rlefko2() and rlefko3(), this function currently does not currently distinguish populations.

Usage

aflefko2(
  year = 1,
  patch = NA,
  stageframe,
  repmatrix = NA,
  overwrite = NA,
  data = NA,
  modelsuite = NA,
  surv_model = NA,
  obs_model = NA,
  size_model = NA,
  repst_model = NA,
  fec_model = NA,
  jsurv_model = NA,
  jobs_model = NA,
  jsize_model = NA,
  jrepst_model = NA,
  paramnames = NA,
  inda = 0,
  indb = 0,
  indc = 0,
  surv_dev = 0,
  obs_dev = 0,
  size_dev = 0,
  repst_dev = 0,
  fec_dev = 0,
  jsurv_dev = 0,
  jobs_dev = 0,
  jsize_dev = 0,
  jrepst_dev = 0,
  repmod = 1,
  yearcol = "year2",
  patchcol = "patchid",
  year.as.random = FALSE,
  patch.as.random = FALSE,
  final_age = 10,
  continue = TRUE,
  randomseed = 0,
  negfec = FALSE,
  reduce = FALSE
)

Arguments

year

A variable corresponding to year or observation time, or a set of such values, given in values associated with the year term used in linear model development. Can also equal all, in which case matrices will be estimated for all years. Defaults to all.

patch

A variable designating which patches or subpopulations will have matrices estimated. Should be set to specific patch names, or to all if matrices should be estimated for all patches. Defaults to all.

stageframe

A stageframe object that includes information on the size, observation status, propagule status, immaturity status, and maturity status of each ahistorical stage. Should also incorporate bin widths if size is continuous.

repmatrix

A matrix composed mostly of 0s, with non-zero values for each potentially new individual (row) born to each reproductive stage (column). Entries act as multipliers on fecundity, with 1 equaling full fecundity.

overwrite

A data frame developed with the overwrite() function describing transitions to be overwritten either with given values or with other estimated transitions.

data

The original historical demographic data frame used to estimate vital rates (class hfvdata). The original data frame is required in order to initialize years and patches properly.

modelsuite

An optional lefkoMod object holding the vital rate models. If given, then surv_model, obs_model, size_model, repst_model, fec_model, jsurv_model, jobs_model, jsize_model, jrepst_model, paramnames, yearcol, and patchcol are not required. No models should include size or reproductive status in time t-1.

surv_model

A linear model predicting survival probability. This can be a model of class glm or glmer, and requires a predicted binomial variable under a logit link. If given, then will overwrite any survival probability model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

obs_model

A linear model predicting sprouting or observation probability. This can be a model of class glm or glmer, and requires a predicted binomial variable under a logit link. If given, then will overwrite any observation probability model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

size_model

A linear model predicting size. This can be a model of class glm or glmer, both of which require a predicted poisson variable under a log link, or a model of class lm or lmer, in which a Gaussian response is assumed. If given, then will overwrite any size model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

repst_model

A linear model predicting reproduction probability. This can be a model of class glm or glmer, and requires a predicted binomial variable under a logit link. If given, then will overwrite any reproduction probability model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

fec_model

A linear model predicting fecundity. This can be a model of class glm or glmer, and requires a predicted poisson variable under a log link. If given, then will overwrite any fecundity model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

jsurv_model

A linear model predicting juvenile survival probability. This can be a model of class glm or glmer, and requires a predicted binomial variable under a logit link. If given, then will overwrite any juvenile survival probability model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

jobs_model

A linear model predicting juvenile sprouting or observation probability. This can be a model of class glm or glmer, and requires a predicted binomial variable under a logit link. If given, then will overwrite any juvenile observation probability model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

jsize_model

A linear model predicting juvenile size. This can be a model of class glm or glmer, both of which require a predicted poisson variable under a log link, or a model of class lm or lmer, in which a Gaussian response is assumed. If given, then will overwrite any juvenile size model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

jrepst_model

A linear model predicting reproduction probability of a mature individual that was immature in the previous year. This can be a model of class glm or glmer, and requires a predicted binomial variable under a logit link. If given, then will overwrite any reproduction probability model given in modelsuite. This model must have been developed in a modeling exercise testing only the impacts of time t.

paramnames

A dataframe with two columns, the first showing the general model terms that will be used in matrix creation, and the second showing the equivalent terms used in modeling. Only required if modelsuite is not supplied.

inda

A numeric value to use for individual covariate a. Defaults to 0.

indb

A numeric value to use for individual covariate b. Defaults to 0.

indc

A numeric value to use for individual covariate c. Defaults to 0.

surv_dev

A numeric value to be added to the y-intercept in the linear model for survival probability.

obs_dev

A numeric value to be added to the y-intercept in the linear model for observation probability.

size_dev

A numeric value to be added to the y-intercept in the linear model for size.

repst_dev

A numeric value to be added to the y-intercept in the linear model for probability of reproduction.

fec_dev

A numeric value to be added to the y-intercept in the linear model for fecundity.

jsurv_dev

A numeric value to be added to the y-intercept in the linear model for juvenile survival probability.

jobs_dev

A numeric value to be added to the y-intercept in the linear model for juvenile observation probability.

jsize_dev

A numeric value to be added to the y-intercept in the linear model for juvenile size.

jrepst_dev

A numeric value to be added to the y-intercept in the linear model for juvenile reproduction probability.

repmod

A scalar multiplier of fecundity. Defaults to 1.

yearcol

The variable name or column number corresponding to year in time t in the dataset. Not needed if a modelsuite is supplied.

patchcol

The variable name or column number corresponding to patch in the dataset. Not needed if a modelsuite is supplied.

year.as.random

A logical term indicating whether coefficients for missing patches within vital rate models should be estimated as random intercepts. Defaults to FALSE, in which case missing time step coefficients are set to 0.

patch.as.random

A logical term indicating whether coefficients for missing patches within vital rate models should be estimated as random intercepts. Defaults to FALSE, in which case missing patch coefficients are set to 0.

final_age

The final age to model in the matrix, where the first age will be age 0.

continue

A logical value designating whether to allow continued survival of individuals going past the final age, using the demographic characteristics of the final age.

randomseed

A numeric value used as a seed to generate random estimates for missing time step and patch coefficients, if either year.as.random or patch.as.random is set to TRUE. Defaults to 0.

negfec

A logical value denoting whether fecundity values estimated to be negative should be reset to 0. Defaults to FALSE.

reduce

A logical value denoting whether to remove ahistorical stages associated solely with 0 transitions. These are only removed in cases where the associated row and column sums in ALL matrices estimated equal 0. Defaults to FALSE.

Value

If all inputs are properly formatted, then this function will return an object of class lefkoMat. Output includes:

A

A list of full projection matrices in order of sorted patches and years.

U

A list of survival-transition matrices sorted as in A.

F

A list of fecundity matrices sorted as in A.

hstages

Null for ahistorical matrices.

ahstages

A data frame detailing the characteristics of associated ahistorical stages.

labels

A data frame giving the patch and year of each matrix in order. In aflefko2(), only one population may be analyzed at once, and so pop = NA

matrixqc

A short vector describing the number of non-zero elements in U and F matrices, and the number of annual matrices.

modelqc

This is the qc portion of the modelsuite input.

Please note that this function will yield incorrect estimates if the models utilized incorporate state in time t-1. Only use models developed testing ahistorical effects.

Examples

# NOT RUN {
data(lathyrus)

sizevector <- c(0, 4.6, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9)
stagevector <- c("Sd", "Sdl", "Dorm", "Sz1nr", "Sz2nr", "Sz3nr", "Sz4nr", "Sz5nr",
                 "Sz6nr", "Sz7nr", "Sz8nr", "Sz9nr", "Sz1r", "Sz2r", "Sz3r", "Sz4r",
                 "Sz5r", "Sz6r", "Sz7r", "Sz8r", "Sz9r")
repvector <- c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1)
obsvector <- c(0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
matvector <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
immvector <- c(1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
minima <- c(0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)
maxima <- c(NA, 1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA)
binvec <- c(0, 4.6, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,
            0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5)

lathframeln <- sf_create(sizes = sizevector, stagenames = stagevector, repstatus = repvector,
                        obsstatus = obsvector, matstatus = matvector, immstatus = immvector,
                        indataset = indataset, binhalfwidth = binvec, propstatus = propvector,
                        minage = minima, maxage = maxima)

lathvertln <- verticalize3(lathyrus, noyears = 4, firstyear = 1988, patchidcol = "SUBPLOT",
                           individcol = "GENET", blocksize = 9, juvcol = "Seedling1988",
                           sizeacol = "lnVol88", repstracol = "Intactseed88",
                           fecacol = "Intactseed88", deadacol = "Dead1988",
                           nonobsacol = "Dormant1988", stageassign = lathframeln,
                           stagesize = "sizea", censorcol = "Missing1988",
                           censorkeep = NA, NAas0 = TRUE, censor = TRUE)

lathvertln$feca2 <- round(lathvertln$feca2)
lathvertln$feca1 <- round(lathvertln$feca1)
lathvertln$feca3 <- round(lathvertln$feca3)

lathrepmln <- matrix(0, 21, 21)
lathrepmln[1, c(13:21)] <- 0.345
lathrepmln[2, c(13:21)] <- 0.054

lathover2 <- overwrite(stage3 = c("Sd", "Sdl"), stage2 = c("Sd", "Sd"),
                       givenrate = c(0.345, 0.054))

lathmodelsln2 <- modelsearch(lathvertln, historical = FALSE, approach = "lme4", suite = "main", 
                             vitalrates = c("surv", "obs", "size", "repst", "fec"), 
                             juvestimate = "Sdl",bestfit = "AICc&k", sizedist = "gaussian", 
                             fecdist = "poisson", indiv = "individ", patch = "patchid", 
                             year = "year2", age = "obsage", year.as.random = TRUE, 
                             patch.as.random = TRUE, show.model.tables = TRUE, quiet = TRUE)
                             
lathmat2age <- aflefko2(year = "all", patch = "all", stageframe = lathframeln, 
                        modelsuite = lathmodelsln2, data = lathvertln, 
                        repmatrix = lathrepmln, overwrite = lathover2,
                        patchcol = "patchid", yearcol = "year2", year.as.random = FALSE,
                        patch.as.random = FALSE, final_age = 2, continue = TRUE, reduce = FALSE)

summary(lathmat2age)
# }
# NOT RUN {
# }