create_lM: Create lefkoMat Object from Given Input Matrices or an MPM...

View source: R/datamanag.R

create_lMR Documentation

Create lefkoMat Object from Given Input Matrices or an MPM Database

Description

Function create_lM() creates lefkoMat objects from supplied matrices and extra information, or from a supplied MPM database such as COMPADRE or COMADRE.

Usage

create_lM(
  mats,
  stageframe = NULL,
  hstages = NA,
  agestages = NA,
  historical = FALSE,
  agebystage = FALSE,
  UFdecomp = TRUE,
  entrystage = 1,
  poporder = 1,
  patchorder = 1,
  yearorder = NA,
  matrix_id = NULL,
  add_FC = TRUE,
  sparse_output = FALSE
)

Arguments

mats

A list of A matrices, or, if importing from a matrix database such as COMPADRE or COMADRE, then the object holding the database.

stageframe

A stageframe describing all stages utilized.

hstages

A data frame outlining the order of historical stages, if matrices provided in mats are historical. Defaults to NA.

agestages

A data frame outlining the order of ahistorical age-stages, if age-by-stage matrices are provided.

historical

A logical value indicating whether input matrices are historical or not. Defaults to FALSE.

agebystage

A logical value indicating whether input matrices are ahistorical age-by-stage matrices. If TRUE, then object agestages is required. Defaults to FALSE.

UFdecomp

A logical value indicating whether U and F matrices should be inferred. Defaults to TRUE.

entrystage

The stage or stages produced by reproductive individuals. Used to determine which transitions are reproductive for U-F decomposition. Defaults to 1, which corresponds to the first stage in the stageframe.

poporder

The order of populations in the list supplied in object mats. Defaults to 1.

patchorder

The order of patches in the list supplied in object mats. Defaults to 1.

yearorder

The order of monitoring occasions in the list supplied in object mats. Defaults to NA, which leads to each matrix within each population-patch combination being a different monitoring occasion.

matrix_id

The values of MatrixID from the used database corresponding to the matrices to import, if importing from a database. Not used if importing a list of matrices.

add_FC

A logical value indicating whether to sum the matF and matC matrices to produce the F matrix. If FALSE, then only uses the matF matrix. Only used if importing from the COMPADRE or COMADRE database. Defaults to TRUE.

sparse_output

A logical value indicating whether to output matrices in sparse format. Defaults to FALSE, in which case all matrices are output in standard matrix format. Does not apply to matrices imported from COMPADRE or COMADRE, which are always in standard format.

Value

A lefkoMat object incorporating the matrices input in object mats as object A, their U and F decompositions in objects U and F (if requested), the provided stageframe as object ahstages, the order of historical stages as object hstages (if historical = TRUE), the order of matrices as object labels, and a short quality control section used by the summary.lefkoMat() function.

Notes for importing lists of matrices

Lists may be composed of a mix of matrices in standard R format (i.e. created via the matrix() function), and in dgCMatrix sparse format (i.e. created via the Matrix::Matrix() function with sparse = TRUE.) All matrices will be forced to one or the other, depending on the value given for the sparse_output argument.

U and F decomposition assumes that elements holding fecundity values are to be interpreted solely as fecundity rates. Users wishing to split these elements between fecundity and survival should do so manually after running this function.

Age-by-stage MPMs require an agestages data frame outlining the order of age-stages. This data frame has 3 variables: stage_id, which is the number of the stage as labelled by the equivalently named variable in the stageframe; stage, which is the official name of the stage as given in the equivalently named variable in the stageframe; and age, which of course gives the age associated with the stage at that time. The number of rows must be equal to the number of rows and columns of each entered matrix.

Users may edit the dataqc object, setting the first NA to the number of individuals sampled, and the second NA to the number of rows in a vertical version of the demographic dataset. This is not required, however.

Notes for importing from COMPADRE or COMADRE

For this function to operate, users must have either the COMPADRE database or the COMADRE database loaded into the global environment. Note that the sample databases supplied within package Rcompadre will not work with this function.

This function does not and cannot replace the wonderful tools offered to explore the COMPADRE and COMADRE packages. Please see package Rcompadre to use those tools. Note that function import_Com() has no relationship to the Rcompadre development team.

Function import_Com() requires that the dimensions of all matrices imported into a single lefkoMat object be equal.

The reproductive and maturity status of each stage is determined by patterns assessed within the F matrices. Users should check that these values make sense.

Stage names may be edited manually afterward.

Users may edit the dataqc object, setting the first NA to the number of individuals sampled, and the second NA to the number of rows in a vertical version of the demographic dataset. This is not required, however.

See Also

add_lM()

delete_lM()

subset_lM()

Examples

# These matrices are of 9 populations of the plant species Anthyllis
# vulneraria, and were originally published in Davison et al. (2010) Journal
# of Ecology 98:255-267 (doi: 10.1111/j.1365-2745.2009.01611.x).

sizevector <- c(1, 1, 2, 3) # These sizes are not from the original paper
stagevector <- c("Sdl", "Veg", "SmFlo", "LFlo")
repvector <- c(0, 0, 1, 1)
obsvector <- c(1, 1, 1, 1)
matvector <- c(0, 1, 1, 1)
immvector <- c(1, 0, 0, 0)
propvector <- c(0, 0, 0, 0)
indataset <- c(1, 1, 1, 1)
binvec <- c(0.5, 0.5, 0.5, 0.5)

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

# POPN C 2003-2004
XC3 <- matrix(c(0, 0, 1.74, 1.74,
0.208333333, 0, 0, 0.057142857,
0.041666667, 0.076923077, 0, 0,
0.083333333, 0.076923077, 0.066666667, 0.028571429), 4, 4, byrow = TRUE)

# 2004-2005
XC4 <- matrix(c(0, 0, 0.3, 0.6,
0.32183908, 0.142857143, 0, 0,
0.16091954, 0.285714286, 0, 0,
0.252873563, 0.285714286, 0.5, 0.6), 4, 4, byrow = TRUE)

# 2005-2006
XC5 <- matrix(c(0, 0, 0.50625, 0.675,
0, 0, 0, 0.035714286,
0.1, 0.068965517, 0.0625, 0.107142857,
0.3, 0.137931034, 0, 0.071428571), 4, 4, byrow = TRUE)

# POPN E 2003-2004
XE3 <- matrix(c(0, 0, 2.44, 6.569230769,
0.196428571, 0, 0, 0,
0.125, 0.5, 0, 0,
0.160714286, 0.5, 0.133333333, 0.076923077), 4, 4, byrow = TRUE)

XE4 <- matrix(c(0, 0, 0.45, 0.646153846,
0.06557377, 0.090909091, 0.125, 0,
0.032786885, 0, 0.125, 0.076923077,
0.049180328, 0, 0.125, 0.230769231), 4, 4, byrow = TRUE)

XE5 <- matrix(c(0, 0, 2.85, 3.99,
0.083333333, 0, 0, 0,
0, 0, 0, 0,
0.416666667, 0.1, 0, 0.1), 4, 4, byrow = TRUE)

mats_list <- list(XC3, XC4, XC5, XE3, XE4, XE5)
yr_ord <- c(1, 2, 3, 1, 2, 3)
pch_ord <- c(1, 1, 1, 2, 2, 2)

anth_lefkoMat <- create_lM(mats_list, anthframe, hstages = NA,
  historical = FALSE, poporder = 1, patchorder = pch_ord, yearorder = yr_ord)

# A theoretical example showcasing historical matrices
sizevector <- c(1, 2, 3) # These sizes are not from the original paper
stagevector <- c("Sdl", "Veg", "Flo")
repvector <- c(0, 0, 1)
obsvector <- c(1, 1, 1)
matvector <- c(0, 1, 1)
immvector <- c(1, 0, 0)
propvector <- c(1, 0, 0)
indataset <- c(1, 1, 1)
binvec <- c(0.5, 0.5, 0.5)

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

A1 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,
  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,
  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,
  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,
  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,
  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,
  0, 0, 2.00, 0, 0, 3.00, 0, 0, 4.00,
  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,
  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)

A2 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,
  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,
  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,
  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,
  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,
  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,
  0, 0, 5.00, 0, 0, 6.00, 0, 0, 7.00,
  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,
  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)

A3 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,
  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,
  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,
  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,
  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,
  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,
  0, 0, 8.00, 0, 0, 9.00, 0, 0, 10.00,
  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,
  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)

B1 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,
  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,
  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,
  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,
  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,
  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,
  0, 0, 11.00, 0, 0, 12.00, 0, 0, 13.00,
  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,
  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)

B2 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,
  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,
  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,
  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,
  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,
  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,
  0, 0, 14.00, 0, 0, 15.00, 0, 0, 16.00,
  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,
  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)

B3 <- matrix(c(0.10, 0, 0, 0.12, 0, 0, 0.15, 0, 0,
  0.15, 0, 0, 0.17, 0, 0, 0.20, 0, 0,
  0.20, 0, 0, 0.22, 0, 0, 0.25, 0, 0,
  0, 0.20, 0, 0, 0.22, 0, 0, 0.25, 0,
  0, 0.25, 0, 0, 0.27, 0, 0, 0.30, 0,
  0, 0.30, 0, 0, 0.32, 0, 0, 0.35, 0,
  0, 0, 17.00, 0, 0, 18.00, 0, 0, 19.00,
  0, 0, 0.35, 0, 0, 0.37, 0, 0, 0.40,
  0, 0, 0.40, 0, 0, 0.42, 0, 0, 0.45), 9, 9, byrow = TRUE)

histmats <- list(A1, A2, A3, B1, B2, B3)
stageframe <- exframe
pch_ord <- c("A", "A", "A", "B", "B", "B")
yr_ord <- c(1, 2, 3, 1, 2, 3)

hist_trial <- create_lM(histmats, exframe, historical = TRUE,
  UFdecomp = TRUE, entrystage = 1, patchorder = pch_ord, yearorder = yr_ord)
  

lefko3 documentation built on May 29, 2024, 6:39 a.m.