Using your own matrix data
Owen Jones
2024-10-16
Source:vignettes/a05_UsingYourOwnData.Rmd
a05_UsingYourOwnData.RmdThe utility of Rcompadre extends beyond the use of data
from the COMPADRE and COMADRE matrix databases. By coercing
user-provided matrix population model (MPM) data (and metadata) into the
standardised format used by the Rcompadre package (a
CompadreDB object) you can make use of all the
functionality of the package. The central function to carry out this is
cdb_build_cdb().
This vignette illustrates some simple use cases.
The CompadreDB object
Before illustrating the construction of a CompadreDB
object using cdb_build_cdb() is it first necessary to
outline the anatomy of the object.
The CompadreDB object consists of four parts: (1) the
matrices; (2) the stage information; (3) the metadata describing the
matrices; and (4) version information. Much of this information can be
generated automatically by the cdb_build_cdb()
function.
The matrices
MPM data can exist as A matrices (i.e. the whole MPM model) but can also exist as a series of submatrices that sum to the A matrix. Typically these matrices are based on demographic processes such as growth and survival, sexual reproduction and clonal reproduction. These matrices are commonly denoted as the U, F and C matrices respectively, and A = U + F + C (Caswell 2001, Salguero-Gomez et al. 2015, 2016).
Thus, each MPM in the CompadreDB object is provided as a
list object with four elements representing this set of matrices:
A, the full MPM and the three demographic process-based
submatrices (U, F and C).
In some cases it is not desirable (or perhaps impossible) to provide
information for the set of submatrices. For example, it may not be
possible to distinguish between sexual (F) and clonal
(C) reproduction, or between growth/survival (U) and
reproduction (F and/or C). Alternatively, it may
simply be that the planned analyses do not require the potentially
laborious splitting of the A matrix into these submatrices.
Nevertheless, CompadreDB requires the full set of four
matrices. Thankfully, the matrices can be provided as NA
matrices and can often be generated automatically from the provided data
(see below).
- If only the A matrices are provided,
cdb_build_cdb()will automatically populate the U, F and c matrices withNAvalues. - If a U matrix is provided, an F and/or C matrix must also be provided. If only one of the F and C matrices is provided, the other is assumed to be 0. The A matrix is then calculated automatically from these sets as A = U + F + C.
Sets of matrices of the same type must be provided as a
list for each type. For example, you could provide two
lists: one for the U matrices and one for the matching
F matrices. The function conducts some error checks to ensure
that these lists have the same length, and that all matrices in each set
has the same dimensions.
The stage information
Each MPM has a life-cycle divided into two or more discrete stages.
The CompadreDB object must include this information, and it
is provided as a list of data.frames (one for
each MPM).
The metadata
A valid CompadreDB object MUST include a
data.frame of metadata with a number of rows equal to the
number of MPMs.
This metadata, can be minimal or very extensive, depending on the users’ needs. In the simplest case, for example with simulated data, this might simply be an ID number, or perhaps parameters used in simulation. In cases with empirical MPMs the metadata will typically include taxonomic information on the species, the geographic location, the name of the study site, the year or time-frame of study and so on. Thus, the metadata data.frame can include anything from one to hundreds of columns.
Data preparation
The main function used for creating a CompadreDB object
from user-defined data is cdb_build_cdb(). This function
takes the components described above, performs some error checks, and
combines them into a single CompadreDB object.
A simple example
First we need to load the library, and the dplyr
package.
library(Rcompadre)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, unionIn this example we generate a series of 2 dimension A matrices using a series of uniform distributions for the U submatrix, and a gamma distribution, to approximate the average of a Poisson process. In this case, the matrices all have the same dimension, but it is not necessary for dimension to be the same. This is a bit long-winded, and there are certainly better ways to simulate these data (e.g. using a Dirichlet distribution), but the example serves a useful purpose here.
nMat <- 20
mort1 <- runif(nMat, 0, 1)
u1 <- runif(nMat, 0, 1 - mort1)
u2 <- 1 - mort1 - u1
mort2 <- runif(nMat, 0, 1)
u3 <- runif(nMat, 0, 1 - mort2)
u4 <- 1 - mort2 - u3
Uvals <- cbind(u1, u2, u3, u4)
Fvals <- rgamma(nMat, rep(1:4, each = 5))
Avals <- Uvals
Avals[, 3] <- Avals[, 3] + Fvals
Alist <- lapply(as.list(as.data.frame(t(Avals))), matrix,
byrow = FALSE,
nrow = 2, ncol = 2
)Next we use cdb_build_cdb() to convert this list of
matrices into a COMPADRE object. Here I am adding an identifier to each
matrix, and a column for the shape parameter for the Gamma distribution
used to simulate the data.
meta <- data.frame(idNum = 1:20, shapeParam = rep(1:4, each = 5))
x <- cdb_build_cdb(mat_a = Alist, metadata = meta)
#> Warning in cdb_build_cdb(mat_a = Alist, metadata = meta): Metadata does not include a `SpeciesAccepted` column, so number
#> of species not provided when viewing object.
x
#> A COM(P)ADRE database ('CompadreDB') object with ?? SPECIES and 20 MATRICES.
#>
#> # A tibble: 20 × 3
#> mat idNum shapeParam
#> <list> <int> <int>
#> 1 <CompdrMt> 1 1
#> 2 <CompdrMt> 2 1
#> 3 <CompdrMt> 3 1
#> 4 <CompdrMt> 4 1
#> 5 <CompdrMt> 5 1
#> 6 <CompdrMt> 6 2
#> 7 <CompdrMt> 7 2
#> 8 <CompdrMt> 8 2
#> 9 <CompdrMt> 9 2
#> 10 <CompdrMt> 10 2
#> 11 <CompdrMt> 11 3
#> 12 <CompdrMt> 12 3
#> 13 <CompdrMt> 13 3
#> 14 <CompdrMt> 14 3
#> 15 <CompdrMt> 15 3
#> 16 <CompdrMt> 16 4
#> 17 <CompdrMt> 17 4
#> 18 <CompdrMt> 18 4
#> 19 <CompdrMt> 19 4
#> 20 <CompdrMt> 20 4We can look at the matrices using the normal Rcompadre
function matA().
matA(x)[1]
#> [[1]]
#> [,1] [,2]
#> [1,] 0.2664835 0.81467771
#> [2,] 0.6527664 0.05590771Now the matrices are stored in a CompadreDB object they
can be manipulated in the same diverse ways as the
CompadreDB object downloaded from the COMPADRE/COMADRE
database.
For example, filtering based on part of the metadata, in this case,
shapeParam.
x %>%
filter(shapeParam > 2)
#> A COM(P)ADRE database ('CompadreDB') object with ?? SPECIES and 10 MATRICES.
#>
#> # A tibble: 10 × 3
#> mat idNum shapeParam
#> <list> <int> <int>
#> 1 <CompdrMt> 11 3
#> 2 <CompdrMt> 12 3
#> 3 <CompdrMt> 13 3
#> 4 <CompdrMt> 14 3
#> 5 <CompdrMt> 15 3
#> 6 <CompdrMt> 16 4
#> 7 <CompdrMt> 17 4
#> 8 <CompdrMt> 18 4
#> 9 <CompdrMt> 19 4
#> 10 <CompdrMt> 20 4Including stage descriptions and version information
In the above example, I did not include any information about the
stage definitions. Since these information were not provided,
cdb_build_cdb() automatically creates some information. You
can view that information like this (using square brackets to choose a
particular matrix model):
matrixClass(x)[1]
#> [[1]]
#> MatrixClassOrganized MatrixClassAuthor MatrixClassNumber
#> 1 active 1 1
#> 2 active 2 2In the following example I illustrate how one can include descriptions of the stages
First I create a data frame describing the matrix stages.
(stageDescriptor <- data.frame(
MatrixClassOrganized = rep("active", 2),
MatrixClassAuthor = c("small", "large"),
stringsAsFactors = FALSE
))
#> MatrixClassOrganized MatrixClassAuthor
#> 1 active small
#> 2 active largeIn this case, all stages are the same and I can simply repeat the
stageDescriptor in a list. However, the size of these data
frames, and the information within them may vary.
y <- cdb_build_cdb(
mat_a = Alist, metadata = meta, stages = stageDesc,
version = "Matrices Rock!"
)
#> Warning in cdb_build_cdb(mat_a = Alist, metadata = meta, stages = stageDesc, : Metadata does not include a `SpeciesAccepted` column, so number
#> of species not provided when viewing object.Now you can access the stage/class description information like this, using square brackets to find the information for particular matrices.
matrixClass(y)[5]
#> [[1]]
#> MatrixClassOrganized MatrixClassAuthor MatrixClassNumber
#> 1 active small 1
#> 2 active large 2
MatrixClassAuthor(y)[5]
#> [[1]]
#> [1] "small" "large"You can also obtain the version information.
Version(y)
#> [1] "Matrices Rock!"
DateCreated(y)
#> [1] "2024-10-16"The newly-created database can be saved like this:
save(y, "myMatrixDatabase.Rdata")References
Caswell, H. (2001). Matrix Population Models: Construction, Analysis, and Interpretation. 2nd edition. Sinauer Associates, Sunderland, MA. ISBN-10: 0878930965
Salguero-Gomez, R. , Jones, O. R., Archer, C. R., Buckley, Y. M., Che-Castaldo, J. , Caswell, H. , Hodgson, D. , Scheuerlein, A. , Conde, D. A., Brinks, E. , Buhr, H. , Farack, C. , Gottschalk, F. , Hartmann, A. , Henning, A. , Hoppe, G. , Romer, G. , Runge, J. , Ruoff, T. , Wille, J. , Zeh, S. , Davison, R. , Vieregg, D. , Baudisch, A. , Altwegg, R. , Colchero, F. , Dong, M. , Kroon, H. , Lebreton, J. , Metcalf, C. J., Neel, M. M., Parker, I. M., Takada, T. , Valverde, T. , Velez-Espino, L. A., Wardle, G. M., Franco, M. and Vaupel, J. W. (2015), The COMPADRE Plant Matrix Database: an open online repository for plant demography. J Ecol, 103: 202-218. <:10.1111/1365-2745.12334>
Salguero-Gomez, R. , Jones, O. R., Archer, C. R., Bein, C. , Buhr, H. , Farack, C. , Gottschalk, F. , Hartmann, A. , Henning, A. , Hoppe, G. , Romer, G. , Ruoff, T. , Sommer, V. , Wille, J. , Voigt, J. , Zeh, S. , Vieregg, D. , Buckley, Y. M., Che-Castaldo, J. , Hodgson, D. , Scheuerlein, A. , Caswell, H. and Vaupel, J. W. (2016), COMADRE: a global data base of animal demography. J Anim Ecol, 85: 371-384. <:10.1111/1365-2656.12482>