The 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
This vignette illustrates some simple use cases.
Before illustrating the construction of a
CompadreDB object using
cdb_build_cdb() is it first necessary to outline the anatomy of the object.
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
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-Gómez 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).
cdb_build_cdb()will automatically populate the U, F and c matrices with
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.
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
data.frames (one for each MPM).
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.
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
First we need to load the library, and the
In 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 x 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 4
We can look at the matrices using the normal
matA(x) #> [] #> [,1] [,2] #> [1,] 0.2953572 1.36111807 #> [2,] 0.3413577 0.05430518
Now 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,
x %>% filter(shapeParam > 2) #> A COM(P)ADRE database ('CompadreDB') object with ?? SPECIES and 10 MATRICES. #> #> # A tibble: 10 x 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 4
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) #> [] #> MatrixClassOrganized MatrixClassAuthor MatrixClassNumber #> 1 active 1 1 #> 2 active 2 2
In 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") )) #> MatrixClassOrganized MatrixClassAuthor #> 1 active small #> 2 active large
In 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) #> [] #> MatrixClassOrganized MatrixClassAuthor MatrixClassNumber #> 1 active small 1 #> 2 active large 2 MatrixClassAuthor(y) #> [] #>  "small" "large"
You can also obtain the version information.
The newly-created database can be saved like this:
Caswell, H. (2001). Matrix Population Models: Construction, Analysis, and Interpretation. 2nd edition. Sinauer Associates, Sunderland, MA. ISBN-10: 0878930965
Salguero‐Gómez, 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. , Römer, 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. , Vélez‐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‐Gómez, R. , Jones, O. R., Archer, C. R., Bein, C. , Buhr, H. , Farack, C. , Gottschalk, F. , Hartmann, A. , Henning, A. , Hoppe, G. , Römer, 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>