Calculate a population-specific mean fecundity matrix for each set of matrices in a COM(P)ADRE database
Source:R/cdb_mean_matF.R
cdb_mean_matF.RdTakes a CompadreDB object and calculates a grand mean fecundity matrix for
each unique population (a mean of all population-specific fecundity matrices,
including fecundity matrices for which MatrixComposite == 'Mean').
Populations are defined based on unique combinations of the columns 'SpeciesAuthor', 'MatrixPopulation', and 'MatrixDimension', (or optionally, a different set of columns supplied by the user).
The main purpose of this function is to identify stage classes that are potentially reproductive (i.e. the absence of fecundity in a given stage class and year does not necessarily indicate that the stage in question is non-reproductive).
Usage
cdb_mean_matF(
cdb,
columns = c("SpeciesAuthor", "MatrixPopulation", "MatrixDimension")
)Value
Returns a list of matrices, representing the mean fecundity matrix associated with each row of the database.
See also
Other data management:
cdb_flatten(),
cdb_id_stages(),
cdb_id_studies(),
cdb_id(),
cdb_rbind(),
cdb_unflatten(),
cdb_unnest(),
mpm_elementwise_apply(),
mpm_mean(),
mpm_median(),
mpm_sd(),
string_representation
Author
Owen R. Jones <jones@biology.sdu.dk>
Danny Buss <dlb50@cam.ac.uk>
Julia Jones <juliajones@biology.sdu.dk>
Iain Stott <stott@biology.sdu.dk>
Patrick Barks <patrick.barks@gmail.com>
Examples
# print matF associated with row 16 of database
Compadre$mat[[16]]
#> MatrixClassOrganized
#> 1 prop
#> 2 active
#> 3 active
#> 4 active
#> 5 active
#> 6 active
#> 7 active
#> MatrixClassAuthor
#> 1 Seeds in the seedbank
#> 2 Seedling: <1 year old
#> 3 Small saplings: ≤ 0,072 cm²
#> 4 Medium saplings: 0,072 < d ≤ 0,787 cm²
#> 5 Large saplings: 0,787 < d ≤ 19,643 cm²
#> 6 Small adults: (with reproduction) 19,643 < d ≤ 63,643 cm²
#> 7 Large adults: (with reproduction) > 63,643 cm²
#>
#> matA:
#> 1 2 3 4 5 6 7
#> 1 0.586 0.000 0.000 0.000 0.000 73.25 87.800
#> 2 0.015 0.000 0.000 0.000 0.000 1.24 1.490
#> 3 0.000 0.384 0.411 0.063 0.000 0.00 0.000
#> 4 0.000 0.384 0.429 0.785 0.000 0.00 0.000
#> 5 0.000 0.000 0.000 0.090 0.923 0.00 0.000
#> 6 0.000 0.000 0.000 0.000 0.020 0.88 0.000
#> 7 0.000 0.000 0.000 0.000 0.000 0.02 0.962
#>
#> matU:
#> 1 2 3 4 5 6 7
#> 1 0.586 0.000 0.000 0.000 0.000 0.00 0.000
#> 2 0.015 0.000 0.000 0.000 0.000 0.00 0.000
#> 3 0.000 0.384 0.411 0.063 0.000 0.00 0.000
#> 4 0.000 0.384 0.429 0.785 0.000 0.00 0.000
#> 5 0.000 0.000 0.000 0.090 0.923 0.00 0.000
#> 6 0.000 0.000 0.000 0.000 0.020 0.88 0.000
#> 7 0.000 0.000 0.000 0.000 0.000 0.02 0.962
#>
#> matF:
#> 1 2 3 4 5 6 7
#> 1 0 0 0 0 0 73.25 87.80
#> 2 0 0 0 0 0 1.24 1.49
#> 3 0 0 0 0 0 0.00 0.00
#> 4 0 0 0 0 0 0.00 0.00
#> 5 0 0 0 0 0 0.00 0.00
#> 6 0 0 0 0 0 0.00 0.00
#> 7 0 0 0 0 0 0.00 0.00
#>
#> matC:
#> 1 2 3 4 5 6 7
#> 1 0 0 0 0 0 0 0
#> 2 0 0 0 0 0 0 0
#> 3 0 0 0 0 0 0 0
#> 4 0 0 0 0 0 0 0
#> 5 0 0 0 0 0 0 0
#> 6 0 0 0 0 0 0 0
#> 7 0 0 0 0 0 0 0
#>
# create list of meanMatFs
meanF <- cdb_mean_matF(Compadre)
# print meanMatF associated with row 16 of database
meanF[[16]]
#> F1 F2 F3 F4 F5 F6 F7
#> F1 0 0 0 0 0 77.316667 261.666667
#> F2 0 0 0 0 0 1.306667 4.196667
#> F3 0 0 0 0 0 0.000000 0.000000
#> F4 0 0 0 0 0 0.000000 0.000000
#> F5 0 0 0 0 0 0.000000 0.000000
#> F6 0 0 0 0 0 0.000000 0.000000
#> F7 0 0 0 0 0 0.000000 0.000000