DAISIE_MW_ML | R Documentation |
This function computes the maximum likelihood estimates of the parameters of the relationships between parameters of the DAISIE model (with clade-specific diversity-dependence) and island area and distance of the island to the mainland for data from lineages colonizing several islands/archipelagos. It also outputs the corresponding loglikelihood that can be used in model comparisons.
A note on the sigmoidal functions used in distance_dep: For anagenesis and cladogenesis, the functional relationship is k * (d/d0)^x/(1 + (d/d0)^x); for colonization the relationship is: k - k * (d/d0)^x/(1 + (d/d0)^x). The d0 parameter is the 11th parameter entered. In 'sigmoidal_col_ana', the 11th parameter is the d0 for colonization and the 12th is the d0 for anagenesis.
DAISIE_MW_ML(
datalist,
initparsopt,
idparsopt,
parsfix,
idparsfix,
res = 100,
ddmodel = 11,
cond = 0,
island_ontogeny = NA,
tol = c(1e-04, 1e-05, 1e-07),
maxiter = 1000 * round((1.25)^length(idparsopt)),
methode = "lsodes",
optimmethod = "subplex",
CS_version = 1,
verbose = 0,
tolint = c(1e-16, 1e-10),
distance_type = "continent",
distance_dep = "power",
parallel = "local",
cpus = 3,
num_cycles = 1
)
datalist |
Data object containing information on colonisation and branching times of species for several islands or archipelagos, as well as the area, isolation and age of each of the islands/archipelagos. See data(archipelagos41) for an example. |
initparsopt |
The initial values of the parameters that must be optimized; they are all positive |
idparsopt |
The ids of the parameters that must be optimized. The ids
are defined as follows (see Valente et al 2020 Supplementary Tables 1 and 2
a better explanation of the models and parameters): |
parsfix |
The values of the parameters that should not be optimized |
idparsfix |
The ids of the parameters that should not be optimized, e.g. c(1,3) if lambda^c and K should not be optimized. |
res |
Sets the maximum number of species for which a probability must be computed, must be larger than the size of the largest clade |
ddmodel |
Sets the model of diversity-dependence: |
cond |
cond = 0 : conditioning on island age |
island_ontogeny |
type of island ontonogeny. If NA, then constant ontogeny is assumed |
tol |
Sets the tolerances in the optimization. Consists of: |
maxiter |
Sets the maximum number of iterations in the optimization |
methode |
Method of the ODE-solver. See package deSolve for details. Default is "lsodes" |
optimmethod |
Method used in likelihood optimization. Default is "subplex" (see subplex package). Alternative is 'simplex' which was the method in previous versions. |
CS_version |
a numeric or list. Default is 1 for the standard DAISIE model, for a relaxed-rate model a list with the following elements:
|
verbose |
sets whether parameters and likelihood should be printed (1) or not (0) |
tolint |
Vector of two elements containing the absolute and relative tolerance of the integration |
distance_type |
Use 'continent' if the distance to the continent should be used, use 'nearest_big' if the distance to the nearest big landmass should be used, and use 'biologically_realistic' if the distance should take into account some biologically realism, e.g. an average of the previous two if both are thought to contribute. |
distance_dep |
Sets what type of distance dependence should be used.
Default is a power law, denoted as 'power' (models M1-14 in Valente et al 2020).
Alternatives are additive or interactive contributions of distance and area
to the rate of cladogenesis ("area_additive_clado"; "area_interactive_clado",
"area_interactive_clado1" and "area_interactive_clado2"). Other alternatives are
exponential relationship denoted by 'exp'; or sigmoids, either
'sigmoidal_col' for a sigmoid in the colonization, 'sigmoidal_ana' for sigmoidal anagenesis,
'sigmoidal_clado' for sigmoidal cladogenesis, and 'sigmoidal_col_ana' for
sigmoids in both colonization and anagenesis. |
parallel |
Sets whether parallel computation should be used. Use 'no' if no parallel computing should be used, 'cluster' for parallel computing on a unix/linux cluster, and 'local' for parallel computation on a local machine. |
cpus |
Number of cpus used in parallel computing. Default is 3. Will not have an effect if parallel = 'no'. |
num_cycles |
The number of cycles the optimizer will go through. Default is 1. |
The output is a dataframe containing estimated parameters and maximum loglikelihood.
lambda_c0 |
gives the maximum likelihood estimate of lambda^c, the rate of cladogenesis for unit area |
y |
gives the maximum likelihood estimate of y, the exponent of area for the rate of cladogenesis |
mu0 |
gives the maximum likelihood estimate of mu0, the extinction rate |
x |
gives the maximum likelihood estimate of x, the exponent of 1/area for the extinction rate |
K0 |
gives the maximum likelihood estimate of K0, the carrying-capacity for unit area |
z |
gives the maximum likelihood estimate of z, the exponent of area for the carrying capacity |
gamma0 |
gives the maximum likelihood estimate of gamma0, the immigration rate for unit distance |
y |
gives the maximum likelihood estimate of alpha, the exponent of 1/distance for the rate of colonization |
lambda_a0 |
gives the maximum likelihood estimate of lambda^a0, the rate of anagenesis for unit distance |
beta |
gives the maximum likelihood estimate of beta, the exponent of 1/distance for the rate of anagenesis |
loglik |
gives the maximum loglikelihood |
df |
gives the number of estimated parameters, i.e. degrees of feedom |
conv |
gives a message on convergence of optimization; conv = 0 means convergence |
Rampal S. Etienne & Luis Valente
Valente L, Phillimore AB, Melo M, Warren BH, Clegg SM, Havenstein K, Tiedemann R, Illera JC, Thébaud C, Aschenbach T, Etienne RS. A simple dynamic model explains island bird diversity worldwide (2020) Nature, 579, 92-96
DAISIE_ML_CS
,
cat("
### Fit the M19 model as in Valente et al 2020, using the ML
parameters as starting values (see Supplementary Tables 1 and 2).
utils::data(archipelagos41)
DAISIE_MW_ML(
datalist= archipelagos41,
initparsopt =
c(0.040073803, 1.945656546, 0.150429656,
67.25643672, 0.293635061, 0.059096872, 0.382688527,
0.026510781),
idparsopt = c(1,3,4,7,8,9,10,11),
parsfix = c(0,Inf,0) ,
idparsfix = c(2,5,6),
res = 100,
ddmodel = 0,
methode = 'lsodes',
cpus = 4,
parallel = 'local',
optimmethod = 'subplex',
tol = c(1E-4, 1E-5, 1E-7),
distance_type = 'continent',
distance_dep = 'area_interactive_clado'
)
")
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