| cxr_er_fit {cxr} | R Documentation |
General optimization for effect-response models
Description
Estimates parameters of user-specified models of competitive effects and responses. NOTE: including covariates on competitive effects is still under development, in this version it is suggested not to use that feature.
Usage
cxr_er_fit(
data,
model_family = c("BH"),
covariates = NULL,
optimization_method = c("Nelder-Mead", "BFGS", "CG", "ucminf", "L-BFGS-B", "nlm",
"nlminb", "Rcgmin", "Rvmmin", "spg", "bobyqa", "nmkb", "hjkb", "nloptr_CRS2_LM",
"nloptr_ISRES", "nloptr_DIRECT_L_RAND", "DEoptimR", "GenSA"),
lambda_cov_form = c("none", "global"),
effect_cov_form = c("none", "global"),
response_cov_form = c("none", "global"),
initial_values = list(lambda = 1, effect = 1, response = 1, lambda_cov = 0, effect_cov
= 0, response_cov = 0),
lower_bounds = NULL,
upper_bounds = NULL,
fixed_terms = NULL,
bootstrap_samples = 0
)
Arguments
data |
either a list of dataframes or a single dataframe. if 'data' is a list, each element is a dataframe with the following columns:
If 'data' is a dataframe, it also needs a 'focal' column. Regardless of the data structure, all focal species need to have the same number of observations (i.e. same number of rows), and the set of neighbour species needs to be the same as the set of focal species, so that the neighbours columns correspond to the names of the list elements or, if 'data' is a dataframe, to the values of the 'focal' column. Future versions will relax this requirement. |
model_family |
family of model to use. Available families are BH (Beverton-Holt), LV (Lotka-Volterra), RK (Ricker), and LW (Law-Watkinson). Users may also define their own families and models (see vignette 4). |
covariates |
a data structure equivalent to 'data', in which each column are the values of a covariate. |
optimization_method |
numerical optimization method. |
lambda_cov_form |
form of the covariate effects on lambda. Either "none" (no covariate effects) or "global" (one estimate per covariate). |
effect_cov_form |
form of the covariate effects on competitive effects. Either "none" (no covariate effects) or "global" (one estimate per covariate) |
response_cov_form |
form of the covariate effects on competitive responses. Either "none" (no covariate effects) or "global" (one estimate per covariate) |
initial_values |
list with components "lambda","effect","response", and optionally "lambda_cov", "effect_cov", "response_cov", specifying the initial values for numerical optimization. Single values are allowed. |
lower_bounds |
optional list with single values for "lambda", "effect","response", and optionally "lambda_cov", "effect_cov", "response_cov". |
upper_bounds |
optional list with single values for "lambda", "effect","response", and optionally "lambda_cov", "effect_cov", "response_cov". |
fixed_terms |
optional list specifying which model parameters are fixed. |
bootstrap_samples |
number of bootstrap samples for error calculation. Defaults to 0, i.e. no error is calculated. |
Value
an object of class 'cxr_er_fit' which is a list with the following components:
model_name: string with the name of the fitness model
model: model function
data: data supplied
taxa: names of the taxa fitted
covariates: covariate data supplied
optimization_method: optimization method used
initial_values: list with initial values
fixed_terms: list with fixed terms
lambda: fitted values for lambdas, or NULL if fixed
effect: fitted values for competitive effects, or NULL if fixed
response: fitted values for competitive responses, or NULL if fixed
lambda_cov: fitted values for effect of covariates on lambdas, or NULL if fixed
effect_cov: fitted values for effect of covariates on competitive effects, or NULL if fixed
response_cov: fitted values for effect of covariates on competitive responses, or NULL if fixed
lambda_standard_error: standard errors for lambdas, if calculated
effect_standard_error: standard errors for competitive effects, if calculated
response_standard_error: standard errors for competitive responses, if calculated
lambda_cov_standard_error: standard errors for effect of covariates on lambdas, if calculated
effect_cov_standard_error: standard errors for effect of covariates on competitive effects, if calculated
response_cov_standard_error: standard errors for effect of covariates on competitive responses, if calculated
log_likelihood: log-likelihood of the fits
Examples
# fit three species at once
data("neigh_list")
# these species all have >250 observations
example_sp <- c("BEMA","LEMA","HOMA")
sp.pos <- which(names(neigh_list) %in% example_sp)
data <- neigh_list[sp.pos]
n.obs <- 250
# keep only fitness and neighbours columns
for(i in 1:length(data)){
data[[i]] <- data[[i]][1:n.obs,c(2,sp.pos+2)]#2:length(data[[i]])]
}
# covariates: salinity
data("salinity_list")
salinity <- salinity_list[example_sp]
# keep only salinity column
for(i in 1:length(salinity)){
salinity[[i]] <- salinity[[i]][1:n.obs,2:length(salinity[[i]])]
}
initial_values = list(lambda = 1,
effect = 1,
response = 1
# lambda_cov = 0,
# effect_cov = 0,
# response_cov = 0
)
lower_bounds = list(lambda = 0,
effect = 0,
response = 0
# lambda_cov = 0,
# effect_cov = 0,
# response_cov = 0
)
upper_bounds = list(lambda = 100,
effect = 10,
response = 10
# lambda_cov = 0,
# effect_cov = 0,
# response_cov = 0
)
er_3sp <- cxr_er_fit(data = data,
model_family = "BH",
# fit without covariates,
# as it may be very computationally expensive
# covariates = salinity,
optimization_method = "bobyqa",
lambda_cov_form = "none",
effect_cov_form = "none",
response_cov_form = "none",
initial_values = initial_values,
lower_bounds = lower_bounds,
upper_bounds = upper_bounds,
# syntaxis for fixed values
# fixed_terms = list("response"),
bootstrap_samples = 3)
# brief summary
summary(er_3sp)