Nothing
R/sar_average.R
In sars: Fit and Compare Species-Area Relationship Models Using Multimodel Inference
Defines functions
sar_pred
sar_average
sar_multi
display_sars_models
sars_models
Documented in
display_sars_models
sar_average
sar_multi
sar_pred
sars_models
#' Display the 21 SAR model names
#'
#' @description Display the 21 SAR model names as a vector. See
#' \code{\link{sar_multi}} for further information.
#' @usage sars_models()
#' @note sar_mmf is included here for now but has been deprecated (see News)
#' @return A vector of model names.
#' @export
sars_models <- function() {
c("power","powerR","epm1","epm2","p1","p2","loga","koba","mmf",
"monod","negexpo","chapman","weibull3","asymp","ratio",
"gompertz","weibull4","betap","logistic","heleg","linear")
}
#NB - Table1 is stored in sysdata.rda
#' Display the model information table
#'
#' @description Display Table 1 of Matthews et al. (2019). See
#' \code{\link{sar_multi}} for further information.
#' @usage display_sars_models()
#' @return A table of model information for 21 SAR models, including the model
#' function, number of parameters and general model shape. This includes the
#' 20 models in Matthews et al. (2019); however, note that the mmf model has
#' now been deprecated, and the standard logistic model listed in Tjorve
#' (2003) added instead. Note also, an error in the Chapman Richards model
#' equation has now been corrected, and the shape of some of the models have
#' been updated from sigmoid to convex/sigmoid.
#' @references Matthews et al. (2019) sars: an R package for fitting, evaluating
#' and comparing species–area relationship models. Ecography, 42, 1446-1455.
#'
#' Tjørve, E. (2003) Shapes and functions of species–area curves: a review of
#' possible models. Journal of Biogeography, 30, 827-835.
#' @export
display_sars_models <- function() {
# display table 1 of the manuscript
print(Table1)
}
#' Create a Collection of SAR Model Fits
#'
#' @description Creates a fit collection of SAR model fits, which can then be
#' plotted using \code{\link{plot.sars}}.
#' @usage sar_multi(data, obj = c("power",
#' "powerR","epm1","epm2","p1","p2","loga","koba",
#' "monod","negexpo","chapman","weibull3","asymp",
#' "ratio","gompertz","weibull4","betap","logistic","heleg","linear"),
#' normaTest = "none", homoTest = "none", homoCor = "spearman", grid_start =
#' "partial", grid_n = NULL, verb = TRUE, display = TRUE)
#' @param data A dataset in the form of a dataframe with two columns: the first
#' with island/site areas, and the second with the species richness of each
#' island/site.
#' @param obj A vector of model names.
#' @param normaTest The test used to test the normality of the residuals of each
#' model. Can be any of "lillie" (Lilliefors Kolmogorov-Smirnov test),
#' "shapiro" (Shapiro-Wilk test of normality), "kolmo" (Kolmogorov-Smirnov
#' test), or "none" (no residuals normality test is undertaken; the default).
#' @param homoTest The test used to check for homogeneity of the residuals of
#' each model. Can be any of "cor.fitted" (a correlation of the squared
#' residuals with the model fitted values), "cor.area" (a correlation of the
#' squared residuals with the area values), or "none" (no residuals
#' homogeneity test is undertaken; the default).
#' @param homoCor The correlation test to be used when \code{homoTest !=
#' "none"}. Can be any of "spearman" (the default), "pearson", or "kendall".
#' @param grid_start Should a grid search procedure be implemented to test
#' multiple starting parameter values. Can be one of 'none', 'partial' or
#' 'exhaustive' The default is set to 'partial'.
#' @param grid_n If \code{grid_start = exhaustive}, the number of points sampled
#' in the starting parameter space (see details).
#' @param verb verbose - Whether or not to print certain warnings
#' (default: \code{verb == TRUE}).
#' @param display Show the model fitting output and related messages.
#' (default: \code{display == TRUE}).
#' @details The \code{sar_models()} function can be used to bring up a list of
#' the 20 model names. \code{display_sars_models()} generates a table of the
#' 20 models with model information.
#' @return A list of class 'sars' with n elements, corresponding to the n
#' individual SAR model fits.
#' @importFrom cli rule symbol
#' @importFrom crayon bold col_align cyan green red yellow
#' @examples
#' data(galap)
#' # construct a fit_collection object of 3 SAR model fits
#' fit2 <- sar_multi(galap, obj = c("power", "loga", "linear"))
#' plot(fit2)
#'
#' # construct a fit_collection object of all 20 SAR model fits
#' # using no grid_start for speed
#' fit3 <- sar_multi(galap, grid_start = "none")
#'
#' @export
sar_multi <- function(data,
obj = c("power", "powerR","epm1","epm2","p1","p2",
"loga","koba","monod","negexpo",
"chapman","weibull3","asymp","ratio",
"gompertz", "weibull4","betap","logistic","heleg",
"linear"),
normaTest = "none",
homoTest = "none",
homoCor = "spearman",
grid_start = "partial",
grid_n = NULL,
verb = TRUE,
display = TRUE){
if ("mmf" %in% obj){
warning("mmf has been deprecated, see News")
}
if (!((is.character(obj)) | (inherits(obj, "sars"))))
stop("obj must be of class character or sars")
if (nrow(data) < 4)
stop("Multi SAR needs at least four data points")
if (nrow(data) == 4 & normaTest == "lillie")
stop("The Lilliefors test cannot be performed with less than 5 data",
" points")
if (is.character(obj) & is.null(data))
stop("if obj is character then data should be provided")
if (is.character(obj)) {
if (any(!(obj %in% c("linear","power","powerR","epm1","epm2","p1",
"p2","loga","koba","mmf","monod","negexpo",
"chapman","weibull3","asymp","ratio","gompertz",
"weibull4","betap","heleg","logistic"))))
stop("provided model names do not match with model functions")
}
if (!(grid_start %in% c("none", "partial", "exhaustive"))){
stop("grid_start should be one of 'none', 'partial' or 'exhaustive'")
}
if (grid_start == "exhaustive"){
if (!is.numeric(grid_n))
stop("grid_n should be numeric if grid_start == exhaustive")
}
if (!is.logical(verb) | !is.logical(display)){
stop('verb / display should be logical')
}
if (length(obj) < 2)
stop("more than 1 fit is required to use sar_multi")
normaTest <- match.arg(normaTest, c("none", "shapiro",
"kolmo", "lillie"))
homoTest <- match.arg(homoTest, c("none","cor.area",
"cor.fitted"))
if (homoTest != "none"){
homoCor <- match.arg(homoCor, c("spearman","pearson",
"kendall"))
}
#if (verb) cat_line(rule(left = paste0(cyan(symbol$bullet),
#bold(" multi_sars")),right="multi-model SAR"))
if (display & is.character(obj)) {
cat("\n", paste("Now attempting to fit the",
length(obj), "SAR models:"), "\n\n")
cat_line(rule(left = bold(" multi_sars"),
right="multi-model SAR"))
}
#if (verb) cat_line(magenta(symbol$arrow_right)," Data set is: ")
#if (verb) cat_line(rule(left = paste0(magenta(symbol$bullet))))
#if (verb) bullet("O | S : model", bullet = blue_arrow())
#if not yet fitted, fit the models to the datasquare_small_filled
if (!is.character(obj))
stop("obj should be a character vector of model names")
mods <- paste0("sar_",obj)
names(mods) <- obj
fits <- lapply(obj, function(x){
#have to do separately because linear does not have grid_start
if (x == "linear"){
f <- eval(parse(text = paste0(mods[x],
"(data", ", normaTest = ",
paste0("'", normaTest, "'"),
", homoTest = ", paste0("'", homoTest,
"'"),
", homoCor = ", paste0("'", homoCor,
"'"),
", verb = ", paste0(verb),")")))
} else{ #if grid_start provided, implement it (for non-linear models)
f <- eval(parse(text = paste0(mods[x],
"(data", ", normaTest = ",
paste0("'", normaTest, "'"),
", homoTest = ", paste0("'", homoTest,
"'"),
", homoCor = ", paste0("'", homoCor,
"'"),
", grid_start = ",
paste0("'",grid_start,"'"), ", grid_n = ",
paste0(grid_n),
", verb = ",paste0(verb),")")))
}
if (display) {
if(is.na(f$value)) {
cat_line(paste0(red(symbol$arrow_right)," ",
col_align(x,max(nchar(obj)))," : ",
red(symbol$cross)))
}else{
if (!is.matrix(f$sigConf)){
cat_line( paste0(yellow(symbol$arrow_right)," ",
col_align(x,max(nchar(obj))),
" : Warning: could not compute parameters statistics"))
}else{
cat_line( paste0(cyan(symbol$arrow_right)," ",
col_align(x,max(nchar(obj)))," : ",
green(symbol$tick)))
}
}
}
f
})#eo suppressWarnings(lapply)
names(fits) <- obj
class(fits) <- "sars"
attr(fits, "type") <- "fit_collection"
return(fits)
}#end of multi_sars
#' Fit a multimodel averaged SAR curve
#'
#' @description Construct a multimodel averaged species-area relationship curve
#' using information criterion weights and up to twenty SAR models.
#' @usage sar_average(obj = c("power",
#' "powerR","epm1","epm2","p1","p2","loga","koba",
#' "monod","negexpo","chapman","weibull3","asymp",
#' "ratio","gompertz","weibull4","betap","logistic", "heleg", "linear"), data =
#' NULL, crit = "Info", normaTest = "none", homoTest = "none", homoCor =
#' "spearman", neg_check = FALSE, alpha_normtest = 0.05, alpha_homotest =
#' 0.05, grid_start = "partial", grid_n = NULL, confInt = FALSE, ciN = 100,
#' verb = TRUE, display = TRUE)
#' @param obj Either a vector of model names or a fit_collection object created
#' using \code{\link{sar_multi}}. If a vector of names is provided,
#' \code{sar_average} first calls \code{sar_multi} before generating the
#' averaged multimodel curve.
#' @param data A dataset in the form of a dataframe with two columns: the first
#' with island/site areas, and the second with the species richness of each
#' island/site. If \code{obj} is a fit_collection object, \code{data} should
#' be NULL.
#' @param crit The criterion used to compare models and compute the model
#' weights. The default \code{crit = "Info"} switches to AIC or AICc depending
#' on the number of data points in the dataset. AIC (\code{crit = "AIC"}) or
#' AICc (\code{crit = "AICc"}) can be chosen regardless of the sample size. For
#' BIC, use \code{crit ="Bayes"}.
#' @param normaTest The test used to test the normality of the residuals of each
#' model. Can be any of "lillie" (Lilliefors Kolmogorov-Smirnov test),
#' "shapiro" (Shapiro-Wilk test of normality), "kolmo" (Kolmogorov-Smirnov
#' test), or "none" (no residuals normality test is undertaken; the default).
#' @param homoTest The test used to check for homogeneity of the residuals of
#' each model. Can be any of "cor.fitted" (a correlation of the squared
#' residuals with the model fitted values), "cor.area" (a correlation of the
#' squared residuals with the area values), or "none" (no residuals
#' homogeneity test is undertaken; the default).
#' @param homoCor The correlation test to be used when \code{homoTest !=
#' "none"}. Can be any of "spearman" (the default), "pearson", or "kendall".
#' @param neg_check Whether or not a check should be undertaken to flag any
#' models that predict negative richness values.
#' @param alpha_normtest The alpha value used in the residual normality test
#' (default = 0.05, i.e. any test with a P value < 0.05 is flagged as failing
#' the test).
#' @param alpha_homotest The alpha value used in the residual homogeneity test
#' (default = 0.05, i.e. any test with a P value < 0.05 is flagged as failing
#' the test).
#' @param grid_start Should a grid search procedure be implemented to test
#' multiple starting parameter values. Can be one of 'none', 'partial' or
#' 'exhaustive' The default is set to 'partial'.
#' @param grid_n If \code{grid_start = exhaustive}, the number of points sampled
#' in the starting parameter space (see details).
#' @param confInt A logical argument specifying whether confidence intervals
#' should be calculated for the multimodel curve using bootstrapping.
#' @param ciN The number of bootstrap samples to be drawn to calculate the
#' confidence intervals (if \code{confInt == TRUE}).
#' @param verb verbose - Whether or not to print certain warnings
#' (default: \code{verb == TRUE})
#' @param display Show the model fitting output and related messages.
#' (default: \code{display == TRUE}).
#' @details The multimodel SAR curve is constructed using information criterion
#' weights (see Burnham & Anderson, 2002; Guilhaumon et al. 2010). If
#' \code{obj} is a vector of n model names the function fits the n models to
#' the dataset provided using the \code{sar_multi} function. A dataset must
#' have four or more datapoints to fit the multimodel curve. If any models
#' cannot be fitted they are removed from the multimodel SAR. If \code{obj} is
#' a fit_collection object (created using the \code{sar_multi} function), any
#' model fits in the collection which are NA are removed. In addition, if any
#' other model checks have been selected (i.e. residual normality and
#' heterogeneity tests, and checks for negative predicted richness values),
#' these are undertaken and any model that fails the selected test(s) is
#' removed from the multimodel SAR. The order of the additional checks inside
#' the function is (if all are turned on): normality of residuals, homogeneity
#' of residuals, and a check for negative fitted values. Once a model fails
#' one test it is removed and thus is not available for further tests. Thus, a
#' model may fail multiple tests but the returned warning will only provide
#' information on a single test. We have now changed the defaults so that
#' no checks are undertaken, so it is up to the user to select any checks if
#' appropriate.
#'
#' The resultant models are then used to construct the multimodel SAR curve.
#' For each model in turn, the model fitted values are multiplied by the
#' information criterion weight of that model, and the resultant values are
#' summed across all models (Burnham & Anderson, 2002). Confidence intervals
#' can be calculated (using \code{confInt}) around the multimodel averaged
#' curve using the bootstrap procedure outlined in Guilhaumon et al (2010).The
#' procedure transforms the residuals from the individual model fits and
#' occasionally NAs / Inf values can be produced - in these cases, the model
#' is removed from the confidence interval calculation (but not the multimodel
#' curve itself). There is also a constraint within the procedure to remove
#' any transformed residuals that result in negative richness values. When
#' several SAR models are used, when grid_start is turned on and when the
#' number of bootstraps (\code{ciN}) is large, generating the confidence
#' intervals can take a (very) long time. Parallel processing will be added to
#' future versions.
#'
#' Choosing starting parameter values for non-linear regression optimisation
#' algorithms is not always straight forward, depending on the data at hand.
#' In the package, we use various approaches to choose default starting
#' parameters. However, we also use a grid search process which creates a
#' large array of different possible starting parameter values (within certain
#' bounds) and then randomly selects a proportion of these to test. There are
#' three options for the \code{grid_start} argument to control this process.
#' The default (\code{grid_start = "partial"}) randomly samples 500 different
#' sets of starting parameter values for each model, adds these to the model's
#' default starting values and tests all of these. A more comprehensive set of
#' starting parameter estimates can be used (\code{grid_start = "exhaustive"})
#' - this option allows the user to choose the number of starting parameter
#' sets to be tested (using the \code{grid_n} argument) and includes a range
#' of additional starting parameter estimates, e.g. very small values and
#' particular values we have found to be useful for individual models. Using
#' \code{grid_start = "exhaustive"} in combination with a large \code{grid_n}
#' can be very time consuming; however, we would recommend it as it makes it
#' more likely that the optimal model fit will be found, particularly for the
#' more complex models. This is particularly true if any of the model fits
#' does not converge, returns a singular gradient at parameter estimates, or
#' the plot of the model fit does not look optimum. The grid start procedure
#' can also be turned off (\code{grid_start = "none"}), meaning just the
#' default starting parameter estimates are used. Note that \code{grid_start}
#' has been disabled for a small number of models (e.g. Weibull 3 par.). See
#' the vignette for more information. Remember that, as grid_start has a
#' random component, when \code{grid_start != "none"}, you can get slightly
#' different results each time you fit a model or run \code{sar_average}.
#'
#' Even with grid_start, occasionally a model fit will be able to be fitted
#' and pass the model fitting checks (e.g. residual normality) but the
#' resulting fit is nonsensical (e.g. a horizontal line with intercept at
#' zero). Thus, it can be useful to plot the resultant 'multi' object to check
#' the individual model fits. To re-run the \code{sar_average} function
#' without a particular model, simply remove it from the \code{obj} argument.
#'
#' The \code{sar_models()} function can be used to bring up a list of the 20
#' model names. \code{display_sars_models()} generates a table of the 20
#' models with model information.
#'
#' @return A list of class "multi" and class "sars" with two elements. The first
#' element ('mmi') contains the fitted values of the multimodel sar curve. The
#' second element ('details') is a list with the following components:
#' \itemize{ \item{mod_names} { Names of the models that were successfully
#' fitted and passed any model check} \item{fits} { A fit_collection object
#' containing the successful model fits} \item{ic} { The information criterion
#' selected} \item{norm_test} { The residual normality test selected}
#' \item{homo_test} { The residual homogeneity test selected}
#' \item{alpha_norm_test} { The alpha value used in the residual normality
#' test} \item{alpha_homo_test} { The alpha value used in the residual
#' homogeneity test} \item{ics} { The information criterion values (e.g. AIC
#' values) of the model fits} \item{delta_ics} { The delta information
#' criterion values} \item{weights_ics} { The information criterion weights of
#' each model fit} \item{n_points} { Number of data points} \item{n_mods} {
#' The number of successfully fitted models} \item{no_fit} { Names of the
#' models which could not be fitted or did not pass model checks}
#' \item{convergence} { Logical value indicating whether \code{\link{optim}}
#' model convergence code = 0, for each model} }
#'
#' The \code{\link{summary.sars}} function returns a more useful summary of
#' the model fit results, and the \code{\link{plot.multi}} plots the
#' multimodel curve.
#' @note There are different types of non-convergence and these are dealt with
#' differently in the package. If the optimisation algorithm fails to return
#' any solution, the model fit is defined as NA and is then removed, and so
#' does not appear in the model summary table or multi-model curve etc.
#' However, the optimisation algorithm (e.g. Nelder-Mead) can also return
#' non-NA model fits but where the solution is potentially non-optimal (e.g.
#' degeneracy of the Nelder–Mead simplex) - these cases are identified by any
#' \code{\link{optim}} convergence code that is not zero. We have decided not
#' to remove these fits (i.e. they are kept in the model summary table and
#' multimodel curve) - as arguably a non-optimal fit is still better than no
#' fit - but any instances can be checked using the returned
#' \code{details$converged} vector and then the model fitting re-run without
#' these models, if preferred. Increasing the starting parameters grid search
#' (see above) may also help avoid this issue.
#'
#' The generation of confidence intervals around the multimodel curve (using
#' \code{confInt == TRUE}), may throw up errors that we have yet to come
#' across. Please report any issues to the package maintainer.
#'
#' There are different formulas for calculating the various information
#' criteria (IC) used for model comparison (e.g. AIC, BIC). For example, some
#' formulas use the residual sum of squares (rss) and others the
#' log-likelihood (ll). Both are valid approaches and will give the same
#' parameter estimates, but it is important to only compare IC values that
#' have been calculated using the same approach. For example, the 'sars'
#' package used to use formulas based on the rss, while the \link[stats]{nls}
#' function function in the stats package uses formulas based on the ll. To
#' increase the compatibility between nls and sars, we have changed our
#' formulas such that now our IC formulas are the same as those used in the
#' \link[stats]{nls} function. See the "On the calculation of information
#' criteria" section in the package vignette for more information.
#'
#' The mmf model was found to be equivalent to the He & Legendre logistic, and
#' so the former has been deprecated (as of Feb 2021). We have removed it from
#' the default models in \code{sar_average}, although it is still available to
#' be used for the time being (using the \code{obj} argument). The standard
#' logistic model has been added in its place, and is now used as default
#' within \code{sar_average}.
#'
#' @references Burnham, K. P., & Anderson, D. R. (2002). Model selection and
#' multi-model inference: a practical information-theoretic approach (2nd
#' ed.). New-York: Springer.
#'
#' Guilhaumon, F., Mouillot, D., & Gimenez, O. (2010). mmSAR: an R-package for
#' multimodel species-area relationship inference. Ecography, 33, 420-424.
#'
#' Matthews, T. J., K. A. Triantis, R. J. Whittaker, & F. Guilhaumon. (2019).
#' sars: an R package for fitting, evaluating and comparing species–area
#' relationship models. Ecography, 42, 1446–55.
#' @examples
#' data(galap)
#' #attempt to construct a multimodel SAR curve using all twenty sar models
#' #using no grid_start just for speed here (not recommended generally)
#' fit <- sar_average(data = galap, grid_start = "none")
#' summary(fit)
#' plot(fit)
#'
#' # construct a multimodel SAR curve using a fit_collection object
#' ff <- sar_multi(galap, obj = c("power", "loga", "monod", "weibull3"))
#' fit2 <- sar_average(obj = ff, data = NULL)
#' summary(fit2)
#'
#' \dontrun{
#' # construct a multimodel SAR curve using a more exhaustive set of starting
#' # parameter values
#' fit3 <- sar_average(data = galap, grid_start = "exhaustive", grid_n = 1000)
#' }
#'
#' @export
sar_average <- function(obj = c("power", "powerR","epm1","epm2","p1","p2",
"loga","koba","monod","negexpo",
"chapman","weibull3","asymp","ratio",
"gompertz", "weibull4","betap","logistic",
"heleg", "linear"), data = NULL,
crit = "Info",
normaTest = "none",
homoTest = "none",
homoCor = "spearman",
neg_check = FALSE,
alpha_normtest = 0.05,
alpha_homotest = 0.05,
grid_start = "partial",
grid_n = NULL,
confInt = FALSE,
ciN = 100,
verb = TRUE,
display = TRUE){
if ("mmf" %in% obj){
warning("mmf has been deprecated, see News")
}
if (!((is.character(obj)) | (inherits(obj, "sars"))))
stop("obj must be of class character or sars")
if (is.character(obj) & is.null(data))
stop("if obj is character then data should be provided")
if (is.character(obj)) {
if (any(!(obj %in% c("linear","power","powerR","epm1","epm2","p1",
"p2","loga","koba","mmf","monod","negexpo",
"chapman","weibull3","asymp","ratio","gompertz",
"weibull4","betap","heleg","logistic"))))
stop("provided model names do not match with model functions")
}
if (!(grid_start %in% c("none", "partial", "exhaustive"))){
stop("grid_start should be one of 'none', 'partial' or 'exhaustive'")
}
if (grid_start == "exhaustive"){
if (!is.numeric(grid_n))
stop("grid_n should be numeric if grid_start == exhaustive")
}
if (!is.logical(verb) | !is.logical(display)){
stop('verb / display should be logical')
}
if (length(obj) < 2)
stop("more than 1 fit is required to construct a sar_multi")
if (display & grid_start != "none"){
cat("\nModels to be fitted using a grid start approach: \n")
}
#if a vector of names is provided, then call sar_multi first
if (is.character(obj)){
fits <- sar_multi(data = data, obj = obj,
normaTest = normaTest,
homoTest = homoTest,
homoCor = homoCor,
grid_start = grid_start,
grid_n = grid_n,
verb = verb,
display = display)
} else{
fits <- obj
}
#save the full list of models that the user wants to fit for use with
#the CI function
if (confInt) obj_all <- obj
#####BAD MODEL CHECKS#######################
normaTest <- match.arg(normaTest, c("none", "shapiro",
"kolmo", "lillie"))
homoTest <- match.arg(homoTest, c("none","cor.area",
"cor.fitted"))
if (homoTest != "none"){
homoCor <- match.arg(homoCor, c("spearman","pearson",
"kendall"))
}
#if obj = character vector, check the test names match up with
#those provide here. Doesn't actually matter but can be brought to
#attention of user
if (!is.character(obj)){
wn <- obj[[1]]$normaTest[[1]]
wh <- obj[[1]]$homoTest[[1]]
if (wn != normaTest) {
if (verb){
warning("normaTest argument does not match the
normaTest stored in the fit collection
object. The multi SAR curve will proceed
with the test used in the fit collection
object.")
}
normaTest <- wn
}
if (wh != homoTest){
if (verb){
warning("homoTest argument does not match the
homoTest stored in the fit collection
object. The multi SAR curve will proceed
with the test used in the fit collection
object.")
}
homoTest <- wh
}}
if (normaTest == "none") alpha_normtest <- "none"
if (homoTest == "none") alpha_homotest <- "none"
#NA CHECKS
f_nas <- unlist(lapply(fits,function(b)b$value))
if(all(is.na(f_nas))){
stop("No model could be fitted, aborting multi_sars\n")
}
badMods <- vector(length = 0, mode = "character")
if(any(is.na(f_nas))){
badNames <- is.na(f_nas)
if (verb){
message("\n", paste(sum(is.na(f_nas)),
"models could not be fitted and have been excluded",
" from the multi SAR"),
"\n")
}
badMods <- obj[badNames] #extract the bad model names from the obj
fits <- fits[!is.na(f_nas)]
}
bml <- length(badMods)
if ((normaTest != "none" | homoTest != "none" | neg_check) & display){
if (is.character(obj)){
if (any(is.na(f_nas))){
cat("\nModel fitting completed. Now undertaking model validation",
" checks. Additional models will be excluded if necessary:\n")
} else {
cat("\nModel fitting completed - all models succesfully fitted.",
"Now undertaking model validation checks. Additional models",
" will be excluded if necessary:\n")
}
} else {
cat("\nNow undertaking model validation checks. Additional models",
" will\nbe excluded if necessary\n")
}
}
#if checks for normality and / or homoscedasticity enabled, then check
#and remove bad fits from fits
if (normaTest != "none") {
np <- vapply(fits, function(x) x$normaTest[[2]]$p.value,
FUN.VALUE = numeric(1))
#sometimes bad models produce calculated values with all same
#richness values and no correlation can be done. Remove these
if (anyNA(np)){
wnn <- is.na(np)
mn <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(is.na(np)),"models returned NAs in the",
" residuals normality test and have been excluded",
" from the multi SAR:"),
"\n",
paste(mn[wnn], collapse = ", "), "\n")
}
badMods <- c(badMods, mn[wnn])#select the model names with NAs
fits <- fits[!wnn]
np <- np[!wnn]
}
whp <- np < alpha_normtest
if (any(whp)) {
mn2 <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(np < alpha_normtest),
"models failed the residuals normality test and",
" have been excluded",
" from the multi SAR:"), "\n",
paste(mn2[whp], collapse = ", "), "\n")
}
badMods <- c(badMods, mn2[whp])
fits <- fits[!whp]#then remove these models from the fit collection
}
}
if (homoTest != "none") {
hp <- vapply(fits, function(x) x$homoTest[[2]]$p.value,
FUN.VALUE = numeric(1))
#sometimes bad models produce calculated values with all same richness
#values and no correlation can be done. Remove these
if (anyNA(hp)){
whn <- is.na(hp)
mn3 <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(is.na(hp)),
"returned NAs in the residuals homogeneity test and have",
" been excluded from the multi SAR:"), "\n",
paste(mn3[whn], collapse = ", "), "\n")
}
badMods <- c(badMods, mn3[whn])#select the model names with NAs
fits <- fits[!whn]
hp <- hp[!whn]
}
whh <- hp < alpha_homotest
if (any(whh)) {
mn4 <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(hp < alpha_homotest),
"models failed the residuals homogeneity test and have been",
" excluded from the multi SAR:"), "\n",
paste(mn4[whh], collapse = ", "), "\n")
}
badMods <- c(badMods, mn4[whh])
fits <- fits[!whh]
}
}
#negative values
if (neg_check){
nc <- vapply(fits, function(x) any(x$calculated < 0),
FUN.VALUE = logical(1))
if (any(nc)) {
mn5 <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(nc), "models have negative fitted values and",
" have been excluded from the multi SAR:"), "\n",
paste(mn5[nc], collapse = ", "), "\n")
}
badMods <- c(badMods, mn5[nc])
fits <- fits[!nc]
}
}
if (length(badMods) == bml & display) {
if((normaTest == "none" & homoTest == "none" & !(neg_check))){
cat("\nNo model validation checks selected\n\n")
} else{
cat("\nAll models passed the model",
"validation checks\n\n")
}
}
if (length(badMods) == 0) badMods <- 0
if (length(fits) < 2) stop("Fewer than two models could be fitted and /",
" or passed the model checks")
sf <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (display) cat(length(sf), "remaining models used to construct the multi",
"SAR:\n",
paste(sf, collapse = ", "), "\n")
####################################
#setting variables
nPoints <- length(fits[[1]]$data$A)
nMods <- length(fits)
modNames <- vapply(fits, FUN = function(x){x$model$name},
FUN.VALUE = character(1))
#choosing an IC criterion (AIC or AICc or BIC)
IC <- switch(crit,
Info= if ( (nPoints / 3) < 40 ) { "AICc" } else { "AIC" },
AIC = "AIC",
AICc = "AICc",
Bayes = "BIC")
#get ICs
ICs <- vapply(X = fits, FUN = function(x){x[[IC]]}, FUN.VALUE = double(1))
#get delta ICs
delta_ICs <- ICs - min(ICs)
#get akaike weights
akaikesum <- sum(exp( -0.5*(delta_ICs)))
weights_ICs <- exp(-0.5*delta_ICs) / akaikesum
#ERROR: produce weight averaged diversity measures
# mmi <- vapply(fits,FUN=function(x){x$calculated},FUN.VALUE=double(nPoints))
# mmi <- apply((mmi * weights_ICs), 1 , sum)
#produce weight averaged diversity measures
mmi <- vapply(fits,FUN=function(x){x$calculated},FUN.VALUE=double(nPoints))
wm <- matrix(nrow = nPoints, ncol = length(fits))
for (i in seq_along(weights_ICs)) {wm[ ,i] <- mmi[ ,i] * weights_ICs[i]}
mmi <- apply(wm, 1 , sum)
res <- mmi
#get convergence info (of models that have passed checks)
conv <- vapply(fits, FUN=function(x){x$verge}, FUN.VALUE = logical(1))
details <- list(
mod_names = modNames,
fits = fits,
crit = crit,
ic = IC,
norm_test = normaTest,
homo_test = homoTest,
alpha_norm_test = alpha_normtest,
alpha_homo_test = alpha_homotest,
ics = ICs,
delta_ics = delta_ICs,
weights_ics = weights_ICs,
n_points = nPoints,
n_mods = nMods,
no_fit = as.vector(badMods),
convergence = conv
)
res <- list(mmi = mmi, details = details)
class(res) <- c("multi", "sars")
attr(res, "type") <- "multi"
#if (verb) cat_line(rule(left = cyan(symbol$bullet)))
if (display) cat_line(rule())
if (confInt){
cat("\nCalculating sar_multi confidence intervals - this may take",
" some time:\n")
cis <- sar_conf_int(fit = res, n = ciN, crit = IC, obj_all = obj_all,
normaTest = normaTest,
homoTest = homoTest,
homoCor = homoCor,
neg_check = neg_check,
alpha_normtest = alpha_normtest,
alpha_homotest = alpha_homotest,
grid_start = grid_start,
grid_n = grid_n,
verb = verb,
display = display)
res$details$confInt <- cis
} else {
res$details$confInt <- NA
}
invisible(res)
}#end of sar_average
#' Use SAR model fits to predict richness on islands of a given size
#'
#' @description Predict the richness on an island of a given size using either
#' individual SAR model fits, a fit_collection of model fits, or a multi-model
#' SAR curve.
#' @usage sar_pred(fit, area)
#' @param fit Either a model fit object, a fit_collection object (generated
#' using \code{\link{sar_multi}}), or a sar_multi object (generated using
#' \code{\link{sar_average}}).
#' @param area A numeric vector of area values (length >= 1).
#' @details Extrapolation (e.g. predicting the richness of areas too large to be
#' sampled) is one of the primary uses of the SAR. The \code{sar_pred}
#' function provides an easy method for undertaking such an exercise. The
#' function works by taking an already fitted SAR model, extacting the
#' parameter values and then using these values and the model function to
#' predict the richness for any value of area provided.
#'
#' If a multi-model SAR curve is used for prediction (i.e. using
#' \code{\link{sar_average}}), the model information criterion weight (i.e.
#' the conditional probabilities for each of the n models) for each of the
#' individual model fits that were used to generate the curve are stored. The
#' n models are then each used to predict the richness of a larger area and
#' these predictions are multiplied by the respective model weights and summed
#' to provide a multi-model averaged prediction.
#'
#' @return A data.frame of class 'sars' with three columns: 1) the name of the
#' model, 2) the area value for which a prediction has been generated, and 3)
#' the prediction from the model extrapolation.
#' @note This function is used in the ISAR extrapolation paper of Matthews &
#' Aspin (2019).
#'
#' Code to calculate confidence intervals around the predictions using
#' bootstrapping will be added in a later version of the package.
#'
#' As grid_start has a random component, when \code{grid_start != "none"} in
#' your model fitting, you can get slightly different results each time you
#' fit a model or run \code{sar_average} and then run \code{sar_pred} on it.
#' We would recommend using \code{grid_start = "exhaustive"} as this is more
#' likely to find the optimum fit for a given model.
#' @references Matthews, T.J. & Aspin, T.W.H. (2019) Model averaging fails to
#' improve the extrapolation capability of the island species–area
#' relationship. Journal of Biogeography, 46, 1558-1568.
#' @examples
#' data(galap)
#' #fit the power model and predict richness on an island of area = 5000
#' fit <- sar_power(data = galap)
#' p <- sar_pred(fit, area = 5000)
#'
#' #fit three SAR models and predict richness on islands of area = 5000 & 10000
#' #using no grid_start for speed
#' fit2 <- sar_multi(galap, obj = c("power", "loga", "koba"), grid_start = "none")
#' p2 <- sar_pred(fit2, area = c(5000, 10000))
#'
#' #calculate a multi-model curve and predict richness on islands of area = 5000 & 10000
#' #using no grid_start for speed
#' fit3 <- sar_average(data = galap, grid_start = "none")
#' p3 <- sar_pred(fit3, area = c(5000, 10000))
#' @export
#test with known dataset from results that uses AICc
sar_pred <- function(fit, area){
if (!any(class(fit) %in% c("multi", "sars")))
stop("fit must be of class multi or sars")
if (!(is.numeric(area) & is.vector(area)))
stop("area should be a numeric vector")
if (attributes(fit)$type == "multi"){ #if a sar_multi object
wei <- fit$details$weights_ics#weights
#get predicted values for area from each of the model fits
#works whether area is single value or vector of values with length > 1
pred0 <- vapply(area, function(a){
predVec <- vector(length = length(fit$details$mod_names))
for (i in seq_along(fit$details$mod_names)){
fi <- fit$details$fits[[i]]
nam <- fit$details$mod_names[i]
pPars <- fi$par
if (nam == "Linear model"){
predVec[i] <- as.vector(pPars[1] + (pPars[2] * a))
} else {
predVec[i] <- as.vector(fi$model$mod.fun(a, pPars))
}#eo if linear model
}#eo for i
if (!identical(names(wei), names(fit$details$mod_names))){
stop ("Model names do not match.")
}
pred <- sum(predVec * wei) #multiply each model's fitted values by its weight
pred
}, FUN.VALUE = numeric(length = 1))
pred <- data.frame("Model" = "Multi", "Area" = area, "Prediction" = pred0)
} else if (attributes(fit)$type == "fit"){ #if a standard fit object
if (fit$model$name == "Linear model"){
pPars <- fit$par
pred0 <- as.vector((pPars[1] + (pPars[2] * area)))
pred <- data.frame("Model" = "Linear", "Area" = area,
"Prediction" = pred0)
} else {
pPars <- fit$par
pred0 <- as.vector(fit$model$mod.fun(area, pPars))
pred <- data.frame("Model" = fit$model$name,"Area" = area,
"Prediction" = pred0)
}#eo if linear model
} else if (attributes(fit)$type == "fit_collection"){ #if a fit collection
pred0 <- vapply(fit, function(f){
if (f$model$name == "Linear model"){
pPars <- f$par
pred2 <- as.vector((pPars[1] + (pPars[2] * area)))
} else {
pPars <- f$par
pred2 <- as.vector(f$model$mod.fun(area, pPars))
}#eo if linear model
pred2
}, FUN.VALUE = numeric(length = length(area)))
#get model names in fit_collection
mns <- vapply(fit, function(x) x$model$name, FUN.VALUE = character(1))
#build data.frame depending on whether one area value provided or multiple
if (length(area) == 1){
pred <- data.frame("Model" = mns, "Area" = rep(area, length(pred0)),
"Prediction" = pred0)
} else{
#have to unpack the results properly to build data.frame in correct order
mns2 <- as.vector(vapply(mns, function(x) rep(x, nrow(pred0)),
FUN.VALUE = character(nrow(pred0))))
p2 <- as.vector(unlist(pred0))
pred <- data.frame("Model" = mns2, "Area" = area, "Prediction" = p2)
}
rownames(pred) <- NULL
} else{
stop("Incorrect fit object provided")
}
class(pred) <- c("sars", "data.frame")
attr(pred, "type") <- "pred"
return(pred)
}
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Defines functions sar_pred sar_average sar_multi display_sars_models sars_models
Documented in display_sars_models sar_average sar_multi sar_pred sars_models
#' Display the 21 SAR model names
#'
#' @description Display the 21 SAR model names as a vector. See
#' \code{\link{sar_multi}} for further information.
#' @usage sars_models()
#' @note sar_mmf is included here for now but has been deprecated (see News)
#' @return A vector of model names.
#' @export
sars_models <- function() {
c("power","powerR","epm1","epm2","p1","p2","loga","koba","mmf",
"monod","negexpo","chapman","weibull3","asymp","ratio",
"gompertz","weibull4","betap","logistic","heleg","linear")
}
#NB - Table1 is stored in sysdata.rda
#' Display the model information table
#'
#' @description Display Table 1 of Matthews et al. (2019). See
#' \code{\link{sar_multi}} for further information.
#' @usage display_sars_models()
#' @return A table of model information for 21 SAR models, including the model
#' function, number of parameters and general model shape. This includes the
#' 20 models in Matthews et al. (2019); however, note that the mmf model has
#' now been deprecated, and the standard logistic model listed in Tjorve
#' (2003) added instead. Note also, an error in the Chapman Richards model
#' equation has now been corrected, and the shape of some of the models have
#' been updated from sigmoid to convex/sigmoid.
#' @references Matthews et al. (2019) sars: an R package for fitting, evaluating
#' and comparing species–area relationship models. Ecography, 42, 1446-1455.
#'
#' Tjørve, E. (2003) Shapes and functions of species–area curves: a review of
#' possible models. Journal of Biogeography, 30, 827-835.
#' @export
display_sars_models <- function() {
# display table 1 of the manuscript
print(Table1)
}
#' Create a Collection of SAR Model Fits
#'
#' @description Creates a fit collection of SAR model fits, which can then be
#' plotted using \code{\link{plot.sars}}.
#' @usage sar_multi(data, obj = c("power",
#' "powerR","epm1","epm2","p1","p2","loga","koba",
#' "monod","negexpo","chapman","weibull3","asymp",
#' "ratio","gompertz","weibull4","betap","logistic","heleg","linear"),
#' normaTest = "none", homoTest = "none", homoCor = "spearman", grid_start =
#' "partial", grid_n = NULL, verb = TRUE, display = TRUE)
#' @param data A dataset in the form of a dataframe with two columns: the first
#' with island/site areas, and the second with the species richness of each
#' island/site.
#' @param obj A vector of model names.
#' @param normaTest The test used to test the normality of the residuals of each
#' model. Can be any of "lillie" (Lilliefors Kolmogorov-Smirnov test),
#' "shapiro" (Shapiro-Wilk test of normality), "kolmo" (Kolmogorov-Smirnov
#' test), or "none" (no residuals normality test is undertaken; the default).
#' @param homoTest The test used to check for homogeneity of the residuals of
#' each model. Can be any of "cor.fitted" (a correlation of the squared
#' residuals with the model fitted values), "cor.area" (a correlation of the
#' squared residuals with the area values), or "none" (no residuals
#' homogeneity test is undertaken; the default).
#' @param homoCor The correlation test to be used when \code{homoTest !=
#' "none"}. Can be any of "spearman" (the default), "pearson", or "kendall".
#' @param grid_start Should a grid search procedure be implemented to test
#' multiple starting parameter values. Can be one of 'none', 'partial' or
#' 'exhaustive' The default is set to 'partial'.
#' @param grid_n If \code{grid_start = exhaustive}, the number of points sampled
#' in the starting parameter space (see details).
#' @param verb verbose - Whether or not to print certain warnings
#' (default: \code{verb == TRUE}).
#' @param display Show the model fitting output and related messages.
#' (default: \code{display == TRUE}).
#' @details The \code{sar_models()} function can be used to bring up a list of
#' the 20 model names. \code{display_sars_models()} generates a table of the
#' 20 models with model information.
#' @return A list of class 'sars' with n elements, corresponding to the n
#' individual SAR model fits.
#' @importFrom cli rule symbol
#' @importFrom crayon bold col_align cyan green red yellow
#' @examples
#' data(galap)
#' # construct a fit_collection object of 3 SAR model fits
#' fit2 <- sar_multi(galap, obj = c("power", "loga", "linear"))
#' plot(fit2)
#'
#' # construct a fit_collection object of all 20 SAR model fits
#' # using no grid_start for speed
#' fit3 <- sar_multi(galap, grid_start = "none")
#'
#' @export
sar_multi <- function(data,
obj = c("power", "powerR","epm1","epm2","p1","p2",
"loga","koba","monod","negexpo",
"chapman","weibull3","asymp","ratio",
"gompertz", "weibull4","betap","logistic","heleg",
"linear"),
normaTest = "none",
homoTest = "none",
homoCor = "spearman",
grid_start = "partial",
grid_n = NULL,
verb = TRUE,
display = TRUE){
if ("mmf" %in% obj){
warning("mmf has been deprecated, see News")
}
if (!((is.character(obj)) | (inherits(obj, "sars"))))
stop("obj must be of class character or sars")
if (nrow(data) < 4)
stop("Multi SAR needs at least four data points")
if (nrow(data) == 4 & normaTest == "lillie")
stop("The Lilliefors test cannot be performed with less than 5 data",
" points")
if (is.character(obj) & is.null(data))
stop("if obj is character then data should be provided")
if (is.character(obj)) {
if (any(!(obj %in% c("linear","power","powerR","epm1","epm2","p1",
"p2","loga","koba","mmf","monod","negexpo",
"chapman","weibull3","asymp","ratio","gompertz",
"weibull4","betap","heleg","logistic"))))
stop("provided model names do not match with model functions")
}
if (!(grid_start %in% c("none", "partial", "exhaustive"))){
stop("grid_start should be one of 'none', 'partial' or 'exhaustive'")
}
if (grid_start == "exhaustive"){
if (!is.numeric(grid_n))
stop("grid_n should be numeric if grid_start == exhaustive")
}
if (!is.logical(verb) | !is.logical(display)){
stop('verb / display should be logical')
}
if (length(obj) < 2)
stop("more than 1 fit is required to use sar_multi")
normaTest <- match.arg(normaTest, c("none", "shapiro",
"kolmo", "lillie"))
homoTest <- match.arg(homoTest, c("none","cor.area",
"cor.fitted"))
if (homoTest != "none"){
homoCor <- match.arg(homoCor, c("spearman","pearson",
"kendall"))
}
#if (verb) cat_line(rule(left = paste0(cyan(symbol$bullet),
#bold(" multi_sars")),right="multi-model SAR"))
if (display & is.character(obj)) {
cat("\n", paste("Now attempting to fit the",
length(obj), "SAR models:"), "\n\n")
cat_line(rule(left = bold(" multi_sars"),
right="multi-model SAR"))
}
#if (verb) cat_line(magenta(symbol$arrow_right)," Data set is: ")
#if (verb) cat_line(rule(left = paste0(magenta(symbol$bullet))))
#if (verb) bullet("O | S : model", bullet = blue_arrow())
#if not yet fitted, fit the models to the datasquare_small_filled
if (!is.character(obj))
stop("obj should be a character vector of model names")
mods <- paste0("sar_",obj)
names(mods) <- obj
fits <- lapply(obj, function(x){
#have to do separately because linear does not have grid_start
if (x == "linear"){
f <- eval(parse(text = paste0(mods[x],
"(data", ", normaTest = ",
paste0("'", normaTest, "'"),
", homoTest = ", paste0("'", homoTest,
"'"),
", homoCor = ", paste0("'", homoCor,
"'"),
", verb = ", paste0(verb),")")))
} else{ #if grid_start provided, implement it (for non-linear models)
f <- eval(parse(text = paste0(mods[x],
"(data", ", normaTest = ",
paste0("'", normaTest, "'"),
", homoTest = ", paste0("'", homoTest,
"'"),
", homoCor = ", paste0("'", homoCor,
"'"),
", grid_start = ",
paste0("'",grid_start,"'"), ", grid_n = ",
paste0(grid_n),
", verb = ",paste0(verb),")")))
}
if (display) {
if(is.na(f$value)) {
cat_line(paste0(red(symbol$arrow_right)," ",
col_align(x,max(nchar(obj)))," : ",
red(symbol$cross)))
}else{
if (!is.matrix(f$sigConf)){
cat_line( paste0(yellow(symbol$arrow_right)," ",
col_align(x,max(nchar(obj))),
" : Warning: could not compute parameters statistics"))
}else{
cat_line( paste0(cyan(symbol$arrow_right)," ",
col_align(x,max(nchar(obj)))," : ",
green(symbol$tick)))
}
}
}
f
})#eo suppressWarnings(lapply)
names(fits) <- obj
class(fits) <- "sars"
attr(fits, "type") <- "fit_collection"
return(fits)
}#end of multi_sars
#' Fit a multimodel averaged SAR curve
#'
#' @description Construct a multimodel averaged species-area relationship curve
#' using information criterion weights and up to twenty SAR models.
#' @usage sar_average(obj = c("power",
#' "powerR","epm1","epm2","p1","p2","loga","koba",
#' "monod","negexpo","chapman","weibull3","asymp",
#' "ratio","gompertz","weibull4","betap","logistic", "heleg", "linear"), data =
#' NULL, crit = "Info", normaTest = "none", homoTest = "none", homoCor =
#' "spearman", neg_check = FALSE, alpha_normtest = 0.05, alpha_homotest =
#' 0.05, grid_start = "partial", grid_n = NULL, confInt = FALSE, ciN = 100,
#' verb = TRUE, display = TRUE)
#' @param obj Either a vector of model names or a fit_collection object created
#' using \code{\link{sar_multi}}. If a vector of names is provided,
#' \code{sar_average} first calls \code{sar_multi} before generating the
#' averaged multimodel curve.
#' @param data A dataset in the form of a dataframe with two columns: the first
#' with island/site areas, and the second with the species richness of each
#' island/site. If \code{obj} is a fit_collection object, \code{data} should
#' be NULL.
#' @param crit The criterion used to compare models and compute the model
#' weights. The default \code{crit = "Info"} switches to AIC or AICc depending
#' on the number of data points in the dataset. AIC (\code{crit = "AIC"}) or
#' AICc (\code{crit = "AICc"}) can be chosen regardless of the sample size. For
#' BIC, use \code{crit ="Bayes"}.
#' @param normaTest The test used to test the normality of the residuals of each
#' model. Can be any of "lillie" (Lilliefors Kolmogorov-Smirnov test),
#' "shapiro" (Shapiro-Wilk test of normality), "kolmo" (Kolmogorov-Smirnov
#' test), or "none" (no residuals normality test is undertaken; the default).
#' @param homoTest The test used to check for homogeneity of the residuals of
#' each model. Can be any of "cor.fitted" (a correlation of the squared
#' residuals with the model fitted values), "cor.area" (a correlation of the
#' squared residuals with the area values), or "none" (no residuals
#' homogeneity test is undertaken; the default).
#' @param homoCor The correlation test to be used when \code{homoTest !=
#' "none"}. Can be any of "spearman" (the default), "pearson", or "kendall".
#' @param neg_check Whether or not a check should be undertaken to flag any
#' models that predict negative richness values.
#' @param alpha_normtest The alpha value used in the residual normality test
#' (default = 0.05, i.e. any test with a P value < 0.05 is flagged as failing
#' the test).
#' @param alpha_homotest The alpha value used in the residual homogeneity test
#' (default = 0.05, i.e. any test with a P value < 0.05 is flagged as failing
#' the test).
#' @param grid_start Should a grid search procedure be implemented to test
#' multiple starting parameter values. Can be one of 'none', 'partial' or
#' 'exhaustive' The default is set to 'partial'.
#' @param grid_n If \code{grid_start = exhaustive}, the number of points sampled
#' in the starting parameter space (see details).
#' @param confInt A logical argument specifying whether confidence intervals
#' should be calculated for the multimodel curve using bootstrapping.
#' @param ciN The number of bootstrap samples to be drawn to calculate the
#' confidence intervals (if \code{confInt == TRUE}).
#' @param verb verbose - Whether or not to print certain warnings
#' (default: \code{verb == TRUE})
#' @param display Show the model fitting output and related messages.
#' (default: \code{display == TRUE}).
#' @details The multimodel SAR curve is constructed using information criterion
#' weights (see Burnham & Anderson, 2002; Guilhaumon et al. 2010). If
#' \code{obj} is a vector of n model names the function fits the n models to
#' the dataset provided using the \code{sar_multi} function. A dataset must
#' have four or more datapoints to fit the multimodel curve. If any models
#' cannot be fitted they are removed from the multimodel SAR. If \code{obj} is
#' a fit_collection object (created using the \code{sar_multi} function), any
#' model fits in the collection which are NA are removed. In addition, if any
#' other model checks have been selected (i.e. residual normality and
#' heterogeneity tests, and checks for negative predicted richness values),
#' these are undertaken and any model that fails the selected test(s) is
#' removed from the multimodel SAR. The order of the additional checks inside
#' the function is (if all are turned on): normality of residuals, homogeneity
#' of residuals, and a check for negative fitted values. Once a model fails
#' one test it is removed and thus is not available for further tests. Thus, a
#' model may fail multiple tests but the returned warning will only provide
#' information on a single test. We have now changed the defaults so that
#' no checks are undertaken, so it is up to the user to select any checks if
#' appropriate.
#'
#' The resultant models are then used to construct the multimodel SAR curve.
#' For each model in turn, the model fitted values are multiplied by the
#' information criterion weight of that model, and the resultant values are
#' summed across all models (Burnham & Anderson, 2002). Confidence intervals
#' can be calculated (using \code{confInt}) around the multimodel averaged
#' curve using the bootstrap procedure outlined in Guilhaumon et al (2010).The
#' procedure transforms the residuals from the individual model fits and
#' occasionally NAs / Inf values can be produced - in these cases, the model
#' is removed from the confidence interval calculation (but not the multimodel
#' curve itself). There is also a constraint within the procedure to remove
#' any transformed residuals that result in negative richness values. When
#' several SAR models are used, when grid_start is turned on and when the
#' number of bootstraps (\code{ciN}) is large, generating the confidence
#' intervals can take a (very) long time. Parallel processing will be added to
#' future versions.
#'
#' Choosing starting parameter values for non-linear regression optimisation
#' algorithms is not always straight forward, depending on the data at hand.
#' In the package, we use various approaches to choose default starting
#' parameters. However, we also use a grid search process which creates a
#' large array of different possible starting parameter values (within certain
#' bounds) and then randomly selects a proportion of these to test. There are
#' three options for the \code{grid_start} argument to control this process.
#' The default (\code{grid_start = "partial"}) randomly samples 500 different
#' sets of starting parameter values for each model, adds these to the model's
#' default starting values and tests all of these. A more comprehensive set of
#' starting parameter estimates can be used (\code{grid_start = "exhaustive"})
#' - this option allows the user to choose the number of starting parameter
#' sets to be tested (using the \code{grid_n} argument) and includes a range
#' of additional starting parameter estimates, e.g. very small values and
#' particular values we have found to be useful for individual models. Using
#' \code{grid_start = "exhaustive"} in combination with a large \code{grid_n}
#' can be very time consuming; however, we would recommend it as it makes it
#' more likely that the optimal model fit will be found, particularly for the
#' more complex models. This is particularly true if any of the model fits
#' does not converge, returns a singular gradient at parameter estimates, or
#' the plot of the model fit does not look optimum. The grid start procedure
#' can also be turned off (\code{grid_start = "none"}), meaning just the
#' default starting parameter estimates are used. Note that \code{grid_start}
#' has been disabled for a small number of models (e.g. Weibull 3 par.). See
#' the vignette for more information. Remember that, as grid_start has a
#' random component, when \code{grid_start != "none"}, you can get slightly
#' different results each time you fit a model or run \code{sar_average}.
#'
#' Even with grid_start, occasionally a model fit will be able to be fitted
#' and pass the model fitting checks (e.g. residual normality) but the
#' resulting fit is nonsensical (e.g. a horizontal line with intercept at
#' zero). Thus, it can be useful to plot the resultant 'multi' object to check
#' the individual model fits. To re-run the \code{sar_average} function
#' without a particular model, simply remove it from the \code{obj} argument.
#'
#' The \code{sar_models()} function can be used to bring up a list of the 20
#' model names. \code{display_sars_models()} generates a table of the 20
#' models with model information.
#'
#' @return A list of class "multi" and class "sars" with two elements. The first
#' element ('mmi') contains the fitted values of the multimodel sar curve. The
#' second element ('details') is a list with the following components:
#' \itemize{ \item{mod_names} { Names of the models that were successfully
#' fitted and passed any model check} \item{fits} { A fit_collection object
#' containing the successful model fits} \item{ic} { The information criterion
#' selected} \item{norm_test} { The residual normality test selected}
#' \item{homo_test} { The residual homogeneity test selected}
#' \item{alpha_norm_test} { The alpha value used in the residual normality
#' test} \item{alpha_homo_test} { The alpha value used in the residual
#' homogeneity test} \item{ics} { The information criterion values (e.g. AIC
#' values) of the model fits} \item{delta_ics} { The delta information
#' criterion values} \item{weights_ics} { The information criterion weights of
#' each model fit} \item{n_points} { Number of data points} \item{n_mods} {
#' The number of successfully fitted models} \item{no_fit} { Names of the
#' models which could not be fitted or did not pass model checks}
#' \item{convergence} { Logical value indicating whether \code{\link{optim}}
#' model convergence code = 0, for each model} }
#'
#' The \code{\link{summary.sars}} function returns a more useful summary of
#' the model fit results, and the \code{\link{plot.multi}} plots the
#' multimodel curve.
#' @note There are different types of non-convergence and these are dealt with
#' differently in the package. If the optimisation algorithm fails to return
#' any solution, the model fit is defined as NA and is then removed, and so
#' does not appear in the model summary table or multi-model curve etc.
#' However, the optimisation algorithm (e.g. Nelder-Mead) can also return
#' non-NA model fits but where the solution is potentially non-optimal (e.g.
#' degeneracy of the Nelder–Mead simplex) - these cases are identified by any
#' \code{\link{optim}} convergence code that is not zero. We have decided not
#' to remove these fits (i.e. they are kept in the model summary table and
#' multimodel curve) - as arguably a non-optimal fit is still better than no
#' fit - but any instances can be checked using the returned
#' \code{details$converged} vector and then the model fitting re-run without
#' these models, if preferred. Increasing the starting parameters grid search
#' (see above) may also help avoid this issue.
#'
#' The generation of confidence intervals around the multimodel curve (using
#' \code{confInt == TRUE}), may throw up errors that we have yet to come
#' across. Please report any issues to the package maintainer.
#'
#' There are different formulas for calculating the various information
#' criteria (IC) used for model comparison (e.g. AIC, BIC). For example, some
#' formulas use the residual sum of squares (rss) and others the
#' log-likelihood (ll). Both are valid approaches and will give the same
#' parameter estimates, but it is important to only compare IC values that
#' have been calculated using the same approach. For example, the 'sars'
#' package used to use formulas based on the rss, while the \link[stats]{nls}
#' function function in the stats package uses formulas based on the ll. To
#' increase the compatibility between nls and sars, we have changed our
#' formulas such that now our IC formulas are the same as those used in the
#' \link[stats]{nls} function. See the "On the calculation of information
#' criteria" section in the package vignette for more information.
#'
#' The mmf model was found to be equivalent to the He & Legendre logistic, and
#' so the former has been deprecated (as of Feb 2021). We have removed it from
#' the default models in \code{sar_average}, although it is still available to
#' be used for the time being (using the \code{obj} argument). The standard
#' logistic model has been added in its place, and is now used as default
#' within \code{sar_average}.
#'
#' @references Burnham, K. P., & Anderson, D. R. (2002). Model selection and
#' multi-model inference: a practical information-theoretic approach (2nd
#' ed.). New-York: Springer.
#'
#' Guilhaumon, F., Mouillot, D., & Gimenez, O. (2010). mmSAR: an R-package for
#' multimodel species-area relationship inference. Ecography, 33, 420-424.
#'
#' Matthews, T. J., K. A. Triantis, R. J. Whittaker, & F. Guilhaumon. (2019).
#' sars: an R package for fitting, evaluating and comparing species–area
#' relationship models. Ecography, 42, 1446–55.
#' @examples
#' data(galap)
#' #attempt to construct a multimodel SAR curve using all twenty sar models
#' #using no grid_start just for speed here (not recommended generally)
#' fit <- sar_average(data = galap, grid_start = "none")
#' summary(fit)
#' plot(fit)
#'
#' # construct a multimodel SAR curve using a fit_collection object
#' ff <- sar_multi(galap, obj = c("power", "loga", "monod", "weibull3"))
#' fit2 <- sar_average(obj = ff, data = NULL)
#' summary(fit2)
#'
#' \dontrun{
#' # construct a multimodel SAR curve using a more exhaustive set of starting
#' # parameter values
#' fit3 <- sar_average(data = galap, grid_start = "exhaustive", grid_n = 1000)
#' }
#'
#' @export
sar_average <- function(obj = c("power", "powerR","epm1","epm2","p1","p2",
"loga","koba","monod","negexpo",
"chapman","weibull3","asymp","ratio",
"gompertz", "weibull4","betap","logistic",
"heleg", "linear"), data = NULL,
crit = "Info",
normaTest = "none",
homoTest = "none",
homoCor = "spearman",
neg_check = FALSE,
alpha_normtest = 0.05,
alpha_homotest = 0.05,
grid_start = "partial",
grid_n = NULL,
confInt = FALSE,
ciN = 100,
verb = TRUE,
display = TRUE){
if ("mmf" %in% obj){
warning("mmf has been deprecated, see News")
}
if (!((is.character(obj)) | (inherits(obj, "sars"))))
stop("obj must be of class character or sars")
if (is.character(obj) & is.null(data))
stop("if obj is character then data should be provided")
if (is.character(obj)) {
if (any(!(obj %in% c("linear","power","powerR","epm1","epm2","p1",
"p2","loga","koba","mmf","monod","negexpo",
"chapman","weibull3","asymp","ratio","gompertz",
"weibull4","betap","heleg","logistic"))))
stop("provided model names do not match with model functions")
}
if (!(grid_start %in% c("none", "partial", "exhaustive"))){
stop("grid_start should be one of 'none', 'partial' or 'exhaustive'")
}
if (grid_start == "exhaustive"){
if (!is.numeric(grid_n))
stop("grid_n should be numeric if grid_start == exhaustive")
}
if (!is.logical(verb) | !is.logical(display)){
stop('verb / display should be logical')
}
if (length(obj) < 2)
stop("more than 1 fit is required to construct a sar_multi")
if (display & grid_start != "none"){
cat("\nModels to be fitted using a grid start approach: \n")
}
#if a vector of names is provided, then call sar_multi first
if (is.character(obj)){
fits <- sar_multi(data = data, obj = obj,
normaTest = normaTest,
homoTest = homoTest,
homoCor = homoCor,
grid_start = grid_start,
grid_n = grid_n,
verb = verb,
display = display)
} else{
fits <- obj
}
#save the full list of models that the user wants to fit for use with
#the CI function
if (confInt) obj_all <- obj
#####BAD MODEL CHECKS#######################
normaTest <- match.arg(normaTest, c("none", "shapiro",
"kolmo", "lillie"))
homoTest <- match.arg(homoTest, c("none","cor.area",
"cor.fitted"))
if (homoTest != "none"){
homoCor <- match.arg(homoCor, c("spearman","pearson",
"kendall"))
}
#if obj = character vector, check the test names match up with
#those provide here. Doesn't actually matter but can be brought to
#attention of user
if (!is.character(obj)){
wn <- obj[[1]]$normaTest[[1]]
wh <- obj[[1]]$homoTest[[1]]
if (wn != normaTest) {
if (verb){
warning("normaTest argument does not match the
normaTest stored in the fit collection
object. The multi SAR curve will proceed
with the test used in the fit collection
object.")
}
normaTest <- wn
}
if (wh != homoTest){
if (verb){
warning("homoTest argument does not match the
homoTest stored in the fit collection
object. The multi SAR curve will proceed
with the test used in the fit collection
object.")
}
homoTest <- wh
}}
if (normaTest == "none") alpha_normtest <- "none"
if (homoTest == "none") alpha_homotest <- "none"
#NA CHECKS
f_nas <- unlist(lapply(fits,function(b)b$value))
if(all(is.na(f_nas))){
stop("No model could be fitted, aborting multi_sars\n")
}
badMods <- vector(length = 0, mode = "character")
if(any(is.na(f_nas))){
badNames <- is.na(f_nas)
if (verb){
message("\n", paste(sum(is.na(f_nas)),
"models could not be fitted and have been excluded",
" from the multi SAR"),
"\n")
}
badMods <- obj[badNames] #extract the bad model names from the obj
fits <- fits[!is.na(f_nas)]
}
bml <- length(badMods)
if ((normaTest != "none" | homoTest != "none" | neg_check) & display){
if (is.character(obj)){
if (any(is.na(f_nas))){
cat("\nModel fitting completed. Now undertaking model validation",
" checks. Additional models will be excluded if necessary:\n")
} else {
cat("\nModel fitting completed - all models succesfully fitted.",
"Now undertaking model validation checks. Additional models",
" will be excluded if necessary:\n")
}
} else {
cat("\nNow undertaking model validation checks. Additional models",
" will\nbe excluded if necessary\n")
}
}
#if checks for normality and / or homoscedasticity enabled, then check
#and remove bad fits from fits
if (normaTest != "none") {
np <- vapply(fits, function(x) x$normaTest[[2]]$p.value,
FUN.VALUE = numeric(1))
#sometimes bad models produce calculated values with all same
#richness values and no correlation can be done. Remove these
if (anyNA(np)){
wnn <- is.na(np)
mn <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(is.na(np)),"models returned NAs in the",
" residuals normality test and have been excluded",
" from the multi SAR:"),
"\n",
paste(mn[wnn], collapse = ", "), "\n")
}
badMods <- c(badMods, mn[wnn])#select the model names with NAs
fits <- fits[!wnn]
np <- np[!wnn]
}
whp <- np < alpha_normtest
if (any(whp)) {
mn2 <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(np < alpha_normtest),
"models failed the residuals normality test and",
" have been excluded",
" from the multi SAR:"), "\n",
paste(mn2[whp], collapse = ", "), "\n")
}
badMods <- c(badMods, mn2[whp])
fits <- fits[!whp]#then remove these models from the fit collection
}
}
if (homoTest != "none") {
hp <- vapply(fits, function(x) x$homoTest[[2]]$p.value,
FUN.VALUE = numeric(1))
#sometimes bad models produce calculated values with all same richness
#values and no correlation can be done. Remove these
if (anyNA(hp)){
whn <- is.na(hp)
mn3 <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(is.na(hp)),
"returned NAs in the residuals homogeneity test and have",
" been excluded from the multi SAR:"), "\n",
paste(mn3[whn], collapse = ", "), "\n")
}
badMods <- c(badMods, mn3[whn])#select the model names with NAs
fits <- fits[!whn]
hp <- hp[!whn]
}
whh <- hp < alpha_homotest
if (any(whh)) {
mn4 <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(hp < alpha_homotest),
"models failed the residuals homogeneity test and have been",
" excluded from the multi SAR:"), "\n",
paste(mn4[whh], collapse = ", "), "\n")
}
badMods <- c(badMods, mn4[whh])
fits <- fits[!whh]
}
}
#negative values
if (neg_check){
nc <- vapply(fits, function(x) any(x$calculated < 0),
FUN.VALUE = logical(1))
if (any(nc)) {
mn5 <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (verb){
message("\n", paste(sum(nc), "models have negative fitted values and",
" have been excluded from the multi SAR:"), "\n",
paste(mn5[nc], collapse = ", "), "\n")
}
badMods <- c(badMods, mn5[nc])
fits <- fits[!nc]
}
}
if (length(badMods) == bml & display) {
if((normaTest == "none" & homoTest == "none" & !(neg_check))){
cat("\nNo model validation checks selected\n\n")
} else{
cat("\nAll models passed the model",
"validation checks\n\n")
}
}
if (length(badMods) == 0) badMods <- 0
if (length(fits) < 2) stop("Fewer than two models could be fitted and /",
" or passed the model checks")
sf <- vapply(fits, function(x) x$model$name, FUN.VALUE = character(1))
if (display) cat(length(sf), "remaining models used to construct the multi",
"SAR:\n",
paste(sf, collapse = ", "), "\n")
####################################
#setting variables
nPoints <- length(fits[[1]]$data$A)
nMods <- length(fits)
modNames <- vapply(fits, FUN = function(x){x$model$name},
FUN.VALUE = character(1))
#choosing an IC criterion (AIC or AICc or BIC)
IC <- switch(crit,
Info= if ( (nPoints / 3) < 40 ) { "AICc" } else { "AIC" },
AIC = "AIC",
AICc = "AICc",
Bayes = "BIC")
#get ICs
ICs <- vapply(X = fits, FUN = function(x){x[[IC]]}, FUN.VALUE = double(1))
#get delta ICs
delta_ICs <- ICs - min(ICs)
#get akaike weights
akaikesum <- sum(exp( -0.5*(delta_ICs)))
weights_ICs <- exp(-0.5*delta_ICs) / akaikesum
#ERROR: produce weight averaged diversity measures
# mmi <- vapply(fits,FUN=function(x){x$calculated},FUN.VALUE=double(nPoints))
# mmi <- apply((mmi * weights_ICs), 1 , sum)
#produce weight averaged diversity measures
mmi <- vapply(fits,FUN=function(x){x$calculated},FUN.VALUE=double(nPoints))
wm <- matrix(nrow = nPoints, ncol = length(fits))
for (i in seq_along(weights_ICs)) {wm[ ,i] <- mmi[ ,i] * weights_ICs[i]}
mmi <- apply(wm, 1 , sum)
res <- mmi
#get convergence info (of models that have passed checks)
conv <- vapply(fits, FUN=function(x){x$verge}, FUN.VALUE = logical(1))
details <- list(
mod_names = modNames,
fits = fits,
crit = crit,
ic = IC,
norm_test = normaTest,
homo_test = homoTest,
alpha_norm_test = alpha_normtest,
alpha_homo_test = alpha_homotest,
ics = ICs,
delta_ics = delta_ICs,
weights_ics = weights_ICs,
n_points = nPoints,
n_mods = nMods,
no_fit = as.vector(badMods),
convergence = conv
)
res <- list(mmi = mmi, details = details)
class(res) <- c("multi", "sars")
attr(res, "type") <- "multi"
#if (verb) cat_line(rule(left = cyan(symbol$bullet)))
if (display) cat_line(rule())
if (confInt){
cat("\nCalculating sar_multi confidence intervals - this may take",
" some time:\n")
cis <- sar_conf_int(fit = res, n = ciN, crit = IC, obj_all = obj_all,
normaTest = normaTest,
homoTest = homoTest,
homoCor = homoCor,
neg_check = neg_check,
alpha_normtest = alpha_normtest,
alpha_homotest = alpha_homotest,
grid_start = grid_start,
grid_n = grid_n,
verb = verb,
display = display)
res$details$confInt <- cis
} else {
res$details$confInt <- NA
}
invisible(res)
}#end of sar_average
#' Use SAR model fits to predict richness on islands of a given size
#'
#' @description Predict the richness on an island of a given size using either
#' individual SAR model fits, a fit_collection of model fits, or a multi-model
#' SAR curve.
#' @usage sar_pred(fit, area)
#' @param fit Either a model fit object, a fit_collection object (generated
#' using \code{\link{sar_multi}}), or a sar_multi object (generated using
#' \code{\link{sar_average}}).
#' @param area A numeric vector of area values (length >= 1).
#' @details Extrapolation (e.g. predicting the richness of areas too large to be
#' sampled) is one of the primary uses of the SAR. The \code{sar_pred}
#' function provides an easy method for undertaking such an exercise. The
#' function works by taking an already fitted SAR model, extacting the
#' parameter values and then using these values and the model function to
#' predict the richness for any value of area provided.
#'
#' If a multi-model SAR curve is used for prediction (i.e. using
#' \code{\link{sar_average}}), the model information criterion weight (i.e.
#' the conditional probabilities for each of the n models) for each of the
#' individual model fits that were used to generate the curve are stored. The
#' n models are then each used to predict the richness of a larger area and
#' these predictions are multiplied by the respective model weights and summed
#' to provide a multi-model averaged prediction.
#'
#' @return A data.frame of class 'sars' with three columns: 1) the name of the
#' model, 2) the area value for which a prediction has been generated, and 3)
#' the prediction from the model extrapolation.
#' @note This function is used in the ISAR extrapolation paper of Matthews &
#' Aspin (2019).
#'
#' Code to calculate confidence intervals around the predictions using
#' bootstrapping will be added in a later version of the package.
#'
#' As grid_start has a random component, when \code{grid_start != "none"} in
#' your model fitting, you can get slightly different results each time you
#' fit a model or run \code{sar_average} and then run \code{sar_pred} on it.
#' We would recommend using \code{grid_start = "exhaustive"} as this is more
#' likely to find the optimum fit for a given model.
#' @references Matthews, T.J. & Aspin, T.W.H. (2019) Model averaging fails to
#' improve the extrapolation capability of the island species–area
#' relationship. Journal of Biogeography, 46, 1558-1568.
#' @examples
#' data(galap)
#' #fit the power model and predict richness on an island of area = 5000
#' fit <- sar_power(data = galap)
#' p <- sar_pred(fit, area = 5000)
#'
#' #fit three SAR models and predict richness on islands of area = 5000 & 10000
#' #using no grid_start for speed
#' fit2 <- sar_multi(galap, obj = c("power", "loga", "koba"), grid_start = "none")
#' p2 <- sar_pred(fit2, area = c(5000, 10000))
#'
#' #calculate a multi-model curve and predict richness on islands of area = 5000 & 10000
#' #using no grid_start for speed
#' fit3 <- sar_average(data = galap, grid_start = "none")
#' p3 <- sar_pred(fit3, area = c(5000, 10000))
#' @export
#test with known dataset from results that uses AICc
sar_pred <- function(fit, area){
if (!any(class(fit) %in% c("multi", "sars")))
stop("fit must be of class multi or sars")
if (!(is.numeric(area) & is.vector(area)))
stop("area should be a numeric vector")
if (attributes(fit)$type == "multi"){ #if a sar_multi object
wei <- fit$details$weights_ics#weights
#get predicted values for area from each of the model fits
#works whether area is single value or vector of values with length > 1
pred0 <- vapply(area, function(a){
predVec <- vector(length = length(fit$details$mod_names))
for (i in seq_along(fit$details$mod_names)){
fi <- fit$details$fits[[i]]
nam <- fit$details$mod_names[i]
pPars <- fi$par
if (nam == "Linear model"){
predVec[i] <- as.vector(pPars[1] + (pPars[2] * a))
} else {
predVec[i] <- as.vector(fi$model$mod.fun(a, pPars))
}#eo if linear model
}#eo for i
if (!identical(names(wei), names(fit$details$mod_names))){
stop ("Model names do not match.")
}
pred <- sum(predVec * wei) #multiply each model's fitted values by its weight
pred
}, FUN.VALUE = numeric(length = 1))
pred <- data.frame("Model" = "Multi", "Area" = area, "Prediction" = pred0)
} else if (attributes(fit)$type == "fit"){ #if a standard fit object
if (fit$model$name == "Linear model"){
pPars <- fit$par
pred0 <- as.vector((pPars[1] + (pPars[2] * area)))
pred <- data.frame("Model" = "Linear", "Area" = area,
"Prediction" = pred0)
} else {
pPars <- fit$par
pred0 <- as.vector(fit$model$mod.fun(area, pPars))
pred <- data.frame("Model" = fit$model$name,"Area" = area,
"Prediction" = pred0)
}#eo if linear model
} else if (attributes(fit)$type == "fit_collection"){ #if a fit collection
pred0 <- vapply(fit, function(f){
if (f$model$name == "Linear model"){
pPars <- f$par
pred2 <- as.vector((pPars[1] + (pPars[2] * area)))
} else {
pPars <- f$par
pred2 <- as.vector(f$model$mod.fun(area, pPars))
}#eo if linear model
pred2
}, FUN.VALUE = numeric(length = length(area)))
#get model names in fit_collection
mns <- vapply(fit, function(x) x$model$name, FUN.VALUE = character(1))
#build data.frame depending on whether one area value provided or multiple
if (length(area) == 1){
pred <- data.frame("Model" = mns, "Area" = rep(area, length(pred0)),
"Prediction" = pred0)
} else{
#have to unpack the results properly to build data.frame in correct order
mns2 <- as.vector(vapply(mns, function(x) rep(x, nrow(pred0)),
FUN.VALUE = character(nrow(pred0))))
p2 <- as.vector(unlist(pred0))
pred <- data.frame("Model" = mns2, "Area" = area, "Prediction" = p2)
}
rownames(pred) <- NULL
} else{
stop("Incorrect fit object provided")
}
class(pred) <- c("sars", "data.frame")
attr(pred, "type") <- "pred"
return(pred)
}
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