Nothing
R/predict.trophicSDMfit.R
In webSDM: Including Known Interactions in Species Distribution Models
Defines functions
predict.trophicSDMfit
Documented in
predict.trophicSDMfit
#' Computes predicted values from the fitted trophicSDMfit model
#'
#' Computes predicted values from the fitted trophicSDMfit model at environmental conditions specified by \code{Xnew}. Once predictions have been obtained, their quality can eventually be evaluated with \code{evaluateModelFit()}.
#' @param object A trophicSDMfit object obtained with trophicSDM()
#' @param Xnew a matrix specifying the environmental covariates for the predictions to be made. If NULL (default), predictions are done on the training dataset (e.g. by setting Xnew = tSDM$data$X).
#' @param prob.cov Parameter to predict with trophicSDM with presence-absence data. Whether to use predicted probability of presence (prob.cov = T) or the transformed presence-absences (default, prov.cov = F) to predict species distribution.
#' @param pred_samples Number of samples to draw from species posterior predictive distribution when method = "stan_glm". If NULL, set by the default to the number of iterations/10.
#' @param run.parallel Whether to use parallelise code when possible. Can speed up computation time.
#' @param verbose Whether to print advances of the algorithm
#' @param fullPost Optional parameter for stan_glm only. Whether to give back the full posterior predictive distribution (default, fullPost = TRUE) or just the posterior mean, and 2.5% and 97.5% quantiles,
#' @param filter.table Optional, default to NULL, should be provided only if the users wants to filter some species predictions. A sites x species matrix of zeros and ones.
#' @param ... additional arguments
#' @return A list containing for each species the predicted value at each sites. If method = "stan_glm", then each element of the list is a sites x pred_samples matrix containing the posterior predictive distribution of the species at each sites.
#' @author Giovanni Poggiato and Jérémy Andréoletti
#' @importFrom igraph V decompose neighbors vcount
#' @importFrom parallel mclapply detectCores
#' @importFrom stats quantile
#' @method predict trophicSDMfit
#' @export
#' @examples
#' data(Y, X, G)
#' # define abiotic part of the model
#' env.formula = "~ X_1 + X_2"
#' # Run the model with bottom-up control using stan_glm as fitting method and no penalisation
#' # (set iter = 1000 to obtain reliable results)
#' m = trophicSDM(Y, X, G, env.formula, iter = 50,
#' family = binomial(link = "logit"), penal = NULL,
#' mode = "prey", method = "stan_glm")
#'# We can now evaluate species probabilities of presence for the environmental conditions c(0.5, 0.5)
#' predict(m, Xnew = data.frame(X_1 = 0.5, X_2 = 0.5))
#' # Obtain 50 draws from the posterior predictive distribution of species (pred_samples = 10)
#' # using predicted presence-absences of species to predict their predators (prob.cov = TRUE)
#' # Since we don't specify Xnew, the function sets Xnew = X by default
#' Ypred = predict(m, fullPost = TRUE, pred_samples = 10, prob.cov = FALSE)
#' # We can ask the function to only give back posterior mean and 95% credible intervals with
#' # fullPost = F
#' \donttest{
#' Ypred = predict(m, fullPost = TRUE, pred_samples = 30, prob.cov = FALSE)
#' }
#' # If we fit the model using in a frequentist way (e.g. glm)
#' m = trophicSDM(Y, X, G, env.formula,
#' family = binomial(link = "logit"), penal = NULL,
#' mode = "prey", method = "glm")
#' # We are obliged to set pred_samples = 1
#' # (this is done by default if pred_samples is not provided)
#' # In the frequentist case, fullPost is useless.
#' Ypred = predict(m, pred_samples = 1, prob.cov = FALSE)
predict.trophicSDMfit = function(object, Xnew = NULL, prob.cov = FALSE,
pred_samples = NULL, run.parallel = FALSE, verbose = FALSE,
fullPost = TRUE, filter.table = NULL, ...){
tSDM = object
############################################################
# checks & errors
if(!inherits(tSDM, "trophicSDMfit")) stop("You must provide a trophicSDMfit object")
if(is.null(pred_samples)){
if(tSDM$model.call$method=="glm") pred_samples = 1
if(tSDM$model.call$method=="stan_glm") pred_samples = round(tSDM$model.call$iter/10)
}
if(tSDM$model.call$method=="glm" & pred_samples != 1 ){stop("glm requires pred_sample = 1")}
if(is.null(Xnew)) Xnew = tSDM$data$X
# checks for filter.table ( must be a list where each element is of length nrow(Xnew))
if(!is.null(filter.table) &
(!all(unlist(lapply(filter.table,function(x) length(x) == nrow(Xnew) ))) |
length(filter.table) != tSDM$data$S)) {
stop("filter.table must be a list where each element is of length nrow(Xnew) ")
}
############################################################
family = tSDM$model.call$family
n = nrow(Xnew)
S = tSDM$data$S
G = tSDM$data$G
mode = tSDM$model.call$mode
method = tSDM$model.call$method
# Sort species
sp.prediction = as.list(vector(length=vcount(G)))
if(mode == "prey"){
#sortedV = igraph::V(G)[order(unlist(lapply(igraph::decompose(G), compute_TL_laplacian)), decreasing=T)]
sortedV = topological.sort(G, mode = "in")
}else{
#sortedV = igraph::V(G)[order(unlist(lapply(igraph::decompose(G), compute_TL_laplacian)), decreasing=F)]
sortedV = topological.sort(G, mode = "out")
}
names(sp.prediction) = sortedV$name
############################################################
# presence/absence case
if(family$family %in% c("bernoulli", "binomial")){
Neighb = lapply(sortedV, function(sV) neighbors(G, sV, ifelse(mode == "prey", "out", "in")))
# core loop on species (as in trophicSDM)
for(j in 1:vcount(G)){
# print (if verbose)
if(verbose){
print(paste("--- Species", names(sortedV[j]), "---"));
print(tSDM$model[[names(sortedV[j])]]$form.all)
}
# neighbor species (hat have already been predicted)
neighb.sp = Neighb[[sortedV$name[j]]]
# create data to predict with a call to predict.SDMfit
newdata = array(data = NA, dim = c(n, (ncol(Xnew)+length(neighb.sp)), pred_samples))
colnames(newdata) = c(colnames(Xnew), names(neighb.sp))
# fill the abiotic variables
newdata[,1:ncol(Xnew),] = as.matrix(Xnew)
#### fill the biotic part of new data
## species that have already been predicted
if (length(neighb.sp)>0){ for(k in 1:length(neighb.sp)){
if(prob.cov){
# if prob.cov=TRUE, use the species predicted probabilities of presence
newdata[, ncol(Xnew)+k,] = sp.prediction[[names(neighb.sp[k])]]$predictions.prob
}else{
newdata[, ncol(Xnew)+k,] = sp.prediction[[names(neighb.sp[k])]]$predictions.bin
}
}
}
# apply the function SDMpredict to each layer of the array (MARGIN=3)
if(run.parallel){
pred.array = mclapply(1:dim(newdata)[3],
FUN = function(x){
predict(object = tSDM$model[[names(sortedV[j])]],
newdata = newdata[,,x],
pred_samples=1,
prob.cov=prob.cov)},
mc.cores = detectCores() - 1)
}else{
pred.array = apply(newdata,
MARGIN = 3,
FUN = function(x){
predict(object = tSDM$model[[names(sortedV[j])]],
newdata = x,
pred_samples = 1,
prob.cov = prob.cov)})
}
# unlist and format
sp.prediction[[names(sortedV[j])]] = list()
sp.prediction[[names(sortedV[j])]]$predictions.prob =
do.call(cbind,
lapply(pred.array,
FUN=function(x) x$predictions.prob))
if(!is.null(filter.table)){
sp.prediction[[names(sortedV[j])]]$predictions.prob.unfiltered =
sp.prediction[[names(sortedV[j])]]$predictions.prob
sp.prediction[[names(sortedV[j])]]$predictions.prob =
filter.table[[names(sortedV[j])]] * do.call(cbind,
lapply(pred.array,
FUN=function(x) x$predictions.prob))
}
if(!prob.cov){# Here we don't care to give back $predictions.bin.unfiltered
if(!is.null(filter.table)){
sp.prediction[[names(sortedV[j])]]$predictions.bin =
filter.table[[names(sortedV[j])]] *
do.call(cbind,
lapply(pred.array,
FUN=function(x) x$predictions.bin))
}else{
sp.prediction[[names(sortedV[j])]]$predictions.bin =
do.call(cbind,
lapply(pred.array,
FUN = function(x) x$predictions.bin))
}
}
} # End loop on species
###### Wrap up results and resume (if needed)
if(pred_samples == 1){
for(j in 1:length(sp.prediction)){
sp.prediction[[j]]$predictions.prob = sp.prediction[[j]]$predictions.prob[,1]
if(!is.null(filter.table)){
sp.prediction[[j]]$predictions.prob.unfiltered = sp.prediction[[j]]$predictions.prob.unfiltered[,1]
}
}
}else{
if(!fullPost){
# predictions.prob
for(j in 1:length(sp.prediction)){
predictions.mean = rowMeans(sp.prediction[[j]]$predictions.prob)
predictions.q975 = apply(sp.prediction[[j]]$predictions.prob, 1, quantile, 0.975)
predictions.q025 = apply(sp.prediction[[j]]$predictions.prob, 1, quantile, 0.025)
sp.prediction[[j]]$predictions.prob = list(predictions.mean = predictions.mean,
predictions.q025 = predictions.q025,
predictions.q975 = predictions.q975
)
}
# predictions.prob.unfiltered (if needed)
if(!is.null(filter.table)){
for(j in 1:length(sp.prediction)){
predictions.mean = rowMeans(sp.prediction[[j]]$predictions.prob.unfiltered)
predictions.q975 = apply(sp.prediction[[j]]$predictions.prob.unfiltered,1,quantile,0.975)
predictions.q025 = apply(sp.prediction[[j]]$predictions.prob.unfiltered,1,quantile,0.025)
sp.prediction[[j]]$predictions.prob.unfiltered = list(predictions.mean = predictions.mean,
predictions.q025 = predictions.q025,
predictions.q975 = predictions.q975
)
}
}
}
}
if(is.null(filter.table)){
sp.prediction = lapply(sp.prediction, function(x) x$predictions.prob)
}
return(sp.prediction = sp.prediction)
}
# Just the same of above but predictions are directly given (no need to choose between prob or not)
if(family$family=="gaussian"){
for(j in 1:vcount(G)){ # eventually modify with apply
neigh.sp = neighbors(G,v=sortedV[j],mode="out")
newdata=array(data=NA,dim=c(n,(ncol(Xnew)+length(neigh.sp)),pred_samples))
colnames(newdata)=c(colnames(Xnew),names(neigh.sp))
# fill the abiotic variables
newdata[,1:ncol(Xnew),]=as.matrix(Xnew)
if(length((neigh.sp)>0)){
for(k in 1:length(neigh.sp)){
newdata[,ncol(Xnew)+k,] = sp.prediction[[names(neigh.sp[k])]]# here select the random samples!
}
}
if(run.parallel){
pred.array = mclapply(1:dim(newdata)[3],
FUN = function(x){
predict(object = tSDM$model[[names(sortedV[j])]],
newdata = newdata[,,x],
pred_samples = 1,
prob.cov = prob.cov)},
mc.cores = detectCores())
}else{
pred.array = apply(newdata,
MARGIN=3,
FUN = function(x){
predict(object = tSDM$model[[names(sortedV[j])]],
newdata = x,
pred_samples = 1,
prob.cov = prob.cov)})
}
if(!is.null(filter.table)){ # eventually we could change this to give back both filtered and unfilted
sp.prediction[[names(sortedV[j])]] = filter.table[[names(sortedV[j])]] * pred.array
}else{
sp.prediction[[names(sortedV[j])]] = pred.array
}
} # End loop on species
if(pred_samples == 1){
for(j in 1:length(sp.prediction)){
sp.prediction[[j]] = sp.prediction[[j]][,1]
}
}else{
if(!fullPost & method == "stan_glm"){
# if !fullPost, only take the mean and intervals
for(j in 1:length(sp.prediction)){
predictions.mean = rowMeans(sp.prediction[[j]])
predictions.q975 = apply(sp.prediction[[j]],1,quantile,0.975)
predictions.q025 = apply(sp.prediction[[j]],1,quantile,0.025)
sp.prediction[[j]] = list(predictions.mean = predictions.mean,
predictions.q025 = predictions.q025,
predictions.q975 = predictions.q975
)
}
}
}
return(sp.prediction)
}
}
Try the webSDM package in your browser
Any scripts or data that you put into this service are public.
webSDM documentation built on June 24, 2024, 5:13 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions predict.trophicSDMfit
Documented in predict.trophicSDMfit
#' Computes predicted values from the fitted trophicSDMfit model
#'
#' Computes predicted values from the fitted trophicSDMfit model at environmental conditions specified by \code{Xnew}. Once predictions have been obtained, their quality can eventually be evaluated with \code{evaluateModelFit()}.
#' @param object A trophicSDMfit object obtained with trophicSDM()
#' @param Xnew a matrix specifying the environmental covariates for the predictions to be made. If NULL (default), predictions are done on the training dataset (e.g. by setting Xnew = tSDM$data$X).
#' @param prob.cov Parameter to predict with trophicSDM with presence-absence data. Whether to use predicted probability of presence (prob.cov = T) or the transformed presence-absences (default, prov.cov = F) to predict species distribution.
#' @param pred_samples Number of samples to draw from species posterior predictive distribution when method = "stan_glm". If NULL, set by the default to the number of iterations/10.
#' @param run.parallel Whether to use parallelise code when possible. Can speed up computation time.
#' @param verbose Whether to print advances of the algorithm
#' @param fullPost Optional parameter for stan_glm only. Whether to give back the full posterior predictive distribution (default, fullPost = TRUE) or just the posterior mean, and 2.5% and 97.5% quantiles,
#' @param filter.table Optional, default to NULL, should be provided only if the users wants to filter some species predictions. A sites x species matrix of zeros and ones.
#' @param ... additional arguments
#' @return A list containing for each species the predicted value at each sites. If method = "stan_glm", then each element of the list is a sites x pred_samples matrix containing the posterior predictive distribution of the species at each sites.
#' @author Giovanni Poggiato and Jérémy Andréoletti
#' @importFrom igraph V decompose neighbors vcount
#' @importFrom parallel mclapply detectCores
#' @importFrom stats quantile
#' @method predict trophicSDMfit
#' @export
#' @examples
#' data(Y, X, G)
#' # define abiotic part of the model
#' env.formula = "~ X_1 + X_2"
#' # Run the model with bottom-up control using stan_glm as fitting method and no penalisation
#' # (set iter = 1000 to obtain reliable results)
#' m = trophicSDM(Y, X, G, env.formula, iter = 50,
#' family = binomial(link = "logit"), penal = NULL,
#' mode = "prey", method = "stan_glm")
#'# We can now evaluate species probabilities of presence for the environmental conditions c(0.5, 0.5)
#' predict(m, Xnew = data.frame(X_1 = 0.5, X_2 = 0.5))
#' # Obtain 50 draws from the posterior predictive distribution of species (pred_samples = 10)
#' # using predicted presence-absences of species to predict their predators (prob.cov = TRUE)
#' # Since we don't specify Xnew, the function sets Xnew = X by default
#' Ypred = predict(m, fullPost = TRUE, pred_samples = 10, prob.cov = FALSE)
#' # We can ask the function to only give back posterior mean and 95% credible intervals with
#' # fullPost = F
#' \donttest{
#' Ypred = predict(m, fullPost = TRUE, pred_samples = 30, prob.cov = FALSE)
#' }
#' # If we fit the model using in a frequentist way (e.g. glm)
#' m = trophicSDM(Y, X, G, env.formula,
#' family = binomial(link = "logit"), penal = NULL,
#' mode = "prey", method = "glm")
#' # We are obliged to set pred_samples = 1
#' # (this is done by default if pred_samples is not provided)
#' # In the frequentist case, fullPost is useless.
#' Ypred = predict(m, pred_samples = 1, prob.cov = FALSE)
predict.trophicSDMfit = function(object, Xnew = NULL, prob.cov = FALSE,
pred_samples = NULL, run.parallel = FALSE, verbose = FALSE,
fullPost = TRUE, filter.table = NULL, ...){
tSDM = object
############################################################
# checks & errors
if(!inherits(tSDM, "trophicSDMfit")) stop("You must provide a trophicSDMfit object")
if(is.null(pred_samples)){
if(tSDM$model.call$method=="glm") pred_samples = 1
if(tSDM$model.call$method=="stan_glm") pred_samples = round(tSDM$model.call$iter/10)
}
if(tSDM$model.call$method=="glm" & pred_samples != 1 ){stop("glm requires pred_sample = 1")}
if(is.null(Xnew)) Xnew = tSDM$data$X
# checks for filter.table ( must be a list where each element is of length nrow(Xnew))
if(!is.null(filter.table) &
(!all(unlist(lapply(filter.table,function(x) length(x) == nrow(Xnew) ))) |
length(filter.table) != tSDM$data$S)) {
stop("filter.table must be a list where each element is of length nrow(Xnew) ")
}
############################################################
family = tSDM$model.call$family
n = nrow(Xnew)
S = tSDM$data$S
G = tSDM$data$G
mode = tSDM$model.call$mode
method = tSDM$model.call$method
# Sort species
sp.prediction = as.list(vector(length=vcount(G)))
if(mode == "prey"){
#sortedV = igraph::V(G)[order(unlist(lapply(igraph::decompose(G), compute_TL_laplacian)), decreasing=T)]
sortedV = topological.sort(G, mode = "in")
}else{
#sortedV = igraph::V(G)[order(unlist(lapply(igraph::decompose(G), compute_TL_laplacian)), decreasing=F)]
sortedV = topological.sort(G, mode = "out")
}
names(sp.prediction) = sortedV$name
############################################################
# presence/absence case
if(family$family %in% c("bernoulli", "binomial")){
Neighb = lapply(sortedV, function(sV) neighbors(G, sV, ifelse(mode == "prey", "out", "in")))
# core loop on species (as in trophicSDM)
for(j in 1:vcount(G)){
# print (if verbose)
if(verbose){
print(paste("--- Species", names(sortedV[j]), "---"));
print(tSDM$model[[names(sortedV[j])]]$form.all)
}
# neighbor species (hat have already been predicted)
neighb.sp = Neighb[[sortedV$name[j]]]
# create data to predict with a call to predict.SDMfit
newdata = array(data = NA, dim = c(n, (ncol(Xnew)+length(neighb.sp)), pred_samples))
colnames(newdata) = c(colnames(Xnew), names(neighb.sp))
# fill the abiotic variables
newdata[,1:ncol(Xnew),] = as.matrix(Xnew)
#### fill the biotic part of new data
## species that have already been predicted
if (length(neighb.sp)>0){ for(k in 1:length(neighb.sp)){
if(prob.cov){
# if prob.cov=TRUE, use the species predicted probabilities of presence
newdata[, ncol(Xnew)+k,] = sp.prediction[[names(neighb.sp[k])]]$predictions.prob
}else{
newdata[, ncol(Xnew)+k,] = sp.prediction[[names(neighb.sp[k])]]$predictions.bin
}
}
}
# apply the function SDMpredict to each layer of the array (MARGIN=3)
if(run.parallel){
pred.array = mclapply(1:dim(newdata)[3],
FUN = function(x){
predict(object = tSDM$model[[names(sortedV[j])]],
newdata = newdata[,,x],
pred_samples=1,
prob.cov=prob.cov)},
mc.cores = detectCores() - 1)
}else{
pred.array = apply(newdata,
MARGIN = 3,
FUN = function(x){
predict(object = tSDM$model[[names(sortedV[j])]],
newdata = x,
pred_samples = 1,
prob.cov = prob.cov)})
}
# unlist and format
sp.prediction[[names(sortedV[j])]] = list()
sp.prediction[[names(sortedV[j])]]$predictions.prob =
do.call(cbind,
lapply(pred.array,
FUN=function(x) x$predictions.prob))
if(!is.null(filter.table)){
sp.prediction[[names(sortedV[j])]]$predictions.prob.unfiltered =
sp.prediction[[names(sortedV[j])]]$predictions.prob
sp.prediction[[names(sortedV[j])]]$predictions.prob =
filter.table[[names(sortedV[j])]] * do.call(cbind,
lapply(pred.array,
FUN=function(x) x$predictions.prob))
}
if(!prob.cov){# Here we don't care to give back $predictions.bin.unfiltered
if(!is.null(filter.table)){
sp.prediction[[names(sortedV[j])]]$predictions.bin =
filter.table[[names(sortedV[j])]] *
do.call(cbind,
lapply(pred.array,
FUN=function(x) x$predictions.bin))
}else{
sp.prediction[[names(sortedV[j])]]$predictions.bin =
do.call(cbind,
lapply(pred.array,
FUN = function(x) x$predictions.bin))
}
}
} # End loop on species
###### Wrap up results and resume (if needed)
if(pred_samples == 1){
for(j in 1:length(sp.prediction)){
sp.prediction[[j]]$predictions.prob = sp.prediction[[j]]$predictions.prob[,1]
if(!is.null(filter.table)){
sp.prediction[[j]]$predictions.prob.unfiltered = sp.prediction[[j]]$predictions.prob.unfiltered[,1]
}
}
}else{
if(!fullPost){
# predictions.prob
for(j in 1:length(sp.prediction)){
predictions.mean = rowMeans(sp.prediction[[j]]$predictions.prob)
predictions.q975 = apply(sp.prediction[[j]]$predictions.prob, 1, quantile, 0.975)
predictions.q025 = apply(sp.prediction[[j]]$predictions.prob, 1, quantile, 0.025)
sp.prediction[[j]]$predictions.prob = list(predictions.mean = predictions.mean,
predictions.q025 = predictions.q025,
predictions.q975 = predictions.q975
)
}
# predictions.prob.unfiltered (if needed)
if(!is.null(filter.table)){
for(j in 1:length(sp.prediction)){
predictions.mean = rowMeans(sp.prediction[[j]]$predictions.prob.unfiltered)
predictions.q975 = apply(sp.prediction[[j]]$predictions.prob.unfiltered,1,quantile,0.975)
predictions.q025 = apply(sp.prediction[[j]]$predictions.prob.unfiltered,1,quantile,0.025)
sp.prediction[[j]]$predictions.prob.unfiltered = list(predictions.mean = predictions.mean,
predictions.q025 = predictions.q025,
predictions.q975 = predictions.q975
)
}
}
}
}
if(is.null(filter.table)){
sp.prediction = lapply(sp.prediction, function(x) x$predictions.prob)
}
return(sp.prediction = sp.prediction)
}
# Just the same of above but predictions are directly given (no need to choose between prob or not)
if(family$family=="gaussian"){
for(j in 1:vcount(G)){ # eventually modify with apply
neigh.sp = neighbors(G,v=sortedV[j],mode="out")
newdata=array(data=NA,dim=c(n,(ncol(Xnew)+length(neigh.sp)),pred_samples))
colnames(newdata)=c(colnames(Xnew),names(neigh.sp))
# fill the abiotic variables
newdata[,1:ncol(Xnew),]=as.matrix(Xnew)
if(length((neigh.sp)>0)){
for(k in 1:length(neigh.sp)){
newdata[,ncol(Xnew)+k,] = sp.prediction[[names(neigh.sp[k])]]# here select the random samples!
}
}
if(run.parallel){
pred.array = mclapply(1:dim(newdata)[3],
FUN = function(x){
predict(object = tSDM$model[[names(sortedV[j])]],
newdata = newdata[,,x],
pred_samples = 1,
prob.cov = prob.cov)},
mc.cores = detectCores())
}else{
pred.array = apply(newdata,
MARGIN=3,
FUN = function(x){
predict(object = tSDM$model[[names(sortedV[j])]],
newdata = x,
pred_samples = 1,
prob.cov = prob.cov)})
}
if(!is.null(filter.table)){ # eventually we could change this to give back both filtered and unfilted
sp.prediction[[names(sortedV[j])]] = filter.table[[names(sortedV[j])]] * pred.array
}else{
sp.prediction[[names(sortedV[j])]] = pred.array
}
} # End loop on species
if(pred_samples == 1){
for(j in 1:length(sp.prediction)){
sp.prediction[[j]] = sp.prediction[[j]][,1]
}
}else{
if(!fullPost & method == "stan_glm"){
# if !fullPost, only take the mean and intervals
for(j in 1:length(sp.prediction)){
predictions.mean = rowMeans(sp.prediction[[j]])
predictions.q975 = apply(sp.prediction[[j]],1,quantile,0.975)
predictions.q025 = apply(sp.prediction[[j]],1,quantile,0.025)
sp.prediction[[j]] = list(predictions.mean = predictions.mean,
predictions.q025 = predictions.q025,
predictions.q975 = predictions.q975
)
}
}
}
return(sp.prediction)
}
}
Try the webSDM package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.