R/modISDM_predict.R
In PhilipMostert/inlabruSDMs: Fit Models Derived from Point Processes to Species Distributions using 'inlabru'
Defines functions
plot.modISDM_predict
predict.modISDM
Documented in
plot.modISDM_predict
predict.modISDM
#' Export class predict.modISDM
#'
#' @export
setClass('modISDM_predict')
#' Predict for modISDM
#' @title Generic predict function for \code{modISDM} objects.
#' @description Predict function for the object produced by \code{\link{fitISDM}}. Should act identically to \pkg{inlabru}'s generic predict function if wanted, but has additional arguments to help predict certain components created by the model. This is needed since \code{\link{startISDM}} creates variable names which might not be directly apparent to the user.
#' @param object A \code{modISDM} object.
#' @param data Data containing points of the map with which to predict on. May be \code{NULL} if one of \code{mesh} or \code{mask} is \code{NULL}.
#' @param formula Formula to predict. May be \code{NULL} if other arguments: \code{covariates}, \code{spatial}, \code{intercepts} are not \code{NULL}.
#' @param mesh An \code{fm_mesh_2d} object.
#' @param mask A mask of the study background. Defaults to \code{NULL}.
#' @param covariates Name of covariates to predict.
#' @param spatial Logical: include spatial effects in prediction. Defaults to \code{FALSE}.
#' @param intercepts Logical: include intercept terms in prediction. Defaults to \code{FALSE}.
#' @param datasets Names of the datasets to include intercept and spatial term.
#' @param bias Logical include bias field in prediction. Defaults to \code{FALSE}.
#' @param biasnames Names of the datasets to include bias term. Defaults to \code{NULL}. Note: the chosen dataset needs to be run with a bias field first; this can be done using \code{.$addBias} with the object produced by \code{\link{startISDM}}.
#' @param predictor Should all terms (except the bias terms) included in the linear predictor be used in the predictions. Defaults to \code{FALSE}.
#' @param fun Function used to predict. Set to \code{'linear'} if effects on the linear scale are desired.
#' @param ... Additional arguments used by the inlabru \code{predict} function.
#'
#' @method predict modISDM
#' @rdname predict
#' @return A list of inlabru predict objects.
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' if (requireNamespace('INLA')) {
#'
#' #Get Data
#' data("SolitaryTinamou")
#' proj <- "+proj=longlat +ellps=WGS84"
#' data <- SolitaryTinamou$datasets
#' mesh <- SolitaryTinamou$mesh
#' mesh$crs <- proj
#'
#' #Set model up
#' organizedData <- startISDM(data, Mesh = mesh,
#' Projection = proj,
#' responsePA = 'Present')
#'
#' ##Run the model
#' modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
#'
#' #Predict spatial field on linear scale
#' predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
#'
#' }
#'}
#'
#TEMPORAL SHOULD NOT BE AN ARGUMENT
predict.modISDM <- function(object, data = NULL, formula = NULL, mesh = NULL,
mask = NULL, covariates = NULL, spatial = FALSE,
intercepts = FALSE, datasets = NULL, bias = FALSE,
biasnames = NULL, predictor = FALSE,
fun = 'linear', ...) {
##If pointsSpatial == correlate, then need to chose a dataset for which to use as the spatial effect. Could use either the first PA dataset or specify using dataset
if (is.null(data) & is.null(mesh)) stop("Either data covering the entire study region or an fm_mesh_2d object is required.")
#Why can't you do both here?
if (bias && spatial) stop('Please choose one of bias and spatial.')
if (is.null(datasets)) datasets <- unique(object$source)
if (predictor) {
intercepts <- TRUE
covariates <- object$spatCovs$name
if (!is.null(object$covariateFormula)) covariates <- c(covariates, 'Fixed__Effects__Comps')
if (!is.null(object$spatCovs$biasFormula)) covariates <- covariates[!covariates %in% labels(terms(object$spatCovs$biasFormula))]
if (is.character(object$spatial$points)) spatial <- TRUE
else {
if(!object$spatial$points) spatial <- FALSE
else spatial <- TRUE
}
}
if (intercepts) {
if (!is.null(datasets)) {
intercept_terms <- paste0(datasets, '_intercept')
if (identical(rownames(object$summary.fixed)[rownames(object$summary.fixed) %in% intercept_terms], character(0))) intercept_terms <- NULL
} else intercept_terms <- NULL
} else intercept_terms <- NULL
if (is.null(formula) && !intercepts && !spatial && is.null(covariates) && !bias && !predictor) stop("Please provide either a formula or components of a formula to be predicted.")
if (is.null(data)) {
if (!is.null(mask)) {
data <- fmesher::fm_pixels(mesh, mask = mask)
}
else data <- fmesher::fm_int(mesh)
}
if (!is.null(object$temporal$temporalVar)) temporal <- TRUE
else temporal <- FALSE
if (!any(names(data) %in% object$spatCovs$name)) {
for (spatCov in object$spatCovs$name) {
data[, spatCov] <- inlabru::eval_spatial(where = data,
data = get('spatialcovariates',
envir = object$spatCov$env)[spatCov],
layer = spatCov)
if (any(is.na( data[, spatCov]))) {
for (indFix in spatCov) {
data[[indFix]] <- inlabru::bru_fill_missing(where = data,
data = get('spatialcovariates',
envir = object$spatCov$env)[spatCov],
layer = spatCov,
values = data[[indFix]])
}
}
}
}
if (is.null(formula)) {
int <- list()
class(object) <- c('bru','inla','iinla')
if (is.null(fun) | fun == 'linear') fun <- ''
if (temporal) {
numeric_time <- order(as.numeric(unique(unlist(object$temporal$temporalIn))))
time_variable <- object$temporal$temporalVar
time_data <- data.frame(seq_len(max(numeric_time)))
names(time_data) <- time_variable
data <- fmesher::fm_cprod(data, data.frame(time_data))
data$.__plot__index__ <- data[[time_variable]]
}
if (bias) {
if (!is.null(object$biasData$Fields)) {
if (is.null(biasnames)) biasnames <- object$biasData$Fields
biasnames <- paste0(biasnames,'_biasField')
} else biasnames <- NULL
if ('Bias__Effects__Comps' %in% names(object$summary.random)) biasnames <- c('Bias__Effects__Comps', biasnames)
else biasnames <- c(biasnames, NULL)
if (!all(biasnames %in% names(object$summary.random))) stop('Either no bias field has been used or an incorrect dataset name was given.')
if (temporal) {
formula <- as.formula(paste0('~ ',as.character(fun),'(',paste(biasnames,')')))
int[['temporalBiasFields']] <- predict(object, newdata = data, formula = formula, ...)
}
else {
for (bias in biasnames) {
formula <- as.formula(paste0('~ ',as.character(fun),'(',paste(bias,')')))
int[['biasFields']][[bias]] <- predict(object, newdata = data, formula = formula, ...)
}
}
class(int) <- c('modISDM_predict', class(int))
return(int)
}
if (spatial) {
if ('shared_spatial' %in% names(object$summary.random)) spatial_obj <- 'shared_spatial'
else
if (object$spatial$points == 'copy') spatial_obj <- paste0(object$source[1], '_spatial')
else
if (!all(paste0(datasets,'_spatial') %in% names(object$summary.random))) stop('Spatial effects not provided in startISDM')
else spatial_obj <- paste0(datasets, '_spatial')
if (object$spatial$points == 'correlate') {
if (any(object$dataType == "Present absence")) data$._dataset_index_var_. <- which(object$dataType == "Present absence")[1]
else data$._dataset_index_var_. <- 1
}
}
else spatial_obj <- NULL
if (predictor) formula_components <- c(row.names(object$summary.fixed), names(object$summary.random)[!names(object$summary.random) %in% paste0(object[['source']], '_biasField')])
else formula_components <- c(covariates, intercept_terms, spatial_obj)
if (!is.null(object$spatCovs$biasFormula)) formula_components <- formula_components[!formula_components %in% c('Bias__Effects__Comps', paste0(unique(object$species$speciesIn),'_Bias__Effects__Comp'))]
if (all(is.null(formula_components))) stop('Please specify at least one of: covariates, spatial, intercepts or biasd.')
formula <- as.formula(paste0('~ ',as.character(fun),'(',paste(formula_components, collapse = ' + '),')'))
int <- predict(object, newdata = data, formula = formula, ...)
int <- list(int)
names(int) <- 'predictions'
class(int) <- c('modISDM_predict', class(int))
return(int)
}
else {
class(object) <- c('bru','inla','iinla')
int <- predict(object, newdata = data, formula = formula, ...)
int <- list(int)
names(int) <- 'predictions'
class(int) <- c('modISDM_predict', class(int))
return(int)
}
}
#' Plot for modISDM_predict
#' @title Generic plot function for \code{modISDM_predict}.
#' @param x A modISDM_predict object.
#' @param variable One of the following statistics to plot: "mean", "sd", "q0.025", "median","q0.975", "smin", "smax", "cv", "var"
#' @param plot Should the plots be printed, defaults to \code{TRUE}. If \code{FALSE} will produce a list of ggplot objects.
#' @param ... Argument not used
#' @return A ggplot2 object.
#'
#' @method plot modISDM_predict
#' @rdname plot
#'
#' @exportS3Method
#'
#' @examples
#' \dontrun{
#'
#' if (requireNamespace('INLA')) {
#'
#' #Get Data
#' data("SolitaryTinamou")
#' proj <- "+proj=longlat +ellps=WGS84"
#' data <- SolitaryTinamou$datasets
#' mesh <- SolitaryTinamou$mesh
#' mesh$crs <- proj
#'
#' #Set model up
#' organizedData <- startISDM(data, Mesh = mesh, Coordinates = c('X', 'Y'),
#' Projection = proj, responsePA = 'Present')
#'
#' ##Run the model
#' modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
#'
#' #Predict spatial field on linear scale
#' predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
#'
#' #Make generic plot of predictions
#' plot(predictions, colourHigh = 'red', colourLow = 'orange')
#'
#' }
#'}
#'
plot.modISDM_predict <- function(x,
variable = 'mean',
plot = TRUE,
...) {
if (any(!variable%in%c("mean", "sd", "q0.025", "median","q0.975",
"smin", "smax", "cv", "var" ))) stop('variable is not a valid variable to plot')
if (length(x) == 1 && '.__plot__index__' %in% names(x[[1]])) {
if (length(variable) > 1) stop('Please only plot one variable at a time for species plots.')
##Need to create a new var called ..temporal_variable_index.. which is the tempVar
#temporalName <- names(x[[1]])[!names(x[[1]]) %in% c(".block", 'geometry', 'weight', 'mean', 'sd', 'q0.025', 'median', 'q0.975', 'q0.5', 'smin', 'smax', 'cv','mean.mc_std_err', 'sd.mc_std_err')]
temporalName <- '.__plot__index__'
x[[1]]$..temporal_variable_index.. <- as.character(data.frame(x[[1]])[, temporalName])
if (inherits(x[[1]], 'sf')) plot_obj <- geom_sf(data = x[[1]], aes(col = .data[[variable]]))
else plot_obj <- inlabru::gg(x[[1]], aes(col = .data[[variable]]))
plot_grid <- ggplot() + plot_obj + facet_wrap(~ ..temporal_variable_index..) + ggtitle('Plot of the temporal predictions')
return(plot_grid)
}
if (!plot) {
all_plots <- list()
prediction_list <- list()
}
if (names(x) == 'biasFields') {
biasPlot <- TRUE
namesBias <- names(x[[1]])
x <- unlist(x, recursive = FALSE, use.names = FALSE)
names(x) <- namesBias
} else biasPlot <- FALSE
##If plots for the individual fields??
datasettoplot <- names(x)
plot_list <- list()
for (plotname in datasettoplot) {
for (stat in variable) {
if (biasPlot) title <- ggtitle(paste('Predictions of', stat, 'for', plotname, 'bias field'))
else title <- ggtitle(paste('Predictions of', stat))
prediction <- inlabru::gg(x[[plotname]], aes(col = .data[[stat]]))
if (!plot) prediction_list[[plotname]][[stat]] <- ggplot() + prediction + title
plot_list[[plotname]][[stat]] <- ggplot() + prediction + title
}
}
if (plot) {
rows <- length(variable)
cols <- length(datasettoplot)
ind <- max(rows, cols)
plot_list <- unlist(plot_list, recursive = FALSE)
plot_grid <- inlabru::multiplot(plotlist = plot_list, cols = ind)
}
else{
all_plots[[plotname]] <- prediction_list
return(all_plots)
}
}
PhilipMostert/inlabruSDMs documentation built on April 14, 2025, 11:39 a.m.
R Package Documentation
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Defines functions plot.modISDM_predict predict.modISDM
Documented in plot.modISDM_predict predict.modISDM
#' Export class predict.modISDM
#'
#' @export
setClass('modISDM_predict')
#' Predict for modISDM
#' @title Generic predict function for \code{modISDM} objects.
#' @description Predict function for the object produced by \code{\link{fitISDM}}. Should act identically to \pkg{inlabru}'s generic predict function if wanted, but has additional arguments to help predict certain components created by the model. This is needed since \code{\link{startISDM}} creates variable names which might not be directly apparent to the user.
#' @param object A \code{modISDM} object.
#' @param data Data containing points of the map with which to predict on. May be \code{NULL} if one of \code{mesh} or \code{mask} is \code{NULL}.
#' @param formula Formula to predict. May be \code{NULL} if other arguments: \code{covariates}, \code{spatial}, \code{intercepts} are not \code{NULL}.
#' @param mesh An \code{fm_mesh_2d} object.
#' @param mask A mask of the study background. Defaults to \code{NULL}.
#' @param covariates Name of covariates to predict.
#' @param spatial Logical: include spatial effects in prediction. Defaults to \code{FALSE}.
#' @param intercepts Logical: include intercept terms in prediction. Defaults to \code{FALSE}.
#' @param datasets Names of the datasets to include intercept and spatial term.
#' @param bias Logical include bias field in prediction. Defaults to \code{FALSE}.
#' @param biasnames Names of the datasets to include bias term. Defaults to \code{NULL}. Note: the chosen dataset needs to be run with a bias field first; this can be done using \code{.$addBias} with the object produced by \code{\link{startISDM}}.
#' @param predictor Should all terms (except the bias terms) included in the linear predictor be used in the predictions. Defaults to \code{FALSE}.
#' @param fun Function used to predict. Set to \code{'linear'} if effects on the linear scale are desired.
#' @param ... Additional arguments used by the inlabru \code{predict} function.
#'
#' @method predict modISDM
#' @rdname predict
#' @return A list of inlabru predict objects.
#' @export
#'
#' @examples
#'
#' \dontrun{
#'
#' if (requireNamespace('INLA')) {
#'
#' #Get Data
#' data("SolitaryTinamou")
#' proj <- "+proj=longlat +ellps=WGS84"
#' data <- SolitaryTinamou$datasets
#' mesh <- SolitaryTinamou$mesh
#' mesh$crs <- proj
#'
#' #Set model up
#' organizedData <- startISDM(data, Mesh = mesh,
#' Projection = proj,
#' responsePA = 'Present')
#'
#' ##Run the model
#' modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
#'
#' #Predict spatial field on linear scale
#' predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
#'
#' }
#'}
#'
#TEMPORAL SHOULD NOT BE AN ARGUMENT
predict.modISDM <- function(object, data = NULL, formula = NULL, mesh = NULL,
mask = NULL, covariates = NULL, spatial = FALSE,
intercepts = FALSE, datasets = NULL, bias = FALSE,
biasnames = NULL, predictor = FALSE,
fun = 'linear', ...) {
##If pointsSpatial == correlate, then need to chose a dataset for which to use as the spatial effect. Could use either the first PA dataset or specify using dataset
if (is.null(data) & is.null(mesh)) stop("Either data covering the entire study region or an fm_mesh_2d object is required.")
#Why can't you do both here?
if (bias && spatial) stop('Please choose one of bias and spatial.')
if (is.null(datasets)) datasets <- unique(object$source)
if (predictor) {
intercepts <- TRUE
covariates <- object$spatCovs$name
if (!is.null(object$covariateFormula)) covariates <- c(covariates, 'Fixed__Effects__Comps')
if (!is.null(object$spatCovs$biasFormula)) covariates <- covariates[!covariates %in% labels(terms(object$spatCovs$biasFormula))]
if (is.character(object$spatial$points)) spatial <- TRUE
else {
if(!object$spatial$points) spatial <- FALSE
else spatial <- TRUE
}
}
if (intercepts) {
if (!is.null(datasets)) {
intercept_terms <- paste0(datasets, '_intercept')
if (identical(rownames(object$summary.fixed)[rownames(object$summary.fixed) %in% intercept_terms], character(0))) intercept_terms <- NULL
} else intercept_terms <- NULL
} else intercept_terms <- NULL
if (is.null(formula) && !intercepts && !spatial && is.null(covariates) && !bias && !predictor) stop("Please provide either a formula or components of a formula to be predicted.")
if (is.null(data)) {
if (!is.null(mask)) {
data <- fmesher::fm_pixels(mesh, mask = mask)
}
else data <- fmesher::fm_int(mesh)
}
if (!is.null(object$temporal$temporalVar)) temporal <- TRUE
else temporal <- FALSE
if (!any(names(data) %in% object$spatCovs$name)) {
for (spatCov in object$spatCovs$name) {
data[, spatCov] <- inlabru::eval_spatial(where = data,
data = get('spatialcovariates',
envir = object$spatCov$env)[spatCov],
layer = spatCov)
if (any(is.na( data[, spatCov]))) {
for (indFix in spatCov) {
data[[indFix]] <- inlabru::bru_fill_missing(where = data,
data = get('spatialcovariates',
envir = object$spatCov$env)[spatCov],
layer = spatCov,
values = data[[indFix]])
}
}
}
}
if (is.null(formula)) {
int <- list()
class(object) <- c('bru','inla','iinla')
if (is.null(fun) | fun == 'linear') fun <- ''
if (temporal) {
numeric_time <- order(as.numeric(unique(unlist(object$temporal$temporalIn))))
time_variable <- object$temporal$temporalVar
time_data <- data.frame(seq_len(max(numeric_time)))
names(time_data) <- time_variable
data <- fmesher::fm_cprod(data, data.frame(time_data))
data$.__plot__index__ <- data[[time_variable]]
}
if (bias) {
if (!is.null(object$biasData$Fields)) {
if (is.null(biasnames)) biasnames <- object$biasData$Fields
biasnames <- paste0(biasnames,'_biasField')
} else biasnames <- NULL
if ('Bias__Effects__Comps' %in% names(object$summary.random)) biasnames <- c('Bias__Effects__Comps', biasnames)
else biasnames <- c(biasnames, NULL)
if (!all(biasnames %in% names(object$summary.random))) stop('Either no bias field has been used or an incorrect dataset name was given.')
if (temporal) {
formula <- as.formula(paste0('~ ',as.character(fun),'(',paste(biasnames,')')))
int[['temporalBiasFields']] <- predict(object, newdata = data, formula = formula, ...)
}
else {
for (bias in biasnames) {
formula <- as.formula(paste0('~ ',as.character(fun),'(',paste(bias,')')))
int[['biasFields']][[bias]] <- predict(object, newdata = data, formula = formula, ...)
}
}
class(int) <- c('modISDM_predict', class(int))
return(int)
}
if (spatial) {
if ('shared_spatial' %in% names(object$summary.random)) spatial_obj <- 'shared_spatial'
else
if (object$spatial$points == 'copy') spatial_obj <- paste0(object$source[1], '_spatial')
else
if (!all(paste0(datasets,'_spatial') %in% names(object$summary.random))) stop('Spatial effects not provided in startISDM')
else spatial_obj <- paste0(datasets, '_spatial')
if (object$spatial$points == 'correlate') {
if (any(object$dataType == "Present absence")) data$._dataset_index_var_. <- which(object$dataType == "Present absence")[1]
else data$._dataset_index_var_. <- 1
}
}
else spatial_obj <- NULL
if (predictor) formula_components <- c(row.names(object$summary.fixed), names(object$summary.random)[!names(object$summary.random) %in% paste0(object[['source']], '_biasField')])
else formula_components <- c(covariates, intercept_terms, spatial_obj)
if (!is.null(object$spatCovs$biasFormula)) formula_components <- formula_components[!formula_components %in% c('Bias__Effects__Comps', paste0(unique(object$species$speciesIn),'_Bias__Effects__Comp'))]
if (all(is.null(formula_components))) stop('Please specify at least one of: covariates, spatial, intercepts or biasd.')
formula <- as.formula(paste0('~ ',as.character(fun),'(',paste(formula_components, collapse = ' + '),')'))
int <- predict(object, newdata = data, formula = formula, ...)
int <- list(int)
names(int) <- 'predictions'
class(int) <- c('modISDM_predict', class(int))
return(int)
}
else {
class(object) <- c('bru','inla','iinla')
int <- predict(object, newdata = data, formula = formula, ...)
int <- list(int)
names(int) <- 'predictions'
class(int) <- c('modISDM_predict', class(int))
return(int)
}
}
#' Plot for modISDM_predict
#' @title Generic plot function for \code{modISDM_predict}.
#' @param x A modISDM_predict object.
#' @param variable One of the following statistics to plot: "mean", "sd", "q0.025", "median","q0.975", "smin", "smax", "cv", "var"
#' @param plot Should the plots be printed, defaults to \code{TRUE}. If \code{FALSE} will produce a list of ggplot objects.
#' @param ... Argument not used
#' @return A ggplot2 object.
#'
#' @method plot modISDM_predict
#' @rdname plot
#'
#' @exportS3Method
#'
#' @examples
#' \dontrun{
#'
#' if (requireNamespace('INLA')) {
#'
#' #Get Data
#' data("SolitaryTinamou")
#' proj <- "+proj=longlat +ellps=WGS84"
#' data <- SolitaryTinamou$datasets
#' mesh <- SolitaryTinamou$mesh
#' mesh$crs <- proj
#'
#' #Set model up
#' organizedData <- startISDM(data, Mesh = mesh, Coordinates = c('X', 'Y'),
#' Projection = proj, responsePA = 'Present')
#'
#' ##Run the model
#' modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb')))
#'
#' #Predict spatial field on linear scale
#' predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
#'
#' #Make generic plot of predictions
#' plot(predictions, colourHigh = 'red', colourLow = 'orange')
#'
#' }
#'}
#'
plot.modISDM_predict <- function(x,
variable = 'mean',
plot = TRUE,
...) {
if (any(!variable%in%c("mean", "sd", "q0.025", "median","q0.975",
"smin", "smax", "cv", "var" ))) stop('variable is not a valid variable to plot')
if (length(x) == 1 && '.__plot__index__' %in% names(x[[1]])) {
if (length(variable) > 1) stop('Please only plot one variable at a time for species plots.')
##Need to create a new var called ..temporal_variable_index.. which is the tempVar
#temporalName <- names(x[[1]])[!names(x[[1]]) %in% c(".block", 'geometry', 'weight', 'mean', 'sd', 'q0.025', 'median', 'q0.975', 'q0.5', 'smin', 'smax', 'cv','mean.mc_std_err', 'sd.mc_std_err')]
temporalName <- '.__plot__index__'
x[[1]]$..temporal_variable_index.. <- as.character(data.frame(x[[1]])[, temporalName])
if (inherits(x[[1]], 'sf')) plot_obj <- geom_sf(data = x[[1]], aes(col = .data[[variable]]))
else plot_obj <- inlabru::gg(x[[1]], aes(col = .data[[variable]]))
plot_grid <- ggplot() + plot_obj + facet_wrap(~ ..temporal_variable_index..) + ggtitle('Plot of the temporal predictions')
return(plot_grid)
}
if (!plot) {
all_plots <- list()
prediction_list <- list()
}
if (names(x) == 'biasFields') {
biasPlot <- TRUE
namesBias <- names(x[[1]])
x <- unlist(x, recursive = FALSE, use.names = FALSE)
names(x) <- namesBias
} else biasPlot <- FALSE
##If plots for the individual fields??
datasettoplot <- names(x)
plot_list <- list()
for (plotname in datasettoplot) {
for (stat in variable) {
if (biasPlot) title <- ggtitle(paste('Predictions of', stat, 'for', plotname, 'bias field'))
else title <- ggtitle(paste('Predictions of', stat))
prediction <- inlabru::gg(x[[plotname]], aes(col = .data[[stat]]))
if (!plot) prediction_list[[plotname]][[stat]] <- ggplot() + prediction + title
plot_list[[plotname]][[stat]] <- ggplot() + prediction + title
}
}
if (plot) {
rows <- length(variable)
cols <- length(datasettoplot)
ind <- max(rows, cols)
plot_list <- unlist(plot_list, recursive = FALSE)
plot_grid <- inlabru::multiplot(plotlist = plot_list, cols = ind)
}
else{
all_plots[[plotname]] <- prediction_list
return(all_plots)
}
}
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