plot.bruSDM_predict {PointedSDMs}

Generic plot function for predict_bru_sdm.

Description

Plot for predict_bru_sdm

Plot for modISDM_predict

Plot for modMarks_predict

Plot for modSpecies_predict

Usage

## S3 method for class 'bruSDM_predict'
plot(
  x,
  whattoplot = c("mean"),
  cols = NULL,
  layout = NULL,
  colourLow = NULL,
  colourHigh = NULL,
  plot = TRUE,
  ...
)

## S3 method for class 'modISDM_predict'
plot(x, variable = "mean", plot = TRUE, ...)

## S3 method for class 'modMarks_predict'
plot(x, variable = "mean", plot = TRUE, ...)

## S3 method for class 'modSpecies_predict'
plot(x, variable = "mean", plot = TRUE, ...)

Arguments

x

A modSpecies_predict object.

whattoplot

One of the following statistics to plot: "mean", "sd", "q0.025", "median","q0.975", "smin", "smax", "cv", "var"

cols

Number of columns required for the plotting. Used by inlabru's multiplot function.

layout

Layout of the plots. Used by inlabru's multiplot function.

colourLow

Colour for the low values in the predictions (see ?scale_colour_gradient from ggplot2). Defaults to NULL. If non-NULL, colourHigh is required.

colourHigh

Colour for the high values in the predictions (see ?scale_colour_gradient from ggplot2). Defaults to NULL. If non-NULL, colourLow is required.

plot

Should the plots be printed, defaults to TRUE. If FALSE will produce a list of ggplot objects.

...

Argument not used

variable

One of the following statistics to plot: "mean", "sd", "q0.025", "median","q0.975", "smin", "smax", "cv", "var"

Value

A ggplot2 object.

A ggplot2 object.

A ggplot2 object.

A ggplot2 object.

Examples

## Not run: 
 
 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 <- intModel(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')
 
 }

## End(Not run)

## Not run: 
 
 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 <- intModel(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')
 
 }

## End(Not run)

## Not run: 
 
 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 <- startMarks(data, Mesh = mesh,
                             Projection = proj, responsePA = 'Present',
                             markNames = 'speciesName', 
                             markFamily = 'multinomial')
 
  ##Run the model
 modelRun <- fitISDM(organizedData, options = list(control.inla = list(int.strategy = 'eb',
                                                                     diagonal = 1)))
   
  #Predict spatial field on linear scale
  predictions <- predict(modelRun, mesh = mesh, spatial = TRUE, fun = 'linear')
   
  #Make generic plot of predictions
  plot(predictions)
 
 }

## End(Not run)

## Not run: 
 
 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 <- intModel(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')
 
 }

## End(Not run)