plot function

Generic plot function for predict_bru_sdm.

Plot for predict_bru_sdm

Plot for modISDM_predict

Plot for modMarks_predict

Plot for modSpecies_predict

## 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"

Returns

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 <- 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')
 
 }
## 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 <- startSpecies(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)