In wallaceEcoMod/wallace: A Modular Platform for Reproducible Modeling of Species Niches and Distributions
```{asis, echo = {{vis_mapPreds_knit & vis_map_bioclim_knit & !vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_bioclim_knit & !vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_bioclim_knit & !vis_map_threshold_knit}}}
Visualize
Generate a map of the Bioclim generated model with no threshold
```r}, include = {{vis_mapPreds_knit & vis_map_bioclim_knit & !vis_map_threshold_knit}}}
# Select current model and obtain raster prediction
m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]]
predSel_{{spAbr}} <- dismo::predict(m_{{spAbr}}, bgMask_{{spAbr}}, useC = FALSE)
#Get values of prediction
mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}")
#Define colors and legend
rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent')
rasPal <- colorNumeric(rasCols, mapPredVals_{{spAbr}}, na.color = 'transparent')
#Generate map
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
leaflet::addLegend("bottomright", pal = legendPal,
title = "Predicted Suitability<br>(Training)",
values = mapPredVals_{{spAbr}}, layerId = "train",
labFormat = reverseLabel(2, reverse_order = TRUE)) %>%
#add occurrence data
addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude,
radius = 5, color = 'red', fill = TRUE, fillColor = "red",
fillOpacity = 0.2, weight = 2, popup = ~pop) %>%
##Add model prediction
addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7,
group = 'vis', layerId = 'mapPred', method = "ngb") %>%
##add background polygons
addPolygons(data = bgExt_{{spAbr}}, fill = FALSE,
weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_bioclim_knit & vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_bioclim_knit & vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_bioclim_knit & vis_map_threshold_knit}}}
Visualize
Generate a map of the Bioclim generated model with a "{{thresholdRule_rmd}}" threshold rule of {{threshold_rmd}}.
```r}, include = {{vis_mapPreds_knit & vis_map_bioclim_knit & vis_map_threshold_knit}}}
# Select current model and obtain raster prediction
m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]]
predSel_{{spAbr}} <- dismo::predict(m_{{spAbr}}, bgMask_{{spAbr}}, useC = FALSE)
# extract the suitability values for all occurrences
occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')]
# determine the threshold based on the current prediction
occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}})
# Define probability of quantile based on selected threshold
thresProb_{{spAbr}} <- switch("{{thresholdRule_rmd}}",
"mtp" = 0, "p10" = 0.1, "qtp" = {{probQuantile_rmd}})
# Define threshold value
thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}},
probs = thresProb_{{spAbr}})
# Applied selected threshold
predSel_{{spAbr}} <- predSel_{{spAbr}} > thres_{{spAbr}}
# Get values of prediction
mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}")
# Define colors and legend
rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent')
rasPal <- c('gray', 'blue')
# Generate map
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
leaflet::addLegend("bottomright", colors = c('gray', 'blue'),
title = "Thresholded Suitability<br>(Training)",
labels = c("predicted absence", "predicted presence"),
opacity = 1, layerId = "train") %>%
# add occurrence data
addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude,
radius = 5, color = 'red', fill = TRUE, fillColor = "red",
fillOpacity = 0.2, weight = 2, popup = ~pop) %>%
## Add model prediction
addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7,
group = 'vis', layerId = 'mapPred', method = "ngb") %>%
##add background polygons
addPolygons(data = bgExt_{{spAbr}},fill = FALSE,
weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_maxent_knit & !vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_maxent_knit & !vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_maxent_knit & !vis_map_threshold_knit}}}
Visualize
Generate a map of the Maxent generated model with no threshold
```r}, include = {{vis_mapPreds_knit & vis_map_maxent_knit & !vis_map_threshold_knit}}}
# Select current model and obtain raster prediction
m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]]
predSel_{{spAbr}} <- dismo::predict(
m_{{spAbr}}, bgMask_{{spAbr}},
args = c(paste0("outputformat=", "{{predType_rmd}}"),
paste0("doclamp=", tolower(as.character({{clamp_rmd}})))),
na.rm = TRUE)
#Get values of prediction
mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}")
#Define colors and legend
rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent')
rasPal <- colorNumeric(rasCols, mapPredVals_{{spAbr}}, na.color = 'transparent')
#Generate map
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
leaflet::addLegend("bottomright", pal = legendPal,
title = "Predicted Suitability<br>(Training)",
values = mapPredVals_{{spAbr}}, layerId = "train",
labFormat = reverseLabel(2, reverse_order = TRUE)) %>%
#add occurrence data
addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude,
radius = 5, color = 'red', fill = TRUE, fillColor = "red",
fillOpacity = 0.2, weight = 2, popup = ~pop) %>%
##Add model prediction
addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7,
group = 'vis', layerId = 'mapPred', method = "ngb") %>%
##add background polygons
addPolygons(data = bgExt_{{spAbr}},fill = FALSE,
weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_maxent_knit & vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_maxent_knit & vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_maxent_knit & vis_map_threshold_knit}}}
Visualize
Generate a map of the Maxent generated model with a "{{thresholdRule_rmd}}" threshold rule of {{threshold_rmd}}.
```r}, include = {{vis_mapPreds_knit & vis_map_maxent_knit & vis_map_threshold_knit}}}
# Select current model and obtain raster prediction
m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]]
predSel_{{spAbr}} <- dismo::predict(
m_{{spAbr}}, bgMask_{{spAbr}},
args = c(paste0("outputformat=", "{{predType_rmd}}"),
paste0("doclamp=", tolower(as.character({{clamp_rmd}})))),
na.rm = TRUE)
# extract the suitability values for all occurrences
occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')]
# determine the threshold based on the current prediction
occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}})
# Define probability of quantile based on selected threshold
thresProb_{{spAbr}} <- switch("{{thresholdRule_rmd}}",
"mtp" = 0, "p10" = 0.1, "qtp" = {{probQuantile_rmd}})
# Define threshold value
thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}}, probs = thresProb_{{spAbr}})
# Applied selected threshold
predSel_{{spAbr}} <- predSel_{{spAbr}} > thres_{{spAbr}}
#Get values of prediction
mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}")
#Define colors and legend
rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent')
rasPal <- c('gray', 'blue')
# Generate map
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
leaflet::addLegend("bottomright", colors = c('gray', 'blue'),
title = "Thresholded Suitability<br>(Training)",
labels = c("predicted absence", "predicted presence"),
opacity = 1, layerId = "train") %>%
#add occurrence data
addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude,
radius = 5, color = 'red', fill = TRUE, fillColor = "red",
fillOpacity = 0.2, weight = 2, popup = ~pop) %>%
##Add model prediction
addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7,
group = 'vis', layerId = 'mapPred', method = "ngb") %>%
##add background polygons
addPolygons(data = bgExt_{{spAbr}},fill = FALSE,
weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_maxnet_knit & !vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_maxnet_knit & !vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_maxnet_knit & !vis_map_threshold_knit}}}
Visualize
Generate a map of the maxnet generated model with no threshold
```r}, include = {{vis_mapPreds_knit & vis_map_maxnet_knit & !vis_map_threshold_knit}}}
# Select current model and obtain raster prediction
m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]]
predSel_{{spAbr}} <- predictMaxnet(m_{{spAbr}}, bgMask_{{spAbr}},
type = "{{predType_rmd}}",
clamp = {{clamp_rmd}})
#Get values of prediction
mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}")
#Define colors and legend
rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent')
rasPal <- colorNumeric(rasCols, mapPredVals_{{spAbr}}, na.color = 'transparent')
#Generate map
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
leaflet::addLegend("bottomright", pal = legendPal,
title = "Predicted Suitability<br>(Training)",
values = mapPredVals_{{spAbr}}, layerId = "train",
labFormat = reverseLabel(2, reverse_order = TRUE)) %>%
#add occurrence data
addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude,
radius = 5, color = 'red', fill = TRUE, fillColor = "red",
fillOpacity = 0.2, weight = 2, popup = ~pop) %>%
##Add model prediction
addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7,
group = 'vis', layerId = 'mapPred', method = "ngb") %>%
##add background polygons
addPolygons(data = bgExt_{{spAbr}},fill = FALSE,
weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_maxnet_knit & vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_maxnet_knit & vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_maxnet_knit & vis_map_threshold_knit}}}
Visualize
Generate a map of the maxnet generated model with with a "{{thresholdRule_rmd}}" threshold rule of {{threshold_rmd}}.
```r}, include = {{vis_mapPreds_knit & vis_map_maxnet_knit & vis_map_threshold_knit}}}
# Select current model and obtain raster prediction
m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]]
predSel_{{spAbr}} <- predictMaxnet(m_{{spAbr}}, bgMask_{{spAbr}},
type = "{{predType_rmd}}",
clamp = {{clamp_rmd}})
# extract the suitability values for all occurrences
occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')]
# determine the threshold based on the current prediction
occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}})
# Define probability of quantile based on selected threshold
thresProb_{{spAbr}} <- switch("{{thresholdRule_rmd}}",
"mtp" = 0, "p10" = 0.1, "qtp" = {{probQuantile_rmd}})
# Define threshold value
thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}}, probs = thresProb_{{spAbr}})
# Applied selected threshold
predSel_{{spAbr}} <- predSel_{{spAbr}} > thres_{{spAbr}}
# Get values of prediction
mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}")
# Define colors and legend
rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent')
rasPal <- c('gray', 'blue')
# Generate map
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
leaflet::addLegend("bottomright", colors = c('gray', 'blue'),
title = "Thresholded Suitability<br>(Training)",
labels = c("predicted absence", "predicted presence"),
opacity = 1, layerId = "train") %>%
#add occurrence data
addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude,
radius = 5, color = 'red', fill = TRUE, fillColor = "red",
fillOpacity = 0.2, weight = 2, popup = ~pop) %>%
##Add model prediction
addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7,
group = 'vis', layerId = 'mapPred', method = "ngb") %>%
##add background polygons
addPolygons(data = bgExt_{{spAbr}}, fill = FALSE,
weight = 4, color = "blue", group = 'proj')
wallaceEcoMod/wallace documentation built on March 24, 2024, 5:15 p.m.
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```{asis, echo = {{vis_mapPreds_knit & vis_map_bioclim_knit & !vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_bioclim_knit & !vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_bioclim_knit & !vis_map_threshold_knit}}}
Visualize
Generate a map of the Bioclim generated model with no threshold
```r}, include = {{vis_mapPreds_knit & vis_map_bioclim_knit & !vis_map_threshold_knit}}} # Select current model and obtain raster prediction m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]] predSel_{{spAbr}} <- dismo::predict(m_{{spAbr}}, bgMask_{{spAbr}}, useC = FALSE) #Get values of prediction mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}") #Define colors and legend rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent') rasPal <- colorNumeric(rasCols, mapPredVals_{{spAbr}}, na.color = 'transparent') #Generate map m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% leaflet::addLegend("bottomright", pal = legendPal, title = "Predicted Suitability<br>(Training)", values = mapPredVals_{{spAbr}}, layerId = "train", labFormat = reverseLabel(2, reverse_order = TRUE)) %>% #add occurrence data addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude, radius = 5, color = 'red', fill = TRUE, fillColor = "red", fillOpacity = 0.2, weight = 2, popup = ~pop) %>% ##Add model prediction addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7, group = 'vis', layerId = 'mapPred', method = "ngb") %>% ##add background polygons addPolygons(data = bgExt_{{spAbr}}, fill = FALSE, weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_bioclim_knit & vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_bioclim_knit & vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_bioclim_knit & vis_map_threshold_knit}}}
Visualize
Generate a map of the Bioclim generated model with a "{{thresholdRule_rmd}}" threshold rule of {{threshold_rmd}}.
```r}, include = {{vis_mapPreds_knit & vis_map_bioclim_knit & vis_map_threshold_knit}}} # Select current model and obtain raster prediction m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]] predSel_{{spAbr}} <- dismo::predict(m_{{spAbr}}, bgMask_{{spAbr}}, useC = FALSE) # extract the suitability values for all occurrences occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')] # determine the threshold based on the current prediction occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}}) # Define probability of quantile based on selected threshold thresProb_{{spAbr}} <- switch("{{thresholdRule_rmd}}", "mtp" = 0, "p10" = 0.1, "qtp" = {{probQuantile_rmd}}) # Define threshold value thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}}, probs = thresProb_{{spAbr}}) # Applied selected threshold predSel_{{spAbr}} <- predSel_{{spAbr}} > thres_{{spAbr}} # Get values of prediction mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}") # Define colors and legend rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent') rasPal <- c('gray', 'blue') # Generate map m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% leaflet::addLegend("bottomright", colors = c('gray', 'blue'), title = "Thresholded Suitability<br>(Training)", labels = c("predicted absence", "predicted presence"), opacity = 1, layerId = "train") %>% # add occurrence data addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude, radius = 5, color = 'red', fill = TRUE, fillColor = "red", fillOpacity = 0.2, weight = 2, popup = ~pop) %>% ## Add model prediction addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7, group = 'vis', layerId = 'mapPred', method = "ngb") %>% ##add background polygons addPolygons(data = bgExt_{{spAbr}},fill = FALSE, weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_maxent_knit & !vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_maxent_knit & !vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_maxent_knit & !vis_map_threshold_knit}}}
Visualize
Generate a map of the Maxent generated model with no threshold
```r}, include = {{vis_mapPreds_knit & vis_map_maxent_knit & !vis_map_threshold_knit}}} # Select current model and obtain raster prediction m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]] predSel_{{spAbr}} <- dismo::predict( m_{{spAbr}}, bgMask_{{spAbr}}, args = c(paste0("outputformat=", "{{predType_rmd}}"), paste0("doclamp=", tolower(as.character({{clamp_rmd}})))), na.rm = TRUE) #Get values of prediction mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}") #Define colors and legend rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent') rasPal <- colorNumeric(rasCols, mapPredVals_{{spAbr}}, na.color = 'transparent') #Generate map m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% leaflet::addLegend("bottomright", pal = legendPal, title = "Predicted Suitability<br>(Training)", values = mapPredVals_{{spAbr}}, layerId = "train", labFormat = reverseLabel(2, reverse_order = TRUE)) %>% #add occurrence data addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude, radius = 5, color = 'red', fill = TRUE, fillColor = "red", fillOpacity = 0.2, weight = 2, popup = ~pop) %>% ##Add model prediction addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7, group = 'vis', layerId = 'mapPred', method = "ngb") %>% ##add background polygons addPolygons(data = bgExt_{{spAbr}},fill = FALSE, weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_maxent_knit & vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_maxent_knit & vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_maxent_knit & vis_map_threshold_knit}}}
Visualize
Generate a map of the Maxent generated model with a "{{thresholdRule_rmd}}" threshold rule of {{threshold_rmd}}.
```r}, include = {{vis_mapPreds_knit & vis_map_maxent_knit & vis_map_threshold_knit}}} # Select current model and obtain raster prediction m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]] predSel_{{spAbr}} <- dismo::predict( m_{{spAbr}}, bgMask_{{spAbr}}, args = c(paste0("outputformat=", "{{predType_rmd}}"), paste0("doclamp=", tolower(as.character({{clamp_rmd}})))), na.rm = TRUE) # extract the suitability values for all occurrences occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')] # determine the threshold based on the current prediction occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}}) # Define probability of quantile based on selected threshold thresProb_{{spAbr}} <- switch("{{thresholdRule_rmd}}", "mtp" = 0, "p10" = 0.1, "qtp" = {{probQuantile_rmd}}) # Define threshold value thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}}, probs = thresProb_{{spAbr}}) # Applied selected threshold predSel_{{spAbr}} <- predSel_{{spAbr}} > thres_{{spAbr}} #Get values of prediction mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}") #Define colors and legend rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent') rasPal <- c('gray', 'blue') # Generate map m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% leaflet::addLegend("bottomright", colors = c('gray', 'blue'), title = "Thresholded Suitability<br>(Training)", labels = c("predicted absence", "predicted presence"), opacity = 1, layerId = "train") %>% #add occurrence data addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude, radius = 5, color = 'red', fill = TRUE, fillColor = "red", fillOpacity = 0.2, weight = 2, popup = ~pop) %>% ##Add model prediction addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7, group = 'vis', layerId = 'mapPred', method = "ngb") %>% ##add background polygons addPolygons(data = bgExt_{{spAbr}},fill = FALSE, weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_maxnet_knit & !vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_maxnet_knit & !vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_maxnet_knit & !vis_map_threshold_knit}}}
Visualize
Generate a map of the maxnet generated model with no threshold
```r}, include = {{vis_mapPreds_knit & vis_map_maxnet_knit & !vis_map_threshold_knit}}} # Select current model and obtain raster prediction m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]] predSel_{{spAbr}} <- predictMaxnet(m_{{spAbr}}, bgMask_{{spAbr}}, type = "{{predType_rmd}}", clamp = {{clamp_rmd}}) #Get values of prediction mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}") #Define colors and legend rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent') rasPal <- colorNumeric(rasCols, mapPredVals_{{spAbr}}, na.color = 'transparent') #Generate map m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% leaflet::addLegend("bottomright", pal = legendPal, title = "Predicted Suitability<br>(Training)", values = mapPredVals_{{spAbr}}, layerId = "train", labFormat = reverseLabel(2, reverse_order = TRUE)) %>% #add occurrence data addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude, radius = 5, color = 'red', fill = TRUE, fillColor = "red", fillOpacity = 0.2, weight = 2, popup = ~pop) %>% ##Add model prediction addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7, group = 'vis', layerId = 'mapPred', method = "ngb") %>% ##add background polygons addPolygons(data = bgExt_{{spAbr}},fill = FALSE, weight = 4, color = "blue", group = 'proj')
```{asis, echo = {{vis_mapPreds_knit & vis_map_maxnet_knit & vis_map_threshold_knit}}, eval = {{vis_mapPreds_knit & vis_map_maxnet_knit & vis_map_threshold_knit}}, include = {{vis_mapPreds_knit & vis_map_maxnet_knit & vis_map_threshold_knit}}}
Visualize
Generate a map of the maxnet generated model with with a "{{thresholdRule_rmd}}" threshold rule of {{threshold_rmd}}.
```r}, include = {{vis_mapPreds_knit & vis_map_maxnet_knit & vis_map_threshold_knit}}} # Select current model and obtain raster prediction m_{{spAbr}} <- model_{{spAbr}}@models[["{{curModel_rmd}}"]] predSel_{{spAbr}} <- predictMaxnet(m_{{spAbr}}, bgMask_{{spAbr}}, type = "{{predType_rmd}}", clamp = {{clamp_rmd}}) # extract the suitability values for all occurrences occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')] # determine the threshold based on the current prediction occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}}) # Define probability of quantile based on selected threshold thresProb_{{spAbr}} <- switch("{{thresholdRule_rmd}}", "mtp" = 0, "p10" = 0.1, "qtp" = {{probQuantile_rmd}}) # Define threshold value thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}}, probs = thresProb_{{spAbr}}) # Applied selected threshold predSel_{{spAbr}} <- predSel_{{spAbr}} > thres_{{spAbr}} # Get values of prediction mapPredVals_{{spAbr}} <- getRasterVals(predSel_{{spAbr}}, "{{predType_rmd}}") # Define colors and legend rasCols <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") legendPal <- colorNumeric(rev(rasCols), mapPredVals_{{spAbr}}, na.color = 'transparent') rasPal <- c('gray', 'blue') # Generate map m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% leaflet::addLegend("bottomright", colors = c('gray', 'blue'), title = "Thresholded Suitability<br>(Training)", labels = c("predicted absence", "predicted presence"), opacity = 1, layerId = "train") %>% #add occurrence data addCircleMarkers(data = occs_{{spAbr}}, lat = ~latitude, lng = ~longitude, radius = 5, color = 'red', fill = TRUE, fillColor = "red", fillOpacity = 0.2, weight = 2, popup = ~pop) %>% ##Add model prediction addRasterImage(predSel_{{spAbr}}, colors = rasPal, opacity = 0.7, group = 'vis', layerId = 'mapPred', method = "ngb") %>% ##add background polygons addPolygons(data = bgExt_{{spAbr}}, fill = FALSE, weight = 4, color = "blue", group = 'proj')
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