In wallaceEcoMod/wallace: A Modular Platform for Reproducible Modeling of Species Niches and Distributions
```{asis, echo = {{xfer_user_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}, eval = {{xfer_user_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}, include = {{xfer_user_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}}
Transfer model
Transferring the model to the same modelling area with no threshold rule. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}}
## Specify the directory with the environmental variables
dir_envsXf_{{spAbr}} <- ""
envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}})
# Load user environmental variables
xferUserEnvs_{{spAbr}} <- envs_userEnvs(
rasPath = envsXf_path,
rasName = {{userXfName_rmd}}
)
# Transfer model
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs(
evalOut = model_{{spAbr}},
curModel= "{{curModel_rmd}}",
envs = xferUserEnvs_{{spAbr}} ,
xfExt = bgExt_{{spAbr}},
alg = "{{alg_rmd}}",
outputType = "{{outputType_rmd}}",
clamp = {{clamp_rmd}}
)
# store the cropped variables of transfer
xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt
###Make map of transfer
bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox
bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2],
bb_{{spAbr}}[2, 2], fraction = 0.05)
mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}}$xferUser, "{{outputType_rmd}}")
rasCols_{{spAbr}} <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
# if no threshold specified
legendPal <- colorNumeric(rev(rasCols_{{spAbr}}), mapXferVals_{{spAbr}}, na.color = 'transparent')
rasPal_{{spAbr}} <- colorNumeric(rasCols_{{spAbr}}, mapXferVals_{{spAbr}}, na.color = 'transparent')
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>%
leaflet::addLegend("bottomright", pal = legendPal,
title = "Predicted Suitability<br>(Transferred)",
values = mapXferVals_{{spAbr}}, layerId = 'xfer',
labFormat = reverseLabel(2, reverse_order = TRUE)) %>%
# map model prediction raster and polygon of transfer
clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>%
addRasterImage(xfer_userEnvs_{{spAbr}}$xferUser, colors = rasPal_{{spAbr}}, opacity = 0.7,
layerId = 'xferRas', group = 'xfer', method = "ngb") %>%
##add polygon of transfer (same modeling area)
addPolygons(data = bgExt_{{spAbr}}, fill = FALSE,
weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & xfer_user_threshold_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}, eval = {{xfer_user_knit & xfer_user_threshold_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}, include = {{xfer_user_knit & xfer_user_threshold_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}}
Transfer model
Transferring the model to the same modelling area with a "{{xfer_thresholdRule_rmd}}" threshold rule of {{xfer_threshold_rmd}}. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & xfer_user_threshold_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}}
## Specify the directory with the environmental variables
dir_envsXf_{{spAbr}} <- ""
envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}})
# Load user environmental variables
xferUserEnvs_{{spAbr}} <- envs_userEnvs(
rasPath = envsXf_path,
rasName = {{userXfName_rmd}}
)
# Transfer model
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs(
evalOut = model_{{spAbr}},
curModel= "{{curModel_rmd}}",
envs = xferUserEnvs_{{spAbr}} ,
xfExt = bgExt_{{spAbr}},
alg = "{{alg_rmd}}",
outputType = "{{outputType_rmd}}",
clamp = {{clamp_rmd}}
)
# store the cropped variables of transfer
xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt
# extract the suitability values for all occurrences
occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')]
# determine the threshold based on the current prediction
occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}})
# Define probability of quantile based on selected threshold
xfer_thresProb_{{spAbr}} <- switch("{{xfer_thresholdRule_rmd}}",
"mtp" = 0, "p10" = 0.1, "qtp" = {{xfer_probQuantile_rmd}})
# Define threshold value
xfer_thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}},
probs = xfer_thresProb_{{spAbr}})
# Add threshold if specified
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferUser > xfer_thres_{{spAbr}}
##Make map
###Make map of transfer
bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox
bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2],
bb_{{spAbr}}[2, 2], fraction = 0.05)
mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}},"{{outputType_rmd}}")
# if threshold specified
rasPal_{{spAbr}} <- c('gray', 'red')
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>%
leaflet::addLegend("bottomright", colors = c('gray', 'red'),
title = "Thresholded Suitability<br>(Transferred)",
labels = c("predicted absence", "predicted presence"),
opacity = 1, layerId = 'xfer')%>%
# map model prediction raster and polygon of transfer
clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>%
addRasterImage(xfer_userEnvs_{{spAbr}}, colors = rasPal_{{spAbr}}, opacity = 0.7,
layerId = 'xferRas', group = 'xfer', method = "ngb") %>%
##add polygon of transfer (same modeling area)
addPolygons(data = bgExt_{{spAbr}}, fill = FALSE,
weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit}}, eval = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit}}, include = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit }}}
Transfer model
Transferring the model to a user drawn area with no threshold rule. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit}}}
## Specify the directory with the environmental variables
dir_envsXf_{{spAbr}} <- ""
envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}})
# Load user environmental variables
xferUserEnvs_{{spAbr}} <- envs_userEnvs(
rasPath = envsXf_path,
rasName = {{userXfName_rmd}}
)
# Generate the area of transfer according to the drawn polygon in the GUI
xfer_draw_{{spAbr}} <- xfer_draw(
polyXfXY = matrix({{polyXfXY_rmd}}, ncol = 2, byrow = FALSE),
polyXfID = {{polyXfID_rmd}},
drawXfBuf = {{BgBuf_rmd}})
# Transfer model
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs(
evalOut = model_{{spAbr}},
curModel= "{{curModel_rmd}}",
envs = xferUserEnvs_{{spAbr}} ,
xfExt = xfer_draw_{{spAbr}},
alg = "{{alg_rmd}}",
outputType = "{{outputType_rmd}}",
clamp = {{clamp_rmd}}
)
#store the cropped variables of transfer
xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt
###Make map of transfer
bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox
bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2],
bb_{{spAbr}}[2, 2], fraction = 0.05)
mapXferVals_{{spAbr}} <- getRasterVals( xfer_userEnvs_{{spAbr}}$xferUser,"{{outputType_rmd}}")
rasCols_{{spAbr}} <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
# if no threshold specified
legendPal <- colorNumeric(rev(rasCols_{{spAbr}}), mapXferVals_{{spAbr}}, na.color = 'transparent')
rasPal_{{spAbr}} <- colorNumeric(rasCols_{{spAbr}}, mapXferVals_{{spAbr}}, na.color = 'transparent')
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>%
leaflet::addLegend("bottomright", pal = legendPal,
title = "Predicted Suitability<br>(Transferred)",
values = mapXferVals_{{spAbr}}, layerId = 'xfer',
labFormat = reverseLabel(2, reverse_order = TRUE)) %>%
# map model prediction raster and polygon of transfer
clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>%
addRasterImage(xfer_userEnvs_{{spAbr}}$xferUser, colors = rasPal_{{spAbr}}, opacity = 0.7,
layerId = 'xferRas', group = 'xfer', method = "ngb") %>%
##add polygon of transfer (user drawn area)
addPolygons(data = xfer_draw_{{spAbr}}, fill = FALSE,
weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit & xfer_user_threshold_knit}}, eval = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit & xfer_user_threshold_knit}}, include = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit & xfer_user_threshold_knit }}}
Transfer model
Transferring the model to a user drawn area with a "{{xfer_thresholdRule_rmd}}" threshold rule of {{xfer_threshold_rmd}}. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit & xfer_user_threshold_knit}}}
## Specify the directory with the environmental variables
dir_envsXf_{{spAbr}} <- ""
envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}})
# Load user environmental variables
xferUserEnvs_{{spAbr}} <- envs_userEnvs(
rasPath = envsXf_path,
rasName = {{userXfName_rmd}}
)
# Generate the area of transfer according to the drawn polygon in the GUI
xfer_draw_{{spAbr}} <-xfer_draw(
polyXfXY = matrix({{polyXfXY_rmd}}, ncol = 2, byrow = FALSE),
polyXfID = {{polyXfID_rmd}},
drawXfBuf = {{BgBuf_rmd}})
# Transfer model
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs(
evalOut = model_{{spAbr}},
curModel= "{{curModel_rmd}}",
envs = xferUserEnvs_{{spAbr}} ,
xfExt = xfer_draw_{{spAbr}},
alg = "{{alg_rmd}}",
outputType = "{{outputType_rmd}}",
clamp = {{clamp_rmd}}
)
# store the cropped variables of transfer
xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt
# 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
xfer_thresProb_{{spAbr}} <- switch("{{xfer_thresholdRule_rmd}}",
"mtp" = 0, "p10" = 0.1, "qtp" = {{xfer_probQuantile_rmd}})
# Define threshold value
xfer_thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}},
probs = xfer_thresProb_{{spAbr}})
# Add threshold if specified
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferUser > xfer_thres_{{spAbr}}
##Make map
###Make map of transfer
bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox
bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2],
bb_{{spAbr}}[2, 2], fraction = 0.05)
mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}},"{{outputType_rmd}}")
# if threshold specified
rasPal_{{spAbr}} <- c('gray', 'red')
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>%
leaflet::addLegend("bottomright", colors = c('gray', 'red'),
title = "Thresholded Suitability<br>(Transferred)",
labels = c("predicted absence", "predicted presence"),
opacity = 1, layerId = 'xfer')%>%
# map model prediction raster and polygon of transfer
clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>%
addRasterImage(xfer_userEnvs_{{spAbr}}, colors = rasPal_{{spAbr}}, opacity = 0.7,
layerId = 'xferRas', group = 'xfer', method = "ngb") %>%
##add polygon of transfer (user drawn area)
addPolygons(data = xfer_draw_{{spAbr}}, fill = FALSE,
weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit}}, eval = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit}}, include = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit }}}
Transfer model
Transferring the model to a user provided area with no threshold rule. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit}}}
## Specify the directory with the environmental variables
dir_envsXf_{{spAbr}} <- ""
envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}})
# Load user environmental variables
xferUserEnvs_{{spAbr}} <- envs_userEnvs(
rasPath = envsXf_path,
rasName = {{userXfName_rmd}}
)
# Generate the area of transfer based on user provided files
##User must input the path to shapefile or csv file and the file name
xfer_userExt_{{spAbr}} <- xfer_userExtent(
bgShp_path = "Input path here",
bgShp_name = "Input file name here",
userBgBuf = {{BgBuf_rmd}})
# Transfer model
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs(
evalOut = model_{{spAbr}},
curModel= "{{curModel_rmd}}",
envs = xferUserEnvs_{{spAbr}} ,
xfExt = xfer_userExt_{{spAbr}},
alg = "{{alg_rmd}}",
outputType = "{{outputType_rmd}}",
clamp = {{clamp_rmd}}
)
# store the cropped variables of transfer
xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt
###Make map of transfer
bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox
bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2],
bb_{{spAbr}}[2, 2], fraction = 0.05)
mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}}$xferUser,"{{outputType_rmd}}")
rasCols_{{spAbr}} <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c")
# if no threshold specified
legendPal <- colorNumeric(rev(rasCols_{{spAbr}}), mapXferVals_{{spAbr}}, na.color = 'transparent')
rasPal_{{spAbr}} <- colorNumeric(rasCols_{{spAbr}}, mapXferVals_{{spAbr}}, na.color = 'transparent')
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>%
leaflet::addLegend("bottomright", pal = legendPal,
title = "Predicted Suitability<br>(Transferred)",
values = mapXferVals_{{spAbr}}, layerId = 'xfer',
labFormat = reverseLabel(2, reverse_order = TRUE)) %>%
# map model prediction raster and polygon of transfer
clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>%
addRasterImage(xfer_userEnvs_{{spAbr}}$xferUser, colors = rasPal_{{spAbr}}, opacity = 0.7,
layerId = 'xferRas', group = 'xfer', method = "ngb") %>%
##add polygon of transfer (user provided area)
addPolygons(data = xfer_userExt_{{spAbr}}, fill = FALSE,
weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit & xfer_user_threshold_knit}}, eval = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit & xfer_user_threshold_knit}}, include = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit & xfer_user_threshold_knit }}}
Transfer model
Transferring the model to a user provided area with a "{{xfer_thresholdRule_rmd}}" threshold rule of {{xfer_threshold_rmd}}. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit & xfer_user_threshold_knit}}}
## Specify the directory with the environmental variables
dir_envsXf_{{spAbr}} <- ""
envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}})
# Load user environmental variables
xferUserEnvs_{{spAbr}} <- envs_userEnvs(
rasPath = envsXf_path,
rasName = {{userXfName_rmd}}
)
# Generate the area of transfer based on user provided files
##User must input the path to shapefile or csv file and the file name
xfer_userExt_{{spAbr}} <- xfer_userExtent(
bgShp_path = "Input path here",
bgShp_name = "Input file name here",
userBgBuf = {{BgBuf_rmd}})
# Transfer model
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs(
evalOut = model_{{spAbr}},
curModel= "{{curModel_rmd}}",
envs = xferUserEnvs_{{spAbr}} ,
xfExt = xfer_userExt_{{spAbr}},
alg = "{{alg_rmd}}",
outputType = "{{outputType_rmd}}",
clamp = {{clamp_rmd}}
)
# store the cropped variables of transfer
xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt
# extract the suitability values for all occurrences
occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')]
# determine the threshold based on the current prediction
occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}})
# Define probability of quantile based on selected threshold
xfer_thresProb_{{spAbr}} <- switch("{{xfer_thresholdRule_rmd}}",
"mtp" = 0, "p10" = 0.1, "qtp" = {{xfer_probQuantile_rmd}})
# Define threshold value
xfer_thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}},
probs = xfer_thresProb_{{spAbr}})
# Add threshold if specified
xfer_userEnvs_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferUser > xfer_thres_{{spAbr}}
##Make map
###Make map of transfer
bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox
bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2],
bb_{{spAbr}}[2, 2], fraction = 0.05)
mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}},"{{outputType_rmd}}")
# if threshold specified
rasPal_{{spAbr}} <- c('gray', 'red')
m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap)
m %>%
fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>%
leaflet::addLegend("bottomright", colors = c('gray', 'red'),
title = "Thresholded Suitability<br>(Transferred)",
labels = c("predicted absence", "predicted presence"),
opacity = 1, layerId = 'xfer')%>%
# map model prediction raster and polygon of transfer
clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>%
addRasterImage(xfer_userEnvs_{{spAbr}}, colors = rasPal_{{spAbr}}, opacity = 0.7,
layerId = 'xferRas', group = 'xfer', method = "ngb") %>%
##add polygon of transfer (user provided area)
addPolygons(data = xfer_userExt_{{spAbr}}, fill = FALSE,
weight = 4, color = "blue", group = 'xfer')
wallaceEcoMod/wallace documentation built on March 24, 2024, 5:15 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.
```{asis, echo = {{xfer_user_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}, eval = {{xfer_user_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}, include = {{xfer_user_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}}
Transfer model
Transferring the model to the same modelling area with no threshold rule. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}} ## Specify the directory with the environmental variables dir_envsXf_{{spAbr}} <- "" envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}}) # Load user environmental variables xferUserEnvs_{{spAbr}} <- envs_userEnvs( rasPath = envsXf_path, rasName = {{userXfName_rmd}} ) # Transfer model xfer_userEnvs_{{spAbr}} <- xfer_userEnvs( evalOut = model_{{spAbr}}, curModel= "{{curModel_rmd}}", envs = xferUserEnvs_{{spAbr}} , xfExt = bgExt_{{spAbr}}, alg = "{{alg_rmd}}", outputType = "{{outputType_rmd}}", clamp = {{clamp_rmd}} ) # store the cropped variables of transfer xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt ###Make map of transfer bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2], bb_{{spAbr}}[2, 2], fraction = 0.05) mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}}$xferUser, "{{outputType_rmd}}") rasCols_{{spAbr}} <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") # if no threshold specified legendPal <- colorNumeric(rev(rasCols_{{spAbr}}), mapXferVals_{{spAbr}}, na.color = 'transparent') rasPal_{{spAbr}} <- colorNumeric(rasCols_{{spAbr}}, mapXferVals_{{spAbr}}, na.color = 'transparent') m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>% leaflet::addLegend("bottomright", pal = legendPal, title = "Predicted Suitability<br>(Transferred)", values = mapXferVals_{{spAbr}}, layerId = 'xfer', labFormat = reverseLabel(2, reverse_order = TRUE)) %>% # map model prediction raster and polygon of transfer clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>% addRasterImage(xfer_userEnvs_{{spAbr}}$xferUser, colors = rasPal_{{spAbr}}, opacity = 0.7, layerId = 'xferRas', group = 'xfer', method = "ngb") %>% ##add polygon of transfer (same modeling area) addPolygons(data = bgExt_{{spAbr}}, fill = FALSE, weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & xfer_user_threshold_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}, eval = {{xfer_user_knit & xfer_user_threshold_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}, include = {{xfer_user_knit & xfer_user_threshold_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}}
Transfer model
Transferring the model to the same modelling area with a "{{xfer_thresholdRule_rmd}}" threshold rule of {{xfer_threshold_rmd}}. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & xfer_user_threshold_knit & !xfer_user_user_knit & !xfer_user_drawn_knit}}} ## Specify the directory with the environmental variables dir_envsXf_{{spAbr}} <- "" envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}}) # Load user environmental variables xferUserEnvs_{{spAbr}} <- envs_userEnvs( rasPath = envsXf_path, rasName = {{userXfName_rmd}} ) # Transfer model xfer_userEnvs_{{spAbr}} <- xfer_userEnvs( evalOut = model_{{spAbr}}, curModel= "{{curModel_rmd}}", envs = xferUserEnvs_{{spAbr}} , xfExt = bgExt_{{spAbr}}, alg = "{{alg_rmd}}", outputType = "{{outputType_rmd}}", clamp = {{clamp_rmd}} ) # store the cropped variables of transfer xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt # 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 xfer_thresProb_{{spAbr}} <- switch("{{xfer_thresholdRule_rmd}}", "mtp" = 0, "p10" = 0.1, "qtp" = {{xfer_probQuantile_rmd}}) # Define threshold value xfer_thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}}, probs = xfer_thresProb_{{spAbr}}) # Add threshold if specified xfer_userEnvs_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferUser > xfer_thres_{{spAbr}} ##Make map ###Make map of transfer bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2], bb_{{spAbr}}[2, 2], fraction = 0.05) mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}},"{{outputType_rmd}}") # if threshold specified rasPal_{{spAbr}} <- c('gray', 'red') m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>% leaflet::addLegend("bottomright", colors = c('gray', 'red'), title = "Thresholded Suitability<br>(Transferred)", labels = c("predicted absence", "predicted presence"), opacity = 1, layerId = 'xfer')%>% # map model prediction raster and polygon of transfer clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>% addRasterImage(xfer_userEnvs_{{spAbr}}, colors = rasPal_{{spAbr}}, opacity = 0.7, layerId = 'xferRas', group = 'xfer', method = "ngb") %>% ##add polygon of transfer (same modeling area) addPolygons(data = bgExt_{{spAbr}}, fill = FALSE, weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit}}, eval = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit}}, include = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit }}}
Transfer model
Transferring the model to a user drawn area with no threshold rule. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit}}} ## Specify the directory with the environmental variables dir_envsXf_{{spAbr}} <- "" envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}}) # Load user environmental variables xferUserEnvs_{{spAbr}} <- envs_userEnvs( rasPath = envsXf_path, rasName = {{userXfName_rmd}} ) # Generate the area of transfer according to the drawn polygon in the GUI xfer_draw_{{spAbr}} <- xfer_draw( polyXfXY = matrix({{polyXfXY_rmd}}, ncol = 2, byrow = FALSE), polyXfID = {{polyXfID_rmd}}, drawXfBuf = {{BgBuf_rmd}}) # Transfer model xfer_userEnvs_{{spAbr}} <- xfer_userEnvs( evalOut = model_{{spAbr}}, curModel= "{{curModel_rmd}}", envs = xferUserEnvs_{{spAbr}} , xfExt = xfer_draw_{{spAbr}}, alg = "{{alg_rmd}}", outputType = "{{outputType_rmd}}", clamp = {{clamp_rmd}} ) #store the cropped variables of transfer xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt ###Make map of transfer bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2], bb_{{spAbr}}[2, 2], fraction = 0.05) mapXferVals_{{spAbr}} <- getRasterVals( xfer_userEnvs_{{spAbr}}$xferUser,"{{outputType_rmd}}") rasCols_{{spAbr}} <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") # if no threshold specified legendPal <- colorNumeric(rev(rasCols_{{spAbr}}), mapXferVals_{{spAbr}}, na.color = 'transparent') rasPal_{{spAbr}} <- colorNumeric(rasCols_{{spAbr}}, mapXferVals_{{spAbr}}, na.color = 'transparent') m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>% leaflet::addLegend("bottomright", pal = legendPal, title = "Predicted Suitability<br>(Transferred)", values = mapXferVals_{{spAbr}}, layerId = 'xfer', labFormat = reverseLabel(2, reverse_order = TRUE)) %>% # map model prediction raster and polygon of transfer clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>% addRasterImage(xfer_userEnvs_{{spAbr}}$xferUser, colors = rasPal_{{spAbr}}, opacity = 0.7, layerId = 'xferRas', group = 'xfer', method = "ngb") %>% ##add polygon of transfer (user drawn area) addPolygons(data = xfer_draw_{{spAbr}}, fill = FALSE, weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit & xfer_user_threshold_knit}}, eval = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit & xfer_user_threshold_knit}}, include = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit & xfer_user_threshold_knit }}}
Transfer model
Transferring the model to a user drawn area with a "{{xfer_thresholdRule_rmd}}" threshold rule of {{xfer_threshold_rmd}}. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & !xfer_user_user_knit & xfer_user_drawn_knit & xfer_user_threshold_knit}}} ## Specify the directory with the environmental variables dir_envsXf_{{spAbr}} <- "" envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}}) # Load user environmental variables xferUserEnvs_{{spAbr}} <- envs_userEnvs( rasPath = envsXf_path, rasName = {{userXfName_rmd}} ) # Generate the area of transfer according to the drawn polygon in the GUI xfer_draw_{{spAbr}} <-xfer_draw( polyXfXY = matrix({{polyXfXY_rmd}}, ncol = 2, byrow = FALSE), polyXfID = {{polyXfID_rmd}}, drawXfBuf = {{BgBuf_rmd}}) # Transfer model xfer_userEnvs_{{spAbr}} <- xfer_userEnvs( evalOut = model_{{spAbr}}, curModel= "{{curModel_rmd}}", envs = xferUserEnvs_{{spAbr}} , xfExt = xfer_draw_{{spAbr}}, alg = "{{alg_rmd}}", outputType = "{{outputType_rmd}}", clamp = {{clamp_rmd}} ) # store the cropped variables of transfer xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt # extract the suitability values for all occurrences occs_xy_{{spAbr}} <- occs_{{spAbr}}[c('longitude', 'latitude')] # determine the threshold based on the current prediction occPredVals_{{spAbr}} <- raster::extract(predSel_{{spAbr}}, occs_xy_{{spAbr}}) # Define probability of quantile based on selected threshold xfer_thresProb_{{spAbr}} <- switch("{{xfer_thresholdRule_rmd}}", "mtp" = 0, "p10" = 0.1, "qtp" = {{xfer_probQuantile_rmd}}) # Define threshold value xfer_thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}}, probs = xfer_thresProb_{{spAbr}}) # Add threshold if specified xfer_userEnvs_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferUser > xfer_thres_{{spAbr}} ##Make map ###Make map of transfer bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2], bb_{{spAbr}}[2, 2], fraction = 0.05) mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}},"{{outputType_rmd}}") # if threshold specified rasPal_{{spAbr}} <- c('gray', 'red') m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>% leaflet::addLegend("bottomright", colors = c('gray', 'red'), title = "Thresholded Suitability<br>(Transferred)", labels = c("predicted absence", "predicted presence"), opacity = 1, layerId = 'xfer')%>% # map model prediction raster and polygon of transfer clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>% addRasterImage(xfer_userEnvs_{{spAbr}}, colors = rasPal_{{spAbr}}, opacity = 0.7, layerId = 'xferRas', group = 'xfer', method = "ngb") %>% ##add polygon of transfer (user drawn area) addPolygons(data = xfer_draw_{{spAbr}}, fill = FALSE, weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit}}, eval = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit}}, include = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit }}}
Transfer model
Transferring the model to a user provided area with no threshold rule. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit}}} ## Specify the directory with the environmental variables dir_envsXf_{{spAbr}} <- "" envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}}) # Load user environmental variables xferUserEnvs_{{spAbr}} <- envs_userEnvs( rasPath = envsXf_path, rasName = {{userXfName_rmd}} ) # Generate the area of transfer based on user provided files ##User must input the path to shapefile or csv file and the file name xfer_userExt_{{spAbr}} <- xfer_userExtent( bgShp_path = "Input path here", bgShp_name = "Input file name here", userBgBuf = {{BgBuf_rmd}}) # Transfer model xfer_userEnvs_{{spAbr}} <- xfer_userEnvs( evalOut = model_{{spAbr}}, curModel= "{{curModel_rmd}}", envs = xferUserEnvs_{{spAbr}} , xfExt = xfer_userExt_{{spAbr}}, alg = "{{alg_rmd}}", outputType = "{{outputType_rmd}}", clamp = {{clamp_rmd}} ) # store the cropped variables of transfer xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt ###Make map of transfer bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2], bb_{{spAbr}}[2, 2], fraction = 0.05) mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}}$xferUser,"{{outputType_rmd}}") rasCols_{{spAbr}} <- c("#2c7bb6", "#abd9e9", "#ffffbf", "#fdae61", "#d7191c") # if no threshold specified legendPal <- colorNumeric(rev(rasCols_{{spAbr}}), mapXferVals_{{spAbr}}, na.color = 'transparent') rasPal_{{spAbr}} <- colorNumeric(rasCols_{{spAbr}}, mapXferVals_{{spAbr}}, na.color = 'transparent') m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>% leaflet::addLegend("bottomright", pal = legendPal, title = "Predicted Suitability<br>(Transferred)", values = mapXferVals_{{spAbr}}, layerId = 'xfer', labFormat = reverseLabel(2, reverse_order = TRUE)) %>% # map model prediction raster and polygon of transfer clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>% addRasterImage(xfer_userEnvs_{{spAbr}}$xferUser, colors = rasPal_{{spAbr}}, opacity = 0.7, layerId = 'xferRas', group = 'xfer', method = "ngb") %>% ##add polygon of transfer (user provided area) addPolygons(data = xfer_userExt_{{spAbr}}, fill = FALSE, weight = 4, color = "blue", group = 'xfer')
```{asis, echo = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit & xfer_user_threshold_knit}}, eval = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit & xfer_user_threshold_knit}}, include = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit & xfer_user_threshold_knit }}}
Transfer model
Transferring the model to a user provided area with a "{{xfer_thresholdRule_rmd}}" threshold rule of {{xfer_threshold_rmd}}. Environmental layers for transferring are user provided.
```r}, include = {{xfer_user_knit & xfer_user_user_knit & !xfer_user_drawn_knit & xfer_user_threshold_knit}}} ## Specify the directory with the environmental variables dir_envsXf_{{spAbr}} <- "" envsXf_path <- file.path(dir_envsXf_{{spAbr}}, {{userXfName_rmd}}) # Load user environmental variables xferUserEnvs_{{spAbr}} <- envs_userEnvs( rasPath = envsXf_path, rasName = {{userXfName_rmd}} ) # Generate the area of transfer based on user provided files ##User must input the path to shapefile or csv file and the file name xfer_userExt_{{spAbr}} <- xfer_userExtent( bgShp_path = "Input path here", bgShp_name = "Input file name here", userBgBuf = {{BgBuf_rmd}}) # Transfer model xfer_userEnvs_{{spAbr}} <- xfer_userEnvs( evalOut = model_{{spAbr}}, curModel= "{{curModel_rmd}}", envs = xferUserEnvs_{{spAbr}} , xfExt = xfer_userExt_{{spAbr}}, alg = "{{alg_rmd}}", outputType = "{{outputType_rmd}}", clamp = {{clamp_rmd}} ) # store the cropped variables of transfer xferExt_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferExt # 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 xfer_thresProb_{{spAbr}} <- switch("{{xfer_thresholdRule_rmd}}", "mtp" = 0, "p10" = 0.1, "qtp" = {{xfer_probQuantile_rmd}}) # Define threshold value xfer_thres_{{spAbr}} <- stats::quantile(occPredVals_{{spAbr}}, probs = xfer_thresProb_{{spAbr}}) # Add threshold if specified xfer_userEnvs_{{spAbr}} <- xfer_userEnvs_{{spAbr}}$xferUser > xfer_thres_{{spAbr}} ##Make map ###Make map of transfer bb_{{spAbr}} <- bgExt_{{spAbr}}@bbox bbZoom <- polyZoom(bb_{{spAbr}}[1, 1], bb_{{spAbr}}[2, 1], bb_{{spAbr}}[1, 2], bb_{{spAbr}}[2, 2], fraction = 0.05) mapXferVals_{{spAbr}} <- getRasterVals(xfer_userEnvs_{{spAbr}},"{{outputType_rmd}}") # if threshold specified rasPal_{{spAbr}} <- c('gray', 'red') m <- leaflet() %>% addProviderTiles(providers$Esri.WorldTopoMap) m %>% fitBounds(bbZoom[1], bbZoom[2], bbZoom[3], bbZoom[4]) %>% leaflet::addLegend("bottomright", colors = c('gray', 'red'), title = "Thresholded Suitability<br>(Transferred)", labels = c("predicted absence", "predicted presence"), opacity = 1, layerId = 'xfer')%>% # map model prediction raster and polygon of transfer clearMarkers() %>% clearShapes() %>% removeImage('xferRas') %>% addRasterImage(xfer_userEnvs_{{spAbr}}, colors = rasPal_{{spAbr}}, opacity = 0.7, layerId = 'xferRas', group = 'xfer', method = "ngb") %>% ##add polygon of transfer (user provided area) addPolygons(data = xfer_userExt_{{spAbr}}, fill = FALSE, weight = 4, color = "blue", group = 'xfer')
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.