`R/shap_spatial_response.R`

`shap_spatial_response.Rd`

Calculate spatially SHAP-based response figures. They can help to diagnose both how and where the species responses to environmental variables.

```
shap_spatial_response(
model,
var_occ,
variables,
target_vars = NULL,
shap_nsim = 10,
seed = 10,
pfun = .pfun_shap
)
```

- model
(

`isolation_forest`

or other model). It could be the item`model`

of`POIsotree`

made by function`isotree_po`

. It also could be other user-fitted models as long as the`pfun`

can work on it.- var_occ
(

`data.frame`

,`tibble`

) The`data.frame`

style table that include values of environmental variables at occurrence locations.- variables
(

`stars`

) The`stars`

of environmental variables. It should have multiple`attributes`

instead of`dims`

. If you have`raster`

object instead, you could use`st_as_stars`

to convert it to`stars`

or use`read_stars`

directly read source data as a`stars`

. You also could use item`variables`

of`POIsotree`

made by function`isotree_po`

.- target_vars
(a

`vector`

of`character`

) The selected variables to process. If it is`NULL`

, all variables will be used.- shap_nsim
(

`integer`

) The number of Monte Carlo repetitions in SHAP method to use for estimating each Shapley value. See details in documentation of function`explain`

in package`fastshap`

. When the number of variables is large, a smaller shap_nsim could be used. Be cautious that making SHAP-based spatial dependence will be slow because of Monte-Carlo computation for all pixels. But it is worth the time because it is much more informative. See details in documentation of function`explain`

in package`fastshap`

. The default is 10. Usually a value 10 - 20 is enough.- seed
(

`integer`

) The seed for any random progress. The default is`10L`

.- pfun
(

`function`

) The predict function that requires two arguments,`object`

and`newdata`

. It is only required when`model`

is not`isolation_forest`

. The default is the wrapper function designed for iForest model in`itsdm`

.

(`SHAPSpatial`

) A list of

A list of

`stars`

object of spatially SHAP-based response of all variables

The values show how each environmental variable affects the modeling prediction in space. These maps could help to answer questions of where in terms of environmental response.

```
# Using a pseudo presence-only occurrence dataset of
# virtual species provided in this package
library(dplyr)
library(sf)
library(stars)
library(itsdm)
# Prepare data
data("occ_virtual_species")
obs_df <- occ_virtual_species %>% filter(usage == "train")
eval_df <- occ_virtual_species %>% filter(usage == "eval")
x_col <- "x"
y_col <- "y"
obs_col <- "observation"
# Format the observations
obs_train_eval <- format_observation(
obs_df = obs_df, eval_df = eval_df,
x_col = x_col, y_col = y_col, obs_col = obs_col,
obs_type = "presence_only")
env_vars <- system.file(
'extdata/bioclim_tanzania_10min.tif',
package = 'itsdm') %>% read_stars() %>%
slice('band', c(1, 5, 12))
# With imperfect_presence mode,
mod <- isotree_po(
obs_mode = "imperfect_presence",
obs = obs_train_eval$obs,
obs_ind_eval = obs_train_eval$eval,
variables = env_vars, ntrees = 10,
sample_size = 0.8, ndim = 2L,
seed = 123L, nthreads = 1,
response = FALSE,
spatial_response = FALSE,
check_variable = FALSE)
shap_spatial <- shap_spatial_response(
model = mod$model,
var_occ = mod$vars_train,
variables = mod$variables,
shap_nsim = 1)
shap_spatial <- shap_spatial_response(
model = mod$model,
target_vars = c("bio1", "bio12"),
var_occ = mod$vars_train,
variables = mod$variables,
shap_nsim = 1)
if (FALSE) {
##### Use Random Forest model as an external model ########
library(randomForest)
# Prepare data
data("occ_virtual_species")
obs_df <- occ_virtual_species %>%
filter(usage == "train")
env_vars <- system.file(
'extdata/bioclim_tanzania_10min.tif',
package = 'itsdm') %>% read_stars() %>%
slice('band', c(1, 5, 12)) %>%
split()
model_data <- stars::st_extract(
env_vars, at = as.matrix(obs_df %>% select(x, y))) %>%
as.data.frame()
names(model_data) <- names(env_vars)
model_data <- model_data %>%
mutate(occ = obs_df[['observation']])
model_data$occ <- as.factor(model_data$occ)
mod_rf <- randomForest(
occ ~ .,
data = model_data,
ntree = 200)
pfun <- function(X.model, newdata) {
# for data.frame
predict(X.model, newdata, type = "prob")[, "1"]
}
shap_spatial <- shap_spatial_response(
model = mod_rf,
target_vars = c("bio1", "bio12"),
var_occ = model_data %>% select(-occ),
variables = env_vars,
shap_nsim = 10,
pfun = pfun)
}
```