Description Usage Arguments Value Author(s) Examples
View source: R/makeSpatialization.R
make a spatialization of a gridded dataset using a mlr model
1 | makeSpatialization(model, pred.grid = grid.df)
|
model |
an object of class mlr::train() that contains the prediction model |
pred.grid |
an object of class sf::st_makegrid(). This object must contains the same column names as the task on which the model has been trained |
A 2 elements named list
snitch
: a boolean. Is TRUE
if function has provided the expected result. Is FALSE
is function throws an error
output
: a named list which elements are :
value
: a named list which elements are :
spatialized
: an element of class data.frame
. colnames are px
(= reference of the pixel), response
(= prediction value), se
(= prediction standard error)
summary
an element of class data.frame
containing summary information about grid prediction. colnames are min.response
, max.response
, mean.response
, min.se
, max.se
, mean.se
condition
: a character specifying the condition encountered by the function: success, warning, or error.
message
: a character specifying the message relative to the condition.
Thomas Goossens
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 | ## Not run:
# load magrittr for pipe use : %>%
library(magrittr)
# create the dataset
myDataset = makeDataset(
dfrom = "2017-03-04T15:00:00Z",
dto = "2017-03-04T18:00:00Z",
sensor = "tsa")
# extract the list of hourly sets of records
myDataset = myDataset$output$value
# create the tasks
myTasks = purrr::map(myDataset, makeTask, target = "tsa")
# extract the tasks from the outputs
myTasks = myTasks %>% purrr::modify_depth(1, ~.$"output"$"value"$"task")
# keep the first task
myTask = myTasks[[1]]
# create the model
myModel = makeModel(
task = myTask,
learner = agrometeorLearners$mulLR_lonLatAlt_NA)
# extract the relevant information
myModel = myModel$output$value
# spatialize using the trained model
mySpatialization = makeSpatialization(
model = myModel$trained,
pred.grid = grid.df) # grid.df comes precompiled with the package
# get the relevant information
mySpatialization = mySpatialization$output$value
# show an excerpt of the spatialized data
head(mySpatialization$spatialized)
# show the summary stats of spatialized data
head(mySpatialization$summary)
## End(Not run)
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