climpred {climodr} | R Documentation |
Predict sensor data area wide
Description
Use the models created using 'calc.model' to predict the modeled data onto a full spatial raster scene.
Usage
climpred(envrmt = .GlobalEnv$envrmt, method = "monthly", mnote, AOA = TRUE)
Arguments
envrmt |
variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt. |
method |
Character. Either "daily", monthly" or "annual". Also depends on the available data. |
mnote |
Character. Model note to filter models for the fitting model run. |
AOA |
Logical. Should the Area of Applicability be calculated additional to the models? |
Value
Multiple models.rds stored in the /workflow/models folder.
See Also
'autocorr', 'predict'
Examples
#create climodr environment and allow terra-functions to use 70% of RAM
envrmt <- envi.create(proj_path = tempdir(),
memfrac = 0.7)
# Load the climodr example data into the current climodr environment
clim.sample(envrmt = envrmt)
#prepare csv-files
prep.csv(envrmt = envrmt,
method = "proc",
save_output = TRUE)
#process csv-files
csv_data <- proc.csv(envrmt = envrmt,
method = "monthly",
rbind = TRUE,
save_output = TRUE)
# Crop all raster bands
crop.all(envrmt = envrmt,
method = "MB_Timeseries",
overwrite = TRUE)
# Calculate Indices from cropped raster bands
calc.indices(envrmt = envrmt,
vi = "all",
bands = c("blue", "green", "red",
"nir", "nirb",
"re1", "re2", "re3",
"swir1", "swir2"),
overwrite = TRUE)
#extract station coordinates
csv_spat <- spat.csv(envrmt = envrmt,
method = "monthly",
des_file = "plot_description.csv",
save_output = TRUE)
#extract predictor values from raster files
csv_fin <- fin.csv(envrmt = envrmt,
method = "monthly",
save_output = TRUE)
# Test data for autocorrelation after running fin.csv
autocorr(envrmt = envrmt,
method = "monthly",
resp = 5,
pred = c(8:23),
plot.corrplot = FALSE)
# Create 36 different models (12 months x 3 classifiers) for every month in 2017
calc.model(envrmt = envrmt,
method = "monthly",
timespan = c(2017),
climresp = 5,
classifier = c("rf",
"pls",
"lm"),
seed = 707,
p = 0.8,
folds = "LLO",
mnote = "normal",
predrows = c(8:23),
tc_method = "cv",
metric = "RMSE",
autocorrelation = TRUE,
doParallel = FALSE)
# Make predictions
climpred(envrmt = envrmt,
method = "monthly",
mnote = "normal",
AOA = TRUE)
predlist <- list.files(envrmt$path_predictions,
pattern = ".tif")
head(predlist)
[Package climodr version 1.0.0 Index]