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]