autocorr {climodr}R Documentation

Autocorrelation

Description

Tests the final.csv created with 'fin.csv' on autocorrelation to produce reliable models.

Usage

autocorr(
  envrmt = .GlobalEnv$envrmt,
  method = "monthly",
  resp,
  pred,
  plot.corrplot = TRUE,
  corrplot = "coef"
)

Arguments

envrmt

variable name of your envrmt list created using climodr's 'envi.create' function. Default = envrmt.

method

character. Choose the time scale your data is preserved in. Either "annual", "monthly" or "daily".

resp

numerical. Vector or single input of the columns in the final.csv that contain your sensor data ("response variables"). The function will create one file per variable.

pred

numerical. Vector or single input. The columns of your predictor variables, that you want to test for autocorrelation with the response variables.

plot.corrplot

logical. Should correlation matrices be plotted?

corrplot

character. Vector or single input. If plot.corrplot is true, you can choose the design of the correlation plot. You can choose from "coef", "crossout", "blank". Default is "coef".

Value

One .csv file per response variable. These will later be used when 'autocorrelation' is set 'TRUE' during 'calc.model'.

See Also

'calc.model'

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)



[Package climodr version 1.0.0 Index]