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)