K_cal_23SEP2015: K_cal_23SEP2015: NIR calibration for Potassium

In griffithdan/plantspecDB: NIR Calibration data for the 'plantspec' package

Description

NIR calibration for Potassium using version 1 of leaf.spec.data, excluding litter and bryophytes.

Usage

data(K_cal_23SEP2015)

Format

An object of class PLScalibration.

Examples

#  library(leaf.spec)
#  library(leaf.spec.DB)

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# K: Code to create this calibration
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  ## LOAD DATA FROM leaf.spec.DB
  # data(leaf.spec.spectra)
  # data(leaf.spec.data)
  # leaf.spec.data <- leaf.spec.data[!(leaf.spec.data$SCAN_FILE=="SCAN_00225.dpt"),] # remove an outlier
  # leaf.spec.data <- leaf.spec.data[leaf.spec.data$VERSION==1,] # use dataset version 1
  # leaf.spec.data <- leaf.spec.data[!is.na(leaf.spec.data$K),] # remove data where response is NA
  ## remove litter and bryophytes 
  # leaf.spec.data <- leaf.spec.data[!(leaf.spec.data$FUNCTIONAL_TYPE=="bryophyte"|leaf.spec.data$FUNCTIONAL_TYPE=="litter"),] 
  # NUTNET_SCANS <- leaf.spec.spectra[leaf.spec.data$SCAN_FILE,] # choose scans based on remaining data
  # plot(NUTNET_SCANS) # inspect scans
  # component_K <- leaf.spec.data$K # for clarity, put the response in a new vector
  ## select a 10 % validation set with KS, but use ! to return TRUE for the training set
  # training_set_KS <- !(subdivideDataset(spectra = NUTNET_SCANS, method = "KS", p=0.1, type = "validation"))
  # K_opt <- optimizePLS(component=component_K, spectra=NUTNET_SCANS, training_set = training_set_KS)
  # K_cal <- calibrate(component=component_K, spectra=NUTNET_SCANS, optimal_params=K_opt, optimal_model=1, validation = "testset", training_set = training_set_KS)

## LOAD CALIBRATION
# data(K_cal_23SEP2015)

griffithdan/plantspecDB documentation built on May 29, 2019, 11:05 p.m.