Use provided model object to predict trait values with input dataset

Source: R/predict_spectra.R

Loads an existing model and cross-validation performance statistics (created with save_model) and makes predictions based on new spectra.

predict_spectra(
  input.data,
  model.stats.location,
  model.location,
  model.method = "pls",
  wavelengths = deprecated()
)

Arguments

input.data

data.frame object of spectral data for input into a spectral prediction model. First column contains unique identifiers followed by spectral columns. Include no other columns to right of spectra! Column names of spectra must start with "X".

model.stats.location

String containing file path (including file name) to save location of "(model.name)_stats.csv" as output from the save_model function.

model.location

String containing file path (including file name) to location where the trained model ("(model.name).Rds") was saved as output by the save_model function.

model.method

Model type to use for training. Valid options include:

  • "pls": Partial least squares regression (Default)

  • "rf": Random forest

  • "svmLinear": Support vector machine with linear kernel

  • "svmRadial": Support vector machine with radial kernel

wavelengths

DEPRECATED wavelengths is no longer supported; this information is now inferred from input.data column names

Value

data.frame object of predictions for each sample (row). First column is unique identifier supplied by input.data and second is predicted values

Author

Jenna Hershberger jmh579@cornell.edu

Examples

if (FALSE) {
ikeogu.2017 %>%
  dplyr::select(sample.id, dplyr::starts_with("X")) %>%
  predict_spectra(
    input.data = .,
    model.stats.location = paste0(
      getwd(),
      "/my_model_stats.csv"
    ),
    model.location = paste0(getwd(), "/my_model.Rds")
  )
}