Package 'PhenoSpectra'

Feature Selection for Spectral Data

Description

This function filters healthy vs diseased samples, selects the most discriminative spectral variables, applies FDR correction, and exports the results.

Usage

feature_selection(
  file_path,
  output_path = "selected_features.xlsx",
  fdr_threshold = 0.01
)

Arguments

file_path	Path to the cleaned dataset (output of qaqcs function).
output_path	Path to save the selected features table.
fdr_threshold	Threshold for filtering significant features (default: 0.01).

Value

A data.table containing selected spectral variables.

Examples

# Create mock spectral data
library(openxlsx)
mock_data <- data.frame(
  treatment = sample(0:1, 100, replace = TRUE),
  var1 = rnorm(100),
  var2 = rnorm(100),
  var3 = rnorm(100)
)
temp_file <- tempfile(fileext = ".xlsx")
write.xlsx(mock_data, temp_file)

# Perform feature selection
output_path <- tempfile(fileext = ".xlsx")
selected_features <- feature_selection(temp_file, output_path, fdr_threshold = 0.01)
head(selected_features)

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Predict Spectral Disease Severity (SDS)

Description

This function predicts Spectral Disease Severity (SDS) using a standard linear regression model (lm()). It automatically handles column names with special characters by using backticks and constrains predictions to the range ⁠[0, 100]⁠.

Usage

predict_SDS(cleaned_data, sf_test, fixed_effects = NULL)

Arguments

cleaned_data	A dataframe containing spectral measurements and treatment labels.
sf_test	A dataframe containing selected important features (from statistical tests).
fixed_effects	A character vector of fixed effects to include (default: NULL). Example: c("Scan.date").

Value

A dataframe with predicted SDS values for all treatments, constrained between 0 and 100.

Examples

# Create mock spectral data
library(openxlsx)
cleaned_data <- data.frame(
  treatment = sample(0:1, 100, replace = TRUE),
  var1 = rnorm(100),
  var2 = rnorm(100),
  var3 = rnorm(100),
  Scan.date = sample(
    seq.Date(
      from = as.Date('2023-01-01'),
      to = as.Date('2023-12-31'),
      by = 'day'
    ),
    100
  ),
  Scan.time = format(Sys.time(), "%H:%M:%S")
)

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Perform QA/QC on spectral data while preserving original column names

Description

Perform QA/QC on spectral data while preserving original column names

Usage

qaqcs(
  file_path,
  output_path,
  handle_missing = "NA",
  handle_outliers = "NA",
  group_by_col = "treatment"
)

Arguments

file_path	Path to the input file
output_path	Path to save the cleaned data
handle_missing	Method to handle missing values ('impute', 'remove', or 'NA')
handle_outliers	Method to handle outliers ('impute', 'remove', or 'NA')
group_by_col	Column name for grouping

Value

A list with cleaned data and a summary table

Examples

library(openxlsx)
# Create mock raw data
raw_data <- data.frame(
  treatment = sample(0:1, 100, replace = TRUE),
  var1 = rnorm(100),
  var2 = rnorm(100),
  var3 = rnorm(100)
)

# Save mock data to a temporary file
raw_data_file <- tempfile(fileext = ".xlsx")
output_file <- tempfile(fileext = ".xlsx")

write.xlsx(raw_data, raw_data_file)

# Run QA/QC with missing values imputed and outliers removed
cleaned_result <- qaqcs(
  file_path = raw_data_file,
  output_path = output_file,
  handle_missing = "impute",
  handle_outliers = "remove",
  group_by_col = "treatment"
)
head(cleaned_result$cleaned_data)

---

Read and merge spectral data using data.table exclusively

Description

Read and merge spectral data using data.table exclusively

Usage

reads(directory, pattern, output_path)

Arguments

directory	Path to the directory containing files
pattern	File pattern to search for (e.g., 'input')
output_path	Path to save the processed output

Value

A merged data.table

Examples

library(data.table)
# Create mock data files with all required columns
mock_data1 <- data.frame(
  treatment = sample(0:1, 50, replace = TRUE),
  var1 = rnorm(50),
  var2 = rnorm(50),
  Scan.date = sample(
    seq.Date(
      from = as.Date('2023-01-01'),
      to = as.Date('2023-12-31'),
      by = 'day'
    ),
    50,
    replace = TRUE
  ),
  Scan.time = format(Sys.time(), "%H:%M:%S"),
  timestamp = Sys.time()  # Add timestamp column
)

mock_data2 <- data.frame(
  treatment = sample(0:1, 50, replace = TRUE),
  var1 = rnorm(50),
  var2 = rnorm(50),
  Scan.date = sample(
    seq.Date(
      from = as.Date('2023-01-01'),
      to = as.Date('2023-12-31'),
      by = 'day'
    ),
    50,
    replace = TRUE
  ),
  Scan.time = format(Sys.time(), "%H:%M:%S"),
  timestamp = Sys.time()  # Add timestamp column
)

# Save mock data to temporary CSV files
temp_dir <- tempdir()
file1 <- file.path(temp_dir, "input_file1.csv")
file2 <- file.path(temp_dir, "input_file2.csv")
fwrite(mock_data1, file1)
fwrite(mock_data2, file2)

# Run the reads() function on mock CSV data
merged_data <- reads(
  directory = temp_dir,
  pattern = "input",
  output_path = tempfile(fileext = ".csv")
)
head(merged_data)

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Setup Environment for PhenoSpectra

Description

Automatically installs and loads all required packages.

This function installs missing packages required by the PhenoSpectra package.

Usage

setup_environment()

setup_environment()

Value

NULL. This function installs required packages if missing and loads them.

Examples

# Example usage:
setup_environment()  # Automatically installs and loads required packages
# Run to manually install missing packages
setup_environment()

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