predict_SDS {PhenoSpectra} | R Documentation |
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")
)
[Package PhenoSpectra version 0.1.0 Index]