Normalize Measurement Data Using Linear Regression

This function normalizes measurement data by subtracting baseline values predicted from a linear regression of pre and post data.

Usage

normalise_drift_lm(
  data,
  data_pre,
  data_post,
  colname_time = "time",
  colname_measure = "co2d_um_m"
)

Arguments

data

A data frame containing the main dataset to be normalized

data_pre

A data frame containing pre-experiment data used for baseline calculation

data_post

A data frame containing post-experiment data used for baseline calculation

colname_time

Character string specifying the column name for time measurements. Default is "time".

colname_measure

Character string specifying the column name for the measurement. Default is "co2d_um_m".

Value

A modified version of the input data with baseline-adjusted measurements

Details

The function performs the following steps:

1. Combines pre and post data

2. Fits a linear regression model using time as the predictor

3. Predicts baseline values for the main dataset

4. Subtracts these baseline values from the original measurements

Examples

if (FALSE) { # \dontrun{
# Assuming you have pre, post, and main datasets
normalized_data <- normalise_drift_lm(
  data = main_data,
  data_pre = pre_experiment_data,
  data_post = post_experiment_data
)
} # }