Nothing

```
#' @title Selective Measuring
#' @description The function [adjust_frm()] is used to modify existing FastRet models based on changes in chromatographic conditions. It requires a set of molecules with measured retention times on both the original and new column. This function selects a sensible subset of molecules from the original dataset for re-measurement. The selection process includes:
#' 1. Generating chemical descriptors from the SMILES strings of the molecules. These are the features used by [train_frm()] and [adjust_frm()].
#' 2. Standardizing chemical descriptors to have zero mean and unit variance.
#' 3. Training a Ridge Regression model with the standardized chemical descriptors as features and the retention times as the target variable.
#' 4. Scaling the chemical descriptors by coefficients of the Ridge Regression model.
#' 5. Applying PAM clustering on the entire dataset, which includes the scaled chemical descriptors and the retention times.
#' 6. Returning the clustering results, which include the cluster assignments, the medoid indicators, and the raw data.
#' @param raw_data The raw data to be processed. Must be a dataframe with columns NAME, RT and SMILES.
#' @param k_cluster The number of clusters for PAM clustering.
#' @param verbose The level of verbosity.
#' @return A list containing the following elements:
#' * `clustering`: a data frame with raw data, cluster assignments, and medoid indicators
#' * `clobj`: the PAM clustering object
#' * `coefs`: the coefficients from the Ridge Regression model
#' * `model`: the Ridge Regression model
#' * `df`: the preprocessed data
#' * `dfz`: the standardized features
#' * `dfzb`: the features scaled by coefficients of the Ridge Regression model
#' @keywords public
#' @examples \donttest{
#' x <- selective_measuring(RP[1:50, ], k = 5, verbose = 0)
#' # For the sake of a short runtime, only the first 50 rows of the RP dataset
#' # were used in this example. In practice, you should always use the entire
#' # dataset to find the optimal subset for re-measurement.
#' }
#' @export
selective_measuring <- function(raw_data, k_cluster = 25, verbose = 1) {
# Configure logging behaviours
catf <- if (verbose >= 1) catf else function(...) {}
catf("Starting Selective Measuring")
# Return pregenerated results if mocking is enabled for this function
if ("selective_measuring" %in% getOption("FastRet.mocks", c())) {
catf("Mocking is enabled. Returning 'mockdata/clustering.rds'")
return(readRDS(pkg_file("mockdata/clustering.rds")))
}
catf("Preprocessing input data")
validate_inputdata(raw_data, min_cds = 0)
df <- preprocess_data(raw_data, verbose = verbose)
catf("Standardizing features")
dfz <- scale(df[, -which(colnames(df) %in% c("NAME", "SMILES"))])
dfz_noRT <- dfz[, -which(colnames(df) == "RT")]
catf("Training Ridge Regression model")
model <- fit_ridge(dfz, verbose = verbose)
coefs <- glmnet::coef.glmnet(model)@x[-1] # remove intercept
catf("Scaling features by coefficients of Ridge Regression model")
dfzb <- data.frame(t(apply(dfz_noRT, 1, function(z) z * coefs)))
dfzb <- `colnames<-`(dfzb, colnames(dfz_noRT))
catf("Applying PAM clustering")
RTcol <- which(colnames(df) == "RT")
clobj <- cluster::pam(dfzb[, -RTcol], k = as.numeric(k_cluster))
catf("Returning clustering results")
CLUSTER = clobj$clustering
IS_MEDOID = seq_len(nrow(raw_data)) %in% clobj$id.med
ret <- list(
clustering = data.frame(raw_data, CLUSTER, IS_MEDOID),
clobj = clobj,
coefs = coefs,
model = model,
df = df,
dfz = dfz,
dfzb = dfzb
)
ret
}
```

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