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```
#' Fit a Gaussian mixture model to a co-elution profile
#'
#' Fit mixtures of one or more Gaussians to the curve formed by a chromatogram
#' profile, and choose the best fitting model using an information criterion
#' of choice.
#'
#' @param chromatogram a numeric vector corresponding to the chromatogram trace
#' @param points optional, the number of non-NA points in the raw data
#' @param max_gaussians the maximum number of Gaussians to fit; defaults to 5.
#' Note that Gaussian mixtures with more parameters than observed (i.e.,
#' non-zero or NA) points will not be fit.
#' @param criterion the criterion to use for model selection;
#' one of "AICc" (corrected AIC, and default), "AIC", or "BIC"
#' @param max_iterations the number of times to try fitting the curve with
#' different initial conditions; defaults to 10
#' @param min_R_squared the minimum R-squared value to accept when fitting the
#' curve with different initial conditions; defaults to 0.5
#' @param method the method used to select the initial conditions for
#' nonlinear least squares optimization (one of "guess" or "random");
#' see \code{\link{make_initial_conditions}} for details
#' @param filter_gaussians_center true or false: filter Gaussians whose centres
#' fall outside the bounds of the chromatogram
#' @param filter_gaussians_height Gaussians whose heights are below this
#' fraction of the chromatogram height will be filtered. Setting this value to
#' zero disables height-based filtering of fit Gaussians
#' @param filter_gaussians_variance_min Gaussians whose variance is below this
#' threshold will be filtered. Setting this value to zero disables filtering.
#' @param filter_gaussians_variance_max Gaussians whose variance is above this
#' threshold will be filtered. Setting this value to zero disables filtering.
#'
#' @return a list with five entries: the number of Gaussians used to fit
#' the curve; the R^2 of the fit; the number of iterations used to
#' fit the curve with different initial conditions; the coefficients of the
#' fit model; and the curve predicted by the fit model.
#'
#' @examples
#' data(scott)
#' chrom <- clean_profile(scott[1, ])
#' gauss <- choose_gaussians(chrom, max_gaussians = 3)
#'
#' @importFrom purrr map_lgl map
#'
#' @export
choose_gaussians <- function(chromatogram, points = NULL,
max_gaussians = 5,
criterion = c("AICc", "AIC", "BIC"),
max_iterations = 10, min_R_squared = 0.5,
method = c("guess", "random"),
filter_gaussians_center = TRUE,
filter_gaussians_height = 0.15,
filter_gaussians_variance_min = 0.1,
filter_gaussians_variance_max = 50) {
criterion <- match.arg(criterion)
# don't fit mixtures with more parameters than (experimental) points
if (!is.null(points)) {
max_gaussians <- min(max_gaussians, floor(points / 3))
}
# fit and choose models
fits <- list()
for (n_gaussians in seq_len(max_gaussians))
fits[[n_gaussians]] <- fit_gaussians(
chromatogram, n_gaussians, max_iterations, min_R_squared,
method = method, filter_gaussians_center,
filter_gaussians_height, filter_gaussians_variance_min,
filter_gaussians_variance_max)
# remove any models that failed to fit
models <- map(fits, "coefs")
drop <- map_lgl(models, is.null)
fits <- fits[!drop]
coefs <- map(fits, "coefs")
if (criterion == "AICc") {
criteria <- lapply(coefs, gaussian_aicc, chromatogram)
} else if (criterion == "AIC") {
criteria <- lapply(coefs, gaussian_aic)
} else if (criterion == "BIC") {
criteria <- lapply(coefs, gaussian_bic)
}
best <- which.min(criteria)
if (length(best) == 0) {
return(NULL)
} else {
return(fits[[best]])
}
}
```

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