Description Usage Arguments Value Examples

View source: R/choose_gaussians.R

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.

1 2 3 4 5 6 | ```
choose_gaussians(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)
``` |

`chromatogram` |
a numeric vector corresponding to the chromatogram trace |

`points` |
optional, the number of non-NA points in the raw data |

`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. |

`criterion` |
the criterion to use for model selection; one of "AICc" (corrected AIC, and default), "AIC", or "BIC" |

`max_iterations` |
the number of times to try fitting the curve with different initial conditions; defaults to 10 |

`min_R_squared` |
the minimum R-squared value to accept when fitting the curve with different initial conditions; defaults to 0.5 |

`method` |
the method used to select the initial conditions for
nonlinear least squares optimization (one of "guess" or "random");
see |

`filter_gaussians_center` |
true or false: filter Gaussians whose centres fall outside the bounds of the chromatogram |

`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 |

`filter_gaussians_variance_min` |
Gaussians whose variance is below this threshold will be filtered. Setting this value to zero disables filtering. |

`filter_gaussians_variance_max` |
Gaussians whose variance is above this threshold will be filtered. Setting this value to zero disables filtering. |

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.

1 2 3 | ```
data(scott)
chrom <- clean_profile(scott[1, ])
gauss <- choose_gaussians(chrom, max_gaussians = 3)
``` |

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