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 7 8 9 10 11 12 13 | 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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