nlsGaussianSumCV: Choose the best number of gaussian components automatically...
In hafenr/MACode: Functions and code snippets for my master thesis
Description
Usage
Arguments
Value
View source: R/nlsGaussianSum.R
Description
Choose the best number of gaussian components automatically using K-fold
cross validation. The best performing model is refitted and returned.
Usage
1
2
nlsGaussianSumCV(y, x = seq_along(y), K = 3, n.range = 1:3,
quiet = TRUE, ...)
Arguments
y
The intensity values on which to fit.
x
The associated x values. Optional.
K
The number of cross validation folds to perform. Larger numbers
yield better error estimates.
n.range
A numeric vector of n values that should be evaluated.
Value
The fitted nls model that performed best.
hafenr/MACode documentation built on May 17, 2019, 2:24 p.m.
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Description Usage Arguments Value
View source: R/nlsGaussianSum.R
Description
Choose the best number of gaussian components automatically using K-fold cross validation. The best performing model is refitted and returned.
Usage
1 2 | nlsGaussianSumCV(y, x = seq_along(y), K = 3, n.range = 1:3,
quiet = TRUE, ...)
|
Arguments
y |
The intensity values on which to fit. |
x |
The associated x values. Optional. |
K |
The number of cross validation folds to perform. Larger numbers yield better error estimates. |
n.range |
A numeric vector of n values that should be evaluated. |
Value
The fitted nls model that performed best.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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