fit_global_dropout_curves: Find the inverse probit parameters that best explain the...
In const-ae/proDD: Identifying Differentially Abundant Proteins from Label-Free Mass Spectrometry Data
View source: R/fit_dropout_curves.R
Description
There are three different modes for fitting the inverse probit dropout
curves:
- fit_global_dropout_curves
One curve for all samples
- fit_sample_dropout_curves
One curve for each sample
- fit_global_scale_dropout_curves
One curve for each sample,
but all have a common scale (zeta)
Usage
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fit_global_dropout_curves(X, mup, sigma2p, experimental_design, prev_zeta,
prev_rho, maxit = 5000)
fit_sample_dropout_curves(X, mup, sigma2p, experimental_design, prev_zeta,
prev_rho, maxit = 5000)
fit_global_scale_dropout_curves(X, mup, sigma2p, experimental_design,
prev_zeta, prev_rho, max_iter = 10, epsilon = 1e-05)
const-ae/proDD documentation built on Jan. 14, 2020, 9:34 a.m.
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View source: R/fit_dropout_curves.R
Description
There are three different modes for fitting the inverse probit dropout curves:
- fit_global_dropout_curves
One curve for all samples
- fit_sample_dropout_curves
One curve for each sample
- fit_global_scale_dropout_curves
One curve for each sample, but all have a common scale (zeta)
Usage
1 2 3 4 5 6 7 8 | fit_global_dropout_curves(X, mup, sigma2p, experimental_design, prev_zeta,
prev_rho, maxit = 5000)
fit_sample_dropout_curves(X, mup, sigma2p, experimental_design, prev_zeta,
prev_rho, maxit = 5000)
fit_global_scale_dropout_curves(X, mup, sigma2p, experimental_design,
prev_zeta, prev_rho, max_iter = 10, epsilon = 1e-05)
|
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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