RPA.iteration: RPA iteration
In microbiome/RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

RPA.iteration

R Documentation

RPA iteration

Description

Estimating model parameters d and tau2.

Usage

RPA.iteration(
  S,
  epsilon = 0.001,
  alpha = NULL,
  beta = NULL,
  tau2.method = "fast",
  d.method = "fast",
  maxloop = 1e+06
)

Arguments

`S`	Matrix of probe-level observations for a single probeset: samples x probes.
`epsilon`	Convergence tolerance. The iteration is deemed converged when the change in all parameters is < epsilon.
`alpha`	alpha prior for inverse Gamma distribution of probe-specific variances. Noninformative prior is obtained with alpha, beta -> 0. Not used with tau2.method 'var'. Scalar alpha and beta are specify equal inverse Gamma prior for all probes to regularize the solution. The defaults depend on the method.
`beta`	beta prior for inverse Gamma distribution of probe-specific variances. Noninformative prior is obtained with alpha, beta -> 0. Not used with tau2.method 'var'. Scalar alpha and beta are specify equal inverse Gamma prior for all probes to regularize the solution. The defaults depend on the method.
`tau2.method`	Optimization method for tau2 (probe-specific variances). "robust": (default) update tau2 by posterior mean, regularized by informative priors that are identical for all probes (user-specified by setting scalar values for alpha, beta). This regularizes the solution, and avoids overfitting where a single probe obtains infinite reliability. This is a potential problem in the other tau2 update methods with non-informative variance priors. The default values alpha = 2; beta = 1 are used if alpha and beta are not specified. "mode": update tau2 with posterior mean "mean": update tau2 with posterior mean "var": update tau2 with variance around d. Applies the fact that tau2 cost function converges to variance with large sample sizes.
`d.method`	Method to optimize d. "fast": (default) weighted mean over the probes, weighted by probe variances The solution converges to this with large sample size. "basic": optimization scheme to find a mode used in Lahti et al. TCBB/IEEE; relatively slow; this is the preferred method with small sample sizes.
`maxloop`	Maximum number of iterations in the estimation process.

Details

Finds point estimates of the model parameters d (estimated true signal underlying probe-level observations), and tau2 (probe-specific variances). Assuming data set S with P observations of signal d with Gaussian noise that is specific for each observation (specified by a vector tau2 of length P), this method gives a point estimate of d and tau2. Probe-level variance priors alpha, beta can be used with tau2.methods 'robust', 'mode', and 'mean'. The d.method = "fast" is the recommended method for point computing point estimates with large samples size.

Value

A list with the following elements: d: A vector. Estimated 'true' signal underlying the noisy probe-level observations.; tau2: A vector. Estimated variances for each measurement (or probe).