R: Compute expected measurements from fitted models

getExpectedMeas {limorhyde2}

R Documentation

Compute expected measurements from fitted models

Description

This function computes expected measurements (corresponding to the fitted curves) for the specified times and features in all combinations of conditions and covariates (if they exist).

Usage

getExpectedMeas(
  fit,
  times,
  fitType = c("posterior_mean", "posterior_samples", "raw"),
  features = NULL,
  dopar = TRUE
)

Arguments

`fit`	A 'limorhyde2' object.
`times`	Numeric vector of times, in units of `fit$metadata[[fit$timeColname]]`.
`fitType`	String indicating which fitted models to use to compute the expected measurements. A typical analysis using `limorhyde2` will be based on 'posterior_mean', the default.
`features`	Vector of names, row numbers, or logical values for subsetting the features. `NULL` indicates all features.
`dopar`	Logical indicating whether to run calculations in parallel if a parallel backend is already set up, e.g., using `doParallel::registerDoParallel()`. Recommended to minimize runtime.

Value

A data.table.

Examples

library('data.table')

y = GSE34018$y
metadata = GSE34018$metadata

fit = getModelFit(y, metadata)
fit = getPosteriorFit(fit)

measObs = mergeMeasMeta(y, metadata, features = c('13170', '12686'))
measFitMean = getExpectedMeas(
  fit, times = seq(0, 24, 0.5), features = c('13170', '12686'))