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Compute expected measurements from fitted models

Source: R/get_expected.R
getExpectedMeas.Rd

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.

See also

getModelFit(), getPosteriorFit(), getPosteriorSamples(), getExpectedMeasIntervals()

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'))