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.

## 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'))
```