modelGradient {tigre} | R Documentation

## Model log-likelihood/objective error function and its gradient.

### Description

`modeGradient` gives the gradient of the objective function for a model. By default the objective function (`modelObjective`) is a negative log likelihood (`modelLogLikelihood`).

### Usage

      modelObjective(params, model, ...)
      modelLogLikelihood(model)
      modelGradient(params, model, ...)

### Arguments

`params` | parameter vector to evaluate at.
`model` | model structure.
`...` | optional additional arguments.

### Value

`g` | the gradient of the error function to be minimised.
`v` | the objective function value (lower is better).
`ll` | the log-likelihood value.

### See Also

`modelOptimise`.

### Examples

      # Load a mmgmos preprocessed fragment of the Drosophila developmental
      # time series
      data(drosophila_gpsim_fragment)

      # The probe identifier for TF 'twi'
      twi <- "143396_at"
      # The probe identifier for the target gene
      targetProbe <- "152715_at"

      # Create the model but do not optimise
      model <- GPLearn(drosophila_gpsim_fragment,
                       TF=twi, targets=targetProbe,
                       useGpdisim=TRUE, quiet=TRUE,
                       dontOptimise=TRUE)
      params <- modelExtractParam(model, only.values=FALSE)
      ll <- modelLogLikelihood(model)
      paramValues <- modelExtractParam(model, only.values=TRUE)
      modelGradient(paramValues, model)

[Package tigre version 1.48.0 Index]