modelGradient {tigre} | R Documentation |
modeGradient
gives the gradient of the objective function for a
model. By default the objective function (modelObjective
) is
a negative log likelihood (modelLogLikelihood
).
modelObjective(params, model, ...) modelLogLikelihood(model) modelGradient(params, model, ...)
params |
parameter vector to evaluate at. |
model |
model structure. |
... |
optional additional arguments. |
g |
the gradient of the error function to be minimised. |
v |
the objective function value (lower is better). |
ll |
the log-likelihood value. |
# 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)