Extract the coefficients of the lcModel
object, if defined.
The returned set of coefficients depends on the underlying type of lcModel
.
The default implementation checks for the existence of a coef()
function for the internal model as defined in the @model
slot, returning the output if available.
# S3 method for lcModel
coef(object, ...)
A named numeric vector
with all coefficients, or a matrix
with each column containing the cluster-specific coefficients. If coef()
is not defined for the given model, an empty numeric vector
is returned.
The lcModel
object.
Additional arguments.
Classes extending lcModel
can override this method to return model-specific coefficients.
coef.lcModelExt <- function(object, ...) {
# return model coefficients
}
Other lcModel functions:
clusterNames()
,
clusterProportions()
,
clusterSizes()
,
clusterTrajectories()
,
converged()
,
deviance.lcModel()
,
df.residual.lcModel()
,
estimationTime()
,
externalMetric()
,
fitted.lcModel()
,
fittedTrajectories()
,
getCall.lcModel()
,
getLcMethod()
,
ids()
,
lcModel-class
,
metric()
,
model.frame.lcModel()
,
nClusters()
,
nIds()
,
nobs.lcModel()
,
plot-lcModel-method
,
plotClusterTrajectories()
,
plotFittedTrajectories()
,
postprob()
,
predict.lcModel()
,
predictAssignments()
,
predictForCluster()
,
predictPostprob()
,
qqPlot()
,
residuals.lcModel()
,
sigma.lcModel()
,
strip()
,
time.lcModel()
,
trajectoryAssignments()
Other lcModel functions:
clusterNames()
,
clusterProportions()
,
clusterSizes()
,
clusterTrajectories()
,
converged()
,
deviance.lcModel()
,
df.residual.lcModel()
,
estimationTime()
,
externalMetric()
,
fitted.lcModel()
,
fittedTrajectories()
,
getCall.lcModel()
,
getLcMethod()
,
ids()
,
lcModel-class
,
metric()
,
model.frame.lcModel()
,
nClusters()
,
nIds()
,
nobs.lcModel()
,
plot-lcModel-method
,
plotClusterTrajectories()
,
plotFittedTrajectories()
,
postprob()
,
predict.lcModel()
,
predictAssignments()
,
predictForCluster()
,
predictPostprob()
,
qqPlot()
,
residuals.lcModel()
,
sigma.lcModel()
,
strip()
,
time.lcModel()
,
trajectoryAssignments()
data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
model <- latrend(method, latrendData, nClusters = 2)
coef(model)
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