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, ...)The lcModel object.
Additional arguments.
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.
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)
#> A B
#> (Intercept) -1.027661 0.6851075
#> Time 1.095947 -0.7306316