postprob {latrend} | R Documentation |
Posterior probability per fitted trajectory
Description
Get the posterior probability matrix with element (i,j)
indicating the probability of trajectory i
belonging to cluster j
.
Usage
postprob(object, ...)
## S4 method for signature 'lcModel'
postprob(object, ...)
Arguments
object |
The model. |
... |
Not used. |
Details
This method should be extended by lcModel
implementations. The default implementation returns uniform probabilities for all observations.
Value
An I-by-K numeric matrix
with I = nIds(object)
and K = nClusters(object)
.
Implementation
Classes extending lcModel
should override this method.
setMethod("postprob", "lcModelExt", function(object, ...) { # return trajectory-specific posterior probability matrix })
Troubleshooting
If you are getting errors about undefined model signatures when calling postprob(model), check whether the postprob() function is still the one defined by the latrend package. It may have been overridden when attaching another package (e.g., lcmm). If you need to attach conflicting packages, load them first.
See Also
trajectoryAssignments predictPostprob predictAssignments
Other lcModel functions:
clusterNames()
,
clusterProportions()
,
clusterSizes()
,
clusterTrajectories()
,
coef.lcModel()
,
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()
,
predict.lcModel()
,
predictAssignments()
,
predictForCluster()
,
predictPostprob()
,
qqPlot()
,
residuals.lcModel()
,
sigma.lcModel()
,
strip()
,
time.lcModel()
,
trajectoryAssignments()
Examples
data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
model <- latrend(method, latrendData)
postprob(model)
if (rlang::is_installed("lcmm")) {
gmmMethod = lcMethodLcmmGMM(
fixed = Y ~ Time,
mixture = ~ Time,
id = "Id",
time = "Time",
idiag = TRUE,
nClusters = 2
)
gmmModel <- latrend(gmmMethod, data = latrendData)
postprob(gmmModel)
}