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latrend

interface-featureBased

featureBased interface

Description

featureBased interface

Usage

## S4 method for signature 'lcMethodFeature'
getName(object)

## S4 method for signature 'lcMethodFeature'
getShortName(object)

## S4 method for signature 'lcMethodFeature'
prepareData(method, data, verbose, ...)

## S4 method for signature 'lcMethodFeature'
fit(method, data, envir, verbose, ...)

## S4 method for signature 'lcMethodGCKM'
getName(object)

## S4 method for signature 'lcMethodGCKM'
getShortName(object)

## S4 method for signature 'lcMethodGCKM'
compose(method, envir = NULL)

## S4 method for signature 'lcMethodGCKM'
preFit(method, data, envir, verbose)

## S4 method for signature 'lcMethodGCKM'
fit(method, data, envir, verbose, ...)

## S4 method for signature 'lcMethodLMKM'
getName(object)

## S4 method for signature 'lcMethodLMKM'
getShortName(object)

## S4 method for signature 'lcMethodLMKM'
prepareData(method, data, verbose)

## S4 method for signature 'lcMethodLMKM'
fit(method, data, envir, verbose, ...)

## S4 method for signature 'lcMethodStratify'
getName(object)

## S4 method for signature 'lcMethodStratify'
getShortName(object)

## S4 method for signature 'lcMethodStratify'
compose(method, envir = NULL, ...)

## S4 method for signature 'lcMethodStratify'
fit(method, data, envir, verbose, ...)

## S4 method for signature 'lcModelFeature'
getName(object, ...)

## S4 method for signature 'lcModelFeature'
getShortName(object, ...)

## S4 method for signature 'lcModelLMKM'
predictForCluster(object, newdata, cluster, what = "mu", ...)

## S3 method for class 'lcModelLMKM'
coef(object, ..., cluster = NULL)

## S4 method for signature 'lcModelLMKM'
converged(object, ...)

## S4 method for signature 'lcModelLMKM'
postprob(object, ...)

Arguments

`object`	The object to extract the label from.
`method`	The `lcMethod` object.
`data`	The data, as a `data.frame`, on which the model will be trained.
`verbose`	A R.utils::Verbose object indicating the level of verbosity.
`...`	Arguments passed on to `stats::predict.lm` `se.fit` A switch indicating if standard errors are required. `scale` Scale parameter for std.err. calculation. `df` Degrees of freedom for scale. `interval` Type of interval calculation. Can be abbreviated. `level` Tolerance/confidence level. `type` Type of prediction (response or model term). Can be abbreviated. `terms` If `type = "terms"`, which terms (default is all terms), a `character` vector. `na.action` function determining what should be done with missing values in `newdata`. The default is to predict `NA`. `pred.var` the variance(s) for future observations to be assumed for prediction intervals. See ‘Details’. `weights` variance weights for prediction. This can be a numeric vector or a one-sided model formula. In the latter case, it is interpreted as an expression evaluated in `newdata`.
`envir`	The `environment` in which the `lcMethod` should be evaluated
`newdata`	Optional `data.frame` for which to compute the model predictions. If omitted, the model training data is used. Cluster trajectory predictions are made when ids are not specified.
`cluster`	The cluster name.
`what`	The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying `what = 'mb'`.

interface-featureBased

Description

Usage

Arguments

See Also

latrend

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