interface-featureBased: featureBased interface

Description

featureBased interface

Usage

# S4 method for lcMethodTwoStep
getName(object)
# S4 method for lcMethodTwoStep
getShortName(object)
# S4 method for lcMethodTwoStep
prepareData(method, data, verbose, ...)
# S4 method for lcMethodTwoStep
fit(method, data, envir, verbose, ...)
# S4 method for lcMethodGCKM
getName(object)
# S4 method for lcMethodGCKM
getShortName(object)
# S4 method for lcMethodGCKM
compose(method, envir = NULL)
# S4 method for lcMethodGCKM
preFit(method, data, envir, verbose)
# S4 method for lcMethodGCKM
fit(method, data, envir, verbose, ...)
# S4 method for lcMethodLMKM
getName(object)
# S4 method for lcMethodLMKM
getShortName(object)
# S4 method for lcMethodLMKM
prepareData(method, data, verbose)
# S4 method for lcMethodLMKM
fit(method, data, envir, verbose, ...)
# S4 method for lcMethodStratify
getName(object)
# S4 method for lcMethodStratify
getShortName(object)
# S4 method for lcMethodStratify
compose(method, envir = NULL, ...)
# S4 method for lcMethodStratify
fit(method, data, envir, verbose, ...)
# S3 method for lcModelLMKM
coef(object, ..., cluster = NULL)
# S4 method for lcModelLMKM
converged(object, ...)
# S4 method for lcModelLMKM
postprob(object, ...)
# S3 method for lcModelLMKM
predict(object, ..., newdata = NULL, what = "mu")
# S4 method for lcModelTwoStep
getName(object, ...)
# S4 method for lcModelTwoStep
getShortName(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

cluster

The cluster name.

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. If the clusters are specified under the Cluster column, output is given only for the specified cluster. Otherwise, a matrix is returned with predictions for all clusters.

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'.

Description

Usage

Arguments

See Also