mvls: mvls

In helpstatanalysis/mvls: Missing Values in Longitudinal Studies

Description

Takes a longitudinal dataset and impute missing value with a machine learning-based method.

Usage

mvls(data, d = 0.1, method = "k", cluster = 6, nstart = 20,
  pre.imp = F, imp.method = "mean")

Arguments

data	A dataset (more than two longitudinal mesurements)
d	Percentage of change between two-sided mesurements to consider it bigger, smaller or the same, It useful to built the var.matrix.
method	It represent the type of machine learning algorithm. 'k' for k-mean and 'h' for hierical.
cluster	It's the number of cluster. Default setting it's 6. It depends on number of longitudinal mesurements. It could be use mvls.print to decide best cluster number.
nstart	It is the nstart setting of function k-mean. Defualt it'20. Not requested for 'h' method.
pre.imp	TRUE/FALSE (default F). It permit to pre-impute data to built the vari.matrix, It could be reduce cluster with only missing value.
imp.method	It's the type of pre-imputation. Defaul it's 'mean', but there is also 'locf' possibility.

Value

$data It's the data-set with imputation.

$cluster It's the cluster matrix.

$matrix It's the vari.matrix.

$sd.1 It contains the sd for each data imputed at single imputation method. Different from sd.2.

$vari.matrix It's the variation matrix

$data.norm It's the imputation dataset normalized

Author(s)

Lorenzo Querci <[e-mail]>

helpstatanalysis/mvls documentation built on May 13, 2019, 3 a.m.