## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----echo=FALSE---------------------------------------------------------------
options(digits=2)

## ----setup,message=FALSE, warning=FALSE---------------------------------------
library(clusterMI)

## ----message=FALSE------------------------------------------------------------
require(stargazer)
set.seed(123456)
data(wine)
stargazer(wine, type = "text")
table(wine$cult)

## -----------------------------------------------------------------------------
ref <- wine$cult # "True" partition
nb.clust <- 3 # Number of clusters
wine.na <- wine
wine.na$cult <- NULL # Remove the reference partition
wine.na <- prodna(wine.na, pct = 1/3)

## -----------------------------------------------------------------------------
# proportion of missing values
colMeans(is.na(wine.na))

# proportion of incomplete individuals
mean(apply(is.na(wine.na), 1, any))

## ----warning=FALSE, results='hide'--------------------------------------------
m <- 20 # Number of imputed data sets
res.imp.JM <- imputedata(data.na = wine.na,
                         nb.clust = nb.clust,
                         m = m)

## ----warning=FALSE, eval=FALSE------------------------------------------------
#  res.imp <- imputedata(data.na = wine.na,
#                           method = "JM-DP",
#                           nb.clust = nb.clust,
#                           m = m)

## ----echo=TRUE, results='hide'------------------------------------------------
res.imp.JM.conv <- imputedata(data.na = wine.na,
                         method = "JM-GL",
                         nb.clust = nb.clust,
                         m = 800,
                         Lstart = 1, # number of iterations for the burn-in period
                         L = 1 # number of iterations between each draw
                         )

## ----echo=TRUE, eval=FALSE----------------------------------------------------
#  res.conv <- res.imp.JM.conv$res.conv
#  res.conv.ts <- ts(t(res.conv)) # conversion as time-series object
#  plot(res.conv.ts[, 1:4]) # diagnostic from the 4 first variables

## ----echo=FALSE, message=FALSE,fig.align='center',results='hide', fig.width=7----
res.conv <- res.imp.JM.conv$res.conv
res.conv.ts <- ts(t(res.conv)) # conversion as time-series object
plot(res.conv.ts[, 1:4],
     nc = 4,
     main="",
     mar.multi = c(0, 4.1, 0, .1),
     xlab = "L") # diagnostic from the 4 first variables

## ----eval=FALSE---------------------------------------------------------------
#  Lstart <- 400
#  # extraction of summaries after Lstart iterations for the 4 first variables
#  res.conv.ts <- res.conv.ts[Lstart:nrow(res.conv.ts), 1:4]
#  apply(res.conv.ts, 2, acf)

## ----message=FALSE,results='hide',fig.align='center',echo=FALSE,fig.width=7----
Lstart <- 400
res.conv.ts <- res.conv.ts[Lstart:nrow(res.conv.ts), 1:4]  # extraction of summaries after Lstart iterations for the 4 first variables
res.acf <- apply(res.conv.ts, 2, acf,plot=FALSE)
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
par(mfrow = c(1,4), mar = c(4, 2, 3, 1) + 0.1)
mapply(FUN = plot,
       res.acf,
       main = names(res.acf),
       MoreArgs = list(xlab = "L"))

## ----warning=FALSE, results='hide'--------------------------------------------
Lstart <- 400
L <- 20
res.imp.JM <- imputedata(data.na = wine.na,
                         nb.clust = nb.clust,
                         Lstart = Lstart,
                         L = L,
                         m = m)

## ----warning=FALSE, results='hide'--------------------------------------------
maxit <- 20 # Number of iterations for FCS imputation, should be larger in practice
res.imp.FCS <- imputedata(data.na = wine.na,
                      method = "FCS-homo",
                      nb.clust = nb.clust,
                      maxit = maxit,
                      m = m)

## ----eval=FALSE---------------------------------------------------------------
#  imputedata(data.na = wine.na,
#             method = "FCS-homo",
#             nb.clust = nb.clust,
#             maxit = maxit,
#             m = m,
#             method.mice = "norm")

## ----conv, fig.height = 7, fig.width = 7, fig.align = "center", results='hide'----
choosemaxit(res.imp.FCS)

## ----eval = FALSE-------------------------------------------------------------
#  res.imp <- imputedata(data.na = wine.na,
#                        method = "FCS-homo",
#                        nb.clust = nb.clust,
#                        maxit = 100,
#                        m = m)
#  choosemaxit(res.imp)

## ----varselecho, eval=FALSE---------------------------------------------------
#  nnodes <- 2
#  # Number of CPU cores used for parallel computation.
#  # Use parallel::detectCores() to choose an appropriate number
#  
#  # variable selection to impute the "alco" variable
#  B <- 50 # number of bootstrap subsets, should be increased in practice
#  res.varsel <- varselbest(res.imputedata = res.imp.FCS, B = B, listvar = "alco",
#                           nnodes = nnodes, graph = FALSE)
#  
#  res.varsel$predictormatrix["alco", ]

## ----varsel, eval=TRUE, echo=FALSE--------------------------------------------
nnodes <- 2
# Number of CPU cores used for parallel computation.
# Use parallel::detectCores() to choose an appropriate number

# variable selection to impute the "alco" variable
B <- 50 # number of bootstrap subsets, should be increased in practice

# res.chooseB<-chooseB(res.varsel)
# sink(file = "C:/Users/vince/OneDrive - LECNAM/Recherche/MI_clustering/Rpackage/vignettes/sink/chooseB.txt");dput(res.chooseB);sink()
# 
# res.varsel.light<-res.varsel
# res.varsel.light$res.varsel$alco$res.detail<- rep(NA,length(res.varsel.light$res.varsel$alco$res.detail))
# str(res.varsel.light$res.varsel$alco$res.detail)
# res.varsel.light$call$res.imputedata<-NULL
# res.varsel.light$res.varsel$alco$res$listvarblock<-NULL
# res.varsel.light$res.varsel$alco$call$knockoff.arg<-NULL
# res.varsel.light$res.varsel$alco$call$glmnet.arg<-NULL
# res.varsel.light$res.varsel$alco$call$stepwise.arg<-NULL
# sink(file = "C:/Users/vince/OneDrive - LECNAM/Recherche/MI_clustering/Rpackage/vignettes/sink/resvarsel.txt");dput(res.varsel.light);sink()

res.varsel <- list(predictormatrix = structure(c(0, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0), dim = c(13L, 13L), dimnames = list(
    c("alco", "malic", "ash", "alca", "mg", "phe", "fla", "nfla", 
    "pro", "col", "hue", "ratio", "prol"), c("alco", "malic", 
    "ash", "alca", "mg", "phe", "fla", "nfla", "pro", "col", 
    "hue", "ratio", "prol"))), res.varsel = list(alco = list(
    res = list(garde = c(malic = 23, ash = 22, alca = 20, mg = 21, 
    phe = 20, fla = 21, nfla = 21, pro = 20, col = 19, hue = 21, 
    ratio = 20, prol = 22), effectif = c(malic = 6, ash = 8, 
    alca = 9, mg = 7, phe = 2, fla = 7, nfla = 3, pro = 9, col = 18, 
    hue = 15, ratio = 12, prol = 8), proportion = c(malic = 0.260869565217391, 
    ash = 0.363636363636364, alca = 0.45, mg = 0.333333333333333, 
    phe = 0.1, fla = 0.333333333333333, nfla = 0.142857142857143, 
    pro = 0.45, col = 0.947368421052632, hue = 0.714285714285714, 
    ratio = 0.6, prol = 0.363636363636364), selection = c("ash", 
    "alca", "mg", "fla", "pro", "col", "hue", "ratio", "prol"
    ), failure = c(malic = 0, ash = 0, alca = 0, mg = 0, phe = 0, 
    fla = 0, nfla = 0, pro = 0, col = 0, hue = 0, ratio = 0, 
    prol = 0)), res.detail = c(NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA), call = list(
        nnodes = 2, X = structure(c(1.71, NA, NA, NA, 1.87, 2.15, 
        1.35, NA, 1.73, 1.87, NA, 1.92, NA, 3.1, 3.8, NA, 1.6, 
        1.81, NA, NA, 1.9, 1.5, NA, 1.83, 1.53, 1.65, NA, 1.71, 
        NA, NA, 3.98, NA, 4.04, 1.68, NA, NA, 1.67, 1.7, 1.97, 
        0.94, NA, 1.36, NA, NA, 1.21, NA, NA, 1.51, 1.67, 1.09, 
        1.88, NA, 3.87, NA, NA, 1.13, NA, NA, 1.61, NA, NA, 1.51, 
        NA, NA, NA, 1.41, NA, 2.08, NA, NA, NA, 1.29, NA, 2.68, 
        1.39, NA, NA, 2.4, 4.43, NA, 4.31, 2.16, 2.13, 4.3, 1.35, 
        2.99, 3.55, 1.24, NA, 5.51, NA, 2.81, 2.56, NA, NA, 3.88, 
        4.61, 3.24, 2.67, NA, 5.19, 4.12, 3.03, 1.68, 1.67, NA, 
        NA, 3.45, 2.76, NA, 2.58, 4.6, 2.39, NA, 3.91, NA, 2.59, 
        4.1, 2.43, 2.14, 2.67, NA, 2.45, 2.61, NA, 2.41, 2.39, 
        2.38, 2.7, 2.72, 2.62, 2.56, 2.65, NA, 2.52, 2.61, NA, 
        NA, NA, NA, 2.36, NA, 2.7, 2.55, 2.51, NA, 2.12, 2.59, 
        2.29, 2.1, 2.44, 2.12, 2.04, NA, NA, 2.3, 2.68, 1.36, 
        NA, NA, 2.16, 2.53, 2.56, NA, 1.75, 2.67, 2.6, 2.3, NA, 
        NA, 2.4, 2, NA, 2.51, 2.32, 2.58, NA, 2.3, 2.32, NA, 
        2.26, 2.28, 2.74, 1.98, NA, 1.7, NA, NA, 2.28, 1.94, 
        2.7, 2.92, 2.5, 2.2, 1.99, 2.42, NA, 2.13, 2.39, 2.17, 
        NA, 2.38, NA, 2.4, 2.36, 2.25, 2.54, 2.64, 2.61, 2.7, 
        2.35, 2.72, 2.35, 2.2, 2.48, NA, 2.48, 2.28, 2.32, 2.38, 
        NA, NA, 2.64, 2.54, NA, 2.35, 2.3, NA, 2.69, NA, 2.28, 
        NA, 2.48, 2.26, 2.37, 2.74, 15.6, NA, NA, 21, NA, 17.6, 
        16, 16, 11.4, NA, NA, NA, 20, 15.2, 18.6, NA, 17.8, 20, 
        16.1, 17, NA, NA, 19.1, NA, NA, NA, 13.2, 16.2, 18.8, 
        NA, NA, 17, NA, NA, 12.4, NA, NA, 16.3, 16.8, 10.6, NA, 
        16.8, 19, 19, 18.1, NA, 16.8, NA, 30, 21, NA, 18, NA, 
        19, NA, 24, 22.5, 18, 22.8, NA, NA, 22, NA, 18, 21.5, 
        16, 18, 17.5, 18.5, 20.5, 22.5, 19, 20, NA, NA, 21.5, 
        20.8, NA, NA, 21.5, 21, 21, 28.5, 22, NA, 20, 21.5, NA, 
        NA, 25, NA, 21, NA, 23.5, 20, NA, NA, NA, 22, 18.5, 22, 
        19.5, NA, 25, NA, NA, NA, 18.5, NA, 22.5, 24.5, 25, 19.5, 
        NA, 23, 20, NA, 24.5, 127, 100, 101, NA, 96, 121, NA, 
        89, 91, 102, 112, 120, NA, 116, 102, NA, 95, NA, 93, 
        NA, 107, 101, 106, 104, NA, NA, NA, 117, NA, 101, NA, 
        107, 111, 101, 92, 111, NA, 118, 102, 88, 101, 100, 87, 
        104, 98, NA, 151, 86, 139, 101, NA, 112, NA, 86, NA, 
        78, 85, NA, 90, 70, 81, NA, NA, NA, 134, 85, NA, 97, 
        88, 85, 84, 92, 94, 103, 84, 85, 86, 96, 102, 86, NA, 
        85, 92, 80, 122, NA, 106, NA, 89, NA, 101, NA, 89, 97, 
        NA, 112, NA, 92, NA, 98, NA, 89, 97, 98, 89, NA, 106, 
        106, 90, 88, 105, 112, 86, 91, 102, NA, 120, NA, 2.8, 
        2.65, 2.8, 2.8, 2.5, 2.6, 2.98, 2.6, 3.1, NA, 2.85, 2.8, 
        NA, NA, 2.41, 2.61, 2.48, 2.53, NA, 2.4, 2.95, 3, NA, 
        2.42, 2.95, 2.45, NA, 3.15, 2.45, 3.25, 2.64, 3, 2.85, 
        3.1, 2.72, 3.88, NA, 3.2, 3, 1.98, 2.05, 2.02, 3.5, 1.89, 
        NA, 2.53, NA, 2.95, NA, NA, NA, NA, 2.83, NA, 2.2, NA, 
        1.65, NA, NA, 2.2, 1.6, 1.45, 1.38, 3.02, NA, 2.55, NA, 
        NA, NA, 2.2, NA, 2.36, 2.74, 1.75, 2.56, 2.46, NA, 2.9, 
        NA, NA, 2.86, NA, 2.13, 2.1, 1.51, NA, 1.7, NA, 1.38, 
        NA, NA, NA, 1.4, NA, 2, 1.38, 1.7, 1.93, 1.48, NA, NA, 
        1.8, 1.9, NA, NA, 1.83, NA, 1.39, 1.35, NA, 1.55, NA, 
        1.39, 1.68, NA, NA, 1.65, 2.05, NA, 2.76, NA, NA, 2.52, 
        2.51, NA, 2.76, NA, 3.64, 2.91, 3.14, 3.4, NA, 2.41, 
        2.88, 2.37, 2.61, 2.94, NA, 2.97, 3.25, 3.19, 2.69, NA, 
        2.43, 3.04, 3.29, 2.68, 3.56, 2.63, 3, 2.65, NA, NA, 
        3.74, 2.9, NA, 3.23, 0.57, NA, 1.41, NA, 1.75, 2.65, 
        1.3, NA, 2.86, 2.89, 2.14, 1.57, NA, NA, 2.26, 2.53, 
        1.58, 1.59, 2.21, 1.69, NA, NA, 1.25, 1.46, NA, 0.99, 
        NA, 2.99, 2.17, 1.36, 1.92, 1.76, NA, 2.92, 2.03, 2.29, 
        2.17, 1.6, NA, NA, NA, 3.03, 2.65, 2.24, 1.75, NA, NA, 
        1.2, 0.58, 0.47, NA, 0.6, 0.5, NA, 0.52, 0.8, NA, 0.65, 
        0.76, 1.36, 0.83, 0.63, NA, 0.58, 1.31, 1.1, NA, 0.6, 
        NA, 0.68, 0.47, 0.84, 0.96, NA, 0.7, NA, 0.69, 0.68, 
        0.76, 0.28, NA, 0.3, 0.39, NA, 0.31, 0.22, 0.29, 0.43, 
        0.29, 0.3, NA, NA, 0.17, 0.25, 0.27, 0.26, NA, 0.34, 
        0.27, NA, 0.29, NA, 0.42, NA, NA, 0.2, 0.34, NA, NA, 
        0.32, 0.28, 0.3, 0.21, 0.17, 0.32, 0.21, 0.26, NA, 0.28, 
        NA, 0.53, NA, 0.45, 0.37, NA, NA, NA, 0.21, 0.13, 0.34, 
        0.43, 0.43, 0.3, NA, 0.4, 0.61, 0.22, NA, NA, NA, 0.5, 
        NA, 0.17, NA, NA, NA, NA, 0.29, NA, 0.48, 0.39, 0.29, 
        NA, NA, 0.52, 0.3, 0.32, 0.43, 0.3, NA, NA, NA, NA, NA, 
        NA, 0.17, 0.6, 0.53, 0.63, 0.53, 0.53, NA, NA, NA, NA, 
        0.47, 0.45, NA, 0.61, NA, NA, 0.63, 0.53, 0.52, 0.5, 
        0.6, 0.4, 0.41, NA, 0.39, NA, 0.48, NA, 0.43, 0.43, 0.53, 
        NA, 2.29, 1.28, 2.81, 1.82, 1.98, 1.25, 1.85, 1.81, NA, 
        2.96, 1.46, NA, NA, 1.66, NA, 1.69, 1.46, 1.66, 1.45, 
        1.35, 1.76, 2.38, 1.95, NA, NA, 1.44, NA, NA, NA, NA, 
        NA, 2.03, NA, 2.14, NA, 1.87, 1.62, NA, 1.66, 0.42, NA, 
        NA, 1.87, 1.03, NA, NA, 2.5, 1.87, NA, NA, 1.15, NA, 
        1.95, 1.43, NA, NA, 1.62, NA, 1.56, 1.38, 1.64, 1.63, 
        NA, 1.35, 1.56, 1.77, 2.81, 1.4, 1.35, NA, 1.63, 2.08, 
        2.49, NA, 1.04, NA, NA, 1.83, 1.71, NA, 2.91, 1.35, NA, 
        NA, 0.94, NA, 0.84, 1.25, 0.8, 1.1, NA, 0.75, NA, 0.55, 
        NA, 1.14, 0.86, 1.25, 1.26, NA, 1.55, 1.56, 1.14, 2.7, 
        2.29, NA, NA, 0.94, NA, 1.15, 1.54, 1.11, 0.64, 1.24, 
        1.41, 1.35, 1.46, 1.35, 5.64, 4.38, 5.68, NA, NA, 5.05, 
        7.22, 5.6, NA, 7.5, NA, NA, NA, 5.1, 4.5, NA, 3.93, 3.52, 
        4.8, 3.95, 4.5, 5.7, NA, NA, 5.4, 4.25, 5.1, 6.13, 4.28, 
        5.43, 4.36, 5.04, 5.24, 6.1, 7.2, NA, 5.85, NA, NA, NA, 
        3.27, 5.75, 4.45, 2.95, 4.6, 3.17, 2.85, 3.38, NA, 3.21, 
        3.8, NA, NA, 2.5, 3.9, NA, 4.8, 3.05, 2.45, 1.74, 2.4, 
        3.6, 3.05, 3.25, NA, 2.9, NA, NA, NA, 2.94, 3.3, 2.7, 
        NA, NA, 2.9, 1.9, NA, 3.25, NA, NA, 2.8, NA, NA, NA, 
        NA, NA, 5, 5.45, NA, 5, 4.92, NA, NA, 4.35, 4.4, 8.21, 
        NA, 8.42, NA, 10.52, 7.9, NA, 7.5, 13, NA, NA, 5.58, 
        NA, NA, 6.62, NA, 8.5, NA, 9.7, 7.3, NA, 9.3, 9.2, 1.04, 
        1.05, NA, NA, 1.02, NA, NA, 1.15, 1.25, 1.2, 1.28, NA, 
        1.13, NA, NA, 1.11, 1.09, 1.12, NA, 1.02, NA, 1.19, 1.09, 
        NA, 1.25, NA, NA, 0.95, 0.91, 0.88, 0.82, NA, 0.87, NA, 
        1.12, NA, NA, 0.94, 1.07, NA, 1.25, 0.98, 1.22, NA, NA, 
        1.02, 1.28, 1.36, 1.31, NA, 1.23, 0.96, 1.19, 1.38, NA, 
        1.31, 0.84, NA, NA, 1.07, 1.08, 1.05, NA, NA, NA, NA, 
        1.42, 1.27, NA, 1.04, NA, 0.86, NA, NA, 0.93, 1.71, 0.95, 
        0.8, 0.92, 0.73, NA, 0.86, 0.97, 0.79, NA, 0.74, 0.78, 
        0.75, 0.75, 0.82, NA, NA, NA, 0.89, NA, 0.65, 0.54, 0.55, 
        0.48, 0.56, 0.6, 0.57, 0.67, 0.57, NA, 0.96, 0.87, 0.68, 
        0.7, 0.78, 0.74, 0.67, NA, NA, 0.7, 0.59, 0.6, 0.61, 
        3.92, 3.4, 3.17, 2.93, 3.58, 3.58, 3.55, 2.9, NA, 3, 
        NA, 2.65, NA, 3.36, 3.52, 4, 3.63, NA, NA, 2.77, NA, 
        2.71, 2.88, 2.87, 3, NA, NA, 3.38, 3, NA, 3, 3.35, 3.33, 
        3.33, NA, 3.26, 3.2, NA, 2.84, 1.82, NA, 1.59, 2.87, 
        NA, 2.3, NA, NA, NA, 3.5, 3.13, 2.14, 2.52, NA, NA, 3.14, 
        2.72, 2.01, NA, NA, 3.21, NA, 2.65, NA, NA, NA, 2.74, 
        2.83, 2.96, 2.77, 3.57, 2.42, 3.02, 3.26, 2.5, 3.19, 
        2.87, 3.33, 3.39, 3.12, NA, 3.64, NA, NA, NA, NA, 1.42, 
        1.29, 1.51, 1.27, 1.69, 2.15, NA, NA, NA, 2.05, 2, NA, 
        1.62, 1.47, 1.51, 1.48, 1.64, 1.73, 1.96, 1.78, NA, NA, 
        1.75, 1.68, 1.75, NA, NA, NA, NA, NA, 1.56, 1.62, 1.6, 
        1065, 1050, NA, 735, 1290, 1295, 1045, 1320, NA, 1547, 
        1310, 1280, NA, 845, 770, 1035, 1015, 845, 1195, 1285, 
        915, 1285, NA, NA, 1235, NA, 760, 795, 1035, 1095, 680, 
        885, 1080, 985, 1150, 1190, 1060, 970, 1270, NA, NA, 
        NA, NA, NA, NA, NA, 718, 410, NA, 886, NA, NA, 463, 278, 
        714, NA, 515, NA, NA, 625, 480, 450, 495, 345, 625, 428, 
        406, NA, 562, 672, NA, 312, 680, NA, 385, NA, NA, NA, 
        365, 380, NA, 378, 466, 580, NA, NA, 600, NA, 720, 515, 
        590, 600, NA, 520, 550, NA, NA, NA, 480, 675, NA, 480, 
        880, 660, NA, 680, 570, NA, 615, NA, NA, NA, 470, NA, 
        NA, 835, 840, NA), dim = c(118L, 12L), dimnames = list(
            NULL, c("V1", "V2", "V3", "V4", "V5", "V6", "V7", 
            "V8", "V9", "V10", "V11", "V12"))), Y = c(14.23, 
        13.2, 13.16, 13.24, 14.39, 14.06, 13.86, 13.75, 14.75, 
        14.38, 13.63, 14.3, 13.83, 13.64, 12.93, 13.71, 12.85, 
        13.5, 13.39, 13.3, 13.87, 13.73, 13.58, 13.68, 13.76, 
        13.05, 14.22, 13.56, 13.41, 13.88, 13.24, 13.05, 14.21, 
        13.9, 13.05, 13.82, 13.74, 14.22, 13.29, 12.37, 12.33, 
        12.64, 12.37, 12.17, 12.37, 13.34, 12.21, 13.86, 12.99, 
        11.96, 11.66, 11.84, 12.7, 12, 12.72, 12.08, 13.05, 11.84, 
        12.16, 12.08, 12.08, 12, 12.69, 11.62, 11.81, 12.29, 
        12.29, 12.08, 12.6, 12.51, 12.72, 12.22, 11.61, 11.76, 
        12.08, 11.03, 11.82, 11.45, 12.42, 13.05, 11.87, 12.07, 
        11.79, 12.04, 12.86, 12.88, 12.7, 12.51, 12.25, 12.53, 
        12.84, 12.93, 13.36, 13.52, 13.62, 12.25, 12.87, 13.32, 
        12.79, 13.23, 13.17, 13.84, 12.45, 14.34, 13.48, 13.69, 
        12.85, 12.96, 13.78, 13.73, 13.58, 13.4, 12.77, 14.16, 
        13.4, 13.27, 13.17, 14.13), B = 50, path.outfile = NULL, 
        methods = "knockoff", sizeblock = 5, printflag = FALSE, 
        r = c(alco = 0.3), seed = 1234567, nb.clust = 3, modelNames = NULL))), 
    proportion = structure(c(0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 0.260869565217391, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 0.363636363636364, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
    0.45, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.333333333333333, 
    1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0.1, 1, 1, 1, 1, 0, 1, 
    1, 1, 1, 1, 1, 1, 0.333333333333333, 1, 1, 1, 1, 1, 0, 1, 
    1, 1, 1, 1, 1, 0.142857142857143, 1, 1, 1, 1, 1, 1, 0, 1, 
    1, 1, 1, 1, 0.45, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0.947368421052632, 
    1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0.714285714285714, 1, 
    1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0.6, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 0, 1, 0.363636363636364, 1, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1, 0), dim = c(13L, 13L), dimnames = list(c("alco", 
    "malic", "ash", "alca", "mg", "phe", "fla", "nfla", "pro", 
    "col", "hue", "ratio", "prol"), c("alco", "malic", "ash", 
    "alca", "mg", "phe", "fla", "nfla", "pro", "col", "hue", 
    "ratio", "prol"))), call = list(data.na = NULL, listvar = "alco", 
        nb.clust = NULL, nnodes = 2, sizeblock = 5, method.select = "knockoff", 
        B = 50, r = 0.3, graph = FALSE, printflag = TRUE, path.outfile = NULL, 
        mar = c(2.1, 4.1, 2.1, 0.6), cex.names = 0.7, modelNames = NULL))


res.varsel$predictormatrix["alco", ]

## ----eval=FALSE---------------------------------------------------------------
#  # multiple imputation with the new model
#  res.imp.select <- imputedata(data.na = wine.na,
#                               method = "FCS-homo",
#                               nb.clust = nb.clust,
#                               maxit = maxit,
#                               m = m,
#                               predictmat = res.varsel$predictormatrix)

## ----eval=FALSE---------------------------------------------------------------
#  varselbest(res.imputedata = res.imp.FCS, B = B, nnodes = nnodes) # (time consuming)

## ----eval=FALSE---------------------------------------------------------------
#  res.B <- chooseB(res.varsel)

## ----convb,fig.height = 4, fig.width = 4, fig.align = "center",echo=FALSE-----
res.chooseB <- list(alco = structure(c(1, 1, 0.5, 0.5, 0.333333333333333, 0.333333333333333, 
0.25, 0.25, 0.25, 0.2, 0.166666666666667, 0.166666666666667, 
0.166666666666667, 0.142857142857143, 0.125, 0.125, 0.125, 0.125, 
0.125, 0.111111111111111, 0.1, 0.1, 0.1, 0.0909090909090909, 
0.0833333333333333, 0.0833333333333333, 0.0833333333333333, 0.0833333333333333, 
0.0833333333333333, 0.153846153846154, 0.214285714285714, 0.214285714285714, 
0.266666666666667, 0.266666666666667, 0.266666666666667, 0.25, 
0.235294117647059, 0.235294117647059, 0.235294117647059, 0.277777777777778, 
0.277777777777778, 0.263157894736842, 0.263157894736842, 0.25, 
0.25, 0.238095238095238, 0.227272727272727, 0.227272727272727, 
0.260869565217391, 0.260869565217391, 0, 0, 0, 0, 0, 0.5, 0.333333333333333, 
0.333333333333333, 0.25, 0.25, 0.25, 0.2, 0.2, 0.333333333333333, 
0.285714285714286, 0.285714285714286, 0.285714285714286, 0.25, 
0.25, 0.25, 0.25, 0.333333333333333, 0.3, 0.3, 0.363636363636364, 
0.363636363636364, 0.363636363636364, 0.333333333333333, 0.333333333333333, 
0.384615384615385, 0.384615384615385, 0.428571428571429, 0.4, 
0.4, 0.375, 0.375, 0.352941176470588, 0.352941176470588, 0.352941176470588, 
0.352941176470588, 0.388888888888889, 0.388888888888889, 0.388888888888889, 
0.368421052631579, 0.35, 0.35, 0.380952380952381, 0.380952380952381, 
0.380952380952381, 0.363636363636364, 1, 1, 0.5, 0.5, 0.5, 0.5, 
0.5, 0.333333333333333, 0.333333333333333, 0.5, 0.4, 0.4, 0.4, 
0.5, 0.5, 0.428571428571429, 0.428571428571429, 0.428571428571429, 
0.428571428571429, 0.428571428571429, 0.428571428571429, 0.5, 
0.444444444444444, 0.444444444444444, 0.444444444444444, 0.444444444444444, 
0.444444444444444, 0.4, 0.363636363636364, 0.363636363636364, 
0.363636363636364, 0.416666666666667, 0.416666666666667, 0.384615384615385, 
0.357142857142857, 0.357142857142857, 0.333333333333333, 0.333333333333333, 
0.3125, 0.3125, 0.352941176470588, 0.352941176470588, 0.352941176470588, 
0.352941176470588, 0.352941176470588, 0.388888888888889, 0.421052631578947, 
0.421052631578947, 0.421052631578947, 0.45, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0.25, 0.2, 0.2, 0.166666666666667, 0.166666666666667, 
0.166666666666667, 0.285714285714286, 0.375, 0.375, 0.375, 0.333333333333333, 
0.4, 0.4, 0.4, 0.363636363636364, 0.363636363636364, 0.333333333333333, 
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.384615384615385, 
0.384615384615385, 0.357142857142857, 0.357142857142857, 0.357142857142857, 
0.357142857142857, 0.333333333333333, 0.3125, 0.3125, 0.294117647058824, 
0.294117647058824, 0.333333333333333, 0.333333333333333, 0.315789473684211, 
0.315789473684211, 0.3, 0.3, 0.3, 0.333333333333333, 0.333333333333333, 
0.333333333333333, 0, 0, 0, 0, 0, 0, 0, 0, 0.2, 0.2, 0.2, 0.2, 
0.166666666666667, 0.166666666666667, 0.166666666666667, 0.166666666666667, 
0.166666666666667, 0.142857142857143, 0.125, 0.125, 0.125, 0.111111111111111, 
0.111111111111111, 0.1, 0.1, 0.1, 0.1, 0.0909090909090909, 0.0909090909090909, 
0.0909090909090909, 0.0833333333333333, 0.0833333333333333, 0.0833333333333333, 
0.0769230769230769, 0.0769230769230769, 0.0769230769230769, 0.0769230769230769, 
0.0714285714285714, 0.0714285714285714, 0.0666666666666667, 0.125, 
0.125, 0.117647058823529, 0.117647058823529, 0.111111111111111, 
0.111111111111111, 0.111111111111111, 0.105263157894737, 0.105263157894737, 
0.1, 0, 0, 0, 0, 0, 0, 0, 0, 0.25, 0.25, 0.25, 0.4, 0.333333333333333, 
0.333333333333333, 0.428571428571429, 0.428571428571429, 0.428571428571429, 
0.375, 0.333333333333333, 0.333333333333333, 0.4, 0.4, 0.363636363636364, 
0.363636363636364, 0.363636363636364, 0.333333333333333, 0.307692307692308, 
0.307692307692308, 0.285714285714286, 0.285714285714286, 0.285714285714286, 
0.285714285714286, 0.333333333333333, 0.333333333333333, 0.333333333333333, 
0.3125, 0.3125, 0.294117647058824, 0.294117647058824, 0.294117647058824, 
0.294117647058824, 0.294117647058824, 0.277777777777778, 0.277777777777778, 
0.315789473684211, 0.315789473684211, 0.35, 0.35, 0.333333333333333, 
0.333333333333333, 0, 1, 0.5, 0.5, 0.333333333333333, 0.333333333333333, 
0.25, 0.25, 0.25, 0.25, 0.2, 0.2, 0.166666666666667, 0.166666666666667, 
0.142857142857143, 0.142857142857143, 0.125, 0.125, 0.125, 0.111111111111111, 
0.1, 0.1, 0.1, 0.0909090909090909, 0.0909090909090909, 0.0833333333333333, 
0.0769230769230769, 0.0769230769230769, 0.0769230769230769, 0.0769230769230769, 
0.0714285714285714, 0.0714285714285714, 0.0714285714285714, 0.0714285714285714, 
0.0666666666666667, 0.0666666666666667, 0.0666666666666667, 0.0666666666666667, 
0.0666666666666667, 0.0625, 0.0625, 0.0588235294117647, 0.0588235294117647, 
0.111111111111111, 0.157894736842105, 0.157894736842105, 0.15, 
0.15, 0.15, 0.142857142857143, 0, 1, 0.5, 0.5, 0.5, 0.5, 0.5, 
0.5, 0.5, 0.666666666666667, 0.666666666666667, 0.75, 0.6, 0.6, 
0.6, 0.5, 0.5, 0.428571428571429, 0.375, 0.375, 0.375, 0.375, 
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.3, 
0.363636363636364, 0.363636363636364, 0.333333333333333, 0.333333333333333, 
0.307692307692308, 0.307692307692308, 0.357142857142857, 0.357142857142857, 
0.4, 0.4, 0.4, 0.4, 0.4, 0.4375, 0.4375, 0.470588235294118, 0.470588235294118, 
0.470588235294118, 0.470588235294118, 0.444444444444444, 0.444444444444444, 
0.473684210526316, 0.45, 0.45, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 0.875, 0.875, 0.888888888888889, 0.888888888888889, 
0.888888888888889, 0.888888888888889, 0.9, 0.9, 0.9, 0.909090909090909, 
0.916666666666667, 0.916666666666667, 0.916666666666667, 0.923076923076923, 
0.923076923076923, 0.928571428571429, 0.928571428571429, 0.928571428571429, 
0.928571428571429, 0.933333333333333, 0.9375, 0.9375, 0.9375, 
0.9375, 0.941176470588235, 0.941176470588235, 0.941176470588235, 
0.944444444444444, 0.944444444444444, 0.944444444444444, 0.947368421052632, 
0.947368421052632, 0, 0, 0, 0, 0, 0, 0.333333333333333, 0.333333333333333, 
0.5, 0.5, 0.5, 0.6, 0.6, 0.6, 0.666666666666667, 0.666666666666667, 
0.714285714285714, 0.714285714285714, 0.714285714285714, 0.625, 
0.625, 0.666666666666667, 0.666666666666667, 0.7, 0.7, 0.727272727272727, 
0.75, 0.75, 0.769230769230769, 0.769230769230769, 0.785714285714286, 
0.785714285714286, 0.785714285714286, 0.733333333333333, 0.75, 
0.75, 0.75, 0.705882352941177, 0.666666666666667, 0.666666666666667, 
0.666666666666667, 0.684210526315789, 0.684210526315789, 0.7, 
0.7, 0.7, 0.7, 0.7, 0.7, 0.714285714285714, 1, 1, 1, 0.5, 0.666666666666667, 
0.666666666666667, 0.666666666666667, 0.5, 0.5, 0.5, 0.6, 0.6, 
0.6, 0.6, 0.6, 0.6, 0.5, 0.5, 0.5, 0.428571428571429, 0.428571428571429, 
0.428571428571429, 0.5, 0.5, 0.555555555555556, 0.555555555555556, 
0.6, 0.6, 0.6, 0.636363636363636, 0.636363636363636, 0.636363636363636, 
0.666666666666667, 0.666666666666667, 0.666666666666667, 0.615384615384615, 
0.642857142857143, 0.642857142857143, 0.642857142857143, 0.666666666666667, 
0.666666666666667, 0.6875, 0.647058823529412, 0.647058823529412, 
0.647058823529412, 0.611111111111111, 0.611111111111111, 0.631578947368421, 
0.6, 0.6, 1, 1, 1, 0.5, 0.5, 0.333333333333333, 0.333333333333333, 
0.25, 0.2, 0.2, 0.2, 0.333333333333333, 0.333333333333333, 0.285714285714286, 
0.285714285714286, 0.25, 0.25, 0.222222222222222, 0.2, 0.2, 0.2, 
0.272727272727273, 0.272727272727273, 0.272727272727273, 0.25, 
0.25, 0.25, 0.230769230769231, 0.230769230769231, 0.285714285714286, 
0.285714285714286, 0.333333333333333, 0.333333333333333, 0.3125, 
0.3125, 0.294117647058824, 0.294117647058824, 0.277777777777778, 
0.263157894736842, 0.263157894736842, 0.3, 0.3, 0.3, 0.333333333333333, 
0.333333333333333, 0.333333333333333, 0.333333333333333, 0.363636363636364, 
0.363636363636364, 0.363636363636364), dim = c(50L, 12L), dimnames = list(
    NULL, c("V1", "V2", "V3", "V4", "V5", "V6", "V7", "V8", "V9", 
    "V10", "V11", "V12"))))
gridB.intern<-seq(1,length(res.varsel$res.varsel$alco$res.detail),ceiling(length(res.varsel$res.varsel$alco$res.detail)/100))

matprop<-res.chooseB$alco
colnames(matprop) <- names(res.varsel$res.varsel$alco$res$proportion)
matprop.plot <- as.data.frame(matprop)
matprop.plot$id <- gridB.intern

plot_data <- reshape2::melt(matprop.plot, id.var = "id", 
                  value.name = "proportion")

linewidth <- 1;linetype <- "dotdash";xlab <- "B";ylab <- "Proportion"

res.ggplot <- ggplot2::ggplot(plot_data, ggplot2::aes(x = id, y = proportion, group = variable, colour = variable)) + 
  ggplot2::geom_line(linetype = linetype, linewidth = linewidth) + 
  ggplot2::labs(x = xlab, y = ylab)+ ggplot2::labs(title = NULL)

print(res.ggplot)

## ----fig.height = 4, fig.width = 4, fig.align = "center"----------------------
# check the variable importance
round(res.varsel$proportion["alco",], 2)
barplot(sort(res.varsel$proportion["alco",], decreasing=TRUE),
        ylab = "proportion",
        main = "alco",
        ylim = c(0, 1),
        las = 2,
        cex.names = .5)

r <- 0.2 # a new threshold value (r = 0.3 by default)
abline(h = r, col = 2, lty = 2)

## ----fig.height = 4, fig.width = 4, fig.align = "center"----------------------
predictormatrix <- res.varsel$predictormatrix
predictormatrix[res.varsel$proportion>r] <- 1
predictormatrix[res.varsel$proportion<=r] <- 0
predictormatrix["alco", ]

## ----eval=FALSE---------------------------------------------------------------
#  chooser(res.varsel = res.varsel)

## ----eval=FALSE---------------------------------------------------------------
#  # kmeans clustering
#  res.pool.kmeans <- clusterMI(res.imp.JM, nnodes = nnodes)

## ----echo=FALSE---------------------------------------------------------------
res.pool.kmeans <- clusterMI(res.imp.JM, nnodes = nnodes, instability = FALSE,verbose = FALSE)
res.pool.kmeans$instability<-list(U = c(0.0181100871102134, 0.00951395025880571, 0.0225299835879308, 
0.0181744729200858, 0.0254525943693978, 0.0190544123216766, 0.0331624794849135, 
0.0271001136220174, 0.0181504860497412, 0.0194470395152127, 0.0241068046963767, 
0.0169953288726171, 0.0383171316752935, 0.0180330766317384, 0.0179093548794344, 
0.0229781593233178, 0.0348604974119429, 0.031046585027143, 0.0191200605984093, 
0.0279977275596516), Ubar = 0.023103017295796, B = 0.0721092390087769, 
    Tot = 0.0952122563045729)

## -----------------------------------------------------------------------------
part <- res.pool.kmeans$part
table(part) #compute cluster sizes
table(part, ref) #compare the partition with the reference partition
res.pool.kmeans$instability # look at instabilitiy measures

## ----eval=FALSE---------------------------------------------------------------
#  res.pool.all <- lapply(c("kmeans", "pam", "clara","hclust", "mixture", "cmeans"),
#                     FUN = clusterMI,
#                     nnodes = nnodes,
#                     output = res.imp.JM)

## ----results='hide', message=FALSE, eval=FALSE--------------------------------
#  library(clustrd)
#  res.ana.rkm <- lapply(res.imp.JM$res.imp,
#                        FUN = cluspca,
#                        nclus = nb.clust,
#                        ndim = 2,
#                        method= "RKM")
#  
#  # extract the set of partitions (as list)
#  res.ana.rkm <- lapply(res.ana.rkm, "[[", "cluster")
#  
#  # pooling by NMF
#  res.pool.rkm <- fastnmf(res.ana.rkm, nb.clust = nb.clust)
#  part.rkm <- res.pool.rkm$best$clust# extract the best solution based on several initialisations

## ----overimpecho, eval=FALSE--------------------------------------------------
#  # Multiple imputation is rerun with more imputed data sets (m = 100)
#  res.imp.over <- imputedata(data.na = wine.na,
#                        nb.clust = nb.clust,
#                        m = 100,
#                        Lstart = Lstart,
#                        L = L,
#                        verbose = FALSE)
#  
#  # selection of 20 complete individuals on variable "alco"
#  plotinds <- sample(which(!is.na(wine.na[, "alco"])),
#                      size = 20)
#  
#  res.over <- overimpute(res.imp.over,
#                         nnodes = nnodes,
#                         plotvars = "alco",
#                         plotinds = plotinds)

## ----overimp, fig.height = 4, fig.width = 4, fig.align = "center", warning=FALSE, echo=FALSE, results='hide'----
# Multiple imputation is rerun with more imputed data sets (m = 100) 
# sink(file = "C:/Users/vince/OneDrive - LECNAM/Recherche/MI_clustering/Rpackage/vignettes/sink/overimpute.txt");dput(res.over);sink()

res.over <- list(res.plot = structure(list(var = c("alco", "alco", "alco", "alco", "alco", 
"alco", "alco", "alco", "alco", "alco", "alco", "alco", "alco", 
"alco", "alco", "alco", "alco", "alco", "alco", "alco"), trueval = c(12.7, 
13.82, 13.27, 12.85, 12.72, 12, 12.04, 14.38, 12.37, 14.16, 13.58, 
13.32, 11.76, 11.84, 12.93, 12.99, 12.84, 13.86, 12.6, 13.69), 
    xbar = c(11.7738376865082, 13.2781194335305, 12.7656078455516, 
    12.3550918675967, 12.1103766286982, 11.3488581416796, 11.689790703598, 
    13.8292914203422, 11.8838061588389, 13.4927187358782, 12.8831079064593, 
    12.7635311892978, 11.4102766462339, 11.6100843994868, 12.3771753207685, 
    12.1498426103087, 12.2019534817579, 12.8181064702831, 12.0241257849814, 
    12.9630596788976), binf = c(10.9002453672607, 12.5906496890719, 
    12.1071831918227, 11.7189914123926, 11.4744279355071, 10.5966941487251, 
    11.047877870576, 13.0803896346415, 11.1022261939148, 12.7714298904175, 
    12.0665951704424, 12.0436735061878, 10.6786684003405, 10.8910995794392, 
    11.7081349527227, 11.0226921594602, 11.4204289240371, 11.6007267438947, 
    11.2195525358362, 12.0458943601711), bsup = c(12.5277787644536, 
    13.7651267363568, 13.2746684227263, 12.8346923847886, 12.6520903164249, 
    11.9232169641688, 12.1702859416548, 14.3855364579454, 12.356405814087, 
    14.0423416960767, 13.4498872617714, 13.1819251692097, 12.253636795827, 
    12.3733229011399, 12.9137110119783, 12.8684270922574, 12.7028857537259, 
    13.7063102013102, 12.5359610362416, 13.5852137169506), pct = c(25, 
    25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 
    25, 25, 25, 25), col = c("blue", "green", "green", "blue", 
    "#FFFF00", "green", "green", "blue", "green", "#FFFF00", 
    "green", "green", "#FFFF00", "#FFFF00", "green", "green", 
    "green", "green", "green", "#FFFF00")), row.names = c("134", 
"53", "176", "24", "82", "81", "130", "15", "60", "173", "169", 
"149", "113", "78", "141", "74", "140", "72", "102", "162"), class = "data.frame"), res.values = structure(c(10.9027571590876, 13.3720282195999, 11.940000220621, 
12.4588572744253, 11.3847526657941, 11.0424938170906, 10.8182234941578, 
13.1903500550807, 11.9867297229711, 12.5651795406012, 11.9050384287735, 
12.2377169380197, 10.9071143748198, 10.7402969152648, 11.8916671774033, 
10.7483687696833, 11.9543405883846, 11.8711675999938, 11.1721688171028, 
12.429957988662, 11.1565466858466, 12.8809103115834, 12.2345463355942, 
12.2721010736648, 11.9472111163214, 10.8626662746027, 10.8245445791266, 
12.9606544401327, 11.4494033653085, 12.3324693055764, 12.2247508157112, 
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12.5832278506997, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 11.5393372050152, 12.8598755811367, 
12.2655549570779, 11.9942309306618, 11.7879214676301, 11.2072814754551, 
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12.5840269295835, 12.5005043043175, 12.4622048366594, 13.6609252976536, 
12.2021373735729, 13.2810745286227, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 12.2730558037744, 
13.4455380456546, 12.8580592427116, 12.4227873006222, 12.3182166527325, 
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13.0309632820284, 13.0539573124124, 12.678302186866, NA, 11.2247269982608, 
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12.9420323614893, 12.4559762046665, 12.5501632641266, 13.4646547050552, 
12.3289598669765, 13.1892612229751, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 11.7763759972509, 
13.2296990083637, 12.3683010450742, 12.4849605810544, 12.2315201278412, 
11.4543741803095, 11.687390988967, 14.092571208206, 11.8923416902844, 
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12.0022759417132, 11.7893223529602, 12.0142533084167, 11.2973065294882, 
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11.7174456170697, 11.9931807583945, 11.91013445003, 11.0777179520053, 
11.6575502862021, 11.7234253422465, 11.983396252915, 10.9401815777881, 
12.8897542175903, 12.7838939031615, 11.6891514994582, 12.1761459083228, 
10.7804594766759, 11.7200262669945, 13.250931943692, 11.4306099502856, 
12.8627963120138, 12.3091211528353, 12.3387405488775, 10.2956639653444, 
11.6109025606084, 11.9330604584893, 11.315884485154, 11.7597563595566, 
12.0529326512517, 11.5204323063685, 12.5542407400717, 10.8901981999531, 
12.6716565800448, 12.1850327902115, 12.1184654996523, 11.5935856373925, 
10.7679568098204, 12.684661604491, 13.5322869899053, 10.8375973140203, 
13.1341414383696, 12.4713608228455, 12.3996735569132, 11.2404611536777, 
11.1879960215822, 11.8149699366855, 11.5263753425494, 11.550562684474, 
11.881954512162, 11.960385266623, 11.9191343945325, 11.9275162461025, 
14.4300776220174, 13.2743343624162, 12.5004372882726, 12.3818166726893, 
11.3172066364164, 12.4489415414244, 14.5518055941913, 12.3814877434305, 
13.6640347229998, 13.004009818708, 12.98641916686, 11.5971294050632, 
13.0919638244835, 12.5969370593044, 12.523375648891, 11.8048025943535, 
12.6716475075252, 12.2891516762832, 12.8799082897072, 12.5791235755281, 
13.7530238239126, 13.2996352538429, 12.8330831864878, 12.6180370732925, 
11.9213345604736, 11.9242170074846, 14.407275051282, 12.5172235205991, 
14.0614967570622, 13.5507890858319, 13.3076807320896, 11.7858001461438, 
11.8584538361961, 12.8273710855852, 12.9510255323964, 12.6674167292068, 
13.8345005412558, 12.5315423088742, 13.5261250542701, 11.2378426019217, 
13.317780686254, 12.8520367085728, 13.2314690414045, 11.6164657431659, 
10.5998799720902, 11.4357432274465, 14.0417669865121, 12.1607754042512, 
13.2466054940719, 12.797111452771, 12.4047191492522, 11.7833124729873, 
12.097653018214, 11.7951612085733, 12.3041048876447, 11.1508412920931, 
12.4501892541409, 12.4610946871315, 12.3261738189918, 10.8552645219384, 
12.9975652341808, 12.6677845732241, 11.7652169789635, 11.6827253641907, 
11.110919985169, 11.8897433664123, 13.3123005479684, 11.7760781785571, 
13.1638079938049, 12.7956196208381, 12.4920841796339, 11.7252484518286, 
11.8268745473843, 11.3318932693566, 11.3357737895019, 11.535325758363, 
12.5461021275932, 12.3602886322043, 12.8926798402372), dim = c(20L, 
100L)))


par(mar=c(5, 4, 4, 2) - 1.9)
by(res.over$res.plot, INDICES = res.over$res.plot$var, FUN = function(xx) {

    plot(x = xx[, "trueval"], y = xx[, "xbar"], 
         col = as.character(xx[, "col"]), xlab = "observed values", 
         ylab = "imputed values", main = paste(xx[1, "var"], " (cov =", xx[1, "pct"],"%)"), 
         ylim = c(min(xx[, "binf"], na.rm = T), max(xx[, 
                                                       "bsup"], na.rm = T)))
    abline(0, 1)
    segments(x0 = xx[, "trueval"], x1 = xx[, "trueval"], 
             y0 = xx[, "binf"], y1 = xx[, "bsup"], 
             col = as.character(xx[, "col"]))
    legend("bottomright", legend = c("0-0.2", "0.2-0.4", 
                                     "0.4-0.6", "0.6-0.8", "0.8-1"), 
           col = c("blue", "green", heat.colors(3)[c(3, 
                                                     2, 1)]),
           bty = "n", lty = 1, horiz =TRUE, 
           cex = .8, lwd = 0.4)
  })

## ----fig.height = 4, fig.width = 4, fig.align = "center"----------------------
res.m <- choosem(res.pool.kmeans)

## ----eval=FALSE---------------------------------------------------------------
#  res.nbclust <- choosenbclust(res.pool.kmeans)

## ----fig.height = 4, fig.width = 4, fig.align = "center", echo=FALSE----------
res.nbclust <- list(nb.clust = 3L, crit = c(`2` = 0.114077699448272, `3` = 0.0973099188526245, 
`4` = 0.124537091242568, `5` = 0.165533521629721))


plot(as.numeric(names(res.nbclust$crit)),res.nbclust$crit,xlab="nb clust",ylab="Total instability",type="b",xaxt = "n")
    axis(1, as.numeric(names(res.nbclust$crit)), as.numeric(names(res.nbclust$crit)))

## ----fig.width=7,fig.height=5,fig.align="center",results=FALSE, warning=FALSE, message=FALSE----
require(reshape2)
require(ggplot2)
dat.m = melt(data.frame(wine.na, part = as.factor(part)), id.var=c("part"))
ggplot(dat.m, aes(part, value, col = part)) +
  facet_wrap(variable~., scales = "free_y") +
  geom_boxplot(width = 0.7)

## ----fig.width=7,fig.height=5,fig.align="center", message=FALSE---------------
library(VIM)
pairsVIM(wine.na,
         pch = 21,
         bg = c("red", "green3", "blue")[part],
         cex = .2,
         gap = 0)

## ----fig.width=7, fig.height=5, fig.align="center"----------------------------
library(FactoMineR)
library(missMDA)
# merge the partition variable with the incomplete data set
data.pca <- cbind.data.frame(class = factor(part, levels = seq(nb.clust)),
                             wine.na)
# perform PCA with missing values by specifying where is the partition variable 
res.imputepca <- imputePCA(data.pca, quali.sup = 1)
res.pca <- PCA(res.imputepca$completeObs, quali.sup = 1, graph = FALSE)
plot(res.pca, habillage = 1)

## -----------------------------------------------------------------------------
library(clusterCrit)
res.crit <- extCriteria(part, ref, crit = "all")
round(unlist(res.crit), 2)