| Type: | Package |
| Title: | K-Means Clustering with Build-in Missing Data Imputation |
| Version: | 0.2.4 |
| Author: | Oliver Pfaffel |
| Maintainer: | Oliver Pfaffel <opfaffel@gmail.com> |
| Description: | This k-means algorithm is able to cluster data with missing values and as a by-product completes the data set. The implementation can deal with missing values in multiple variables and is computationally efficient since it iteratively uses the current cluster assignment to define a plausible distribution for missing value imputation. Weights are used to shrink early random draws for missing values (i.e., draws based on the cluster assignments after few iterations) towards the global mean of each feature. This shrinkage slowly fades out after a fixed number of iterations to reflect the increasing credibility of cluster assignments. See the vignette for details. |
| License: | GPL-3 |
| Encoding: | UTF-8 |
| Imports: | ClusterR, copula, dplyr, magrittr, tidyr, ggplot2, rlang, knitr |
| Suggests: | ggExtra, rmarkdown, testthat (≥ 2.1.0), Hmisc, tictoc, spelling, corrplot, covr |
| VignetteBuilder: | knitr |
| RoxygenNote: | 7.1.0 |
| Language: | en-US |
| NeedsCompilation: | no |
| Packaged: | 2021-05-27 20:22:28 UTC; opfaf |
| Repository: | CRAN |
| Date/Publication: | 2021-05-31 07:40:11 UTC |
Pipe operator
Description
See magrittr::%>% for details.
Usage
lhs %>% rhs
Value
No return value, called for side effects
K-means clustering with build-in missing data imputation
Description
Clustering algorithm that produces a missing value imputation using on the go. The (local) imputation distribution is defined by the currently assigned cluster. The first draw is by random imputation.
Usage
ClustImpute(
X,
nr_cluster,
nr_iter = 10,
c_steps = 1,
wf = default_wf,
n_end = 10,
seed_nr = 150519,
assign_with_wf = TRUE,
shrink_towards_global_mean = TRUE
)
Arguments
X |
Data frame with only numeric values or NAs |
nr_cluster |
Number of clusters |
nr_iter |
Iterations of procedure |
c_steps |
Number of clustering steps per iteration |
wf |
Weight function. Linear up to n_end by default. Used to shrink X towards zero or the global mean (default). See shrink_towards_global_mean |
n_end |
Steps until convergence of weight function to 1 |
seed_nr |
Number for set.seed() |
assign_with_wf |
Default is TRUE. If set to False, then the weight function is only applied in the centroid computation, but ignored in the cluster assignment. |
shrink_towards_global_mean |
By default TRUE. The weight matrix w is applied on the difference of X from the global mean m, i.e, (x-m)*w+m |
Value
- complete_data
Completed data without NAs
- clusters
For each row of complete_data, the associated cluster
- centroids
For each cluster, the coordinates of the centroids in tidy format
- centroids_matrix
For each cluster, the coordinates of the centroids in matrix format
- imp_values_mean
Mean of the imputed variables per draw
- imp_values_sd
Standard deviation of the imputed variables per draw
Examples
# Random Dataset
set.seed(739)
n <- 750 # numer of points
nr_other_vars <- 2
mat <- matrix(rnorm(nr_other_vars*n),n,nr_other_vars)
me<-4 # mean
x <- c(rnorm(n/3,me/2,1),rnorm(2*n/3,-me/2,1))
y <- c(rnorm(n/3,0,1),rnorm(n/3,me,1),rnorm(n/3,-me,1))
dat <- cbind(mat,x,y)
dat<- as.data.frame(scale(dat)) # scaling
# Create NAs
dat_with_miss <- miss_sim(dat,p=.1,seed_nr=120)
# Run ClustImpute
res <- ClustImpute(dat_with_miss,nr_cluster=3)
# Plot complete data set and cluster assignment
ggplot2::ggplot(res$complete_data,ggplot2::aes(x,y,color=factor(res$clusters))) +
ggplot2::geom_point()
# View centroids
res$centroids
Check and replace duplicate (centroid) rows
Description
Internal function of ClustImpute: check new centroids for duplicate rows and replace with random draws in this case.
Usage
check_replace_dups(centroids, X, seed)
Arguments
centroids |
Matrix of centroids |
X |
Underlying data matrix (without missings) |
seed |
Seed used for random sampling |
Value
Returns centroids where duplicate rows are replaced by random draws
K-means clustering with build-in missing data imputation
Description
Default weight function. One minus the return value is multiplied with missing(=imputed) values. It starts with 1 and goes to 0 at n_end.
Usage
default_wf(n, n_end = 10)
Arguments
n |
current step |
n_end |
steps until convergence of weight function to 0 |
Value
value between 0 and 1
Examples
x <- 0:20
plot(x,1-default_wf(x))
Simulation of missings
Description
Simulates missing at random using a normal copula to create correlations between the missing (type="MAR"). Missings appear in each column of the provided data frame with the same ratio.
Usage
miss_sim(dat, p = 0.2, type = "MAR", seed_nr = 123)
Arguments
dat |
Data frame with only numeric values |
p |
Fraction of missings (for entire data frame) |
type |
Type of missingness. Either MCAR (=missing completely at random) or MAR (=missing at random) |
seed_nr |
Number for set.seed() |
Value
data frame with only numeric values and NAs
Examples
data(cars)
cars_with_missings <- miss_sim(cars,p = .2,seed_nr = 4)
summary(cars_with_missings)
Plot showing marginal distribution by cluster assignment
Description
Returns a plot with the marginal distributions by cluster and feature. The plot shows histograms or boxplots and , as a ggplot object, can be modified further.
Usage
## S3 method for class 'kmeans_ClustImpute'
plot(
x,
type = "hist",
vline = "centroids",
hist_bins = 30,
color_bins = "#56B4E9",
color_vline = "#E69F00",
size_vline = 2,
...
)
Arguments
x |
an object returned from ClustImpute |
type |
either "hist" to plot a histogram or "box" for a boxplot |
vline |
for "hist" a vertical line is plotted showing either the centroid value or the mean of all data points grouped by cluster and feature |
hist_bins |
number of bins for histogram |
color_bins |
color for the histogram bins |
color_vline |
color for the vertical line |
size_vline |
size of the vertical line |
... |
currently unused |
Value
Returns a ggplot object
Prediction method
Description
Prediction method
Usage
## S3 method for class 'kmeans_ClustImpute'
predict(object, newdata, ...)
Arguments
object |
Object of class kmeans_ClustImpute |
newdata |
Data frame |
... |
additional arguments affecting the predictions produced - not currently used |
Value
integer value (cluster assignment)
Examples
# Random Dataset
set.seed(739)
n <- 750 # numer of points
nr_other_vars <- 2
mat <- matrix(rnorm(nr_other_vars*n),n,nr_other_vars)
me<-4 # mean
x <- c(rnorm(n/3,me/2,1),rnorm(2*n/3,-me/2,1))
y <- c(rnorm(n/3,0,1),rnorm(n/3,me,1),rnorm(n/3,-me,1))
dat <- cbind(mat,x,y)
dat<- as.data.frame(scale(dat)) # scaling
# Create NAs
dat_with_miss <- miss_sim(dat,p=.1,seed_nr=120)
res <- ClustImpute(dat_with_miss,nr_cluster=3)
predict(res,newdata=dat[1,])
Print method for ClustImpute
Description
Returns a plot with the marginal distributions by cluster and feature. The plot shows histograms or boxplots and , as a ggplot object, can be modified further.
Usage
## S3 method for class 'kmeans_ClustImpute'
print(x, ...)
Arguments
x |
an object returned from ClustImpute |
... |
currently unused |
Value
No return value (print function)
Reduction of variance
Description
Computes one minus the ratio of the sum of all within cluster variances by the overall variance
Usage
var_reduction(clusterObj)
Arguments
clusterObj |
Object of class kmeans_ClustImpute |
Value
integer value typically between 0 and 1
Examples
# Random Dataset
set.seed(739)
n <- 750 # numer of points
nr_other_vars <- 2
mat <- matrix(rnorm(nr_other_vars*n),n,nr_other_vars)
me<-4 # mean
x <- c(rnorm(n/3,me/2,1),rnorm(2*n/3,-me/2,1))
y <- c(rnorm(n/3,0,1),rnorm(n/3,me,1),rnorm(n/3,-me,1))
dat <- cbind(mat,x,y)
dat<- as.data.frame(scale(dat)) # scaling
# Create NAs
dat_with_miss <- miss_sim(dat,p=.1,seed_nr=120)
res <- ClustImpute(dat_with_miss,nr_cluster=3)
var_reduction(res)