Package 'tglkmeans'

Downsample the columns of a matrix to a target number

Description

This function takes a matrix and downsamples it to a target number of samples. It uses a random seed for reproducibility and allows for removing columns with small sums.

Usage

downsample_matrix(
  mat,
  target_n = NULL,
  target_q = NULL,
  seed = NULL,
  remove_columns = FALSE
)

Arguments

mat	An integer matrix to be downsampled. Can be a matrix or sparse matrix (dgCMatrix). If the matrix contains NAs, the function will run significantly slower. Values that are not integers will be coerced to integers using floor().
target_n	The target number of samples to downsample to.
target_q	A target quantile of sums to downsample to. Only one of 'target_n' or 'target_q' can be provided.
seed	The random seed for reproducibility (default is NULL)
remove_columns	Logical indicating whether to remove columns with small sums (default is FALSE)

Value

The downsampled matrix

Examples

mat <- matrix(1:12, nrow = 4)
downsample_matrix(mat, 2)

# Remove columns with small sums
downsample_matrix(mat, 12, remove_columns = TRUE)

# sparse matrix
mat_sparse <- Matrix::Matrix(mat, sparse = TRUE)
downsample_matrix(mat_sparse, 2)

# with a quantile
downsample_matrix(mat, target_q = 0.5)

---

Simulate normal data for kmeans tests

Description

Creates nclust clusters normally distributed around 1:nclust

Usage

simulate_data(
  n = 100,
  sd = 0.3,
  nclust = 30,
  dims = 2,
  frac_na = NULL,
  add_true_clust = TRUE,
  id_column = TRUE
)

Arguments

n	number of observations per cluster
sd	sd
nclust	number of clusters
dims	number of dimensions
frac_na	fraction of NA in the first dimension
add_true_clust	add a column with the true cluster ids
id_column	add a column with the id

Value

simulated data

Examples

simulate_data(n = 100, sd = 0.3, nclust = 5, dims = 2)

# add 20% missing data
simulate_data(n = 100, sd = 0.3, nclust = 5, dims = 2, frac_na = 0.2)

---

kmeans++ with return value similar to R kmeans

Description

kmeans++ with return value similar to R kmeans

Usage

TGL_kmeans(
  df,
  k,
  metric = "euclid",
  max_iter = 40,
  min_delta = 0.0001,
  verbose = FALSE,
  keep_log = FALSE,
  id_column = FALSE,
  reorder_func = "hclust",
  hclust_intra_clusters = FALSE,
  seed = NULL,
  use_cpp_random = FALSE
)

Arguments

df	a data frame or a matrix. Each row is a single observation and each column is a dimension. the first column can contain id for each observation (if id_column is TRUE), otherwise the rownames are used.
k	number of clusters. Note that in some cases the algorithm might return less clusters than k.
metric	distance metric for kmeans++ seeding. can be 'euclid', 'pearson' or 'spearman'
max_iter	maximal number of iterations
min_delta	minimal change in assignments (fraction out of all observations) to continue iterating
verbose	display algorithm messages
keep_log	keep algorithm messages in 'log' field
id_column	df's first column contains the observation id
reorder_func	function to reorder the clusters. operates on each center and orders by the result. e.g. reorder_func = mean would calculate the mean of each center and then would reorder the clusters accordingly. If reorder_func = hclust the centers would be ordered by hclust of the euclidean distance of the correlation matrix, i.e. hclust(dist(cor(t(centers)))) if NULL, no reordering would be done.
hclust_intra_clusters	run hierarchical clustering within each cluster and return an ordering of the observations.
seed	seed for the c++ random number generator
use_cpp_random	use c++ random number generator instead of R's. This should be used for only for backwards compatibility, as from version 0.4.0 onwards the default random number generator was changed o R.

Value

list with the following components:

cluster:

A vector of integers (from ‘1:k’) indicating the cluster to which each point is allocated.

centers:

A matrix of cluster centers.

size:

The number of points in each cluster.

log:

messages from the algorithm run (only if id_column == TRUE).

order:

A vector of integers with the new ordering if the observations. (only if hclust_intra_clusters = TRUE)

See Also

TGL_kmeans_tidy

Examples

# create 5 clusters normally distributed around 1:5
d <- simulate_data(
    n = 100,
    sd = 0.3,
    nclust = 5,
    dims = 2,
    add_true_clust = FALSE,
    id_column = FALSE
)

head(d)

# cluster
km <- TGL_kmeans(d, k = 5, "euclid", verbose = TRUE)
names(km)
km$centers
head(km$cluster)
km$size

---

TGL kmeans with 'tidy' output

Description

TGL kmeans with 'tidy' output

Usage

TGL_kmeans_tidy(
  df,
  k,
  metric = "euclid",
  max_iter = 40,
  min_delta = 0.0001,
  verbose = FALSE,
  keep_log = FALSE,
  id_column = FALSE,
  reorder_func = "hclust",
  add_to_data = FALSE,
  hclust_intra_clusters = FALSE,
  seed = NULL,
  use_cpp_random = FALSE
)

Arguments

df	a data frame or a matrix. Each row is a single observation and each column is a dimension. the first column can contain id for each observation (if id_column is TRUE), otherwise the rownames are used.
k	number of clusters. Note that in some cases the algorithm might return less clusters than k.
metric	distance metric for kmeans++ seeding. can be 'euclid', 'pearson' or 'spearman'
max_iter	maximal number of iterations
min_delta	minimal change in assignments (fraction out of all observations) to continue iterating
verbose	display algorithm messages
keep_log	keep algorithm messages in 'log' field
id_column	df's first column contains the observation id
reorder_func	function to reorder the clusters. operates on each center and orders by the result. e.g. reorder_func = mean would calculate the mean of each center and then would reorder the clusters accordingly. If reorder_func = hclust the centers would be ordered by hclust of the euclidean distance of the correlation matrix, i.e. hclust(dist(cor(t(centers)))) if NULL, no reordering would be done.
add_to_data	return also the original data frame with an extra 'clust' column with the cluster ids ('id' is the first column)
hclust_intra_clusters	run hierarchical clustering within each cluster and return an ordering of the observations.
seed	seed for the c++ random number generator
use_cpp_random	use c++ random number generator instead of R's. This should be used for only for backwards compatibility, as from version 0.4.0 onwards the default random number generator was changed o R.

Value

list with the following components:

cluster:

tibble with 'id' column with the observation id ('1:n' if no id column was supplied), and 'clust' column with the observation assigned cluster.

centers:

tibble with 'clust' column and the cluster centers.

size:

tibble with 'clust' column and 'n' column with the number of points in each cluster.

data:

tibble with 'clust' column the original data frame.

log:

messages from the algorithm run (only if id_column = FALSE).

order:

tibble with 'id' column, 'clust' column, 'order' column with a new ordering if the observations and 'intra_clust_order' column with the order within each cluster. (only if hclust_intra_clusters = TRUE)

See Also

TGL_kmeans

Examples

# create 5 clusters normally distributed around 1:5
d <- simulate_data(
    n = 100,
    sd = 0.3,
    nclust = 5,
    dims = 2,
    add_true_clust = FALSE,
    id_column = FALSE
)

head(d)

# cluster
km <- TGL_kmeans_tidy(d, k = 5, "euclid", verbose = TRUE)
km

---

Set parallel threads

Description

Set parallel threads

Usage

tglkmeans.set_parallel(thread_num)

Arguments

thread_num

number of threads. use '1' for non parallel behavior

Value

None

Examples

tglkmeans.set_parallel(8)

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