| Type: | Package |
| Title: | Mixtures of Discrete Laplace Distributions using Numerical Optimisation |
| Version: | 0.6.1 |
| Date: | 2023-06-11 |
| Description: | Fit a mixture of Discrete Laplace distributions using plain numerical optimisation. This package has similar applications as the 'disclapmix' package that uses an EM algorithm. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Imports: | Rcpp (≥ 1.0.3), cluster |
| LinkingTo: | Rcpp |
| RoxygenNote: | 7.2.1 |
| Encoding: | UTF-8 |
| Suggests: | testthat, disclapmix, readxl |
| NeedsCompilation: | yes |
| Packaged: | 2023-04-11 00:19:17 UTC; mkruijver |
| Author: | Maarten Kruijver 
[aut, cre],
Duncan Taylor
[aut] |
| Maintainer: | Maarten Kruijver <maarten.kruijver@esr.cri.nz> |
| Repository: | CRAN |
| Date/Publication: | 2023-04-12 11:30:05 UTC |
Discrete Laplace mixture inference using Numerical Optimisation
Description
An extension to the *disclapmix* method in the *disclapmix* package that supports duplicated loci and other non-standard haplotypes.
Description of your package
Usage
disclapmix2(
x,
number_of_clusters,
include_2_loci = FALSE,
remove_non_standard_haplotypes = TRUE,
use_stripped_data_for_initial_clustering = FALSE,
initial_y_method = "pam",
verbose = 0L
)
Arguments
x |
DataFrame. Columns should be one character vector for each locus |
number_of_clusters |
The number of clusters to fit the model for. |
include_2_loci |
Should duplicated loci be included or excluded from the analysis? |
remove_non_standard_haplotypes |
Should observations that are not single integer alleles be removed? |
use_stripped_data_for_initial_clustering |
Should non_standard data be removed for the initial clustering? |
initial_y_method |
Which cluster method to use for finding initial central haplotypes, y: pam (recommended) or clara. |
verbose |
Set to 1 (or higher) to print optimisation details. Default is 0. |
Value
List.
Author(s)
you
Examples
require(disclapmix)
data(danes)
x <- as.matrix(danes[rep(seq_len(nrow(danes)), danes$n), -ncol(danes)])
x2 <- as.data.frame(sapply(danes[rep(seq_len(nrow(danes)), danes$n), -ncol(danes)], as.character))
dlm_fit <- disclapmix(x, clusters = 3L)
dlm2_fit <- disclapmix2(x2, number_of_clusters = 3)
stopifnot(all.equal(dlm_fit$logL_marginal, dlm2_fit$log_lik))
Count the number of times each haplotype occurs
Description
Count the number of times each haplotype occurs
Usage
haplotype_counts(x)
Arguments
x |
DataFrame (by locus) of character vectors containing haplotypes (rows) where alleles are separated by comma's, e.g. "13,14.2" is a haplotype |
Value
Integer vector with count for each row in DataFrame
Examples
# read haplotypes
h <- readxl::read_excel(system.file("extdata","South_Australia.xlsx",
package = "disclapmix2"),
col_types = "text")[-c(1,2)]
# obtain counts
counts <- disclapmix2::haplotype_counts(h)
# all haplotypes in the dataset are unique
stopifnot(all(counts == 1))
Compute Profile Probability from fit
Description
Compute the profile probability for a new profile that was not used in the original fit.
Usage
profile_pr_by_locus_and_cluster(x, fit)
Arguments
x |
DataFrame. Columns should be one character vector for each locus |
fit |
Output from disclapmix2 |
Value
Numeric.
Examples
require(disclapmix)
data(danes)
x <- as.data.frame(sapply(danes[rep(seq_len(nrow(danes)), danes$n), -ncol(danes)], as.character))
dlm2_fit <- disclapmix2(x, number_of_clusters = 3)
new_profile <- structure(list(DYS19 = "14", DYS389I = "13", DYS389II = "29",
DYS390 = "22", DYS391 = "9", DYS392 = "15", DYS393 = "13",
DYS437 = "14", DYS438 = "11", DYS439 = "12"),
row.names = 1L, class = "data.frame")
profile_pr_by_locus_and_cluster(x = new_profile, dlm2_fit)
List unique haplotypes with their counts
Description
List unique haplotypes with their counts
Usage
unique_haplotype_counts(x)
Arguments
x |
DataFrame (by locus) of character vectors containing haplotypes (rows) where alleles are separated by comma's, e.g. "13,14.2" is a haplotype |
Value
DataFrame with unique rows and a Count column added at the end
Examples
# read haplotypes
h <- readxl::read_excel(system.file("extdata","South_Australia.xlsx",
package = "disclapmix2"),
col_types = "text")[-c(1,2)]
# obtain counts
unique_counts <- disclapmix2::unique_haplotype_counts(h)
# all haplotypes in the dataset are unique
stopifnot(all(unique_counts$Count == 1))