1. vsclust
  2. man
  3. determine_fuzz.Rd
Browse code

added now function for biocondcutor and many fixes

Veit Schwammle authored on 23/09/2022 11:37:56
Showing 1 changed files
man/determine_fuzz.Rd
History View file @ b634e98
... ... 
@@ -1,5 +1,5 @@
1 1 
 % Generated by roxygen2: do not edit by hand
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-% Please edit documentation in R/HelperFuncs.R
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+% Please edit documentation in R/RunClustering.R
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 \name{determine_fuzz}
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 \alias{determine_fuzz}
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 \title{Determine individual fuzzifier values}
... ... 
@@ -7,32 +7,42 @@
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 determine_fuzz(dims, NClust, Sds = 1)
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 }
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 \arguments{
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-\item{dims}{vector of two integers containing the dimensions of the data matrix for the clustering}
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+\item{dims}{vector of two integers containing the dimensions of the data
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+matrix for the clustering}
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-\item{NClust}{Number of cluster for running vsclust on (does no influence the calculation of `mm`)}
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+\item{NClust}{Number of cluster for running vsclust on (does no influence the
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+calculation of `mm`)}
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 \item{Sds}{individual standard deviations, set to 1 if not available}
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 }
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 \value{
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-list of `m`: individual fuzzifiers, `mm`: standard fuzzifier for fcm clustering when not using vsclust algorithm
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+list of `m`: individual fuzzifiers, `mm`: standard fuzzifier for fcm
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+clustering when not using vsclust algorithm
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 }
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 \description{
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-This function calculated the values of the fuzzifier from a) the dimensions
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-of the considered data set and b)
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+This function calculated the values of the fuzzifier from a) the dimensions
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+of the considered data set and b)
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 from the individual feature standard deviations.
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 }
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 \examples{
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 # Generate some random data
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-data <- matrix(rnorm(1:1000), nrow=100)
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+data <- matrix(rnorm(seq_len(1000)), nrow=100)
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 # Estimate fuzzifiers
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 fuzz_out <- determine_fuzz(dim(data), 1)
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 # Run clustering
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 clres <- vsclust_algorithm(data, centers=5, m=fuzz_out$mm)
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 }
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 \references{
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-Schwaemmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering of omics data. Bioinformatics. 2018 Sep 1;34(17):2965-2972. doi: 10.1093/bioinformatics/bty224. PMID: 29635359.
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+Schwaemmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering
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+of omics data. Bioinformatics. 2018 Sep 1;34(17):2965-2972. doi:
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+10.1093/bioinformatics/bty224. PMID: 29635359.
34 39
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-Schwaemmle V, Hagensen CE. A Tutorial for Variance-Sensitive Clustering and the Quantitative Analysis of Protein Complexes. Methods Mol Biol. 2021;2228:433-451. doi: 10.1007/978-1-0716-1024-4_30. PMID: 33950508.
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+Schwaemmle V, Hagensen CE. A Tutorial for Variance-Sensitive Clustering and
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+the Quantitative Analysis of Protein Complexes. Methods Mol Biol.
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+2021;2228:433-451. doi: 10.1007/978-1-0716-1024-4_30. PMID: 33950508.
36 43
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-Schwaemmle V, Jensen ON. A simple and fast method to determine the parameters for fuzzy c-means cluster analysis. Bioinformatics. 2010 Nov 15;26(22):2841-8. doi: 10.1093/bioinformatics/btq534. Epub 2010 Sep 29. PMID: 20880957.
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+Schwaemmle V, Jensen ON. A simple and fast method to determine the parameters
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+for fuzzy c-means cluster analysis. Bioinformatics. 2010 Nov 15;
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+26(22):2841-8. doi: 10.1093/bioinformatics/btq534. Epub 2010 Sep 29.
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+PMID: 20880957.
38 48 
 }
Browse code

donttest to shiny call

veitveit authored on 17/08/2022 07:29:11
Showing 1 changed files
man/determine_fuzz.Rd
History View file @ 14bdeef
... ... 
@@ -27,7 +27,7 @@ data <- matrix(rnorm(1:1000), nrow=100)
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 # Estimate fuzzifiers
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 fuzz_out <- determine_fuzz(dim(data), 1)
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 # Run clustering
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-clres <- vsclust_algorithm(data, centers=10, m=fuzz_out$mm)
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+clres <- vsclust_algorithm(data, centers=5, m=fuzz_out$mm)
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 }
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 \references{
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 Schwaemmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering of omics data. Bioinformatics. 2018 Sep 1;34(17):2965-2972. doi: 10.1093/bioinformatics/bty224. PMID: 29635359.
Browse code

removed example

veitveit authored on 17/08/2022 04:35:44
Showing 1 changed files
man/determine_fuzz.Rd
History View file @ bac03e8
... ... 
@@ -17,7 +17,8 @@ determine_fuzz(dims, NClust, Sds = 1)
17 17 
 list of `m`: individual fuzzifiers, `mm`: standard fuzzifier for fcm clustering when not using vsclust algorithm
18 18 
 }
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 \description{
20 
-This function calculated the values of the fuzzifier from a) the dimensions of the considered data set and b)
20 
+This function calculated the values of the fuzzifier from a) the dimensions
21 
+of the considered data set and b)
21 22 
 from the individual feature standard deviations.
22 23 
 }
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 \examples{
Browse code

ready for bioconductor

veitveit authored on 18/07/2022 11:59:30
Showing 1 changed files
man/determine_fuzz.Rd
History View file @ ad4be2b
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new file mode 100644
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@@ -0,0 +1,37 @@
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+% Generated by roxygen2: do not edit by hand
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+% Please edit documentation in R/HelperFuncs.R
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+\name{determine_fuzz}
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+\alias{determine_fuzz}
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+\title{Determine individual fuzzifier values}
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+\usage{
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+determine_fuzz(dims, NClust, Sds = 1)
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+}
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+\arguments{
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+\item{dims}{vector of two integers containing the dimensions of the data matrix for the clustering}
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+
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+\item{NClust}{Number of cluster for running vsclust on (does no influence the calculation of `mm`)}
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+
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+\item{Sds}{individual standard deviations, set to 1 if not available}
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+}
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+\value{
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+list of `m`: individual fuzzifiers, `mm`: standard fuzzifier for fcm clustering when not using vsclust algorithm
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+}
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+\description{
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+This function calculated the values of the fuzzifier from a) the dimensions of the considered data set and b)
21 
+from the individual feature standard deviations.
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+}
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+\examples{
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+# Generate some random data
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+data <- matrix(rnorm(1:1000), nrow=100)
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+# Estimate fuzzifiers
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+fuzz_out <- determine_fuzz(dim(data), 1)
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+# Run clustering
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+clres <- vsclust_algorithm(data, centers=10, m=fuzz_out$mm)
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+}
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+\references{
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+Schwaemmle V, Jensen ON. VSClust: feature-based variance-sensitive clustering of omics data. Bioinformatics. 2018 Sep 1;34(17):2965-2972. doi: 10.1093/bioinformatics/bty224. PMID: 29635359.
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+
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+Schwaemmle V, Hagensen CE. A Tutorial for Variance-Sensitive Clustering and the Quantitative Analysis of Protein Complexes. Methods Mol Biol. 2021;2228:433-451. doi: 10.1007/978-1-0716-1024-4_30. PMID: 33950508.
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+
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+Schwaemmle V, Jensen ON. A simple and fast method to determine the parameters for fuzzy c-means cluster analysis. Bioinformatics. 2010 Nov 15;26(22):2841-8. doi: 10.1093/bioinformatics/btq534. Epub 2010 Sep 29. PMID: 20880957.
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+}