Vsmatrix object (Multivariate Analysis Toolbox for Matlab®)

This object describes a matrix that captures the very common situation where measurements of a number of variables are taken for a multitude of samples. This objects inherits from the parent object datamatrix.

Navigate to:     General Description     Class Structure     Class Construction     Class Functions

Each field can be accessed by the dot (.) operation, or by the GET function. The GET function can work on multiple instances simultaneously. Most fields, except for those that are Dependent, can be modified using the dot (.) operation, or by the SET function.

		Field	Description	Type	Default	Dedicated Get/Set Function
		type	Type of matrix. It is always 'variable-sample'	string	'variable-sample'
		units	units of the variables.	cell	{}
		level	level of the variables. Can be nominal/ordinal/numerical/unknown	cell	{}
		name	Inherited from datamatrix. name of object, should be short and used as identifier. This field will never be empty.	string	'unnamed'
		description	Inherited from datamatrix. verbal description of the class content.	string	''
		source	Inherited from datamatrix. verbal description of the source of information.	string	''
		row_name	Inherited from datamatrix. Name of the row-category (for example, in gene arrays it can be "tissue").	string	''
		row_sampleset	Inherited from datamatrix. A sampleset describing the row-samples.	sampleset	[]
		row_groupings	Inherited from datamatrix. grouping objects associated with the row-samples.	grouping array	[]
		col_name	Inherited from datamatrix. Name of the column-category (for example, in gene arrays it can be "genes").	string	''
		col_sampleset	Inherited from datamatrix. A sampleset describing the column-samples.	sampleset	[]
		col_groupings	Inherited from datamatrix. grouping objects associated with the column-samples.	grouping array	[]
		matrix	Inherited from datamatrix. the data matrix.	double	[]
		transformations	Inherited from datamatrix. keeping track on transformations that had been applied previously on the matrix.	structure	[]
	Dependent	no_rows	Inherited from datamatrix. number of row-samples.	integer scalar	0	norows
	Dependent	no_cols	Inherited from datamatrix. number of column-samples.	integer scalar	0	nocols

Empty instance (scalar)

an empty vsmatrix instance, with all fields initialized to their default values.

syntax: vsm = vsmatrix;

Empty instance (matrix)

an array of empty vsmatrix instances.

syntax: vsm = vsmatrix(size,'size');

Copy constructor

one vsmatrix instance is copied into another.

syntax: vsm_destination = vsmatrix(vsm_origin);

Construction by field names

an instance is formed by directly providing field values. Any field which is not dependent is permitted.

syntax: vsm = vsmatrix(field_name, field_value, ...);

Reading-in a matrix

A numerical variable-by-sample matrix is read into a vsmatrix instance.

syntax: vsm = vsmatrix(matrix);

Display:

show

displays class content.

showvariables

displays information about the variables in a VSMATRIX.

I/O functions:

dump

writes the VSMATRIX matrix into an excel sheet.

Inference:

cimean

finds CI for multidimensional mean.

correlate

computes correlation matrix.

detectcore

picks a dense group of samples forming the core of the data.

detectoutliers

detects outliers.

emutualentropy

estimates pairwise mutual entropy

pcorrelate

computes partial correlation matrix.

testmean

tests mean value or mean difference.

Information extraction:

colid

finds the IDs of a list of column-variables. Source: datamatrix.

groupid

finds the IDs of a list of groupings.

iscomplete

checks for complete variables in VSMATRIX.

isnominal

checks for nominal variables in VSMATRIX.

isnumeric

checks for numeric variables in VSMATRIX.

isordinal

checks for ordinal variables in VSMATRIX.

nomissing

finds the number of missing values in each variable.

rowid

finds the IDs of a list of row-variables. Source: datamatrix.

sampid

finds the IDs of a list of samples.

varid

finds the IDs of a list of variables.

Operators:

eq

element-wise logical operator.

ge

element-wise logical operator.

gt

element-wise logical operator.

le

element-wise logical operator.

lt

element-wise logical operator.

ne

element-wise logical operator.

or

restricts (by condition) the samples in a vsmatrix instance.

plus

adds variables and groupings to a vsmatrix

SET/GET functions:

colnames

retrieves the column-variable names. Source: datamatrix.

get

get method

matrix

retrieves the variables-by-samples matrix.

nocols

retrieves the number of columns in DATAMATRIX instances. Source: datamatrix.

nogroupings

retrieves the number of col-groupings in vsmatrix.

norows

retrieves the number of rows in DATAMATRIX instances. Source: datamatrix.

nosamples

retrieves the number of samples in VSMATRIX.

novariables

retrieves the number of variables in vsmatrix.

rownames

retrieves the row-variable names. Source: datamatrix.

samplenames

retrieves the sample names of vsmatrix.

set

set method

variablenames

retrieves the variable names of VSMATRIX.

Transformations:

deletecols

eliminate samples from a VSMATRIX instance.

deleterows

eliminate variables from a VSMATRIX instance.

deletesamples

eliminate samples from a VSMATRIX instance.

deletevariables

eliminate variables from a VSMATRIX instance.

fillin

fills missing data.

filterout

filters out variables or samples.

reorder

selects a subsets of samples in a specific order.

split

splits data matrix into training set and test set.

subset

extracts a subset of the original vsmatrix.

transform

transforms the data matrix.

Variable-wise computations:

entropy

entropy of each variable.

iqr

computes the inter-quartile range of each variable.

max

calculates max of each variable.

mean

calculates mean of each variable.

median

calculates median of each variable.

min

calculates min of each variable.

minmax

calculates min and max of each variable.

quantile

calculates the p-th quantile of each variable.

std

calculates standard deviation of each variable.

sum

calculates sum of each variable.

var

calculates variance of each variable.

Visualization:

hist

computes and plots the histogram of a variable.

plot

plots variables versus samples.

scatter

plots variables versus variables.