standardW

This functions work similar to <code><a href="/link/scale?package=wrMisc&version=1.15.3.1" data-mini-rdoc="wrMisc::scale">scale</a></code>, however, it evaluates the entire input and not column-wise (and independeltly as <code>scale</code> does). 
With Standarizing we speak of transforming the data to end up with mean=O and sd=1.
Furthermore, in case of 3-dim arrays, this function returns also an object with the same dimensions as the input.

The efficient treatment and convenient analysis of experimental high-throughput (omics) data gets facilitated through this collection of diverse functions.
Several functions address advanced object-conversions, like manipulating lists of lists or lists of arrays, reorganizing lists to arrays or into separate vectors, merging of multiple entries, etc.
Another set of functions provides speed-optimized calculation of standard deviation (sd), coefficient of variance (CV) or standard error of the mean (SEM)
for data in matrixes or means per line with respect to additional grouping (eg n groups of replicates).
A group of functions facilitate dealing with non-redundant information, by indexing unique, adding counters to redundant or eliminating lines with respect redundancy in a given reference-column, etc.
Help is provided to identify very closely matching numeric values to generate (partial) distance matrixes for very big data in a memory efficient manner or to reduce the complexity of large data-sets by combining very close values.
Other functions help aligning a matrix or data.frame to a reference using partial matching or to mine an experimental setup to extract patterns of replicate samples.
Many times large experimental datasets need some additional filtering, adequate functions are provided.
Convenient data normalization is supported in various different modes, parameter estimation via permutations or boot-strap as well as flexible testing of multiple pair-wise combinations using the framework of 'limma' is provided, too.
Batch reading (or writing) of sets of files and combining data to arrays is supported, too.

Wolfgang Raffelsberger

wrMisc

Analyze Experimental High-Throughput (Omics) Data

standardW function

<dl><dt>mat</dt>
<dd>(matrix, data.frame or array) data that need to get standardized.</dd>
<dt>byColumn</dt>
<dd>(logical) if <code>TRUE</code> the function will be run independently over all columns such as as <code>apply(mat,2,standardW)</code></dd>
<dt>na.rm</dt>
<dd>(logical) if <code>NA</code>s in the data don't get ignored via this argument, the output will be all <code>NA</code></dd>
<dt>silent</dt>
<dd>(logical) suppress messages</dd>
<dt>debug</dt>
<dd>(logical) additional messages for debugging</dd>
<dt>callFrom</dt>
<dd>(character) allow easier tracking of messages produced</dd></dl>

Arguments

This functions work similar to <code><a href='https://rdrr.io/r/base/scale.html'>scale</a></code>, however, it evaluates the entire input and not column-wise (and independeltly as <code>scale</code> does). 
With Standarizing we speak of transforming the data to end up with mean=O and sd=1.
Furthermore, in case of 3-dim arrays, this function returns also an object with the same dimensions as the input.

Standardize (scale) data — standardW

<dl>

<dt>mat</dt>
<dd>(matrix, data.frame or array) data that need to get standardized.</dd>


<dt>byColumn</dt>
<dd>(logical) if <code>TRUE</code> the function will be run independently over all columns such as as <code>apply(mat,2,standardW)</code></dd>


<dt>na.rm</dt>
<dd>(logical) if <code>NA</code>s in the data don't get ignored via this argument, the output will be all <code>NA</code></dd>


<dt>silent</dt>
<dd>(logical) suppress messages</dd>


<dt>debug</dt>
<dd>(logical) additional messages for debugging</dd>


<dt>callFrom</dt>
<dd>(character) allow easier tracking of messages produced</dd>

</dl>

standardW: Standardize (scale) data

Description

Usage

Value

Arguments

See Also

Examples