valid_followup

Sometimes some samples are collected and analyzed, later another batch of
samples is analyzed.
This function tries to detect if there are problems with the data or when
the data is combined in a single analysis.
To know specific problems with the data you need to use check_data()

Distributes samples in batches while making batches
homogeneous according to their description. Allows for an arbitrary
number of variables, both numeric and categorical. For quality control
it provides functions to subset a representative sample.

Llu<c3><ad>s Revilla Sancho

experDesign

Design Experiments for Batches

Lluís Revilla Sancho

Juanjo Lozano

Azucena Salas Martinez

valid_followup function

<dl><dt>old_data, new_data</dt>
<dd>A data.frame with the old and new data respectively.</dd>
<dt>all_data</dt>
<dd>A <code>data.frame</code> with all the data about the samples.
Each row is a sample.</dd>
<dt>omit</dt>
<dd>Name of the columns of the <code>pheno</code> that will be omitted.</dd>
<dt>column</dt>
<dd>The name of the column where the old data has the batch
information, or whether the data is new or not (<code>NA</code>) in the case of all_data.</dd></dl>

Arguments

Check the data for a follow up experiment. — valid_followup

<dl>

<dt>old_data, new_data</dt>
<dd>A data.frame with the old and new data respectively.</dd>


<dt>all_data</dt>
<dd>A <code>data.frame</code> with all the data about the samples.
Each row is a sample.</dd>


<dt>omit</dt>
<dd>Name of the columns of the <code>pheno</code> that will be omitted.</dd>


<dt>column</dt>
<dd>The name of the column where the old data has the batch
information, or whether the data is new or not (<code>NA</code>) in the case of all_data.</dd>

</dl>

valid_followup: Check the data for a follow up experiment.

Description

Usage

Value

Arguments

See Also

Examples