ConModelTest_disc

Covariate model test in longdat_disc() pipeline

This tool takes longitudinal dataset as input and analyzes if there is significant
change of the features over time (a proxy for treatments), while detects and controls
for 'covariates' simultaneously. 'LongDat' is able to take in several data types as input,
including count, proportion, binary, ordinal and continuous data. The output table contains
p values, effect sizes and 'covariates' of each feature, making the downstream analysis easy.

Chia-Yu Chen

LongDat

A Tool for 'Covariate'-Sensitive Longitudinal Analysis on
'omics' Data

Sofia Forslund

ConModelTest_disc function

<dl><dt>N</dt>
<dd>Internal function argument.</dd>
<dt>variables</dt>
<dd>Internal function argument.</dd>
<dt>melt_data</dt>
<dd>Internal function argument.</dd>
<dt>sel_fac</dt>
<dd>Internal function argument.</dd>
<dt>data_type</dt>
<dd>Internal function argument.</dd>
<dt>test_var</dt>
<dd>Internal function argument.</dd>
<dt>verbose</dt>
<dd>Internal function argument.</dd></dl>

Arguments

Covariate model test in longdat_disc() pipeline — ConModelTest_disc

<dl>

<dt>N</dt>
<dd>Internal function argument.</dd>


<dt>variables</dt>
<dd>Internal function argument.</dd>


<dt>melt_data</dt>
<dd>Internal function argument.</dd>


<dt>sel_fac</dt>
<dd>Internal function argument.</dd>


<dt>data_type</dt>
<dd>Internal function argument.</dd>


<dt>test_var</dt>
<dd>Internal function argument.</dd>


<dt>verbose</dt>
<dd>Internal function argument.</dd>

</dl>

ConModelTest_disc: Covariate model test in longdat_disc() pipeline

Description

Arguments