compute_F_test_with_limma

(Required) list of models for which the test will be done.
The F-test will be computed for the first independent variable that was
specified in the <code>independent.variables</code> argument to the
function <code><a rd-options="" href="/link/compute_models_with_limma?package=lipidomeR&version=0.1.2" data-mini-rdoc="lipidomeR::compute_models_with_limma">compute_models_with_limma</a></code>.

(Optional) numeric value specifying the threshold
for statistical significance of difference after correction for multiple
testing.

p.adj.threshold

(Optional) <code>TRUE</code> or <code>FALSE</code>: Print the results
of the test?

print.table

Use this to achieve analysis of variance (ANOVA) or analysis of covariance
 (ANCOVA). The F-test is based on a model produced by the function
 <code><a rd-options="" href="/link/compute_models_with_limma?package=lipidomeR&version=0.1.2" data-mini-rdoc="lipidomeR::compute_models_with_limma">compute_models_with_limma</a></code>.
 To use this function, first call the function
 <code><a rd-options="" href="/link/compute_models_with_limma?package=lipidomeR&version=0.1.2" data-mini-rdoc="lipidomeR::compute_models_with_limma">compute_models_with_limma</a></code>.

Create lipidome-wide heatmaps of statistics with the 'lipidomeR'.
The 'lipidomeR' provides a streamlined pipeline for the systematic
interpretation of the lipidome through publication-ready visualizations
of regression models fitted on lipidomics data.
With 'lipidomeR', associations between covariates and the lipidome
can be interpreted systematically and intuitively through heatmaps, where
lipids are categorized by the lipid class and are presented on
two-dimensional maps organized by the lipid size and level of saturation.
This way, the 'lipidomeR' helps you gain an immediate understanding of
the multivariate patterns in the lipidome already at first glance.
You can create lipidome-wide heatmaps of statistical associations, changes,
differences, variation, or other lipid-specific values.
The heatmaps are provided with publication-ready quality and the results
behind the visualizations are based on rigorous statistical models.

Tommi Suvitaival

lipidomeR

Integrative Visualizations of the Lipidome

compute_F_test_with_limma function

(Required) list of models for which the test will be done.
The F-test will be computed for the first independent variable that was
specified in the <code>independent.variables</code> argument to the
function <code><a rd-options='' href='compute_models_with_limma'>compute_models_with_limma</a></code>.

Use this to achieve analysis of variance (ANOVA) or analysis of covariance
 (ANCOVA). The F-test is based on a model produced by the function
 <code><a rd-options='' href='compute_models_with_limma'>compute_models_with_limma</a></code>.
 To use this function, first call the function
 <code><a rd-options='' href='compute_models_with_limma'>compute_models_with_limma</a></code>.

compute_F_test_with_limma: Compute an F-test for a multi-level factor independent variable

Description

Usage

Arguments

Value

See Also