AICc_smooth_spline

Calculate the Akaike Information Criterion Corrected for small observation numbers (AICc) for a fitted <code><a href="/link/smooth.spline?package=santaR&version=1.2.4" data-mini-rdoc="santaR::smooth.spline">smooth.spline</a></code>. The smaller the AICc, the better the spline fit.

A graphical and automated pipeline for the analysis
of short time-series in R ('santaR'). This approach is designed to accommodate asynchronous
time sampling (i.e. different time points for different individuals),
inter-individual variability, noisy measurements and large numbers of variables.
Based on a smoothing splines functional model, 'santaR' is able to detect variables
highlighting significantly different temporal trajectories between study groups.
Designed initially for metabolic phenotyping, 'santaR' is also suited for other Systems Biology
disciplines. Command line and graphical analysis (via a 'shiny' application) enable fast and
parallel automated analysis and reporting, intuitive visualisation and comprehensive plotting
options for non-specialist users.

Arnaud Wolfer

santaR

Short Asynchronous Time-Series Analysis

Timothy Ebbels

Joe Cheng

AICc_smooth_spline function

<dl><dt>fittedSmoothSpline</dt>
<dd>A fitted <code><a href="/link/smooth.spline?package=santaR&version=1.2.4" data-mini-rdoc="santaR::smooth.spline">smooth.spline</a></code></dd></dl>

Arguments

Calculate the Akaike Information Criterion Corrected for small observation numbers (AICc) for a fitted <code><a href='https://rdrr.io/r/stats/smooth.spline.html'>smooth.spline</a></code>. The smaller the AICc, the better the spline fit.

Calculate the Akaike Information Criterion Corrected for small observation numbers for a smooth.spline — AICc_smooth_spline

<dl>

<dt>fittedSmoothSpline</dt>
<dd>A fitted <code><a href='https://rdrr.io/r/stats/smooth.spline.html'>smooth.spline</a></code></dd>

</dl>

AICc_smooth_spline: Calculate the Akaike Information Criterion Corrected for small observation numbers for a smooth.spline

Description

Usage

Value

Arguments