# Generalised Semi-Linear Canonical Correlation Analysis

### Description

GSLCCA estimates the parameters of a given nonlinear model to maximize the correlation with a linear combination of multiple response variables.

### Details

GSLCCA was developed to characterize brain activity monitored using EEG (electroencephalogram) under different treatment regimens in research and development projects at Pfizer.

EEG results are typically summarised as a matrix of power spectra, giving the power (microvolts squared) over multiple frequencies (Hz) recorded at different times. GSLCCA is then used to find a linear combination of the powers that is maximally correlated to a nonlinear pharmacometric model.

The `gslcca`

package can be used to relate any multivariate
response to any function of time, however it provides shortcuts for
specifying standard PK/PD models (Critical Exponential or Double
Exponential). It also has options to analyse subjects separately;
to estimate parameters separately for different treatments and to
partial out covariates prior to analysis.

Utility functions are provided to plot the results and to investigating the effect of pre-smoothing on the analysis.

### Author(s)

Foteini Strimenopoulou and Heather Turner

Maintainer: Heather Turner <ht@heatherturner.net>

### References

Brain, P., Strimenopoulou, F. and Ivarsson, M. (2011). Analysing electroencephalogram (EEG) data using Extended Semi-Linear Canonical Correlation Analysis. Submitted.

### See Also

`gslcca`

for the main function, with links to associated functions.

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