View source for T-Squared Q residuals and Contributions

Hotelling's T<sup>2</sup> (T-Squared), Q residuals, and their corresponding contributions, are summary statistics which help describe how well a model is describing the data in a given sample, and why that sample has its observed scores for a given model. These statistics are generally only available for factor-based models like [[Pca|Principal Components Analysis]], [[Pls| Partial Least Squares]], [[Pcr|Principal Components Regression]], or [[Mcr| Multivariate Curve Resolution]] models. But some non-factor-based models (e.g. [[Mlr|Multiple Linear Regression]]) also provide similar statistics.

Given a model which describes data generically using loadings (<b>P</b>) and scores (<b>T</b>), the following equation describes the relationship between the data and the model:

::<math>X = TP^T + E</math>

Where X is a matrix of ''m'' rows (samples) and ''n'' columns (variables). Based on this equation, the Q residuals, Hotelling's T<sup>2</sup>, and contributions for each can be calculated. This page describes the calculation, theory, and software how-to for calculating and using these statistics.

==Q Residuals==

Q Residuals are a lack-of-fit statistic calculated as the sum of squares of each row (sample) of E; i.e., for the i<sup>th</sup> sample in <math>X</math>, <math>x_i</math>:

::<math>Q_i = e_ie_i^T = x_i(I - P_kP_k^T)x_i^T</math>

where <math>e_i</math> is the i<sup>th</sup> row of <math>E</math>, <math>P_k</math> is the matrix of the k loadings vectors retained in the model (where each vector is a column of <math>P_k</math>) and I is the identity matrix of appropriate size (n by n). The Q statistic indicates how well each sample conforms to the model. It is a measure of the difference, or residual, between a sample and its projection into the k factors retained in the model.

===Q Contributions===

A single row of the ''m'' by ''n'' <b>E</b> matrix, <math>e_i</math>, represents the Q contributions for a given sample. The Q contributions show how much each variable contributes to the overall Q statistic for the sample while retaining the sign of the deviation in the given variable. Such contributions can be useful in identifying the variables which contribute most to a given sample's sum-squared residual error. Such residuals can often be used to determine whether the lack-of-fit is due to systematic deviation of that sample or simple random variation.

Q contributions are plotted while viewing a [[Model_Building:_Plotting_Scores|scores plot]]. Use the "Q con" button on the Plot Controls to request Q contributions. You will be prompted to select one or more samples for which you wish to see contributions. Select the samples and a new figure will be created showing the contributions.

:'''Hints:'''
:* Left-click on a variable in the figure to view the raw data for that variable (column of X).
:* Right-clicking the figure will make the figure "interactive" allowing you to view different subsets of samples when more than one has been selected.

====Relative Q Contributions====

Another form of contributions are "relative" Q contributions. Normally, the Q contributions are calculated as the residuals relative to the model. In some cases, comparing the Q contributions of two or more samples relative to each other can be useful in determining whether the same measurement phenomenon is responsible for both samples' Q residuals. In this case, relative contributions can be used in which one or more samples are selected as a "reference" set, then Q contributions

Relative Q contributions can be viewed when viewing a Scores plot. On the Plot Controls, select the "Q con Ref." button and, when prompted, select the sample(s) you want to use as the reference. When complete, a new class set will be created showing you which sample(s) are being used for the relative contributions. Next, use the "Q con" button to select the samples for which you want to view contributions. The result will be a contributions plot with relative contributions calculated and noted on the Y-axis.

==Hotelling's T-Squared==

Whereas Q residuals represent the magnitude of the variation remaining in each sample after projection through the model, the Hotelling's T<sup>2</sup> values represent a measure of
the variation in each sample within the model. It indicates how far each sample is from the center (scores = 0) of the model.

Hotelling’s T<sup>2</sup> is the sum of the normalized squared scores and is defined as:

::<math>T_i^2 = t_i\lambda^{-1}t_i^T = x_iP_k\lambda^{-1}P_k^Tx_i^T</math>

where <math>t_i</math> refers to the i<sup>th</sup> row of <math>T_k</math>, the ''m'' by ''k'' matrix of scores from the model, and <math>\lambda</math> is a diagonal matrix containing the eigenvalues (<math>\lambda_1</math> through <math>\lambda_k</math>) corresponding to ''k'' principal components, factors, or latent variables retained in the model.

===T-Squared Contributions===
T<sup>2</sup> contributions describe how individual variables contribute to the Hotelling's T<sup>2</sup> value for a given sample. The contributions to <math>T_i^2</math> for the i<sup>th</sup> sample, <math>t_{con,i}</math>, is a 1 by ''k'' vector calculated from:

::<math>t_{con,i} = t_i\lambda^{-1/2}P_k^T = x_iP_k\lambda^{-1/2}P_k^T</math>

The term <math>t_{con}</math> can be considered a scaled version of the data within the model. The
data are scaled to equalize the variance captured by each factor. Note that this formulation for T<sup>2</sup> contributions has the property that the sum-squared contributions give <math>T_i^2</math> for the given sample. That is:

::<math>T_i^2 = t_{con,i}t_{con,i}^T