Download The Unscrambler Methods

The Unscrambler User Manual
Camo Software AS
Make Predictions
Use an existing regression model to predict response values for new samples.
Principles of Prediction on New Samples
Prediction (computation of unknown response values using a regression model) is the purpose of most
regression applications.
When Can You Use Prediction?
Prerequisites for prediction of response values on new samples for which X-values are available are the
following:

You need a regression model (MLR or PCR or PLS) which expresses the response variable or variables
(Y) as a function of the X-variables;

The model should have been calibrated on samples covering the region your new samples belong to, i.e.
on similar samples (similarity being determined by the X-values);

The model should also have been validated on samples covering the region your new samples belong to.
Note that model validation can only be considered successful if you have

used a proper validation method (test set or cross validation);

dealt with outliers in a proper way (not just removed all the samples which did not fit well);

and obtained a value of RMSEP that you can live with.
How Does Prediction Work?
Prediction consists in feeding observed X-values for new samples into a regression model so as to obtain
computed (predicted) Y-values.
As the next sections will show, this operation may be done in more than one way, at least for projection
methods.
Prediction from an MLR Model
When you choose MLR as a regression method, there is only one way to compute predictions. It is based on
the model equation, using the observed values for the X-variables, and the regression coefficients (b0, b1, …,
bk) for the MLR model:
Ypred = b0 + b1 X1 + ... + b kXk
This prediction method is simple and easy to understand. However it has a disadvantage, as we will see when
we compare it to another approach presented in the next section.
Prediction from a PCR or PLS Model
If you choose PCR or PLS as a regression method, you may still compute predicted Y-values using X and the
b-coefficients.
The Unscrambler Methods
Principles of Prediction on New Samples 133