**Residual
and Predicted**

This function was used to adjust some variables by other
covariate(s) using generalized linear or additive model (spline smoothing),
calculate the predicted value and residual value. The predicted and/or residual value will be
used in further analysis.

For example, we want to look the association of a SNP marker
(SNP1) with blood pressure (SBP and DBP), we know SBP and DBP were affected by
age, body mass index (BMI), cigarette smoking and education. We want to adjust SBP and DBP by age, BMI,
smoke and education and calculate SBP and DBP residuals first.

**A screen
shot of sample input window:**

Drag and drop variables from variables list to relevant box
(Dependent, Independent linear terms, Smoothing terms).

**Technical
notes:**

1) If you want
derive the regression model from a subset of population (called reference
population), you can specify the reference population by type in the
conditions. For example, your study
population include random selected subjects (POP=0), and affected subjects
(POP=1), you want to generate the predictive model from random selected subjects
only, you just type in “POP=1” in the text box.

2) You can
apply generalized additive model to generate the predicted value and
residuals. Drag and drop the smoothing
term (eg. age and body mass index) to smoothing terms list. The default degree of freedom for each
smoothing term is using GCV method to determine. You also can specify the degree of freedom
manually by right click the variable and then click “change degree of freedom”

3) For each
dependent variable, the default new variable name for holding the predicted
value is the original name plus “.pred” for holding the residual value is the
original name plus “.resid”. You can
change the name by click the “.pred” and/or “.resid” text.

4) You can
specify a stratified variable to get separate predictive model for each
stratum.

Click
“Save”, new variables will be added to the variables list.