Performs Partial Least Squares analyses with a list of continuous dependent variables, and lists of continuous and categorical predictor variables arranged into an ANCOVA-like design.

**Estimation
and Design**

**Analysis syntax**. Analysis syntax string for general Partial Least
Squares models. You can specify here the complete syntax, as, for example,
copied form a Statistica analysis. Set this string to empty,
or just PLS; to create the syntax from the specific options specified
below.

**Detail of computed results reported**. Specifies the level of computed
results reported. If Minimal results is requested, Statistica
will report summary statistics for the components and the regression coefficients;
if All results is requested, summary plots, weights, and other statistics
will be reported. Residual (observational) statistics can be requested
as a separate option.

**PLS method**.
Select the algorithm (method) that is to be used to compute the PLS components;
the default is PLS. If PLS is specified, a standard PLS analysis using
the NIPALS algorithm (Rannar, Lindgren, Geladi, and Wold, 1994) will be
performed; if you specify SIMPLS, the factor scores will be computed via
the SIMPLS algorithm (de Jong, 1993).

**Max number
of components**. Specifies the maximum number of components to
be extracted; the default value is 120.

**Delta for
R-square; 1.E-**. Specifies the negative exponent for a base-10
constant delta (delta = 10^-RDelta); the default value is 12. Delta is
used by the program as a criterion for determining whether to stop extracting
additional PLS components.

**Design**.
Required; specify the ANCOVA-like design for the categorical and continuous
predictors.

Use the syntax:

DESIGN = Design specifications

Example 1.

DESIGN = GROUP | GENDER | TIME | PAID; {makes a full factorial design}

Example 2.

DESIGN = SEQUENCE + PERSON(SEQUENCE) + TREATMNT + SEQUENCE*TREATMNT;

Example 3.

DESIGN = MULLET | SHEEPSHD | CROAKER @2; {Makes factorial design to degree
2}

Example 4.

DESIGN = TEMPERAT | MULLET | SHEEPSHD | CROAKER - TEMPERAT; {Removes main
effect for TEMPERAT from factorial design}

Example 5.

DESIGN = BLOCK + DEGREES + DEGREES*DEGREES + TIME + TIME*TIME + TIME*DEGREES;

**Parameterization
of effects**. Parameterization for coding the ANCOVA-like design
vectors for categorical predictor effects. Specify either the sigma-restricted
model or the overparameterized model; sigma restricted parameterization
is the default.

**Intercept**.
Specifies whether the intercept (constant) is to be included in the model
(i.e., a parameter is to be estimated for the intercept); the default
is INTERCEPT=INCLUDE.

**Auto scaling**.
Each column in the predictor design matrix X and matrix of dependent (response)
variables Y will be divided by its respective standard deviation, and
all computations will be performed on these scaled matrices X and Y. Note
that the coefficients that are computed when the

**Residuals
and Observational Statistics**

**Residual
analysis**. Creates predicted and residual values, and factor
scores for each observation.

**Normal probability
plot**. Creates a normal probability plot of residuals.

**Parameters
for Estimation**

**Delta for
eigenvalues; 1.E-**. Specifies the negative exponent for a base-10
constant delta (delta = 10^-Edelta); the default value is 12. Delta is
used for checking the convergence of the iterative computation of eigenvectors
for each PLS component.

**Maximum
number of iterations**. Specifies the maximum number of iterations
for the iterative computation of eigenvectors for each PLS component.
The default value is 200. PLS uses an iterative power method (see Golub
and van Loan, 1996) to compute the eigenvector of

**Generates
data source, if N for input less than**. Generates a data source
for further analyses with other Data Miner nodes if the input data source
has fewer than k observations, as specified in this edit field; note that
parameter k (number of observations) will be evaluated against the number
of observations in the input data source, not the number of valid or selected
observations.