for Using SAS Pls Procedue,please read

http://www.ats.ucla.edu/stat/sas/library/pls.pdf

If you elect r package pls,

http://cran.r-project.org/web/packages/pls/index.html

data(yarn)
## Default methods:
yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV")
yarn.pls <- plsr(density ~ NIR, 6, data = yarn, validation = "CV")
yarn.cppls <- cppls(density ~ NIR, 6, data = yarn, validation = "CV")
## Alternative methods:
yarn.oscorespls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV",
method = "oscorespls")
yarn.simpls <- mvr(density ~ NIR, 6, data = yarn, validation = "CV",
method = "simpls")
## Not run:
## Parallelised cross-validation, using transient cluster:
pls.options(parallel = 4) # use mclapply
pls.options(parallel = quote(makeCluster(4, type = "PSOCK"))) # use parLapply
## A new cluster is created and stopped for each cross-validation:
yarn.pls <- plsr(density ~ NIR, 6, data = yarn, validation = "CV")
yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV")
## Parallelised cross-validation, using persistent cluster:
library(parallel)
## This creates the cluster:
pls.options(parallel = makeCluster(4, type = "PSOCK"))
## The cluster can be used several times:
yarn.pls <- plsr(density ~ NIR, 6, data = yarn, validation = "CV")
yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV")
## The cluster should be stopped manually afterwards:
stopCluster(pls.options()$parallel)
## Parallelised cross-validation, using persistent MPI cluster:
## This requires the packages snow and Rmpi to be installed
library(parallel)
## This creates the cluster:
pls.options(parallel = makeCluster(4, type = "MPI"))
## The cluster can be used several times:
yarn.pls <- plsr(density ~ NIR, 6, data = yarn, validation = "CV")
yarn.pcr <- pcr(density ~ NIR, 6, data = yarn, validation = "CV")
## The cluster should be stopped manually afterwards:
stopCluster(pls.options()$parallel)
## It is good practice to call mpi.exit() or mpi.quit() afterwards:
mpi.exit()
## End(Not run)
## Multi-response models:
data(oliveoil)
sens.pcr <- pcr(sensory ~ chemical, ncomp = 4, scale = TRUE, data = oliveoil)
sens.pls <- plsr(sensory ~ chemical, ncomp = 4, scale = TRUE, data = oliveoil)
## Classification
# A classification example utilizing additional response information
# (Y.add) is found in the cppls.fit manual (’See also’ above).

For SPSS,  http://pic.dhe.ibm.com/infocenter/spssstat/v20r0m0/index.jsp?topic=%2Fcom.ibm.spss.statistics.help%2Fidh_idd_pls_variables.htm
Partial Least Squares Regression

The Partial Least Squares Regression procedure estimates partial least squares (PLS, also known as "projection to latent structure") regression models. PLS is a predictive technique that is an alternative to ordinary least squares (OLS) regression, canonical correlation, or structural equation modeling, and it is particularly useful when predictor variables are highly correlated or when the number of predictors exceeds the number of cases.

PLS combines features of principal components analysis and multiple regression. It first extracts a set of latent factors that explain as much of the covariance as possible between the independent and dependent variables. Then a regression step predicts values of the dependent variables using the decomposition of the independent variables.

Availability. PLS is an extension command that requires the IBM® SPSS® Statistics - Integration Plug-In for Python to be installed on the system where you plan to run PLS (see How to Get Integration Plug-Ins). The PLS Extension Module must be installed separately, and can be downloaded fromhttp://www.ibm.com/developerworks/spssdevcentral.

Tables. Proportion of variance explained (by latent factor), latent factor weights, latent factor loadings, independent variable importance in projection (VIP), and regression parameter estimates (by dependent variable) are all produced by default.

Charts. Variable importance in projection (VIP), factor scores, factor weights for the first three latent factors, and distance to the model are all produced from the Options tab.

[url=]Partial Least Squares Regression Data Considerations[/url]

[url=]To Obtain Partial Least Squares Regression[/url]

This procedure pastes PLS command syntax.

See PLS Algorithms for computational details for this procedure.

Related Topics[size=1em]Model (Partial Least Squares Regression)
[size=1em]Options (Partial Least Squares Regression)
[size=1em]PLS

全是英文的就没得中文的啊