plsreg1 {plspm}R Documentation

PLS-R1: Partial Least Squares Regression 1

Description

Calculates partial least squares regression for the univariate case (i.e. one response variable)

Usage

  plsreg1(x, y, nc = 2, cv = FALSE)

Arguments

x A numeric matrix or data frame with the predictor variables (which may contain missing data).
y A numeric vector for the reponse or predictand variable.
nc The number of extracted PLS components (2 by default).
cv A logical value indicating whether cross-validation should be performed (FALSE by default).

Details

The minimum number of PLS components nc to be extracted is 2.

The argument x may contain missing data. Conversely, the argument y must not contain missing values.

The data is scaled to standardized values (mean=0, variance=1).

The argument cv gives the option to perform leave-one-out cross validation to choose the best number of PLS components.

Value

An object of class "plsreg1", basically a list with the following elements:

scores PLS components.
x.loads loadings of the predictor variables.
y.loads loadings of the predictand variable.
u.scores u scores of the predictand variable.
raw.wgs weights to calculate the PLS scores with the deflated matrices of predictor variables.
mod.wgs modified weights to calculate the PLS scores with the matrix of predictor variables.
std.coef Vector of standardized regression coefficients.
coeffs Vector of regression coefficients (used with the original data scale).
R2 Vector of PLS R-squared.
y.pred Vector of predicted values.
resid Vector of residuals.
cor.sco Correlations between the variables and the PLS components.
T2 Table of Hotelling T2 values (used to detect atypical observations).
Q2 Table with the cross validation results. Includes: PRESS, RSS, Q2, and cummulated Q2. Only available when cv=TRUE

Author(s)

Gaston Sanchez

References

Geladi, P., and Kowalski, B. (1986) Partial Least Squares Regression: A Tutorial. Analytica Chimica Acta, 185, pp. 1-17.

Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Editions TECHNIP, Paris.

Tenenhaus, M., Gauchi, J.-P., and Menardo, C. (1995) Regression PLS et applications. Revue de statistique appliquee, 43, pp. 7-63.

Valencia, J.L., Diaz-Llanos, F.J. (2004) Metodos de Prediccion en Situaciones Limite. Editorial La Muralla, S.A. Madrid.

See Also

print.plsreg1, plot.plsreg1, plsreg2.

Examples

  ## Not run: 
  ## example of PLSR1 with the vehicles dataset
  ## predictand variable: price of vehicles
  data(vehicles)
  pls1 <- plsreg1(vehicles[,1:12], vehicles[,13], cv=TRUE)
  pls1
  plot(pls1)
  
## End(Not run)

[Package plspm version 0.1-4 Index]