textir (version 1.8-5)

pls: Partial Least Squares

Description

A simple partial least squares procedure.

Usage

pls(X, y, K=1, scale=TRUE, verb=TRUE)

Arguments

X

The covariate matrix, in either simple_triplet_matrix or matrix format.

y

The response vector.

K

The number of desired PLS directions.

scale

An indicator for whether to standardize X; usually a good idea. If scale=TRUE, X will be scaled to have variance-one columns.

verb

Whether or not to print a small progress script.

Value

• A pls object list with the following entries
• y
  The response vector.
• X
  The covariate matrix. If scale=TRUE, scaled to have variance-one columns.
• directions
  The pls directions X%*%loadings.
• factors
  Response factors.
• phi
  The pls loadings.
• fitted
  K columns of fitted y values for each number of directions.
• fwdmod
  The lm object from forward regression lm(as.numeric(y)~directions).
• scale
  If scale=TRUE on input, the standard deviations used to scale X.

Details

Fits the Partial Least Squares algorithm described in Taddy (2011; Section 3.1). In particular, we obtain loadings loadings[,k] as the correlation between X and factors factors[,k], where factors[,1] is initialized at normalize(as.numeric(y)) and subsequent factors are orthogonal to the k'th pls direction, directions[,k]=X%*%loadings[,k].

References

Taddy (2011), Inverse Regression for Analysis of Sentiment in Text. http://arxiv.org/abs/1012.2098

Wold, H. (1975), Soft modeling by latent variables: The nonlinear iterative partial least squares approach. In Perspectives in Probability and Statistics, Papers in Honour of M.S. Bartlett.

See Also

plot.pls, normalize, freq, corr, we8there, congress109

Examples

data(congress109)
summary( fit <- pls(freq(congress109Counts), congress109Ideology$repshare, K=3) )
plot(fit, pch=21, bg=c(4,3,2)[congress109Ideology$party])
predict(fit, newdata=freq(congress109Counts[c(68,388),]))

data(we8there)
summary( fit <- pls(freq(we8thereCounts),as.factor(we8thereRatings$Overall)) )
plot(fit, col=c(2,2,2,3,3))