Polypls: Difference between revisions
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===Purpose=== | ===Purpose=== | ||
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===Description=== | ===Description=== | ||
POLYPLS creates a partial least squares regression model with polynomial fit for the inner relation. Inputs are a matrix of predictor variables (x-block) x, a matrix of predicted variables (y-block) y, the number of latent variables lv, and the order of the polynomial n. | POLYPLS creates a partial least squares regression model with polynomial fit for the inner relation. Inputs are a matrix of predictor variables (x-block) <tt>x</tt>, a matrix of predicted variables (y-block) <tt>y</tt>, the number of latent variables <tt>lv</tt>, and the order of the polynomial <tt>n</tt>. | ||
Outputs are p the x-block latent variable loadings, q the y-block variable loadings, w the x-block latent variable weights, t the x-block latent variable scores, u the y-block latent variable scores, b a matrix of polynomial coefficients for the inner relationship, and ssqdif a table of x- and y-block variance captured by the PLS model. | Outputs are <tt>p</tt> the x-block latent variable loadings, <tt>q</tt> the y-block variable loadings, <tt>w</tt> the x-block latent variable weights, <tt>t</tt> the x-block latent variable scores, <tt>u</tt> the y-block latent variable scores, <tt>b</tt> a matrix of polynomial coefficients for the inner relationship, and <tt>ssqdif</tt> a table of x- and y-block variance captured by the PLS model. | ||
Use POLYPRED to make predictions with new data. | Use POLYPRED to make predictions with new data. | ||
Revision as of 14:47, 10 October 2008
Purpose
Calculate partial least squares regression models with polynomial inner relations.
Synopsis
- [p,q,w,t,u,b,ssqdif] = polypls(x,y,lv,n)
Description
POLYPLS creates a partial least squares regression model with polynomial fit for the inner relation. Inputs are a matrix of predictor variables (x-block) x, a matrix of predicted variables (y-block) y, the number of latent variables lv, and the order of the polynomial n.
Outputs are p the x-block latent variable loadings, q the y-block variable loadings, w the x-block latent variable weights, t the x-block latent variable scores, u the y-block latent variable scores, b a matrix of polynomial coefficients for the inner relationship, and ssqdif a table of x- and y-block variance captured by the PLS model.
Use POLYPRED to make predictions with new data.