Lwr

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Revision as of 13:32, 25 January 2010 by imported>Donal (→‎Inputs)
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Purpose

LWR locally weighted regression for multivariate Y.

Synopsis

model = lwr(x,y,ncomp,npts,options); %identifies model (calibration step)
pred = lwr(x,model,options); %makes predictions with a new X-block
options = lwr(options); %returns a default options structure

Description

LWR calculates a single locally weighted regression model using the given number of principal components ncomp to predict a dependent variable y from a set of independent variables x.

Predictions are made using a locally weighted regression model defined by the number principal components used to model the independent variables (ncomp), and the number of points defined as local (npts).

Alternatively, LWR can be used in 'predicton mode' to apply a previously built LWR model in model to an external set of test data in x (2-way array class "double" or "dataset"), in order to generate y-values for these data.

Furthermore, if matching x-block and y-block measurements are available for an external test set, then LWR can be used in 'validation mode' to predict the y-values of the test data from the model model and x, and allow comparison of these predicted y-values to the known y-values y.

Note: Calling lwr with no inputs starts the graphical user interface (GUI) for this analysis method.

Inputs

  • x = X-block (predictor block) class "double" or "dataset"
  • y = Y-block (predicted block) class "double" or "dataset"
  • ncomp = the number of latent variables to be calculated (positive integer scalar)
  • npts = the number of points to use in local regression (positive integer scalar)
  • model = previously generated lwr model

Outputs

  • model = a standard model structure model with the following fields (see MODELSTRUCT):
    • modeltype: 'PLS',
    • datasource: structure array with information about input data,
    • date: date of creation,
    • time: time of creation,
    • info: additional model information,
    • reg: regression vector,
    • loads: cell array with model loadings for each mode/dimension,
    • pred: 2 element cell array with
      • model predictions for each input block (when options.blockdetail='normal' x-block predictions are not saved and this will be an empty array),and
      • the y-block predictions.
    • wts: double array with X-block weights,
    • tsqs: cell array with T2 values for each mode,
    • ssqresiduals: cell array with sum of squares residuals for each mode,
    • description: cell array with text description of model, and
    • detail: sub-structure with additional model details and results.
  • pred a structure, similar to model, that contains scores, predictions, etc. for the new data.
  • valid a structure, similar to model, that contains scores, predictions, and additional y-block statistics, etc. for the new data.

Options

options = a structure array with the following fields:

  • display: [ 'off' | {'on'} ], governs level of display to command window,
  • plots [ 'none' | {'final'} ], governs level of plotting,
  • outputversion: [ 2 | {3} ], governs output format (see below),
  • preprocessing: {[] []}, two element cell array containing preprocessing structures (see PREPROCESS) defining preprocessing to use on the x- and y-blocks (first and second elements respectively)
  • algorithm: [ 'nip' | {'sim'} | 'robustpls' ], PLS algorithm to use: NIPALS or SIMPLS {default}, and
  • blockdetails: [ {'standard'} | 'all' ], extent of predictions and residuals included in model, 'standard' = only y-block, 'all' x- and y-blocks.
  • confidencelimit: [ {'0.95'} ], confidence level for Q and T2 limits, a value of zero (0) disables calculation of confidence limits,
  • weights: [ 'hist' | [vector] ], governs sample weighting. If set to the string 'hist', y-block histogram weighting is done on the samples. If set to a vector, each element is used as a weight for the corresponding sample. If empty, no sample weighting is done.
  • roptions: structure of options to pass to rsimpls (robust PLS engine from the Libra Toolbox).
alpha: [ {0.75} ], (1-alpha) measures the number of outliers the algorithm should resist. Any value between 0.5 and 1 may be specified. These options are only used when algorithm is 'robustpls'.

The default options can be retreived using: options = pls('options');.

OUTPUTVERSION

By default (options.outputversion = 3) the output of the function is a standard model structure model. If options.outputversion = 2, the output format is:

[b,ssq,p,q,w,t,u,bin] = pls(x,y,ncomp,options)

where the outputs are

  • b = matrix of regression vectors or matrices for each number of principal components up to ncomp,
  • ssq = the sum of squares information,
  • p = x-block loadings,
  • q = y-block loadings,
  • w = x-block weights,
  • t = x-block scores
  • u = y-block scores, and
  • bin = inner relation coefficients.

Note: The regression matrices are ordered in b such that each Ny (number of y-block variables) rows correspond to the regression matrix for that particular number of principal components.

Algorithm

Note that unlike previous versions of the PLS function, the default algorithm (see Options, above) is the faster SIMPLS algorithm. If the alternate NIPALS algorithm is to be used, the options.algorithm field should be set to 'nip'.

See Also

analysis, crossval, modelstruct, nippls, pcr, plsda, preprocess, ridge, simpls