# innerEstim: PLS inner estimation In matrixpls: Matrix-Based Partial Least Squares Estimation

## Description

Calculates a set of inner weights.

## Usage

 ```1 2 3 4 5 6 7 8 9``` ```innerEstim.centroid(S, W, inner.mod, ignoreInnerModel = FALSE, ...) innerEstim.path(S, W, inner.mod, ...) innerEstim.factor(S, W, inner.mod, ignoreInnerModel = FALSE, ...) innerEstim.identity(S, W, inner.mod, ...) innerEstim.gsca(S, W, inner.mod, ...) ```

## Arguments

 `S` Covariance matrix of the data. `W` Weight matrix, where the indicators are on colums and composites are on the rows. `inner.mod` A square matrix specifying the relationships of the composites in the model. `ignoreInnerModel` Should the inner model be ignored and all correlations be used. `...` Other arguments are ignored.

## Details

In the centroid scheme, inner weights are set to the signs (1 or -1) of correlations between composites that are connected in the model specified in `inner.mod` and zero otherwise.

In the path scheme, inner weights are based on regression estimates of the relationships between composites that are connected in the model specified in `inner.mod`, and correlations for the inverse relationships. If a relationship is reciprocal, regression is used for both directions.

In the factor scheme, inner weights are set to the correlations between composites that are connected in the model specified in `inner.mod` and zero otherwise.

In the identity scheme identity matrix is used as the inner weight matrix `E`.

Centroid, innner, and path schemes fall back to to identity scheme for composites that are not connected to any other composites.

For information about GSCA weights, see GSCA.

## Value

A matrix of unscaled inner weights `E` with the same dimesions as `inner.mod`.

## Functions

• `innerEstim.centroid`: inner estimation with centroid scheme.

• `innerEstim.path`: inner estimation with path scheme.

• `innerEstim.factor`: inner estimation with factor scheme.

• `innerEstim.identity`: inner estimation with identity scheme.

• `innerEstim.gsca`: inner estimation with generalized structured component analysis.

## References

Lohm<c3><b6>ller J.-B. (1989) Latent variable path modeling with partial least squares. Heidelberg: Physica-Verlag.

matrixpls documentation built on May 30, 2017, 7:12 a.m.