Package 'morepls' reference manual

Title:	Interpretation Tools for Partial Least Squares Regression
Description:	Various kinds of plots (observations, variables, correlations, weights, regression coefficients and Variable Importance in the Projection) and aids to interpretation (coefficients, Q2, correlations, redundancies) for partial least squares regressions computed with the 'pls' package, following Tenenhaus (1998, ISBN:2-7108-0735-1).
Authors:	Nicolas Robette [aut, cre]
Maintainer:	Nicolas Robette <[email protected]>
License:	GPL (>= 2)
Version:	0.1
Built:	2024-11-05 06:17:37 UTC
Source:	CRAN

Standardized and raw coefficients

Description

Computes the standardized and raw coefficients of a PLS regression, with p-values and confidence intervals from a jackknife procedure.

Usage

get_coef(object, y = NULL, ncomp = NULL,
 ci = 0.95, raw = FALSE)
get_coef(object, y = NULL, ncomp = NULL,
 ci = 0.95, raw = FALSE)

Arguments

`object`	an object of class `mvr` from `pls` package. It must be cross-validated with `jackknife = TRUE`
`y`	the name of the response variable whose coefficients are plotted. If `NULL` (default), the first response variable is used.
`ncomp`	the number of components to use for computing coefficients
`ci`	the confidence level of the confidence interval. Default is 0.95.
`raw`	logical. If `FALSE` (default), standardized coefficients are computed. If `TRUE`, raw coefficients are computed.

Value

A data frame with coefficients, standard deviation, t-values, p-values and confidence intervals.

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls",
           jackknife = TRUE)
res <- get_coef(pls)
head(res)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls",
           jackknife = TRUE)
res <- get_coef(pls)
head(res)

Correlations between variables and scores

Description

Computes correlations between variables and scores from a PLS regression.

Usage

get_cor(object)
get_cor(object)

Arguments

object

an object of class mvr from pls package.

Value

A list with the following elements :

`Xt`	correlations between X variables and X scores
`Yt`	correlations between Y variables and X scores
`Xu`	correlations between X variables and Y scores
`Yu`	correlations between Y variables and Y scores
`XY`	correlations between X variables and Y variables
`tu`	correlations between X scores and Y scores

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
res <- get_cor(pls)
str(res)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
res <- get_cor(pls)
str(res)

Q2 and cumulative Q2 indexes

Description

Computes Q2 and cumulative Q2 indexes from a PLS regression.

Usage

get_Q2(object)
get_Q2(object)

Arguments

object

an object of class mvr from pls package. It has to be cross-validated

Value

A list with the following elements :

`Q2kh`	Q2 index by X variable and number of components
`Q2h`	Q2 index by number of components
`Q2cumkh`	cumulative Q2 index by X variable and number of components
`Q2cumh`	cumulative Q2 index by number of components

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
res <- get_Q2(pls)
str(res)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
res <- get_Q2(pls)
str(res)

R2 and redundancies

Description

Computes R2 and redundancies between variables and scores from a PLS regression.

Usage

get_red(object)
get_red(object)

Arguments

object

an object of class mvr from pls package.

Value

A list with the following elements :

`Xt`	R2 and redundancies between X variables and X scores
`Yt`	R2 and redundancies between Y variables and X scores
`Xu`	R2 and redundancies between X variables and Y scores
`Yu`	R2 and redundancies between Y variables and Y scores
`Xtcum`	cumulative R2 and redundancies between X variables and X scores
`Ytcum`	cumulative R2 and redundancies between Y variables and X scores
`Xucum`	cumulative R2 and redundancies between X variables and Y scores
`Yucum`	cumulative R2 and redundancies between Y variables and Y scores

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
res <- get_red(pls)
str(res)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
res <- get_red(pls)
str(res)

Plot of coefficients

Description

Plots the coefficients from a PLS regression.

Usage

plo_coef(object, y = NULL, ncomp = NULL, sort = FALSE,
                     col = "darkgreen", repel = FALSE,
                     max.pval = NULL, whiskers = FALSE, ci = 0.95)
plo_coef(object, y = NULL, ncomp = NULL, sort = FALSE,
                     col = "darkgreen", repel = FALSE,
                     max.pval = NULL, whiskers = FALSE, ci = 0.95)

Arguments

`object`	an object of class `mvr` from `pls` package
`y`	the name of the response variable whose coefficients are plotted. If `NULL` (default), the first response variable is used.
`ncomp`	the number of components to use for computing coefficients
`sort`	logical. If `TRUE`, bars are sorted by decreasing coefficients. Default is `FALSE`.
`col`	color of the bars
`repel`	logical. If `TRUE`, the names of the variables are repelled with `geom_text_repel`. Default is `FALSE`
`max.pval`	coefficients with jack-knife p-values higher than `max.pval` have a more transparent color bar. If `NULL` (default), all bars have the same opacity. If not `NULL`, `object` must be cross-validated with `jackknife = TRUE`.
`whiskers`	logical. If `TRUE`, whiskers are added to represent the confidence interval of the coefficients. Default is `FALSE`. If `TRUE`, `object` must be cross-validated with `jackknife = TRUE`.
`ci`	the confidence level of the confidence interval. Only used if `whiskers` is `TRUE`. Default is 0.95.

Value

a ggplot2 object

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls",
           jackknife = TRUE)
plo_coef(pls)
plo_coef(pls, max.pval = 0.05)
plo_coef(pls, whiskers = TRUE)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls",
           jackknife = TRUE)
plo_coef(pls)
plo_coef(pls, max.pval = 0.05)
plo_coef(pls, whiskers = TRUE)

Plot of correlations

Description

Plots the correlations between (X and Y) variables and the components (X scores) of a PLS regression.

Usage

plo_cor(object, comps = 1:2, which = "both", min.cor = NULL,
        lim = NULL, circles = NULL, col = NULL, size = 3.88)
plo_cor(object, comps = 1:2, which = "both", min.cor = NULL,
        lim = NULL, circles = NULL, col = NULL, size = 3.88)

Arguments

`object`	an object of class `mvr` from `pls` package
`comps`	the components to use. Default is `c(1,2)`.
`which`	character string. If `"both"` (default), X and Y variables are plotted. If `"X"`, only X variables are plotted. If `"Y"`, only Y variables are plotted.
`min.cor`	numerical value. The minimal correlation with one or the other component for a variable to be plotted. If `NULL` (default), all the variables are plotted.
`lim`	numerical value. The limit of the scale (in absolute value). If `NULL` (default), the limits are automatically determined from the range of tha data.
`circles`	vector of numeric values. Circles are added to the plot at radiuses specified in `circles`. If `NULL` (default), no circle is plotted.
`col`	colors for the names of the variables. Only one value should be provided if `which` is "X" or "Y", a vector of two if `which` is "both". If `NULL` (default), colors are set automatically.
`size`	numerical value. The size of the names of the variables.

Value

a ggplot2 object

Note

This is what Tenenhaus calls the univariate interpretation of the PLS components, as opposed to the multivariate interpretation (see plo_var).

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_cor(pls)
# plot with circles corresponding to
# correlations of 0.5 and 1
plo_cor(pls, lim = 1, circles = c(0.5, 1), col = c("pink", "purple"))
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_cor(pls)
# plot with circles corresponding to
# correlations of 0.5 and 1
plo_cor(pls, lim = 1, circles = c(0.5, 1), col = c("pink", "purple"))

Plot of weights

Description

Plots the weights of X variables from a PLS regression.

Usage

plo_ctr(object, comp = 1, sort = FALSE, col = "tomato4", repel = FALSE)
plo_ctr(object, comp = 1, sort = FALSE, col = "tomato4", repel = FALSE)

Arguments

`object`	an object of class `mvr` from `pls` package
`comp`	the component to use. Default is 1.
`sort`	logical. If `TRUE`, bars are sorted by decreasing VIPs. Default is `FALSE`.
`col`	color of the bars
`repel`	logical. If `TRUE`, the names of the variables are repelled with `geom_text_repel`. Default is `FALSE`

Details

According to Tenenhaus, the contribution of a variable to the construction of a component is measured by the squared loading weight. For a given component, the sum af the squared loading weights is equal to 1. This plot represents the loading weights, which keeps the information about their sign. Dashed lines are added at +/- sqrt(1/p), with p the number of X variables, which corresponds to the average contribution to the construction of the component.

Value

a ggplot2 object

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_ctr(pls)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_ctr(pls)

Plot of scores

Description

Plots the scores of the observations of a PLS regression.

Usage

plo_obs(object, comps = 1:2, col = "black", size = 1.5)
plo_obs(object, comps = 1:2, col = "black", size = 1.5)

Arguments

`object`	an object of class `mvr` from `pls` package
`comps`	the components to use. Default is `c(1,2)`.
`col`	the color of the points.
`size`	numerical value. The size of the points.

Value

a ggplot2 object

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_obs(pls)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_obs(pls)

Plot of loadings

Description

Plots the loadings of the variables of a PLS regression.

Usage

plo_var(object, comps = 1:2, which = "both", col = NULL,
 size = 3.88, Yline = TRUE, col.Yline = "firebrick3")
plo_var(object, comps = 1:2, which = "both", col = NULL,
 size = 3.88, Yline = TRUE, col.Yline = "firebrick3")

Arguments

`object`	an object of class `mvr` from `pls` package
`comps`	the components to use. Default is `c(1,2)`.
`which`	character string. If `"both"` (default), X and Y variables are plotted. If `"X"`, only X variables are plotted. If `"Y"`, only Y variables are plotted.
`col`	colors for the names of the variables. Only one value should be provided if `which` is "X" or "Y", a vector of two if `which` is "both". If `NULL` (default), colors are set automatically.
`size`	numerical value. The size of the names of the variables.
`Yline`	logical. If `TRUE` (default), a line is drawn through the origin and the coordinates of the response variable, and a second line orthogonal to the first one. This is aimed at facilitating the interpretation.
`col.Yline`	the color of the lines drawn if `Yline` is `TRUE`. Default is "firebrick3".

Value

a ggplot2 object

Note

This is what Tenenhaus calls the multivariate interpretation of the PLS components, as opposed to the univariate interpretation provided by the correlations (see plo_cor). This superposes Y loadings (vectors from the C matrix) and projections, i.e. modified weights (vectors of the W* matrix).

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_var(pls)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_var(pls)

Plot of VIPs

Description

Plots the Variable Importance in Projections (VIP) indexes of a PLS regression.

Usage

plo_vip(object, ncomp = NULL, sort = FALSE,
col = "steelblue4", repel = FALSE)
plo_vip(object, ncomp = NULL, sort = FALSE,
col = "steelblue4", repel = FALSE)

Arguments

`object`	an object of class `mvr` from `pls` package
`ncomp`	the number of components to use for computing VIPs
`sort`	logical. If `TRUE`, bars are sorted by decreasing VIPs. Default is `FALSE`.
`col`	color of the bars
`repel`	logical. If `TRUE`, the names of the variables are repelled with `geom_text_repel`. Default is `FALSE`

Value

a ggplot2 object

Author(s)

Nicolas Robette

References

Martens, H., Næs, T. (1989) Multivariate calibration. Chichester: Wiley.

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

Examples

library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_vip(pls)
library(pls)
data(yarn)
pls <- mvr(density ~ NIR,
           ncomp = 5,
           data = yarn,
           validation = "CV",
           method = "oscorespls")
plo_vip(pls)

Package 'morepls'

Help Index

Standardized and raw coefficients

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Correlations between variables and scores

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Q2 and cumulative Q2 indexes

Description

Usage

Arguments

Value

Author(s)

References

Examples

R2 and redundancies

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Plot of coefficients

Description

Usage

Arguments

Value

Author(s)

References

See Also

Examples

Plot of correlations

Description

Usage

Arguments

Value

Note

Author(s)

References

See Also

Examples

Plot of weights

Description

Usage

Arguments

Details

Value

Author(s)

References

See Also

Examples

Plot of scores

Description

Usage

Arguments

Value

Author(s)

References

Examples

Plot of loadings

Description

Usage

Arguments

Value

Note