Type:	Package
Title:	Partial Least Squares (PLS) Data Analysis Methods
Version:	0.2.0
Date:	2023-03-12
Author:	Frederic Bertrand [cre], Gaston Sanchez [aut]
Maintainer:	Frederic Bertrand <frederic.bertrand@utt.fr>
Description:	Different methods for PLS analysis of one or two data tables such as Tucker's Inter-Battery, NIPALS, SIMPLS, SIMPLS-CA, PLS Regression, and PLS Canonical Analysis. The main reference for this software is the awesome book (in French) 'La Regression PLS: Theorie et Pratique' by Michel Tenenhaus.
Depends:	R (≥ 2.15.1)
Suggests:	FactoMineR
License:	GPL-3
LazyLoad:	yes
Collate:	'nipals.R' 'plot.nipals.R' 'plot.plsca.R' 'plot.plsreg1.R' 'plot.plsreg2.R' 'plsca.R' 'plsreg1.R' 'plsreg2.R' 'print.nipals.R' 'print.plsca.R' 'print.plsreg1.R' 'print.plsreg2.R' 'interbat.R' 'plot.interbat.R' 'print.interbat.R' 'print.simpls.R' 'print.simplsca.R' 'simpls.R' 'simplsca.R' 'plot.simpls.R' 'plot.simplsca.R'
Encoding:	UTF-8
RoxygenNote:	7.2.1
NeedsCompilation:	no
Packaged:	2023-03-13 09:26:12 UTC; fbertran
Repository:	CRAN
Date/Publication:	2023-03-13 10:00:02 UTC

carscomplete data set

Description

Data set with six variables measured on 24 cars (no missing values)

Usage

data(carscomplete)

Format

A data frame with 6 variables measured on the 24 cars.

Details

This is data with no missing values

Source

Tenenhaus M. (1998) La Regression PLS: theorie et pratique. Table 4, page 67

References

Tenenhaus M. (1998) La Regression PLS: theorie et pratique Paris: Editions Technip

Examples

data(carscomplete)
head(carscomplete)

carsmissing data set

Description

Data set with six variables measured on 24 cars

Usage

data(carsmissing)

Format

A data frame with 6 variables measured on the 24 cars (contains missing values).

Details

This is data contains one missing value in each row

Source

Tenenhaus M. (1998) La Regression PLS: theorie et pratique. Table 5, page 69

References

Tenenhaus M. (1998) La Regression PLS: theorie et pratique Paris: Editions Technip

Examples

data(carsmissing)
head(carsmissing)

Cornell data set

Description

Data describing the composition of 12 octane mixtures (units measured in proportions)

Usage

data(cornell)

Format

A data frame with 12 observations on the following 8 variables.

Source

Tenenhaus M. (1998) Table 6, page 78

References

Tenenhaus M. (1998) La Regression PLS: theorie et pratique Paris: Editions Technip

Examples

data(cornell)
head(cornell)

Tucker's Inter-Battery Method of Factor Analysis

Description

The function interbat performs Tucker's Inter-Battery method of factor analysis as described in Michel Tenenhaus book La Regression PLS, chapter 3

Usage

  interbat(X, Y, scaled = TRUE)

Arguments

Details

Arguments X and Y must contain more than one variable. No missing data are allowed.

Value

An object of class "interbat", basically a list with the following elements:

Author(s)

Gaston Sanchez

References

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

Tucker, L.R. (1958) An inter-battery method of factor analysis. Psychometrika, 23(2): 111-136.

See Also

plot.interbat, plsca

Examples

## Not run: 
 # load data linnerud
 data(linnerud)

 # apply inter-battery method
 ib = interbat(linnerud[,1:3], linnerud[,4:6])

 # plot variables
 plot(ib, what="variables")

 # plot observations
 plot(ib, what="observations", comps=c(1,1), where=c("t","u"))
 
## End(Not run)

Linnerud data set

Description

Thris data set contains three physiological and three exercise variables measured on 20 men

Usage

data(linnerud)

Format

A data frame with 20 observations on the following 6 variables.

Source

Tenenhaus M. (1998) Table 1, page 15

References

Tenenhaus M. (1998) La Regression PLS: theorie et pratique Paris: Editions Technip

Examples

data(linnerud)
head(linnerud)

NIPALS: Non-linear Iterative Partial Least Squares

Description

Principal Components Analysis with NIPALS algorithm

Usage

  nipals(Data, comps = 2, scaled = TRUE)

Arguments

Details

The function nipals performs Principal Components Analysis of a data matrix that may contain missing values.

Value

An object of class "nipals", basically a list with the following elements:

When the analyzed data contain missing values, the help interpretation tools (e.g. cor.xt, disto, contrib, cos, dmod) may not be meaningful, that is to say, some of the results may not be coherent.

Author(s)

Gaston Sanchez

References

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

Tenenhaus, M. (2007) Statistique. Methodes pour decrire, expliquer et prevoir. Paris: Dunod.

See Also

plot.nipals, plsreg1

Examples

## Not run: 
 # load datasets carscomplete and carsmissing
 data(carscomplete) # complete data
 data(carsmissing)  # missing values

 # apply nipals
 my_nipals1 = nipals(carscomplete)
 my_nipals2 = nipals(carsmissing)

 # plot variables (circle of correlations)
 plot(my_nipals1, what="variables", main="Complete data")
 plot(my_nipals2, what="variables", main="Missing data")

 # plot observations with labels
 plot(my_nipals1, what="observations", show.names=TRUE, main="Complete data")
 plot(my_nipals2, what="observations", show.names=TRUE, main="Missing data")

 # compare results between my_nipals1 and my_nipals2
 plot(my_nipals1$scores[,1], my_nipals2$scores[,1], type="n")
 title("Scores comparison: my_nipals1  -vs-  my_nipals2", cex.main=0.9)
 abline(a=0, b=1, col="gray85", lwd=2)
 points(my_nipals1$scores[,1], my_nipals2$scores[,1], pch=21,
        col="#5592e3", bg = "#5b9cf277", lwd=1.5)
 
## End(Not run)

Plot inter-battery basic results

Description

Plot method for objects of class "interbat". This function plots either the variables or the observations, on the selected components (i.e. scores). Variables are plotted inside the circle of correlations. Observations are plotted on a scatter plot.

Usage

  ## S3 method for class 'interbat'
 plot(x, what = "variables",
    comps = c(1, 2), where = c("t", "t"), cex = 1,
    col.xlabels = "#5592e3", col.ylabels = "#e3a654",
    yfont = 1, pos = NULL, offset = 0.1,
    col.xarrows = "#5b9cf255", col.yarrows = "#e3a65455",
    lwd = 3, length = 0, angle = 0, col.points = "#5592e3",
    pch = 21, pt.bg = "#5b9cf255", show.names = FALSE,
    xpd = TRUE, xlab = NULL, ylab = NULL, main = NULL,
    col.main = "gray35", cex.main = 1.2,
    col.axis = "gray40", show.grid = TRUE,
    col.grid = "gray95", ...)

Arguments

Details

Variables are displayed using the correlations of each block of variables with the components of the other block: $cor.xu -vs- $cor.yt.

Author(s)

Gaston Sanchez

See Also

interbat

Examples

## Not run: 
 # load dataset linnerud
 data(linnerud)

 # apply nipals
 ib = interbat(linnerud[,1:3], linnerud[,4:6])

 # plot variables (circle of correlations)
 plot(ib, what="variables")

 # plot observations (as points) using components (t1,u1)
 plot(ib, what="observations", comps=c(1,1), where=c("t","u"))

 # plot observations with names using components (t1,u1)
 plot(ib, what="observations", comps=c(1,1),
      where=c("t","u"), show.names=TRUE)

 # plot observations (as points) using components (t1,t2)
 plot(ib, what="observations", comps=c(1,2), where=c("t","t"))

 # plot observations (as points) using components (u1,u2)
 plot(ib, what="observations", comps=c(1,2), where=c("u","u"))
 
## End(Not run)

Plot NIPALS basic results

Description

Plot method for objects of class "nipals". This function plots either the variables or the observations, on the selected components (i.e. scores). Variables are plotted inside the circle of correlations. Observations are plotted on a scatter plot.

Usage

  ## S3 method for class 'nipals'
 plot(x, what = "variables",
    comps = c(1, 2), cex = 1, col.labels = "#5592e3",
    pos = NULL, offset = 0.1, col.arrows = "#5b9cf255",
    lwd = 3.5, length = 0, angle = 0,
    col.points = "#5592e3", pch = 21, pt.bg = "#5b9cf255",
    show.names = FALSE, xpd = TRUE, xlab = NULL,
    ylab = NULL, main = NULL, col.main = "gray35",
    cex.main = 1.2, col.axis = "gray40", show.grid = TRUE,
    col.grid = "gray95", ...)

Arguments

Details

Variables are displayed using the correlations in $cor.xt.

Author(s)

Gaston Sanchez

See Also

nipals

Examples

## Not run: 
 # load data climbing ropes
 data(ropes)

 # apply nipals with 3 components
 nip1 = nipals(ropes[,-1], comps=3)

 # plot variables (correlations)
 plot(nip1)

 # plot observations
 plot(nip1, what="obs")

 # plot observations with names
 plot(nip1, what="obs", show.names=TRUE)
 
## End(Not run)

Plot PLS-CA basic results

Description

Plot method for objects of class "plsca". This function plots either the variables or the observations, on the selected components (i.e. scores). Variables are plotted inside the circle of correlations. Observations are plotted on a scatter plot.

Usage

  ## S3 method for class 'plsca'
 plot(x, what = "variables",
    comps = c(1, 2), where = c("t", "t"), cex = 1,
    col.xlabels = "#5592e3", col.ylabels = "#fe9429",
    yfont = 2, pos = NULL, offset = 0.1,
    col.xarrows = "#5b9cf255", col.yarrows = "#fe942955",
    lwd = 3, length = 0, angle = 0, col.points = "#5592e3",
    pch = 21, pt.bg = "#5b9cf255", show.names = FALSE,
    xpd = TRUE, xlab = NULL, ylab = NULL, main = NULL,
    col.main = "gray35", cex.main = 1.2,
    col.axis = "gray40", show.grid = TRUE,
    col.grid = "gray95", ...)

Arguments

Details

Variables are displayed using the correlations of each block of variables with its set of components: $cor.xt and $cor.yu.

Author(s)

Gaston Sanchez

See Also

plsca

Examples

## Not run: 
 # load dataset linnerud
 data(linnerud)

 # apply plsca
 my_plsca = plsca(linnerud[,1:3], linnerud[,4:6])

 # plot variables (circle of correlations)
 plot(my_plsca, what="variables")

 # plot observations (as points) using components (t1,u1)
 plot(my_plsca, what="observations", comps=c(1,1), where=c("t","u"))

 # plot observations with names using components (t1,u1)
 plot(my_plsca, what="observations", comps=c(1,1),
      where=c("t","u"), show.names=TRUE)

 # plot observations (as points) using components (t1,t2)
 plot(my_plsca, what="observations", comps=c(1,2), where=c("t","t"))

 # plot observations (as points) using components (u1,u2)
 plot(my_plsca, what="observations", comps=c(1,2), where=c("u","u"))
 
## End(Not run)

Plot PLS-R1 basic results

Description

Plot method for objects of class "plsreg1". This function plots either the variables or the observations, on the selected components (i.e. scores). Variables are plotted inside the circle of correlations. Observations are plotted on a scatter plot.

Usage

  ## S3 method for class 'plsreg1'
 plot(x, what = "variables",
    comps = c(1, 2), where = c("t", "t"), cex = 1,
    col.xlabels = "#5592e3", col.ylabel = "#fe9429",
    yfont = 2, pos = NULL, offset = 0.1,
    col.xarrows = "#5b9cf255", col.yarrows = "#fe942955",
    lwd = 3, length = 0, angle = 0, col.points = "#5592e3",
    pch = 21, pt.bg = "#5b9cf255", show.names = FALSE,
    xpd = TRUE, xlab = NULL, ylab = NULL, main = NULL,
    col.main = "gray35", cex.main = 1.2,
    col.axis = "gray40", show.grid = TRUE,
    col.grid = "gray95", ...)

Arguments

Details

Variables are displayed using the correlations in $cor.xyt.

Author(s)

Gaston Sanchez

See Also

plsreg1

Examples

## Not run: 
 # load dataset cornell
 data(cornell)

 # apply plsreg1
 myplsr1 = plsreg1(cornell[,1:7], cornell[,8,drop=FALSE])

 # plot variables (circle of correlations)
 plot(myplsr1, what="variables")

 # plot observations (as points) using components (t1,u1)
 plot(myplsr1, what="observations", comps=c(1,1), where=c("t","u"))

 # plot observations with names using components (t1,u1)
 plot(myplsr1, what="observations", comps=c(1,1),
      where=c("t","u"), show.names=TRUE)

 # plot observations (as points) using components (t1,t2)
 plot(myplsr1, what="observations", comps=c(1,2), where=c("t","t"))

 # plot observations (as points) using components (u1,u2)
 plot(myplsr1, what="observations", comps=c(1,2), where=c("u","u"))
 
## End(Not run)

Plot PLS-R2 basic results

Description

Plot method for objects of class "plsreg2". This function plots either the variables or the observations, on the selected components (i.e. scores). Variables are plotted inside the circle of correlations. Observations are plotted on a scatter plot.

Usage

  ## S3 method for class 'plsreg2'
 plot(x, what = "variables",
    comps = c(1, 2), where = c("t", "t"), cex = 1,
    col.xlabels = "#5592e3", col.ylabels = "#fe9429",
    yfont = 2, pos = NULL, offset = 0.1,
    col.xarrows = "#5b9cf255", col.yarrows = "#fe942955",
    lwd = 3, length = 0, angle = 0, col.points = "#5592e3",
    pch = 21, pt.bg = "#5b9cf255", show.names = FALSE,
    xpd = TRUE, xlab = NULL, ylab = NULL, main = NULL,
    col.main = "gray35", cex.main = 1.2,
    col.axis = "gray40", show.grid = TRUE,
    col.grid = "gray95", ...)

Arguments

Details

Variables are displayed using the correlations of each block of variables with its set of components: $cor.xt and $cor.yt.

Author(s)

Gaston Sanchez

See Also

plsreg2

Examples

## Not run: 
 # load dataset vehicles
 data(vehicles)

 # apply plsreg2
 pls2 = plsreg2(vehicles[,1:12], vehicles[,13:16])

 # plot variables (circle of correlations)
 plot(pls2, what="variables")

 # plot observations (as points)
 plot(pls2, what="observations")

 # plot observations with labels
 plot(pls2, what="observations", show.names=TRUE)
 
## End(Not run)

Plot simpls basic results

Description

Plot method for objects of class "simpls". This function plots either the variables or the observations, on the selected components (i.e. scores). Variables are plotted inside the circle of correlations. Observations are plotted on a scatter plot.

Usage

  ## S3 method for class 'simpls'
 plot(x, what = "variables",
    comps = c(1, 2), cex = 1, col.xlabels = "#5592e3",
    col.ylabels = "#fe9429", yfont = 2, pos = NULL,
    offset = 0.1, col.xarrows = "#5b9cf255",
    col.yarrows = "#FE992955", lwd = 3, length = 0,
    angle = 0, col.points = "#5592e3", pch = 21,
    pt.bg = "#5b9cf255", show.names = FALSE, xpd = TRUE,
    xlab = NULL, ylab = NULL, main = NULL,
    col.main = "gray35", cex.main = 1.2,
    col.axis = "gray40", show.grid = TRUE,
    col.grid = "gray95", ...)

Arguments

Details

Variables are displayed using the correlations of each block of variables with the X-components: $cor.xt and $cor.yt.

Author(s)

Gaston Sanchez

See Also

simpls

Examples

## Not run: 
 # load dataset linnerud
 data(linnerud)

 # apply simpls
 sim = simpls(linnerud[,1:3], linnerud[,4:6])

 # plot variables (circle of correlations)
 plot(sim, what="variables")

 # plot observations (as points)
 plot(sim, what="observations")

 # plot observations with names
 plot(sim, what="observations", show.names=TRUE)
 
## End(Not run)

Plot SIMPLS-CA basic results

Description

Plot method for objects of class "simplsca". This function plots either the variables or the observations, on the selected components (i.e. scores). Variables are plotted inside the circle of correlations. Observations are plotted on a scatter plot.

Usage

  ## S3 method for class 'simplsca'
 plot(x, what = "variables",
    comps = c(1, 2), where = c("t", "t"), cex = 1,
    col.xlabels = "#5592e3", col.ylabels = "#fe9429",
    yfont = 2, pos = NULL, offset = 0.1,
    col.xarrows = "#5b9cf255", col.yarrows = "#fe942955",
    lwd = 3, length = 0, angle = 0, col.points = "#5592e3",
    pch = 21, pt.bg = "#5b9cf255", show.names = FALSE,
    xpd = TRUE, xlab = NULL, ylab = NULL, main = NULL,
    col.main = "gray35", cex.main = 1.2,
    col.axis = "gray40", show.grid = TRUE,
    col.grid = "gray95", ...)

Arguments

Details

Variables are displayed using the correlations of each block of variables with its set of components: $cor.xt and $cor.yu.

Author(s)

Gaston Sanchez

See Also

simplsca

Examples

## Not run: 
 # load dataset linnerud
 data(linnerud)

 # apply simplsca
 simca = simplsca(linnerud[,1:3], linnerud[,4:6])

 # plot variables (circle of correlations)
 plot(simca, what="variables")

 # plot observations (as points) using components (t1,u1)
 plot(simca, what="observations", comps=c(1,1), where=c("t","u"))

 # plot observations with names using components (t1,u1)
 plot(simca, what="observations", comps=c(1,1),
      where=c("t","u"), show.names=TRUE)

 # plot observations (as points) using components (t1,t2)
 plot(simca, what="observations", comps=c(1,2), where=c("t","t"))

 # plot observations (as points) using components (u1,u2)
 plot(simca, what="observations", comps=c(1,2), where=c("u","u"))
 
## End(Not run)

PLS-CA: Partial Least Squares Canonical Analysis

Description

Performs partial least squares canonical analysis for two blocks of data. Compared to PLSR2, the blocks of variables in PLS-CA play a symmetric role (i.e. there is neither predictors nor responses)

Usage

  plsca(X, Y, comps = NULL, scaled = TRUE)

Arguments

Value

An object of class "plsca", basically a list with the following elements:

Author(s)

Gaston Sanchez

References

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

See Also

plot.plsca

Examples

## Not run: 
 ## example of PLSCA with the vehicles dataset
 data(vehicles)

 # apply plsca
 my_plsca = plsca(vehicles[,1:12], vehicles[,13:16])
 my_plsca

 # plot variables
 plot(my_plsca)
 
## End(Not run)

PLS-R1: Partial Least Squares Regression 1

Description

The function plsreg1 performs Partial Least Squares Regression for the univariate case (i.e. one response variable)

Usage

  plsreg1(predictors, response, comps = 2, crosval = TRUE)

Arguments

Details

The minimum number of PLS components (comps) to be extracted is 2.

The data is scaled to standardized values (mean=0, variance=1).

The argument crosval gives the option to perform cross-validation. This parameter takes into account how comps is specified. When comps=NULL, the number of components is obtained by cross-validation. When a number of components is specified, cross-validation results are calculated for each component.

Value

An object of class "plsreg1", basically a list with the following elements:

Author(s)

Gaston Sanchez

References

Geladi, P., and Kowalski, B. (1986) Partial Least Squares Regression: A Tutorial. Analytica Chimica Acta, 185, pp. 1-17.

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

Tenenhaus, M., Gauchi, J.-P., and Menardo, C. (1995) Regression PLS et applications. Revue de statistique appliquee, 43, pp. 7-63.

See Also

plot.plsreg1, plsreg2.

Examples

## Not run: 
 ## example of PLSR1 with the vehicles dataset
 # predictand variable: price of vehicles
 data(vehicles)

 # apply plsreg1 extracting 2 components (no cross-validation)
 pls1_one = plsreg1(vehicles[,1:12], vehicles[,13,drop=FALSE], comps=2, crosval=FALSE)

 # apply plsreg1 with selection of components by cross-validation
 pls1_two = plsreg1(vehicles[,1:12], vehicles[,13,drop=FALSE], comps=NULL, crosval=TRUE)

 # apply plsreg1 extracting 5 components with cross-validation
 pls1_three = plsreg1(vehicles[,1:12], vehicles[,13,drop=FALSE], comps=5, crosval=TRUE)

 # plot variables
 plot(pls1_one)
 
## End(Not run)

PLS-R2: Partial Least Squares Regression 2

Description

The function plsreg2 performs partial least squares regression for the multivariate case (i.e. more than one response variable)

Usage

  plsreg2(predictors, responses, comps = 2, crosval = TRUE)

Arguments

Details

The minimum number of PLS components comps to be extracted is 2.

The data is scaled to standardized values (mean=0, variance=1).

The argument crosval gives the option to perform cross-validation. This parameter takes into account how comps is specified. When comps=NULL, the number of components is obtained by cross-validation. When a number of components is specified, cross-validation results are calculated for each component.

Value

An object of class "plsreg2", basically a list with the following elements:

Author(s)

Gaston Sanchez

References

Geladi, P., and Kowlaski, B. (1986) Partial Least Squares Regression: A Tutorial. Analytica Chimica Acta, 185, pp. 1-17.

Hoskuldsson, A. (1988) PLS Regression Methods. Journal of Chemometrics, 2, pp. 211-228.

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

See Also

plot.plsreg2, plsreg1.

Examples

## Not run: 
 ## example of PLSR2 with the vehicles dataset
 data(vehicles)

 # apply plsreg2 extracting 2 components (no cross-validation)
 pls2_one = plsreg2(vehicles[,1:12], vehicles[,13:16], comps=2, crosval=FALSE)

 # apply plsreg2 with selection of components by cross-validation
 pls2_two = plsreg2(vehicles[,1:12], vehicles[,13:16], comps=NULL, crosval=TRUE)

 # apply plsreg2 extracting 5 components with cross-validation
 pls2_three = plsreg2(vehicles[,1:12], vehicles[,13:16], comps=5, crosval=TRUE)

 # plot variables
 plot(pls2_one)
 
## End(Not run)

Climbing Ropes data set

Description

This dataset gives the measurements of 101 climbing ropes available in the market by spring 2011. The data was collected from the brands websites.

Usage

data(ropes)

Format

A data frame with 101 observations on the following 7 variables.

Source

Personal collection of data. (Gaston Sanchez)

Examples

data(ropes)
summary(ropes)

SIMPLS: Alternative Approach to PLS Regression

Description

The function simpls performs the SIMPLS Algorithm as described in Michel Tenenhaus book La Regression PLS, chapter 5.

Usage

  simpls(X, Y, comps = 2)

Arguments

Details

No missing data are allowed.

Value

An object of class "simpls", basically a list with the following elements:

Author(s)

Gaston Sanchez

References

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

de Jong, S. (1993) SIMPLS: An alternative approach to partial least squares regression. Chemometrics and Intelligent Laboratory Systems, 18: 251-263.

See Also

plot.simpls, simplsca

Examples

## Not run: 
 # load data linnerud
 data(linnerud)

 # apply inter-battery method
 my_simpls = simpls(linnerud[,1:3], linnerud[,4:6])

 # plot variables
 plot(my_simpls, what="variables")
 
## End(Not run)

SIMPLS-CA: SIMPLS Canonical Analysis

Description

The function simplsca performs the SIMPLS Canonical Analysis algorithm as described in Michel Tenenhaus book La Regression PLS, chapter 5.

Usage

  simplsca(X, Y, comps = 2)

Arguments

Details

No missing data are allowed.

Value

An object of class "simplsca", basically a list with the following elements:

Author(s)

Gaston Sanchez

References

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

See Also

plot.simplsca, simpls

Examples

## Not run: 
 # load data linnerud
 data(linnerud)

 # apply inter-battery method
 my_simca = simplsca(linnerud[,1:3], linnerud[,4:6])

 # plot variables
 plot(my_simca, what="variables")
 
## End(Not run)

Vehicles data set

Description

These data are the specification of 30 vehicles in terms of various characteristics.

Format

A data frame with 30 observations and 16 variables.

Source

1) 1985 Model Import Car and Truck Specifications, 1985 Ward's Automotive Yearbook.
2) Personal Auto Manuals, Insurance Services Office, 160 Water Street, New York, NY 10038.
3) Insurance Collision Report, Insurance Institute for Highway Safety, Watergate 600, Washington, DC 20037.

Machine Learning Repository. https://archive.ics.uci.edu/ml/datasets/Automobile

Examples

data(vehicles)
vehicles