, Gaston Sanchez [aut]| Title: | Partial Least Squares (PLS) Data Analysis Methods |
|---|---|
| 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. |
| Authors: | Frederic Bertrand [cre]
, Gaston Sanchez [aut] |
| Maintainer: | Frederic Bertrand <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2.0 |
| Built: | 2024-11-10 05:15:36 UTC |
| Source: | https://github.com/cran/plsdepot |
Data set with six variables measured on 24 cars (no missing values)
data(carscomplete)data(carscomplete)
A data frame with 6 variables measured on the 24 cars.
| Num | Variable | Description |
| 1 | Cylindree | Engine |
| 2 | Puissance | Power |
| 3 | Vitese | Speed |
| 4 | Poids | Weight |
| 5 | Longueur | Length |
| 6 | Largeur | Height |
This is data with no missing values
Tenenhaus M. (1998) La Regression PLS: theorie et pratique. Table 4, page 67
Tenenhaus M. (1998) La Regression PLS: theorie et pratique Paris: Editions Technip
data(carscomplete) head(carscomplete)data(carscomplete) head(carscomplete)
Data set with six variables measured on 24 cars
data(carsmissing)data(carsmissing)
A data frame with 6 variables measured on the 24 cars (contains missing values).
| Num | Variable | Description |
| 1 | Cylindree | Engine |
| 2 | Puissance | Power |
| 3 | Vitese | Speed |
| 4 | Poids | Weight |
| 5 | Longueur | Length |
| 6 | Largeur | Height |
This is data contains one missing value in each row
Tenenhaus M. (1998) La Regression PLS: theorie et pratique. Table 5, page 69
Tenenhaus M. (1998) La Regression PLS: theorie et pratique Paris: Editions Technip
data(carsmissing) head(carsmissing)data(carsmissing) head(carsmissing)
Data describing the composition of 12 octane mixtures (units measured in proportions)
data(cornell)data(cornell)
A data frame with 12 observations on the following 8 variables.
| Num | Variable | Description |
| 1 | Distillation | Direct Distillation |
| 2 | Reformed | Reformed |
| 3 | NaphthaTher | Thermal cracking Naphtha |
| 4 | NaphthaCat | Catalytic cracking Naphtha |
| 5 | Polymer | Polymer |
| 6 | Alkylat | Alkylat |
| 7 | NatEssence | Natural Essence |
| 8 | Octane | Octane |
Tenenhaus M. (1998) Table 6, page 78
Tenenhaus M. (1998) La Regression PLS: theorie et pratique Paris: Editions Technip
data(cornell) head(cornell)data(cornell) head(cornell)
The function interbat performs Tucker's
Inter-Battery method of factor analysis as described in
Michel Tenenhaus book La Regression PLS, chapter 3
interbat(X, Y, scaled = TRUE)interbat(X, Y, scaled = TRUE)
X |
Numeric matrix or data frame with two or more columns (X-block). |
Y |
Numeric matrix or data frame with two or more columns (Y-block). |
scaled |
Logical value indicating whether to scale
the data ( |
Arguments X and Y must contain more than
one variable. No missing data are allowed.
An object of class "interbat", basically a list
with the following elements:
values |
The extracted eigenvalues |
x.scores |
scores of the X-block (also known as T components) |
x.wgs |
weights of the X-block |
y.scores |
scores of the Y-block (also known as U components) |
y.wgs |
weights of the Y-block |
cor.xt |
correlations between X and T |
cor.yu |
correlations between Y and U |
cor.xu |
correlations between X and U |
cor.yt |
correlations between Y and T |
cor.tu |
correlations between T and U |
R2X |
explained variance of X by T |
R2Y |
explained variance of Y by U |
com.xu |
communality of X with U |
com.yt |
communality of Y with T |
statistic |
Phi statistic values for assessing the number of relevant components |
Gaston Sanchez
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.
## 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)## 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)
Thris data set contains three physiological and three exercise variables measured on 20 men
data(linnerud)data(linnerud)
A data frame with 20 observations on the following 6 variables.
| Num | Variable | Description |
| 1 | Weight | Weight |
| 2 | Waist | Waist size |
| 3 | Pulse | Pulse |
| 4 | Pulls | Pull-ups |
| 5 | Squats | Squats |
| 6 | Jumps | Jumps |
Tenenhaus M. (1998) Table 1, page 15
Tenenhaus M. (1998) La Regression PLS: theorie et pratique Paris: Editions Technip
data(linnerud) head(linnerud)data(linnerud) head(linnerud)
Principal Components Analysis with NIPALS algorithm
nipals(Data, comps = 2, scaled = TRUE)nipals(Data, comps = 2, scaled = TRUE)
Data |
A numeric matrix or data frame (which may contain missing values). |
comps |
Number of components to be calculated (by default 2) |
scaled |
A logical value indicating whether to scale
the data ( |
The function nipals performs Principal Components
Analysis of a data matrix that may contain missing
values.
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.
values |
The pseudo eigenvalues |
scores |
The extracted scores (i.e. components) |
loadings |
The loadings |
cor.xt |
Correlations between the variables and the scores |
disto |
Squared distance of the observations to the origin |
contrib |
Contributions of the observations (rows) |
cos |
Squared cosinus |
dmod |
Distance to the Model |
Gaston Sanchez
Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Paris: Editions TECHNIP.
Tenenhaus, M. (2007) Statistique. Methodes pour decrire, expliquer et prevoir. Paris: Dunod.
## 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)## 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 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.
## 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", ...)## 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", ...)
x |
An object of class |
what |
What to plot. Options are |
comps |
An integer vector of length two to indicate which components to plot. |
where |
Where to plot the observations. A character
vector of length two to indicate which components to use
when plotting observations. This parameter will take into
account the values in |
cex |
Character expansion for labels and points. |
col.xlabels |
Color for labels of X-block variables. |
col.ylabels |
Color for labels of Y-block variables. |
yfont |
Integer for specifying which font to use for
Y-block labels. See |
pos |
Position for the text (see graphical
paramaters |
offset |
When |
col.xarrows |
Color for the X-block arrows. |
col.yarrows |
Color for the Y-block arrows. |
lwd |
The line width of arrows. |
length |
Length of the edges of the arrow head (in inches). |
angle |
Angle from the shaft of the arrow to the edge of the arrow head. |
col.points |
Color for the points when
|
pch |
Plotting character symbol to use (see
|
pt.bg |
Background (fill) color for the points given
by |
show.names |
Logical indicating whether to show
labels of points. Only used when
|
xpd |
Logical for controlling clipping region of names and labels. |
xlab |
A title for the x axis. |
ylab |
A title for the y axis. |
main |
Main title of the plot. |
col.main |
Color of main title. |
cex.main |
Character expansion of main title. |
col.axis |
Color of axis annotations (tick marks and labels). |
show.grid |
Logical indicating whether to show grid lines. |
col.grid |
Color of grid lines. Only used when
|
... |
Further arguments are passed to labels or points. |
Variables are displayed using the correlations of each
block of variables with the components of the other
block: $cor.xu -vs- $cor.yt.
Gaston Sanchez
## 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)## 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 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.
## 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", ...)## 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", ...)
x |
An object of class |
what |
What to plot. Options are |
comps |
An integer vector of length two to indicate which components to plot |
cex |
Character expansion for labels and points. |
col.labels |
Color for labels of variables. |
pos |
Position for the labels text (see
|
offset |
When |
col.arrows |
Color for the arrows when plotting variables. |
lwd |
The line width of arrows. |
length |
Length of the edges of the arrow head (in inches). |
angle |
Angle from the shaft of the arrow to the edge of the arrow head. |
col.points |
Color for the points when
|
pch |
Plotting character symbol to use (see
|
pt.bg |
Background (fill) color for the points given
by |
show.names |
Logical indicating whether to show
names of points. Only used when
|
xpd |
Logical for controlling clipping region of labels and names. |
xlab |
Title for the x axis. |
ylab |
Title for the y axis. |
main |
Main title of the plot. |
col.main |
Color of main title. |
cex.main |
Character expansion of main title. |
col.axis |
Color of axis annotations (tick marks and labels). |
show.grid |
Logical indicating whether to show grid lines. |
col.grid |
Color of grid lines. Only used when
|
... |
Further arguments are passed to labels or points. |
Variables are displayed using the correlations in
$cor.xt.
Gaston Sanchez
## 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)## 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 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.
## 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", ...)## 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", ...)
x |
An object of class |
what |
What to plot. Options are |
comps |
An integer vector of length two to indicate which components to plot. |
where |
Where to plot the observations. A character
vector of length two to indicate which components to use
when plotting observations. This parameter will take into
account the values in |
cex |
Character expansion for labels and points. |
col.xlabels |
Color for labels of X-block variables. |
col.ylabels |
Color for labels of Y-block variables. |
yfont |
Integer for specifying which font to use for
Y-block labels. See |
pos |
Position for the text (see graphical
paramaters |
offset |
When |
col.xarrows |
Color for the X-block arrows. |
col.yarrows |
Color for the Y-block arrows. |
lwd |
The line width of arrows. |
length |
Length of the edges of the arrow head (in inches). |
angle |
Angle from the shaft of the arrow to the edge of the arrow head. |
col.points |
Color for the points when
|
pch |
Plotting character symbol to use (see
|
pt.bg |
Background (fill) color for the points given
by |
show.names |
Logical indicating whether to show
labels of points. Only used when
|
xpd |
Logical for controlling clipping region of names and labels. |
xlab |
A title for the x axis. |
ylab |
A title for the y axis. |
main |
Main title of the plot. |
col.main |
Color of main title. |
cex.main |
Character expansion of main title. |
col.axis |
Color of axis annotations (tick marks and labels). |
show.grid |
Logical indicating whether to show grid lines. |
col.grid |
Color of grid lines. Only used when
|
... |
Further arguments are passed to labels or points. |
Variables are displayed using the correlations of each
block of variables with its set of components:
$cor.xt and $cor.yu.
Gaston Sanchez
## 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)## 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 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.
## 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", ...)## 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", ...)
x |
An object of class |
what |
What to plot. Options are |
comps |
An integer vector of length two to indicate which components to plot. |
where |
Where to plot the observations. A character
vector of length two to indicate which components to use
when plotting observations. This parameter will take into
account the values in |
cex |
Character expansion for labels and points. |
col.xlabels |
Color for labels of X-block variables. |
col.ylabel |
Color for labels of Y-block variables. |
yfont |
Integer for specifying which font to use for
Y-block labels. See |
pos |
Position for the text (see graphical
paramaters |
offset |
When |
col.xarrows |
Color for the X-block arrows. |
col.yarrows |
Color for the Y-block arrows. |
lwd |
The line width of arrows. |
length |
Length of the edges of the arrow head (in inches). |
angle |
Angle from the shaft of the arrow to the edge of the arrow head. |
col.points |
Color for the points when
|
pch |
Plotting character symbol to use (see
|
pt.bg |
Background (fill) color for the points given
by |
show.names |
Logical indicating whether to show
labels of points. Only used when
|
xpd |
Logical for controlling clipping region of names and labels. |
xlab |
A title for the x axis. |
ylab |
A title for the y axis. |
main |
Main title of the plot. |
col.main |
Color of main title. |
cex.main |
Character expansion of main title. |
col.axis |
Color of axis annotations (tick marks and labels). |
show.grid |
Logical indicating whether to show grid lines. |
col.grid |
Color of grid lines. Only used when
|
... |
Further arguments are passed to labels or points. |
Variables are displayed using the correlations in
$cor.xyt.
Gaston Sanchez
## 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)## 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 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.
## 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", ...)## 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", ...)
x |
An object of class |
what |
What to plot. Options are |
comps |
An integer vector of length two to indicate which components to plot. |
where |
Where to plot the observations. A character
vector of length two to indicate which components to use
when plotting observations. This parameter will take into
account the values in |
cex |
Character expansion for labels and points. |
col.xlabels |
Color for labels of X-block variables. |
col.ylabels |
Color for labels of Y-block variables. |
yfont |
Integer for specifying which font to use for
Y-block labels. See |
pos |
Position for the text (see graphical
paramaters |
offset |
When |
col.xarrows |
Color for the X-block arrows. |
col.yarrows |
Color for the Y-block arrows. |
lwd |
The line width of arrows. |
length |
Length of the edges of the arrow head (in inches). |
angle |
Angle from the shaft of the arrow to the edge of the arrow head. |
col.points |
Color for the points when
|
pch |
Plotting character symbol to use (see
|
pt.bg |
Background (fill) color for the points given
by |
show.names |
Logical indicating whether to show
labels of points. Only used when
|
xpd |
Logical for controlling clipping region of names and labels. |
xlab |
A title for the x axis. |
ylab |
A title for the y axis. |
main |
Main title of the plot. |
col.main |
Color of main title. |
cex.main |
Character expansion of main title. |
col.axis |
Color of axis annotations (tick marks and labels). |
show.grid |
Logical indicating whether to show grid lines. |
col.grid |
Color of grid lines. Only used when
|
... |
Further arguments are passed to labels or points. |
Variables are displayed using the correlations of each
block of variables with its set of components:
$cor.xt and $cor.yt.
Gaston Sanchez
## 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)## 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 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.
## 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", ...)## 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", ...)
x |
An object of class |
what |
What to plot. Options are |
comps |
An integer vector of length two to indicate which components to plot |
cex |
Character expansion for labels and points. |
col.xlabels |
Color for labels of X-block variables. |
col.ylabels |
Color for labels of Y-block variables. |
yfont |
Integer for specifying which font to use for
Y-block labels. See |
pos |
Position for the text (see graphical
paramaters |
offset |
When |
col.xarrows |
Color for the X-block arrows. |
col.yarrows |
Color for the Y-block arrows. |
lwd |
The line width of arrows. |
length |
Length of the edges of the arrow head (in inches). |
angle |
Angle from the shaft of the arrow to the edge of the arrow head. |
col.points |
Color for the points when
|
pch |
Plotting character symbol to use (see
|
pt.bg |
Background (fill) color for the points given
by |
show.names |
Logical indicating whether to show
labels of points. Only used when
|
xpd |
Logical for controlling clipping region of names and labels. |
xlab |
A title for the x axis. |
ylab |
A title for the y axis. |
main |
Main title of the plot. |
col.main |
Color of main title. |
cex.main |
Character expansion of main title. |
col.axis |
Color of axis annotations (tick marks and labels). |
show.grid |
Logical indicating whether to show grid lines. |
col.grid |
Color of grid lines. Only used when
|
... |
Further arguments are passed to labels or points. |
Variables are displayed using the correlations of each
block of variables with the X-components: $cor.xt
and $cor.yt.
Gaston Sanchez
## 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)## 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 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.
## 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", ...)## 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", ...)
x |
An object of class |
what |
What to plot. Options are |
comps |
An integer vector of length two to indicate which components to plot. |
where |
Where to plot the observations. A character
vector of length two to indicate which components to use
when plotting observations. This parameter will take into
account the values in |
cex |
Character expansion for labels and points. |
col.xlabels |
Color for labels of X-block variables. |
col.ylabels |
Color for labels of Y-block variables. |
yfont |
Integer for specifying which font to use for
Y-block labels. See |
pos |
Position for the text (see graphical
paramaters |
offset |
When |
col.xarrows |
Color for the X-block arrows. |
col.yarrows |
Color for the Y-block arrows. |
lwd |
The line width of arrows. |
length |
Length of the edges of the arrow head (in inches). |
angle |
Angle from the shaft of the arrow to the edge of the arrow head. |
col.points |
Color for the points when
|
pch |
Plotting character symbol to use (see
|
pt.bg |
Background (fill) color for the points given
by |
show.names |
Logical indicating whether to show
labels of points. Only used when
|
xpd |
Logical for controlling clipping region of names and labels. |
xlab |
A title for the x axis. |
ylab |
A title for the y axis. |
main |
Main title of the plot. |
col.main |
Color of main title. |
cex.main |
Character expansion of main title. |
col.axis |
Color of axis annotations (tick marks and labels). |
show.grid |
Logical indicating whether to show grid lines. |
col.grid |
Color of grid lines. Only used when
|
... |
Further arguments are passed to labels or points. |
Variables are displayed using the correlations of each
block of variables with its set of components:
$cor.xt and $cor.yu.
Gaston Sanchez
## 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)## 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)
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)
plsca(X, Y, comps = NULL, scaled = TRUE)plsca(X, Y, comps = NULL, scaled = TRUE)
X |
A numeric matrix or data frame (X-block) with more than one variable. No missing data are allowed |
Y |
A numeric matrix or data frame (Y-block) with more than one variable. No missing data are allowed |
comps |
The number of extracted PLS components
( |
scaled |
A logical value indicating whether scaling
data should be performed ( |
An object of class "plsca", basically a list with
the following elements:
x.scores |
scores of the X-block (also known as T components) |
x.wgs |
weights of the X-block |
x.loads |
loadings of the X-block |
y.scores |
scores of the Y-block (also known as U components) |
y.wgs |
weights of the Y-block |
y.loads |
loadings of the Y-block |
cor.xt |
correlations between X and T |
cor.yu |
correlations between Y and U |
cor.tu |
correlations between T and U |
cor.xu |
correlations between X and U |
cor.yt |
correlations between Y and T |
R2X |
explained variance of X by T |
R2Y |
explained variance of Y by U |
com.xu |
communality of X with U |
com.yt |
communality of Y with T |
Gaston Sanchez
Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Editions TECHNIP, Paris.
## 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)## 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)
The function plsreg1 performs Partial Least Squares Regression for the univariate case (i.e. one response variable)
plsreg1(predictors, response, comps = 2, crosval = TRUE)plsreg1(predictors, response, comps = 2, crosval = TRUE)
predictors |
A numeric matrix or data frame with the predictor variables (which may contain missing data). |
response |
A numeric vector for the reponse variable. No missing data allowed. |
comps |
The number of extracted PLS components (2 by default). |
crosval |
Logical indicating whether
cross-validation should be performed ( |
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.
An object of class "plsreg1", basically a list
with the following elements:
x.scores |
PLS components (also known as T-components) |
x.loads |
loadings of the predictor variables |
y.scores |
scores of the response variable (also known as U-components) |
y.loads |
loadings of the response variable |
cor.xyt |
Correlations between the variables and the PLS components |
raw.wgs |
weights to calculate the PLS scores with the deflated matrices of predictor variables |
mod.wgs |
modified weights to calculate the PLS scores with the matrix of predictor variables |
std.coefs |
Vector of standardized regression coefficients |
reg.coefs |
Vector of regression coefficients (used with the original data scale) |
R2 |
Vector of PLS R-squared |
R2Xy |
explained variance of variables by PLS-components |
y.pred |
Vector of predicted values |
resid |
Vector of residuals |
T2 |
Table of Hotelling T2 values (used to detect atypical observations) |
Q2 |
Table with the cross validation results.
Includes: PRESS, RSS, Q2, and cummulated Q2. Only
available when |
Gaston Sanchez
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.
## 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)## 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)
The function plsreg2 performs partial least squares regression for the multivariate case (i.e. more than one response variable)
plsreg2(predictors, responses, comps = 2, crosval = TRUE)plsreg2(predictors, responses, comps = 2, crosval = TRUE)
predictors |
A numeric matrix or data frame containing the predictor variables. |
responses |
A numeric matrix or data frame containing the response variables. |
comps |
The number of extracted PLS components (2 by default) |
crosval |
Logical indicating whether
cross-validation should be performed ( |
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.
An object of class "plsreg2", basically a list
with the following elements:
x.scores |
components of the predictor variables (also known as T-components) |
x.loads |
loadings of the predictor variables |
y.scores |
components of the response variables (also known as U-components) |
y.loads |
loadings of the response variables |
cor.xt |
correlations between X and T |
cor.yt |
correlations between Y and T |
cor.xu |
correlations between X and U |
cor.yu |
correlations between Y and U |
cor.tu |
correlations between T and U |
raw.wgs |
weights to calculate the PLS scores with the deflated matrices of predictor variables |
mod.wgs |
modified weights to calculate the PLS scores with the matrix of predictor variables |
std.coefs |
Vector of standardized regression coefficients (used with scaled data) |
reg.coefs |
Vector of regression coefficients (used with the original data) |
y.pred |
Vector of predicted values |
resid |
Vector of residuals |
expvar |
table with R-squared coefficients |
VIP |
Variable Importance for Projection |
Q2 |
table of Q2 indexes (i.e. leave-one-out cross validation) |
Q2cum |
table of cummulated Q2 indexes |
Gaston Sanchez
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.
## 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)## 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)
This dataset gives the measurements of 101 climbing ropes available in the market by spring 2011. The data was collected from the brands websites.
data(ropes)data(ropes)
A data frame with 101 observations on the following 7 variables.
| Num | Variable | Description |
| 1 | brand | a factor with the brand names |
| 2 | diameter | the diameter of the ropes measured in mm |
| 3 | weight | the weight measured in grams per meter |
| 4 | falls | the number of UIAA falls |
| 5 | imp.force | the impact force value |
| 6 | stat.elong | the static elongation value |
| 7 | dyn.elong | the dynamic elongation value |
Personal collection of data. (Gaston Sanchez)
data(ropes) summary(ropes)data(ropes) summary(ropes)
The function simpls performs the SIMPLS Algorithm
as described in Michel Tenenhaus book La Regression
PLS, chapter 5.
simpls(X, Y, comps = 2)simpls(X, Y, comps = 2)
X |
Numeric matrix or data frame with two or more columns (X-block). |
Y |
Numeric matrix or data frame with two or more columns (Y-block). |
comps |
Number of components to be extracted.
( |
No missing data are allowed.
An object of class "simpls", basically a list with
the following elements:
x.scores |
scores of the X-block (also known as T components) |
x.wgs |
weights of the X-block |
y.wgs |
weights of the Y-block |
cor.xt |
correlations between X and T |
cor.yt |
correlations between Y and T |
R2X |
explained variance of X by T |
R2Y |
explained variance of Y by T |
Gaston Sanchez
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.
## 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)## 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)
The function simplsca performs the SIMPLS
Canonical Analysis algorithm as described in Michel
Tenenhaus book La Regression PLS, chapter 5.
simplsca(X, Y, comps = 2)simplsca(X, Y, comps = 2)
X |
Numeric matrix or data frame with two or more columns (X-block). |
Y |
Numeric matrix or data frame with two or more columns (Y-block). |
comps |
Number of components to be extracted.
( |
No missing data are allowed.
An object of class "simplsca", basically a list
with the following elements:
x.scores |
scores of the X-block (also known as T components) |
x.wgs |
weights of the X-block |
y.scores |
scores of the Y-block (also known as U components) |
y.wgs |
weights of the Y-block |
cor.xt |
correlations between X and T |
cor.yu |
correlations between Y and U |
cor.xu |
correlations between X and U |
cor.yt |
correlations between Y and T |
cor.tu |
correlations between T and U |
R2XT |
explained variance of X by T |
R2YT |
explained variance of Y by T |
R2YU |
explained variance of Y by U |
R2XU |
explained variance of X by U |
Gaston Sanchez
Tenenhaus, M. (1998) La Regression PLS. Theorie et Pratique. Paris: Editions TECHNIP.
## 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)## 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)
These data are the specification of 30 vehicles in terms of various characteristics.
A data frame with 30 observations and 16 variables.
| Num | Variable | Description |
| 1 | diesel | Diesel fuel-type |
| 2 | turbo | Turbo aspiration |
| 3 | two.doors | Vechicles with two doors |
| 4 | hatchback | Hatchback body-style |
| 5 | wheel.base | Wheel base |
| 6 | length | Length |
| 7 | width | Width |
| 8 | height | Height |
| 9 | curb.weight | Curb weight |
| 10 | eng.size | Engine size |
| 11 | horsepower | Horsepower |
| 12 | peak.rpm | Peak revolutions per minute |
| 13 | price | Price in dollars |
| 14 | symbol | Insurance risk rating |
| 15 | city.mpg | Fuel consume in city |
| 16 | highway.mpg | Fuel consume in highway |
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
data(vehicles) vehiclesdata(vehicles) vehicles