bv.boxplot: Bivariate boxplots
In asbio: A Collection of Statistical Tools for Biologists
bv.boxplot R Documentation
Bivariate boxplots
Description
Creates diagnostic bivariate quelplot ellipses (bivariate boxplots) using the method of Goldberg and Iglewicz (1992).
The output can be used to check assumptions of bivariate normality and to identify multivariate outliers. The default robust=TRUE
option relies on on a biweight correlation estimator function written by Everitt (2006). Quelplots,
are potentially asymmetric, although the method currently employed here uses a
single "fence" definition and creates symmetric ellipses.
Usage
bv.boxplot(X, Y, robust = TRUE, D = 7, xlab = "X", ylab="Y", pch = 21,
pch.out = NULL, bg = "gray", bg.out = NULL, hinge.col = 1, fence.col = 1,
hinge.lty = 2, fence.lty = 3, xlim = NULL, ylim = NULL, names = 1:length(X),
ID.out = FALSE, cex.ID.out = 0.7, uni.CI = FALSE, uni.conf = 0.95,
uni.CI.col = 1, uni.CI.lty = 1, uni.CI.lwd = 2, show.points = TRUE, ...)
Arguments
X
First of two quantitative variables making up the bivariate distribution.
Y
Second of two quantitative variables making up the bivariate distribution.
robust
Logical. Robust estimators, i.e. robust = TRUE are recommended.
D
The default D = 7 lets the fence be equal to a 99 percent confidence interval for an individual observation.
xlab
Caption for X axis.
ylab
Caption for Y axis.
pch
Plotting character(s) for scatterplot.
pch.out
Plotting character for outliers.
hinge.col
Hinge color.
fence.col
Fence color.
hinge.lty
Hinge line type.
fence.lty
Fence line type.
xlim
A two element vector defining the X-limits of the plot.
ylim
The Y-limits of the plot.
bg
Background color for points in scatterplot, defaults to black if pch is not in the range 21:26.
bg.out
Background color for outlying points in scatterplot, defaults to black if pch is not in the range 21:26.
names
An optional vector of names for X, Y coordinates.
ID.out
Logical. Whether or not outlying points should be given labels (from argument name in plot.
cex.ID.out
Character expansion for outlying ID labels.
uni.CI
Logical. If true, univariate confidence intervals for the true median at confidence uni.CI are shown.
uni.conf
Univariate confidence, only used if CI.uni = TRUE.
uni.CI.col
Univariate confidence bound line color, only used if CI.uni = TRUE.
uni.CI.lty
Univariate confidence bound line type, only used if CI.uni = TRUE.
uni.CI.lwd
Univariate confidence bound line width, only used if CI.uni = TRUE.
show.points
Logical. Whether points should be shown in graph.
...
Additional arguments from points.
Details
Two ellipses are drawn. The inner is the "hinge" which contains 50 percent of the data. The outer is the "fence".
Observations outside of the "fence" constitute possible troublesome outliers.
The function bivariate from Everitt (2004) is used to calculate robust biweight measures of correlation, scale, and location if robust = TRUE (the default).
We have the following form to the quelplot model:
E_i =
\sqrt{\frac{X^2_{si} + Y^2_{si} - 2R^*X_{si}Y_{si}}{1-R^{*2}}}.
where X_{si} = (X_i - T^*_X)/S^*_X, and Y_{si} = (Y_i - T^*_X)/S^*_Y are standardized values for X_i and Y_i, respectively,
T^*_X and T^*_Y are location estimators for X and Y, S^*_X and S^*_Y are scale estimators for
X and Y, and R^* is a correlation estimator for X and Y. We have:
E_m = median\{E_i:i=1,2,...,n\},
and
E_{max} = max\{E_i: E_i^2 < DE^2_m\}.
where D is a constant that regulates the distance of the "fence" and "hinge".
To draw the "hinge" we have:
R_1 = E_m\sqrt{\frac{1 + R^*}{2}},
R_2 = E_m\sqrt{\frac{1 - R^*}{2}}.
To draw the "fence" we have:
R_1 = E_{max}\sqrt{\frac{1 + R^*}{2}},
R_2 = E_{max}\sqrt{\frac{1 - R^*}{2}}.
For \theta = 0 to 360, let:
\Theta_1 = R_1cos(\theta),
\Theta_2 = R_2sin(\theta).
The Cartesian coordinates of the "hinge" and "fence" are:
X=T^*_X=(\Theta_1+\Theta_2)S^*_X,
Y=T^*_Y=(\Theta_1-\Theta_2)S^*_Y.
Quelplots, are potentially asymmetric, although the current (and only) method used here defines a single value for E_{max}
and hence creates symmetric ellipses. Under this implementation at least one point will define E_{max},
and lie on the "fence".
Value
A diagnostic plot is returned. Invisible objects from the function include location, scale and correlation estimates for X and Y,
estimates for E_m and E_{max}, and a list of outliers (that exceed E_{max}).
Author(s)
Ken Aho, the function relies on an Everitt (2006) function for robust M-estimation.
References
Everitt, B. (2006) An R and S-plus Companion to Multivariate Analysis. Springer.
Goldberg, K. M., and B. Ingelwicz (1992) Bivariate extensions of the boxplot.
Technometrics 34: 307-320.
See Also
boxplot
Examples
Y1<-rnorm(100, 17, 3)
Y2<-rnorm(100, 13, 2)
bv.boxplot(Y1, Y2)
X <- c(-0.24, 2.53, -0.3, -0.26, 0.021, 0.81, -0.85, -0.95, 1.0, 0.89, 0.59,
0.61, -1.79, 0.60, -0.05, 0.39, -0.94, -0.89, -0.37, 0.18)
Y <- c(-0.83, -1.44, 0.33, -0.41, -1.0, 0.53, -0.72, 0.33, 0.27, -0.99, 0.15,
-1.17, -0.61, 0.37, -0.96, 0.21, -1.29, 1.40, -0.21, 0.39)
b <- bv.boxplot(X, Y, ID.out = TRUE, bg.out = "red")
b
asbio documentation built on May 29, 2024, 5:57 a.m.
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| bv.boxplot | R Documentation |
Bivariate boxplots
Description
Creates diagnostic bivariate quelplot ellipses (bivariate boxplots) using the method of Goldberg and Iglewicz (1992).
The output can be used to check assumptions of bivariate normality and to identify multivariate outliers. The default robust=TRUE
option relies on on a biweight correlation estimator function written by Everitt (2006). Quelplots,
are potentially asymmetric, although the method currently employed here uses a
single "fence" definition and creates symmetric ellipses.
Usage
bv.boxplot(X, Y, robust = TRUE, D = 7, xlab = "X", ylab="Y", pch = 21,
pch.out = NULL, bg = "gray", bg.out = NULL, hinge.col = 1, fence.col = 1,
hinge.lty = 2, fence.lty = 3, xlim = NULL, ylim = NULL, names = 1:length(X),
ID.out = FALSE, cex.ID.out = 0.7, uni.CI = FALSE, uni.conf = 0.95,
uni.CI.col = 1, uni.CI.lty = 1, uni.CI.lwd = 2, show.points = TRUE, ...)
Arguments
X |
First of two quantitative variables making up the bivariate distribution. |
Y |
Second of two quantitative variables making up the bivariate distribution. |
robust |
Logical. Robust estimators, i.e. |
D |
The default |
xlab |
Caption for X axis. |
ylab |
Caption for Y axis. |
pch |
Plotting character(s) for scatterplot. |
pch.out |
Plotting character for outliers. |
hinge.col |
Hinge color. |
fence.col |
Fence color. |
hinge.lty |
Hinge line type. |
fence.lty |
Fence line type. |
xlim |
A two element vector defining the X-limits of the plot. |
ylim |
The Y-limits of the plot. |
bg |
Background color for points in scatterplot, defaults to black if |
bg.out |
Background color for outlying points in scatterplot, defaults to black if |
names |
An optional vector of names for X, Y coordinates. |
ID.out |
Logical. Whether or not outlying points should be given labels (from argument |
cex.ID.out |
Character expansion for outlying ID labels. |
uni.CI |
Logical. If true, univariate confidence intervals for the true median at confidence |
uni.conf |
Univariate confidence, only used if |
uni.CI.col |
Univariate confidence bound line color, only used if |
uni.CI.lty |
Univariate confidence bound line type, only used if |
uni.CI.lwd |
Univariate confidence bound line width, only used if |
show.points |
Logical. Whether points should be shown in graph. |
... |
Additional arguments from |
Details
Two ellipses are drawn. The inner is the "hinge" which contains 50 percent of the data. The outer is the "fence".
Observations outside of the "fence" constitute possible troublesome outliers.
The function bivariate from Everitt (2004) is used to calculate robust biweight measures of correlation, scale, and location if robust = TRUE (the default).
We have the following form to the quelplot model:
E_i =
\sqrt{\frac{X^2_{si} + Y^2_{si} - 2R^*X_{si}Y_{si}}{1-R^{*2}}}.
where X_{si} = (X_i - T^*_X)/S^*_X, and Y_{si} = (Y_i - T^*_X)/S^*_Y are standardized values for X_i and Y_i, respectively,
T^*_X and T^*_Y are location estimators for X and Y, S^*_X and S^*_Y are scale estimators for
X and Y, and R^* is a correlation estimator for X and Y. We have:
E_m = median\{E_i:i=1,2,...,n\},
and
E_{max} = max\{E_i: E_i^2 < DE^2_m\}.
where D is a constant that regulates the distance of the "fence" and "hinge".
To draw the "hinge" we have:
R_1 = E_m\sqrt{\frac{1 + R^*}{2}},
R_2 = E_m\sqrt{\frac{1 - R^*}{2}}.
To draw the "fence" we have:
R_1 = E_{max}\sqrt{\frac{1 + R^*}{2}},
R_2 = E_{max}\sqrt{\frac{1 - R^*}{2}}.
For \theta = 0 to 360, let:
\Theta_1 = R_1cos(\theta),
\Theta_2 = R_2sin(\theta).
The Cartesian coordinates of the "hinge" and "fence" are:
X=T^*_X=(\Theta_1+\Theta_2)S^*_X,
Y=T^*_Y=(\Theta_1-\Theta_2)S^*_Y.
Quelplots, are potentially asymmetric, although the current (and only) method used here defines a single value for E_{max}
and hence creates symmetric ellipses. Under this implementation at least one point will define E_{max},
and lie on the "fence".
Value
A diagnostic plot is returned. Invisible objects from the function include location, scale and correlation estimates for X and Y,
estimates for E_m and E_{max}, and a list of outliers (that exceed E_{max}).
Author(s)
Ken Aho, the function relies on an Everitt (2006) function for robust M-estimation.
References
Everitt, B. (2006) An R and S-plus Companion to Multivariate Analysis. Springer.
Goldberg, K. M., and B. Ingelwicz (1992) Bivariate extensions of the boxplot. Technometrics 34: 307-320.
See Also
boxplot
Examples
Y1<-rnorm(100, 17, 3)
Y2<-rnorm(100, 13, 2)
bv.boxplot(Y1, Y2)
X <- c(-0.24, 2.53, -0.3, -0.26, 0.021, 0.81, -0.85, -0.95, 1.0, 0.89, 0.59,
0.61, -1.79, 0.60, -0.05, 0.39, -0.94, -0.89, -0.37, 0.18)
Y <- c(-0.83, -1.44, 0.33, -0.41, -1.0, 0.53, -0.72, 0.33, 0.27, -0.99, 0.15,
-1.17, -0.61, 0.37, -0.96, 0.21, -1.29, 1.40, -0.21, 0.39)
b <- bv.boxplot(X, Y, ID.out = TRUE, bg.out = "red")
b
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