Description Usage Arguments Details Value Author(s) References See Also Examples
Plot of the factor effects with significance levels based on robust estimation of contrast standard errors.
1 2 3 |
obj |
object of class |
alpha |
numeric. Significance level used for the margin of error (ME) and simultaneous margin of error (SME). See Lenth(1989). |
plt |
logical. If |
limits |
logical. If |
xlab |
character string. Used to label the x-axis. "factors" as default. |
ylab |
character string. Used to label the y-axis. "effects" as default. |
faclab |
list with components |
cex.fac |
numeric. Character size used for the factor labels. |
cex.axis |
numeric. Character size used for the axis. |
adj |
numeric between 0 and 1. Determines where to place the
"ME" (margin of error) and the "SME" (simultaneous margin of error) labels
(character size of 0.9* |
... |
extra parameters passed to |
If obj
is of class lm
, 2*coef(obj)
is used as factor
effect with the intercept term removed. Otherwise, obj
should be a
vector with the factor effects. Robust estimate of the contrasts standard
error is used to calculate marginal (ME) and simultaneous margin
of error (SME) for the provided significance (1 - alpha
) level.
See Lenth(1989). Spikes are used to display the factor effects.
If faclab
is NULL
, factors are labelled with the effects or
coefficient names. Otherwise, those faclab\$idx
factors are labelled
as faclab\$lab
. The rest of the factors are blanked.
The function is called mainly for its side effect. It returns a vector with the value of alpha used, the estimated PSE, ME and SME.
Ernesto Barrios. Extension provided by Kjetil Kjernsmo (2013).
Lenth, R. V. (1989). "Quick and Easy Analysis of Unreplicated Factorials". Technometrics Vol. 31, No. 4. pp. 469–473.
DanielPlot
, BsProb
and plot.BsProb
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ### Tensile Strength Experiment. Taguchi and Wu. 1980
library(BsMD)
# Data
data(BM86.data,package="BsMD") # Design matrix and responses
print(BM86.data) # from Box and Meyer (1986)
# Model Fitting. Box and Meyer (1986) example 2.
tensileStrength.lm <- lm(y2 ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + X8 + X9 +
X10 + X11 + X12 + X13 + X14 + X15, data = BM86.data)
print(coef(tensileStrength.lm)) # Model coefficients
par(mfrow=c(1,2),pty="s")
DanielPlot(tensileStrength.lm, main = "Daniel Plot")
LenthPlot(tensileStrength.lm, main = "Lenth's Plot")
|
X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 X14 X15 y1 y2 y3 y4
1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 1 -1 -1 1 0.23 43.7 14.0 0.08
2 1 -1 -1 -1 -1 1 1 -1 -1 1 1 1 1 -1 -1 0.30 40.2 16.8 0.04
3 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1 -1 1 -1 0.52 42.4 15.0 0.53
4 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 1 1 1 1 0.54 44.7 15.4 0.43
5 -1 -1 1 1 -1 -1 1 -1 1 1 -1 -1 1 1 -1 0.70 42.4 27.6 0.31
6 1 -1 -1 1 1 -1 -1 -1 -1 1 1 -1 -1 1 1 0.76 45.9 24.0 0.09
7 -1 1 -1 1 -1 1 -1 -1 1 -1 1 -1 1 -1 1 1.00 42.2 27.4 0.12
8 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 0.96 40.6 22.6 0.36
9 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 1 -1 0.32 42.4 22.3 0.79
10 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 1 1 0.39 45.5 17.1 0.68
11 -1 1 -1 -1 1 -1 1 1 -1 1 -1 -1 1 -1 1 0.61 43.6 21.5 0.73
12 1 1 1 -1 -1 -1 -1 1 1 1 1 -1 -1 -1 -1 0.66 40.6 17.5 0.08
13 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 0.89 44.0 15.9 0.77
14 1 -1 -1 1 1 -1 -1 1 1 -1 -1 1 1 -1 -1 0.97 40.2 21.9 0.38
15 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1 -1 1.07 42.5 16.7 0.49
16 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1.21 46.5 20.3 0.23
(Intercept) X1 X2 X3 X4 X5
42.9625 0.0625 -0.0750 0.1500 0.0750 0.2000
X6 X7 X8 X9 X10 X11
-0.0125 0.1875 0.2000 -0.0250 0.2125 0.0625
X12 X13 X14 X15
0.0625 -0.1875 1.0750 1.5500
alpha PSE ME SME
0.0500000 0.2250000 0.5783809 1.1741965
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