Description Usage Arguments Details Author(s) Examples
Two different plots to summarize graphically the results in an object of class predictreality.SAVE
.
1 2 |
x |
An object of class |
option |
One of "biascorr" or "biasfun" (see details) |
... |
Additional graphical parameters to be passed |
If option
="biascorr" this function returns a plot with point predictions and 95% tolerance bounds of reality at the given set of controllable inputs. If option
="biasfun" the plot represents the estimated bias and 95% credible bounds.
Jesus Palomo, Rui Paulo and Gonzalo Garcia-Donato.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | ## Not run:
library(SAVE)
#############
# load data
#############
data(spotweldfield,package='SAVE')
data(spotweldmodel,package='SAVE')
##############
# create the SAVE object which describes the problem and
# compute the corresponding mle estimates
##############
gfsw <- SAVE(response.name="diameter", controllable.names=c("current", "load", "thickness"),
calibration.names="tuning", field.data=spotweldfield,
model.data=spotweldmodel, mean.formula=~1,
bestguess=list(tuning=4.0))
##############
# obtain the posterior distribution of the unknown parameters
##############
gfsw <- bayesfit(object=gfsw, prior=c(uniform("tuning", upper=8, lower=0.8)),
n.iter=20000, n.burnin=100, n.thin=2)
#########
# bias-corrected prediction at a set of inputs
# using predictreality
##########
load <- c(4.0,5.3)
curr <- seq(from=20,to=30,length=20)
g <- c(1,2)
xnew<- expand.grid(current = curr, load = load, thickness=g)
# Obtain samples
prsw <- predictreality(object=gfsw, newdesign=xnew, tol=1.E-12)
#Plot the results:
#Represent reality and tolerance bounds:
plot(prsw, option="biascorr")
#Represent bias and tolerance bounds:
plot(prsw, option="biasfun")
## End(Not run)
|
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