Description Usage Arguments Value Note Author(s) See Also Examples
Extract all necessary information for plotting from resamplingbased prediction error results and store it in a data frame.
1 2 3 4 5 6 7 8 9 10 11 12  ## S3 method for class 'perry'
fortify(model, data, select = NULL,
reps = model$splits$R > 1, seFactor = NA, ...)
## S3 method for class 'perrySelect'
fortify(model, data,
subset = NULL, select = NULL,
reps = model$splits$R > 1, seFactor = model$seFactor,
...)
## S3 method for class 'perryTuning'
fortify(model, data, ...)

model 
an object inheriting from class

data 
currently ignored. 
subset 
a character, integer or logical vector indicating the subset of models to be converted. 
select 
a character, integer or logical vector indicating the columns of prediction error results to be converted. 
reps 
a logical indicating whether to convert the
results from all replications ( 
seFactor 
a numeric value giving the multiplication
factor of the standard error for displaying error bars in
dot plots or line plots. Error bars in those plots can
be suppressed by setting this to 
... 
for the 
A data frame containing the columns listed below, as well
as additional information stored in the attribute
"facets"
(default faceting formula for the plots).
Fit 
a vector or factor containing the identifiers of the models. 
Name 
a factor containing the names of the predictor error results (not returned in case of only one column of prediction error results with the default name). 
PE 
the estimated prediction errors. 
Lower 
the lower end points of the error bars (only
returned if 
Upper 
the upper end points of the error bars (only
returned if 
Duplicate indices in subset
or select
are
removed such that all models and prediction error results
are unique.
Andreas Alfons
fortify
, perryPlot
,
perryFit
, perrySelect
,
perryTuning
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  library("perryExamples")
data("coleman")
set.seed(1234) # set seed for reproducibility
## set up folds for crossvalidation
folds < cvFolds(nrow(coleman), K = 5, R = 10)
## compare LS, MM and LTS regression
# perform crossvalidation for an LS regression model
fitLm < lm(Y ~ ., data = coleman)
cvLm < perry(fitLm, splits = folds,
cost = rtmspe, trim = 0.1)
# perform crossvalidation for an MM regression model
fitLmrob < lmrob(Y ~ ., data = coleman, k.max = 500)
cvLmrob < perry(fitLmrob, splits = folds,
cost = rtmspe, trim = 0.1)
# perform crossvalidation for an LTS regression model
fitLts < ltsReg(Y ~ ., data = coleman)
cvLts < perry(fitLts, splits = folds,
cost = rtmspe, trim = 0.1)
# combine results into one object
cv < perrySelect(LS = cvLm, MM = cvLmrob, LTS = cvLts)
cv
## convert MM regression results to data frame for plotting
# all replications for box plot
cvLmrobBox < fortify(cvLmrob, reps = TRUE)
perryPlot(cvLmrobBox)
# aggregated results for dot plot
cvLmrobDot < fortify(cvLmrob, reps = FALSE, seFactor = 1)
perryPlot(cvLmrobDot)
## convert combined results to data frame for plotting
# all replications for box plot
cvBox < fortify(cv, reps = TRUE)
perryPlot(cvBox)
# aggregated results for dot plot
cvDot < fortify(cv, reps = FALSE, seFactor = 1)
perryPlot(cvDot)

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