| Daim {Daim} | R Documentation |
Estimation of prediction error based on cross-validation (CV) or various bootstrap techniques.
Daim(formula, model=NULL, data=NULL, control = Daim.control(),
thres = seq(0,1,by=0.01), cutoff = 0.5,
labpos = "1", returnSample = FALSE,
cluster = NULL, seed.cluster = NULL, ...)
formula |
formula of the form y ~ x1 + x2 + ...,
where y must be a factor and x1,x2,... are numeric or factor. |
model |
function. Modelling technique whose error rate is to be estimated.
The function model returns the predicted probability for each observation. |
data |
an optional data frame containing the variables in the model (training data). |
control |
See Daim.control. |
thres |
a numeric vector with the cutoff values. |
cutoff |
the cutoff value for error estimation. This can be a numeric value or a character string.
If the cutoff set to:".632" - the estimated cut-point corresponding the .632 estimation of the sensitivity and the specificity.".632+" - the estimated cut-point corresponding the .632+ estimation of the sensitivity and the specificity. |
labpos |
a character string of the response variable that defines a "positive" event. The labels of the "positive" events will be set to "pos" and other to "neg". |
returnSample |
a logical value for saving the data from each sample. |
cluster |
the name of the cluster, if parallel computing will be used. |
seed.cluster |
an integer value used as seed for the RNG. |
... |
additional parameters. |
a list with the following components :
call |
the matched call. |
formula |
the formula supplied. |
method |
the list of control parameters. |
err632p |
the .632+ estimation of the misclassification error. |
err632 |
the .632 estimation of the misclassification error. |
errloob |
the LOOB estimation of the misclassification error. |
errapp |
the apparent error. |
sens632p |
the .632+ estimation of the sensitivity. |
spec632p |
the .632+ estimation of the specificity. |
sens632 |
the .632 estimation of the sensitivity. |
spec632 |
the .632 estimation of the specificity. |
sensloob |
the LOOB estimation of the sensitivity. |
specloob |
the LOOB estimation of the specificity. |
sensapp |
the apparent sensitivity. |
specapp |
the apparent specificity. |
roc |
a data frame with estimated values of sensitivity and specificity for a variety of cutoffs. |
sample.roc |
a list in which each entry contains the values of the ROC curve of this special sample or cross-validation run. |
sample.data |
a data frame with the results of this particular sample or cross-validation run. |
Werner Adler and Berthold Lausen (2009).
Bootstrap Estimated True and False Positive Rates and ROC Curve.
Computational Statistics & Data Analysis, 53, (3), 718–729.
Tom Fawcett (2006).
An introduction to ROC analysis.
Pattern Recognition Letters, 27, (8).
Bradley Efron and Robert Tibshirani (1997).
Improvements on cross-validation: The.632+ bootstrap method.
Journal of the American Statistical Association, 92, (438), 548–560.
plot.Daim, performDaim, auc.Daim, roc.area.Daim
library(ipred)
data(GlaucomaM)
head(GlaucomaM)
mylda <- function(formula,train,test){
model <- lda(formula,train)
predict(model,test)$posterior[,"pos"]
}
ACC <- Daim(Class~.,model=mylda,data=GlaucomaM,labpos="glaucoma")
ACC
summary(ACC)
####
#### for parallel computing with snow cluster
####
# library(snow)
###
### create cluster with two slave nodes
# cl <- makeCluster(2)
###
### Load used library on all slaves and execute the Daim in parallel
###
# clusterEvalQ(cl, library(ipred))
# ACC <- Daim(Class~.,model=mylda,data=GlaucomaM,labpos="glaucoma",cluster=cl)
# ACC
####
#### for parallel computing with multicore package
#### you need only to load this library
####
# library(multicore)
# ACC <- Daim(Class~.,model=mylda,data=GlaucomaM,labpos="glaucoma")
# ACC
library(randomForest)
myRF <- function(formula,train,test){
model <- randomForest(formula,train)
predict(model,test,type="prob")[,"pos"]
}
ACC2 <- Daim(Class~.,model=myRF,data=GlaucomaM,labpos="glaucoma",
control=Daim.control(number=25))
ACC2
summary(ACC2)
####
#### for parallel computing with snow cluster
####
# library(snow)
###
### create cluster with two slave nodes
# cl <- makeCluster(2)
###
### Load used library on all slaves and execute the Daim in parallel
###
# clusterEvalQ(cl, library(randomForest))
# ACC2 <- Daim(Class~.,model=myRF,data=GlaucomaM,labpos="glaucoma",cluster=cl)
# ACC2
####
#### for parallel computing with multicore package
####
# library(multicore)
# ACC2 <- Daim(Class~.,model=myRF,data=GlaucomaM,labpos="glaucoma")
# ACC2