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