Description Usage Arguments Value References See Also Examples
Estimation of misclassification rate, sensitivity, specificity and AUC based on cross-validation (CV) or various bootstrap techniques.
1 2 3 |
formula |
formula of the form |
model |
function. Modelling technique whose error rate is to be estimated.
The function |
data |
an optional data frame containing the variables in the model (training data). |
control |
See |
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: |
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 others to "neg". |
returnSample |
a logical value for saving the data from each sample. |
cluster |
the name of the cluster, if parallel computing is used. |
seed.cluster |
an integer value used as seed for the RNG. |
multicore |
a logical indicating whether multiple cores (if available) should be used for the computations. |
... |
additional parameters passed to |
An object of class Daim-class
.
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
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 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 | #############################
## Evaluation of ##
## LDA ##
#############################
library(TH.data)
library(MASS)
data(GlaucomaM)
head(GlaucomaM)
mylda <- function(formula, train, test){
model <- lda(formula, train)
predict(model, test)$posterior[,"pos"]
}
set.seed(1102013)
ACC <- Daim(Class~., model=mylda, data=GlaucomaM, labpos="glaucoma",
control=Daim.control(method="boot", number=50))
ACC
summary(ACC)
## Not run:
## just because of checking time on CRAN
####
#### optimal cut point determination
####
set.seed(1102013)
ACC <- Daim(Class~., model=mylda, data=GlaucomaM, labpos="glaucoma",
control=Daim.control(method="boot", number=50), cutoff="0.632+")
ACC
summary(ACC)
####
#### for parallel execution on multicore CPUs and computer clusters
####
library(parallel)
###
### create cluster with two slave nodes
cl <- makeCluster(2)
###
### Load used package on all slaves and execute Daim in parallel
###
clusterEvalQ(cl, library(ipred))
ACC <- Daim(Class~., model=mylda, data=GlaucomaM, labpos="glaucoma", cluster=cl)
ACC
####
#### for parallel computing on multicore CPUs
####
ACC <- Daim(Class~., model=mylda, data=GlaucomaM, labpos="glaucoma", multicore=TRUE)
ACC
#############################
## Evaluation of ##
## randomForrest ##
#############################
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=50))
ACC2
summary(ACC2)
####
#### optimal cut point determination
####
set.seed(1102013)
ACC2 <- Daim(Class~., model=myRF, data=GlaucomaM, labpos="glaucoma",
control=Daim.control(method="boot", number=50), cutoff="0.632+")
summary(ACC2)
####
#### for parallel execution on multicore CPUs and computer clusters
####
library(parallel)
###
### create cluster with two slave nodes
cl <- makeCluster(2)
###
### Load used package on all slaves and execute Daim in parallel
###
clusterEvalQ(cl, library(randomForest))
ACC2 <- Daim(Class~., model=myRF, data=GlaucomaM, labpos="glaucoma", cluster=cl)
ACC2
####
#### for parallel computing on multicore CPUs
####
ACC2 <- Daim(Class~., model=myRF, data=GlaucomaM, labpos="glaucoma", multicore=TRUE)
ACC2
## End(Not run)
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