Displaying 4 results from an estimated 4 matches for "dxrule".
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darle
2005 Dec 15
3
Name conflict between Epi and ROC packages
...he name conflicts in Epi and ROC packages (2 'ROC' functions are the
problem) cause the following code
to work once, but not twice:
library(MASS); data(cats);
x = cats[,2]
y = ifelse(cats[,1]=='F',0,1)
library(Epi); ROC(x,y,grid=0)$AUC
library(ROC); AUC(rocdemo.sca(y, x, dxrule.sca))
What is the standard way of resolving name conflicts? Ask maintainers to
resolve it or rename the younger function, use namespaces somehow, or
something else?
My machine is WinXP, R-2.2.0, ROC and Epi packages are the latest versions.
Jarek Tuszynski
[[alternative HTML version delete...
2005 Sep 28
1
Fast AUC computation
...y large data set (20,000
observations) for which I want to calculate the area under the Receiver
Operator Curve (AUC) for many parameter combinations. I am using the ROC
library and the following commands to generate each AUC:
rocobj=rocdemo.sca(truth = ymis, data = model$fitted.values, rule =
dxrule.sca) #generation of observed ROC object
aucobj=AUC(rocobj) #pulling out just the observed AUC - trapezoidal not
integrated
but they are pretty slow.
Does anyone know of a faster way to get the AUC?
Thanks,
Nina
[[alternative HTML version deleted]]
2003 May 28
0
plot() error-msg: "need finite xlim values"
...finite xlim values"
---
> set.seed(123)
> state <- c(0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, + 1, 1,
1)
> markers <- c(1, 2, 1, 1, 2, 3, 3, 4, 2, 1, 1, 3, 2, 3, 2, 4, + 5, 2,
3, 4) + runif(20, -1, 1)
> roc1 <- rocdemo.sca(truth = state, data = markers, rule = dxrule.sca,
seqlen = 5)
> plot(roc1)
Error in plot.window(xlim, ylim, log, asp, ...) :
need finite xlim values
In addition: Warning messages:
1: no finite arguments to min; returning Inf
2: no finite arguments to max; returning -Inf
3: no finite arguments to min; returning Inf
4: no finite argu...
2005 Sep 22
2
Survey of ROC AUC / wilcoxon test functions
...colAUC(x, y, alg="ROC")
auc[2,i] = colAUC(x, y, alg="Wilcox")
auc[3,i] = wilcox.test(x1, x2, exact=0)$statistic / (n1*n2)
auc[4,i] = (sum(r[1:n1]) - n1*(n1+1)/2) / (n1*n2)
auc[5,i] = roc.area(y, x)$A.tilda
auc[6,i] = AUC(rocdemo.sca(y, x, dxrule.sca))
auc[7,i] = performance(prediction( x, y),"auc")@y.values[[1]]
auc[8,i] = ROC(x,y,grid=0)$AUC # get AUC by 'ROC'
auc[9,i] = auROC(y, x) # get AUC by 'auROC'
pval[1,i] = wilcox.test(x1, x2, exact=0)$p.value
pval[2,i] = wilcox.test(x1, x2, exact=1)$...