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...lt;- -0.01335756 c <- -2.368057 d <- -0.00600052 return(exp(a+b*xtime)+exp(c+d*xtime)) } > numericDeriv(fitterma,"xtime") *Error in numericDeriv(fitterma, "xtime") : * * cannot coerce type 'closure' to vector of type 'double'* * * *Thanks,* *parmee* [[alternative HTML version deleted]]

...w (or for a censored data) using survfit.coxph, can anyone point me to a link or a pdf as to how the probabilities appearing in bold under "summary(pred$surv)" are calculated? Do these represent acumulative probability distribution in time (not including censored time)? Thanks very much, parmee *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)* *pred <- survfit(fit, newdata=data.frame(age=60))* *summary(pred)* time n.risk n.event survival std.err lower 95% CI upper 95% CI 59 26 1 *0.978* 0.0240 0.932 1.000 115 25 1 *0.952* 0....

Hello All, Does anyone know why length(fit1$time) < length(fit2$n) in survfit.coxph output? Why is the predicted time length is not the same as the number of samples (n)? I tried: example(survfit.coxph). Thanks, parmee > fit2$n [1] 241 > fit2$time [1] 0 31 32 60 61 152 153 174 273 277 362 365 499 517 518 547 [17] 566 638 700 760 791 792 809 822 845 944 1005 1077 1116 1125 1218 1369 [33] 1392 1400 1431 1492 1625 1642 1673 1674 1706...

...I have defined the following function (fitterma as a sum of exponentials) that best fits my cumulative distribution. I am also attaching the "xtime" values that I have. I want to try two things as indicated below and am experiencing problems. Any help will be greatly appreciated. Best, Parmee ----------------------- *fitterma <- function(xtime) { * *a <- -0.09144115* *b <- -0.01335756* *c <- -2.368057* *d <- -0.00600052* *return(exp(a+b*xtime)+exp(c+d*xtime))* *}* I want to do two things: *First, take the numerical derivative of this function (fitterma)* to o...

Hey! Can anyone help me coding in R a normalized kernel matrix. Basically, I want K(x,y)/sqrt(*K*(*x, x*)*K*(*y, y*)) Anyone has a piece of code that you could share? Many thanks, Parmee [[alternative HTML version deleted]]

...vary when you predict odd or even number of samples. Why? Here I provide e.g. with Iris data in R, keep reducing prediction cases one-by-one, you will see the discrepancy I am talking about. In my own data, this discrepancy between odd and even number of cases is enhanced by a huge factor. Thanks, Parmee iris <- unique(iris) rbf <- rbfdot(0.5) lssvm> k <- kernelMatrix(rbf, as.matrix(iris[,-5])) lssvm> klir <- lssvm(k, iris[, 5]) lssvm> pre <- predict(klir, k) > ktest <- as.kernelMatrix(k[1:148,]) > pretest <- predict(klir, ktest) > table(pretest,iris...

Hey! How do I get the confusion matrix after performing 10-fold cross validation from SVM in R? When I try to print it, I get the confusion matrix without cross validation. I need to compute PPV. Should I report PPV without CV and total accuracy with CV? I am confused. > svmtrain <- svm(xtrain,ytrain,kernel="sigmoid",cross=10) > pred <- predict(svmtrain, xtrain) >