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...52216404,0.42018794,0.92168073,0.76893929,0.83362668,0.38251162,0.70803701,0.49165923,0.94462558) real<-c(0,1,0,0,0,0,1,1,1,1,1,0,1,0,1)datos_OPTIMO<-cbind(prediccion,real) cutpoint1 <- optimal.cutpoints(X = "prediccion", status = "real",tag.healthy = 1, methods = "Youden", data = datos_OPTIMO,categorical.cov =NULL, pop.prev = NULL,control = control.cutpoints(), ci.fit = TRUE) Me sale el siguiente error: Error: Not all needed variables are supplied in 'data'. ¿Alguien me podría decir qué estoy haciendo mal? gracias, Fernando [[alternative HTML version...

Hello All,  I have used logistic regression glm in R and I am evaluating two models both learned with glm but with different predictors. model1 <- glm (Y ~ x4+ x5+ x6+ x7, data = dat, family = binomial(link=logit))model2 <- glm (Y~ x1 + x2 +x3 , data = dat, family = binomial(link=logit))  and I would like to compare these two models based on the prediction that I get from each model: pred1 =

Dear Colleagues, John Mandel ( Chain block designs with two-way elimination of heterogeneity. Biometrics 10, 251-272 ,1954). extended the class of chain block designs (Youden & Conner (1953) to elimination of both row and column (blocks) effects. These experimental designs can be useful in engineering and other fields. I am having difficulty obtaining his adjusted treatment means in his example, shown below, by the use of lm(), or glm(), or ols(). However, I can o...

Hi, My questions concerns the ROCR package and I hope somebody here on the list can help - or point me to some better place. When evaluating a model's performane, like this: pred1 <- predict(model, ..., type="response") pred2 <- prediction(pred1, binary_classifier_vector) perf <- performance(pred, "sens", "spec") (Where "prediction" and