predict

TL;DR : Is there something I can flag in the original randomForest call to avoid having to re-run the predict function to get predicted categorical probabilities, instead of just the likely category? Details: I am using the randomForest package. I have a model something like: model <- randomForest(x=out.data[train.rows, feature.cols], y=out.data[train.rows, response.col], xtest=out.data[test.rows, feature.cols], ytest=out.data[test.rows, response.col], importance= TRUE) where out.data is a data frame, with feature.cols a mixture of numeric and categorical features, while response.col is a TRUE

I did a multiple linear regression in R using the function lm and I want to use it to predict several values. So I'm trying to use the function predict() . Here is my code: new=data.frame(t=c(10, 20, 30)) v=1/t LinReg<-lm(p ~ log(t) + v) Pred=predict(LinReg, new, interval="confidence") So I would like to predict the values of p when t=c(10,20,30...) . However, this is not working and I don't see why. The error message I get is: "Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : variable lengths differ (found for 'vart') In addition: Warning message:

Good afternoon, I could post reproducible code and certainly will if everyone agrees that something is wrong, but right now I think my question is quite simple and someone will point me the right path. I am working in a data set like this: created_as_free_user t c <fctr> <int> <int> 1 true 36 0 2 true 36 0 3 true 0 1 4 true 28 0 5 true 9 0 6 true 0 1 7 true 13 0 8 true 19 0 9 true 9 0 10 true 16 0 I fitted a Cox Regression model like this: fit_train = coxph(Surv(time = t,event = c) ~ created_as_free_user ,data = teste) summary(fit_train) And received: Call: coxph(formula = Surv(time = t, event

The Problem I trained a linear regression in R to predict this.target from city , variables in data frame data . This trainig is done on a subset of the data, which is specified by train.index . model = glm('data[, this.target] ~ data$city', data = data, subset = train.index) I am trying to test this model on the held out data, which is specified by test.index . predictions = predict(model, data[test.index, ]) For whatever reason, this second step creates an error and a warning. Error in model.frame.default(Terms, newdata, na.action = na.action, xlev = object$xlevels) : invalid type (NULL) for

I have a linear model: mod=lm(weight~age, data=f2) I would like to input an age value and have returned the corresponding weight from this model. This is probably simple, but I have not found a simple way to do this. If your purposes are related to just one prediction you can just grab your coefficient with coef(mod) Or you can just build a simple equation like this. coef(mod)[1] + "Your_Value"*coef(mod)[2] Its usually more robust to use the predict method of lm : f2<-data.frame(age=c(10,20,30),weight=c(100,200,300)) f3<-data.frame(age=c(15,25)) mod<-lm(weight~age,data=f2) pred3<-predict(mod