Re: [R] Predictions with missing inputs
On 25.04.2013 18:12, tonitogomez wrote: Hi Bill, Very clear response. How about when the missing values are on the response variable being predicted (y)? That is, the model is fitted only to complete cases, but then I want to have predictions for all individual y (including those missing). Can I use the mean for that variable 'y'? EXAMPLE: mynewdata <- mydata mynewdata$y<-mean(mydata$y) mypred <- predict(mymodel, mynewdata) Err, if y is your response, you do not need them for prediction... Best, Uwe Ligges Thanks, Manuel -- View this message in context: http://r.789695.n4.nabble.com/Predictions-with-missing-inputs-tp3302303p4665411.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Predictions with missing inputs
Hi Bill, Very clear response. How about when the missing values are on the response variable being predicted (y)? That is, the model is fitted only to complete cases, but then I want to have predictions for all individual y (including those missing). Can I use the mean for that variable 'y'? EXAMPLE: mynewdata <- mydata mynewdata$y<-mean(mydata$y) mypred <- predict(mymodel, mynewdata) Thanks, Manuel -- View this message in context: http://r.789695.n4.nabble.com/Predictions-with-missing-inputs-tp3302303p4665411.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Predictions with missing inputs
On Fri, 2011-02-11 at 20:51 -0500, Axel Urbiz wrote: > Dear users, > > I'll appreciate your help with this (hopefully) simple problem. > > I have a model object which was fitted to inputs X1, X2, X3. Now, I'd like > to use this object to make predictions on a new data set where only X1 and > X2 are available (just use the estimated coefficients for these variables in > making predictions and ignoring the coefficient on X3). Here's my attempt > but, of course, didn't work. > > #fit some linear model to random data > > x=matrix(rnorm(100*3),100,3) > y=sample(1:2,100,replace=TRUE) > mydata <- data.frame(y,x) > mymodel <- lm(y ~ ns(X1, df=3) + X2 + X3, data=mydata) > summary(mymodel) > > #create new data with 1 missing input > > mynewdata <- data.frame(matrix(rnorm(100*2),100,2)) > mypred <- predict(mymodel, mynewdata) > Thanks in advance for your help! > > Axel. Axel, I think mice package solve your problem -- Bernardo Rangel Tura, M.D,MPH,Ph.D National Institute of Cardiology Brazil __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Predictions with missing inputs
With R it is always possible to shoot yourself squarely in the foot, as you seem keen to do, but R does at least often make it difficult. When you predict, you need to have values for ALL variables used in the model. Just leaving out the coefficients corresponding to absent predictors is equivalent to assuming that those coefficients are zero, and there is no basis whatever for so assuming. (In this constructed example things are different because the missing variable is a nonsense variable and the coefficient should be roughly zero, as it is, but in general that is not going to be the case.) So you need to supply some value for each of the missing predictors if you are going to use the standard prediction tools. An obvious plug is the mean of that variable in the training data, though more sophisticated alternatives would often be available. Here is a suggestion for your case. ## fit some linear model to random data x <- matrix(rnorm(100*3),100,3) y <- sample(1:2, 100, replace = TRUE) mydata <- data.frame(y, x) library(splines)## missing from your code. mymodel <- lm(y ~ ns(X1, df = 3) + X2 + X3, data = mydata) summary(mymodel) ## create new data with 1 missing input mynewdata <- within(data.frame(matrix(rnorm(100*2), 100, 2)), ## add in an X3 X3 <- mean(mydata$X3)) mypred <- predict(mymodel, mynewdata) From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of Axel Urbiz [axel.ur...@gmail.com] Sent: 12 February 2011 11:51 To: R-help@r-project.org Subject: [R] Predictions with missing inputs Dear users, I'll appreciate your help with this (hopefully) simple problem. I have a model object which was fitted to inputs X1, X2, X3. Now, I'd like to use this object to make predictions on a new data set where only X1 and X2 are available (just use the estimated coefficients for these variables in making predictions and ignoring the coefficient on X3). Here's my attempt but, of course, didn't work. #fit some linear model to random data x=matrix(rnorm(100*3),100,3) y=sample(1:2,100,replace=TRUE) mydata <- data.frame(y,x) mymodel <- lm(y ~ ns(X1, df=3) + X2 + X3, data=mydata) summary(mymodel) #create new data with 1 missing input mynewdata <- data.frame(matrix(rnorm(100*2),100,2)) mypred <- predict(mymodel, mynewdata) Thanks in advance for your help! Axel. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
