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As I understand it,  predict.lm(l ,newdata=nd ,interval="confidence") yields confidence bands for the predicted mean of new observations and lm.predict(l ,newdata=nd ,interval="prediction") yields confidence bands for new observations themselves, given an lm object l.   However with regard to {mgcv} although  predict.gam (g ,se.fit=TRUE ,interval= "prediction")

Dear List I am using R with mcgv package to model spatial variation in density estimates of dorcas gazelle in Sinai. I have 59 points of data and 4 explanatory variables(distance from mountain edge, camel presence, Latitude & Longitude). I want to test the model fir via bootstraping. I have used the jacknife bootstraping but i...

...and calculate the expected value of Y (predictand -January to June). After variable selection, GAMs (Model 1)were bulit up! R-square :0.40 NOW, I want to use new X'( predictor -July - December data) and put into Model 1, then get the expected value of Y' (predictand-July -December) I used mcgv package, what should I do now? -- View this message in context: http://r.789695.n4.nabble.com/GAMs-in-R-How-to-put-the-new-data-into-the-model-tp3849851p3849851.html Sent from the R help mailing list archive at Nabble.com.