# Random forest fit to residuals from GAM model Fit model using `gam()` from mgcv, then use random forest regression with residuals. Check perfomance of this hybrid model for predictions to `newdata`, if supplied. ```r gamRF(formlist, yvar, data, newdata = NULL, rfVars, method = "GCV.Cp", printit = TRUE, seed = NULL) ``` ## Arguments - `formlist`: List of rght hand sides of formulae for GAM models. - `yvar`: Character string holding y-variable name. - `data`: Data - `newdata`: Optionally, supply test data. - `rfVars`: Names of explanatory variables for the `randomForest` model. - `method`: Smoothing parameter estimation method for use of `gam()`. See `gam`. - `printit`: Should a summary of results (error rates) be printed? - `seed`: Set a seed to make result repeatable. ## Returns A vector of test data accuracies for the hybrid models (one for each element of `formlist`), plus test error mean square and OOB error mean square for the use of `randomForest()`. ## References J. Li, A. D. Heap, A. Potter and J. J. Daniell. 2011. Application of Machine Learning Methods to Spatial Interpolation of Environmental Variables. Environmental Modelling and Software 26: 1647-1656. DOI: 10.1016/j.envsoft.2011.07.004. ## Author(s) John Maindonald ## Note The best results are typically obtained when a relatively low degree of freedom GAM model is used. It seems advisable to use those variables for the GAM fit that seem likely to be similar in their effect irrespective of geographic location. ## See Also `CVgam` ## Examples ```r if(length(find.package("sp", quiet=TRUE))>0){ data("meuse", package="sp") meuse <- within(meuse, {levels(soil) <- c("1","2","2") ffreq <- as.numeric(ffreq) loglead <- log(lead)} ) form <- ~ dist + elev + ffreq + soil rfVars <- c("dist", "elev", "soil", "ffreq", "x", "y") ## Select 90 out of 155 rows sub <- sample(1:nrow(meuse), 90) meuseOut <- meuse[-sub,] meuseIn <- meuse[sub,] gamRF(formlist=list("lm"=form), yvar="loglead", rfVars=rfVars, data=meuseIn, newdata=meuseOut) } ## The function is currently defined as function (formlist, yvar, data, newdata = NULL, rfVars, method = "GCV.Cp", printit = TRUE, seed = NULL) { if(!is.null(seed))set.seed(seed) errRate <- numeric(length(formlist)+2) names(errRate) <- c(names(formlist), "rfTest", "rfOOB") ytrain <- data[, yvar] xtrain <- data[, rfVars] xtest <- newdata[, rfVars] ytest = newdata[, yvar] res.rf <- randomForest(x = xtrain, y = ytrain, xtest=xtest, ytest=ytest) errRate["rfOOB"] <- mean(res.rf$mse) errRate["rfTest"] <- mean(res.rf$test$mse) GAMhat <- numeric(nrow(data)) for(nam in names(formlist)){ form <- as.formula(paste(c(yvar, paste(formlist[[nam]])), collapse=" ")) train.gam <- gam(form, data = data, method = method) res <- resid(train.gam) cvGAMms <- sum(res^2)/length(res) if (!all(rfVars %in% names(newdata))) { missNam <- rfVars[!(rfVars %in% names(newdata))] stop(paste("The following were not found in 'newdata':", paste(missNam, collapse = ", "))) } GAMtesthat <- predict(train.gam, newdata = newdata) GAMtestres <- ytest - GAMtesthat Gres.rf <- randomForest(x = xtrain, y = res, xtest = xtest, ytest = GAMtestres) errRate[nam] <- mean(Gres.rf$test$mse) } if (printit) print(round(errRate, 4)) invisible(errRate) } ```

gamRF function