# Predictions from a fitted CGAIM object Uses a fitted `cgaim` object and computes prediction for the observed data or new data. Predicts the response, indices or ridge functions values at the provided data. ```r ## S3 method for class 'cgaim' predict(object, newdata, type = c("response", "terms", "scterms", "indices"), select = NULL, na.action = "na.pass", ...) ``` ## Arguments - `object`: A `gaim` object. - `newdata`: A list or data.frame containing the new data to predict. If missing, fitted values from the model are returned. - `type`: A character indicating the type of prediction to return. `type = "response"` returns the predicted response. `type = "terms"`, returns ridge and smooth functions evaluated at index predicted for `newdata`. `type = "scterms"` is the same, except that terms are postmultiplied by their scaling coefficients beta. `type = "indices"` returns predicted indices values. - `select`: A numeric or character vector indicating terms to return for all types except `"response"`. - `na.action`: A function indicating how to treat NAs. See `na.fail`. - `...`: For compatibility with the default `predict` method. Unused at the moment. ## Returns When `type = "response"` returns a vector of predicted response. When `type = "terms"` or `"scterms"`, returns a matrix of evaluated ridge and smooth terms. When `type = "indices"`, returns a matrix of evaluated indices. ## Details Predictions of ridge functions at new index values is made through natural interpolation splines, which ensures shape-constraints are enforced. When predicted index result in values outside the observed range, natural splines ensure linear extrapolation, which respects any shape-constraint. `type = "terms"` returns the scaled ridge functions, i.e. before being multiplied by scaling coefficients beta. ## Examples ```r ## Simulate some data n <- 200 x1 <- rnorm(n) x2 <- rnorm(n) x3 <- rnorm(n) x4 <- rnorm(n) mu <- 4 * exp(8 * x1) / (1 + exp(8 * x1)) + exp(x3) y <- mu + rnorm(n) df1 <- data.frame(y, x1, x2, x3, x4) ## Fit an unconstrained the model ans <- cgaim(y ~ g(x1, x2, label = "foo") + g(x3, x4, label = "bar"), data = df1) ## Get fitted values yhat <- predict(ans) ## Predict on new data newdf <- as.data.frame(matrix(rnorm(100), 25, 4)) names(newdf) <- sprintf("x%i", 1:4) # predicted response ypred <- predict(ans, newdf) # Indices indices <- predict(ans, newdata = newdf, type = "indices") # Ridge functions funs <- predict(ans, newdata = newdf, type = "terms") ## Select specific terms ind1 <- predict(ans, newdata = newdf, select = "foo", type = "indices") fun1 <- predict(ans, newdata = newdf, select = "foo", type = "terms") # Plot plot(ans, select = "foo") points(ind1, fun1) ## Scaled terms fun2 <- predict(ans, newdata = newdf, select = "foo", type = "scterms") # Plot plot(ans, select = "foo", yscale = TRUE) points(ind1, fun2) ``` ## See Also `cgaim` for main fitting function

predict.cgaim function