# Predict categorical responses. Predict categorical responses on new data given the fitted model input. ```r ## S3 method for class 'catch' predict(object, newx, z = NULL, ztest = NULL, gamma = NULL,...) ``` ## Arguments - `object`: Input `catchobj` class object as fitted model. - `newx`: Input adjusted testing tensor or matrix list. Each element of the list is a tensor. The tensor should of the same dimension as training data. - `z`: Input training covariates matrix. `z` can be omitted if there is no covariate. - `ztest`: Input testing covariates matrix. `ztest` can be omitted if there is no covariate. - `gamma`: Coefficients of covariates obtained from `adjten`. `gamma` is `NULL` if there is no covariate. - ``...``: Other arguments that can be passed to `predict`. ## Details The function fits LDA model on selected discriminant vectors. Call `predict` or `predict.catch` to perform predictions. There are two ways to make predictions. One way is to directly predict at the same time as fitting model by `catch` since `predict.catch` has already been embedded in `catch` and it will predicts response when testing data is provided. The other way is to first use `adjten` to adjuste tensor and `catch` to fit model. `predict.catch` will take the input adjusted tensor list `newx`, covariate coefficient `gamma` from `adjten` and the fitted model from `catch` to perform prediction. The prediction is identical to providing `catch` testing data. ## Returns - **pred**: Predicted response of `newx` for each `lambda` in model `object`. ## References Pan, Y., Mai, Q., and Zhang, X. (2018) **Covariate-Adjusted Tensor Classification in High-Dimensions**, arXiv:1805.04421. ## Author(s) Yuqing Pan, Qing Mai, Xin Zhang ## See Also `catch`, `adjten` ## Examples ```r #generate training data n <- 20 p <- 4 k <- 2 nvars <- p*p*p x <- array(list(),n) vec_x <- matrix(rnorm(n*nvars),nrow=n,ncol=nvars) vec_x[1:10,] <- vec_x[1:10,]+2 z <- matrix(rnorm(n*2),nrow=n,ncol=2) z[1:10,] <- z[1:10,]+0.5 y <- c(rep(1,10),rep(2,10)) for (i in 1:n){ x[[i]] <- array(vec_x[i,],dim=c(p,p,p)) } #generate testing data newx <- array(list(),n) vec_newx <- matrix(rnorm(n*nvars),nrow=n,ncol=nvars) vec_newx[1:10,] <- vec_newx[1:10,]+2 newz <- matrix(rnorm(n*2),nrow=n,ncol=2) newz[1:10,] <- newz[1:10,]+0.5 for (i in 1:n){ newx[[i]] <- array(vec_newx[i,],dim=c(p,p,p)) } #Make adjustment and fit model obj <- adjten(x, z, y, newx, newz) fit <- catch(x, z, y) #Predict pred <- predict.catch(fit, obj$testxres, z, newz, obj$gamma) #The adjusting, fitting model and predicting step can also be completed #by one command. pred <- catch(x, z, y, newx, newz)$pred ```

predictcatch function