# DDCpredict Based on a `DDC` fit on an initial (training) data set `X`, this function analyzes a new (test) data set `Xnew`. ```r DDCpredict(Xnew, InitialDDC, DDCpars = NULL) ``` ## Arguments - `Xnew`: The new data (test data), which must be a matrix or a data frame. It must always be provided. Its columns (variables) should correspond to those of `InitialDDC$remX`. - `InitialDDC`: The output of the `DDC` function on the initial (training) dataset. Must be provided. - `DDCpars`: The input options to be used for the prediction. By default the options of InitialDDC are used. ## Returns A list with components: - **DDCpars**: the options used in the call, see `DDC`. - **locX**: the locations of the columns, from `InitialDDC`. - **scaleX**: the scales of the columns, from `InitialDDC`. - **Z**: `Xnew` standardized by `locX` and `scaleX`. - **nbngbrs**: predictions use a combination of `nbngbrs` columns. - **ngbrs**: for each column, the list of its neighbors, from `InitialDDC`. - **robcors**: for each column, the correlations with its neighbors, from `InitialDDC`. - **robslopes**: slopes to predict each column by its neighbors, from `InitialDDC`. - **deshrinkage**: for each connected column, its deshrinkage factor used in `InitialDDC`. - **Xest**: predicted values for every cell of `Xnew`. - **scalestres**: scale estimate of the residuals (`Xnew` - `Xest`), from `InitialDDC`. - **stdResid**: columnwise standardized residuals of `Xnew`. - **indcells**: positions of cellwise outliers in `Xnew`. - **Ti**: outlyingness of rows in `Xnew`. - **medTi**: median of the `Ti` in `InitialDDC`. - **madTi**: mad of the `Ti` in `InitialDDC`. - **indrows**: row numbers of the outlying rows in `Xnew`. - **indNAs**: positions of the `NA`'s in `Xnew`. - **indall**: positions of `NA`'s and outlying cells in `Xnew`. - **Ximp**: `Xnew` where all cells in indall are imputed by their prediction. ## References Hubert, M., Rousseeuw, P.J., Van den Bossche W. (2019). MacroPCA: An all-in-one PCA method allowing for missing values as well as cellwise and rowwise outliers. **Technometrics**, 61 (4), 459-473. [(link to open access pdf)](https://www.tandfonline.com/doi/full/10.1080/00401706.2018.1562989) ## Author(s) Rousseeuw P.J., Van den Bossche W. ## See Also `checkDataSet`, `cellMap`, `DDC` ## Examples ```r library(MASS) set.seed(12345) n <- 100; d <- 10 A <- matrix(0.9, d, d); diag(A) = 1 x <- mvrnorm(n, rep(0,d), A) x[sample(1:(n * d), 50, FALSE)] <- NA x[sample(1:(n * d), 50, FALSE)] <- 10 x <- cbind(1:n, x) DDCx <- DDC(x) xnew <- mvrnorm(50, rep(0,d), A) xnew[sample(1:(50 * d), 50, FALSE)] <- 10 predict.out <- DDCpredict(xnew, DDCx) cellMap(D = xnew, R = predict.out$stdResid, columnlabels = 1:d, rowlabels = 1:50) # For more examples, we refer to the vignette: ## Not run: vignette("DDC_examples") ## End(Not run) ```

DDCpredict function