# depthgram: Depthgram for univariate and multivariate functional data... In roahd: Robust Analysis of High Dimensional Data

## Description

This function computes the three 'DepthGram' representations from a p-variate functional data set.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34``` ```depthgram( Data, marginal_outliers = FALSE, boxplot_factor = 1.5, outliergram_factor = 1.5, ids = NULL ) ## Default S3 method: depthgram( Data, marginal_outliers = FALSE, boxplot_factor = 1.5, outliergram_factor = 1.5, ids = NULL ) ## S3 method for class 'fData' depthgram( Data, marginal_outliers = FALSE, boxplot_factor = 1.5, outliergram_factor = 1.5, ids = NULL ) ## S3 method for class 'mfData' depthgram( Data, marginal_outliers = FALSE, boxplot_factor = 1.5, outliergram_factor = 1.5, ids = NULL ) ```

## Arguments

 `Data` A `list` of length `L` (number of components) in which each element is an `N x P` matrix with `N` individuals and `P` time points. Alternatively, it can also be an object of class `fData` or of class `mfData`. `marginal_outliers` A boolean specifying whether the function should return shape and amplitude outliers over each dimension. Defaults to `FALSE`. `boxplot_factor` A numeric value specifying the inflation factor for marginal functional boxplots. This is ignored if `marginal_outliers == FALSE`. Defaults to `1.5`. `outliergram_factor` A numeric value specifying the inflation factor for marginal outliergrams. This is ignored if `marginal_outliers == FALSE`. Defaults to `1.5`. `ids` A character vector specifying labels for individual observations. Defaults to `NULL`, in which case observations will remain unlabelled.

## Value

An object of class `depthgram` which is a list with the following items:

• `mbd.mei.d`: vector MBD of the MEI dimension-wise.

• `mei.mbd.d`: vector MEI of the MBD dimension-wise.

• `mbd.mei.t`: vector MBD of the MEI time-wise.

• `mei.mbd.t`: vector MEI of the MEI time-wise.

• `mbd.mei.t2`: vector MBD of the MEI time/correlation-wise.

• `mei.mbd.t2`: vector MEI of the MBD time/correlation-wise.

• `shp.out.det`: detected shape outliers by dimension.

• `mag.out.det`: detected magnitude outliers by dimension.

• `mbd.d`: matrix `n x p` of MBD dimension-wise.

• `mei.d`: matrix `n x p` of MEI dimension-wise.

• `mbd.t`: matrix `n x p` of MBD time-wise.

• `mei.t`: matrix `n x p` of MEI time-wise.

• `mbd.t2`: matrix `n x p` of MBD time/correlation-wise

• `mei.t2`: matrix `n x p` of MBD time/correlation-wise.

## References

Aleman-Gomez, Y., Arribas-Gil, A., Desco, M. Elias-Fernandez, A., and Romo, J. (2021). "Depthgram: Visualizing Outliers in High Dimensional Functional Data with application to Task fMRI data exploration".

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```N <- 2e2 P <- 1e3 grid <- seq(0, 1, length.out = P) Cov <- exp_cov_function(grid, alpha = 0.3, beta = 0.4) Data <- list() Data[] <- generate_gauss_fdata( N, centerline = sin(2 * pi * grid), Cov = Cov ) Data[] <- generate_gauss_fdata( N, centerline = sin(2 * pi * grid), Cov = Cov ) names <- paste0("id_", 1:nrow(Data[])) DG1 <- depthgram(Data, marginal_outliers = TRUE, ids = names) fD <- fData(grid, Data[]) DG2 <- depthgram(fD, marginal_outliers = TRUE, ids = names) mfD <- mfData(grid, Data) DG3 <- depthgram(mfD, marginal_outliers = TRUE, ids = names) ```

roahd documentation built on Nov. 4, 2021, 1:07 a.m.