Description Usage Arguments Details References See Also Examples

This function plots various multivariate medians in the two-dimensional case. The grey dots presented in the figure are the data points and the Spatial, Component-wise (CWmed), Tukey's, Oja's, Liu's, Projection median as well as the mean value of the data set are plotted in the figure.

1 2 3 4 | ```
Plot2dMedian(data, xvec, yvec, yamm.nprojs = 2000,
PmedMCInt.nprojs = 20000,
no.subinterval = 36, opt.method = "BFGS",
xlab = "Component1", ylab = "Component2")
``` |

`data` |
The data as a matrix or data frame, with each row being viewed as one multivariate observation. |

`xvec` |
A numeric vector containing the maximum and minimum values you desire for the x-axis. |

`yvec` |
A numeric vector containing the maximum and minimum values you desire for the y-axis. |

`yamm.nprojs` |
The number of projections for the dataset when computing |

`PmedMCInt.nprojs` |
The number of projections for the dataset when computing |

`no.subinterval` |
The number of subintervals while using the trapezoidal rule to approximate the projection median with |

`opt.method` |
The method chosen for the optimiser when computing the |

`xlab` |
Title for x-axis. Must be a character string. |

`ylab` |
Title for y-axis. Must be a character string. |

The Spatial median is obtained using `l1median`

in the Rpackage pacPP. The Component-wise (CWmed), Liu's and Tukey's median are produced using function `med`

in the Rpackage depth. Oja's median is produced using function `ojaMedian`

in the Rpackage `OjaNP`

. When computing the projection median, three approximations are implemented and diplayed in the plot, where `PmedMCInt`

uses Monte Carlo method, `PmedTrapz`

is computed by the trapezoidal rule, and `yamm`

uses an optimiser.

The argument `xvec`

and `yvec`

are useful when there are outliers in the data set, which are not expected to be shown in the figure in some cases. Determining the x-axis and y-axis allows you to zoom in the plot and see the difference between multivariate medians and mean value.

Chen, F. and Nason, Guy P. (2020) A new method for computing the projection medi
an, its influence curve and techniques for the production of projected quantile
plots. *PLOS One*, (to appear)

`PmedTrapz`

,
`PmedMCInt`

,
`yamm`

,
`yamm.obj`

,
`optim`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
# Load a data frame with 103 rows and 2 columns.
# The last two rows of the data are the outliers.
data(clusters2d)
#
# Remove the outliers of the dataset.
cluster_without_outlier <- clusters2d[c(1:101),]
myxvec <- c(min(cluster_without_outlier[,1]),
max(cluster_without_outlier[,1]))
myyvec <- c(min(cluster_without_outlier[,2]),
max(cluster_without_outlier[,2]))
#
# Plot the figure.
set.seed(5)
Plot2dMedian(clusters2d, myxvec, myyvec, yamm.nprojs = 2000,
PmedMCInt.nprojs = 20000, no.subinterval = 36,
opt.method = "BFGS", xlab = "Component1",
ylab = "Component2")
``` |

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