View source: R/locOuts_methods.R

plot.locOuts | R Documentation |

This function plots different diagnostic plots for local outlier detection.
It can be applied to an object of class `"locOuts"`

which is the output of the function `local_outliers_ssMRCD`

.

```
## S3 method for class 'locOuts'
plot(
x,
type = c("hist", "spatial", "lines", "3D"),
colour = "all",
focus = NULL,
pos = NULL,
alpha = 0.3,
data = NULL,
add_map = TRUE,
...
)
```

`x` |
a locOuts object obtained by the function |

`type` |
vector containing the types of plots that should be plotted, possible values |

`colour` |
character specifying the color scheme (see details). Possible values |

`focus` |
an integer being the index of the observation whose neighborhood should be analysed more closely. |

`pos` |
integer specifying the position of the text "cut-off" in the histogram (see |

`alpha` |
scalar specifying the transparancy level of the points plotted for plot type |

`data` |
optional data frame or matrix used for plot of type |

`add_map` |
TRUE if a map should be plotted along the line plot ( |

`...` |
further parameters passed on to base-R plotting functions. |

Regarding the parameter `type`

the value `"hist"`

corresponds to a plot of the
histogram of the next distances together with the used cutoff-value.
When using `"spatial"`

the coordinates of each observation are plotted and colorized according to the color setting.
The `"lines"`

plot is used with the index `focus`

of one observation whose out/inlyingness to its neighborhood
should by plotted. The whole data set is scaled to the range [0,1] and the scaled value of the selected observation and
its neighbors are plotted. Outliers are plotted in orange.
The `"3D"`

setting leads to a 3D-plot using the colour setting as height.
The view can be adapted using the parameters `theta`

and `phi`

.

For the `colour`

setting possible values are `"all"`

(all next distances are
used and colored in an orange palette), `"onlyOuts"`

(only outliers are
plotted in orange, inliers are plotted in grey) and `"outScore"`

(the next
distance divided by the cutoff value is used to colourize the points; inliers are colorized in blue, outliers in orange).

Returns plots regarding next distances and spatial context.

`local_outliers_ssMRCD`

```
# set seed
set.seed(1)
# make locOuts object
data = matrix(rnorm(2000), ncol = 4)
coords = matrix(rnorm(1000), ncol = 2)
N_assignments = sample(1:10, 500, replace = TRUE)
lambda = 0.3
# local outlier detection
outs = local_outliers_ssMRCD(data = data,
coords = coords,
N_assignments = N_assignments,
lambda = lambda,
k = 10)
# plot results
plot(outs, type = "hist")
plot(outs, type = "spatial", colour = "outScore")
plot(outs, type = "3D", colour = "outScore", theta = 0)
plot(outs, type ="lines", focus = outs$outliers[1])
```

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