mdrplot {fsdaR}

Plots the trajectory of minimum deletion residual (mdr)

Description

Plots the trajectory of minimum deletion residual (mdr).

Usage

    mdrplot(out, quant = c(0.01, 0.5, 0.99), sign = TRUE, 
        mplus1 = FALSE, envm, 
        xlim, ylim, xlab, ylab, main, 
        lwdenv, lwd, cex.lab, cex.axis, 
        tag, datatooltip, label, nameX, namey, databrush, 
        ...)

Arguments

out

An object returned by FSReda() (see FSReda_control).

The needed elements of out are

1. mdr: Minimum deletion residual. A matrix containing the monitoring of minimum deletion residual in each step of the forward search. The first column of mdr must contain the fwd search index.

2. Un: (for FSR only) - matrix containing the order of entry in the subset of each unit (required only when datatooltip is true or databrush is not empty).

3. y: a vector containing the response (required only when option databrush is requested).

4. X: a matrix containing the explanatory variables (required only when option databrush is requested).

5. Bols: (n-init+1) x (p+1) matrix containing the estimated beta coefficients monitored in each step of the robust procedure (required only when option databrush is requested and suboption multivarfit is requested).

quant

Quantiles for which envelopes have to be computed. The default is to produce 1%, 50% and 99% envelopes. In other words the default is quant=c(0.01, 0.5, 0.99).

sign

Wheather to use MDR with sign: if sign=TRUE (default) we distinguish steps for which minimum deletion residual was associated with positive or negative value of the residual. Steps associated with positive values of mdr are plotted in black, while other steps are plotted in red.

mplus1

Wheather to plot the (m+1)-th order statistic. Specifies if it is necessary to plot the curve associated with (m+1)-th order statistic.

envm

Sample size for drawing enevlopes. Specifies the size of the sample which is used to superimpose the envelope. The default is to add an envelope based on all the observations (size n envelope).

ylim

Control y scale in plot. Vector with two elements controlling minimum and maximum on the y axis. Default is to use automatic scale.

xlim

Control x scale in plot. Vector with two elements controlling minimum and maximum on the x axis. Default is to use automatic scale.

xlab

a title for the x axis

ylab

a title for the y axis

main

an overall title for the plot

lwdenv

Controls the width of the lines associated with the envelopes, default is lvdenv=1.

lwd

Controls the linewidth of the curve which contains the monitoring of minimum deletion residual.

cex.lab

The magnification to be used for x and y labels relative to the current setting of cex

cex.axis

The magnification to be used for axis annotation relative to the current setting of cex

tag

Plot handle. String which identifies the handle of the plot which is about to be created. The default is to use tag 'pl_mdr'. Notice that if the program finds a plot which has a tag equal to the one specified by the user, then the output of the new plot overwrites the existing one in the same window else a new window is created.

datatooltip

If datatooltip is not empty the user can use the mouse in order to have information about the unit selected, the step in which the unit enters the search and the associated label. If datatooltip is a list, it is possible to control the aspect of the data cursor (see MATLAB function datacursormode() for more details or see the examples below). The default options are DisplayStyle="Window" and SnapToDataVertex="on".

label

Character vector containing the labels of the units (optional argument used when datatooltip=TRUE. If this field is not present labels row1, ..., rown will be automatically created and included in the pop up datatooltip window).

nameX

Add variable labels in plot. A vector of strings of length p containing the labels of the variables of the regression dataset. If it is empty (default) the sequence X1, ..., Xp will be created automatically

namey

Add response label. A string containing the label of the response

databrush

interactive mouse brushing. If databrush is missing or empty (default), no brushing is done. The activation of this option (databrush is a scalar or a list) enables the user to select a set of trajectories in the current plot and to see them highlighted in the y|X plot, i.e. a matrix of scatter plots of y against each column of X, grouped according to the selection(s) done by brushing. If the plot y|X does not exist it is automatically created. In addition, brushed units are automatically highlighted in the minimum deletion residual plot if it is already open. The extension to the following plots will be available in future versions of the toolbox:

1. monitoring leverage plot;

2. maximum studentized residual;

3. s^2 and R^2;

4. Cook distance and modified Cook distance;

5. deletion t statistics.

Note that the window style of the other figures is set equal to that which contains the monitoring residual plot. In other words, if the monitoring residual plot is docked all the other figures will be docked too

If databrush=TRUE the default selection tool is a rectangular brush and it is possible to brush only once (that is persist=”).

If databrush=list(...), it is possible to use all optional arguments of function selectdataFS() and the following optional argument:

1. persist. Persist is an empty value or a character containing 'on' or 'off'. The default value is persist="", that is brushing is allowed only once. If persist="on" or persis="off" brushing can be done as many time as the user requires. If persist='on' then the unit(s) currently brushed are added to those previously brushed. It is possible, every time a new brushing is done, to use a different color for the brushed units. If persist='off' every time a new brush is performed units previously brushed are removed.

2. bivarfit. Wheather to superimpose bivariate least square lines on the plot (if plot=TRUE. This option adds one or more least squares lines, based on SIMPLE REGRESSION of y on Xi, to the plots of y|Xi. The default is bivarfit=FALSE: no line is fitted. If bivarfit=1, a single OLS line is fitted to all points of each bivariate plot in the scatter matrix y|X. If bivarfit=2, two OLS lines are fitted: one to all points and another to the group of the genuine observations. The group of the potential outliers is not fitted. If bivarfit=0 one OLS line is fitted to each group. This is useful for the purpose of fitting mixtures of regression lines. If bivarfit='i1' or bivarfit='i2', etc. an OLS line is fitted to a specific group, the one with index 'i' equal to 1, 2, 3 etc. Again, useful in case of mixtures.

3. multivarfit. Wheather to superimpose multivariate least square lines. This option adds one or more least square lines, based on MULTIVARIATE REGRESSION of y on X, to the plots of y|Xi. The default is multivarfit=FALSE: no line is fitted. If bivarfit=1, a single OLS line is fitted to all points of each bivariate plot in the scatter matrix y|X. The line added to the scatter plot y|Xi is avconst + Ci*Xi, where Ci is the coefficient of Xi in the multivariate regression and avconst is the effect of all the other explanatory variables different from Xi evaluated at their centroid (that is overline(y)'C)). If multivarfit=2, same action as with multivarfit=1 but this time we also add the line based on the group of unselected observations (i.e. the normal units).

4. labeladd. Add outlier labels in plot. If labeladd=TRUE, we label the outliers with the unit row index in matrices X and y. The default value is labeladd=FALSE, i.e. no label is added.

...

potential further arguments passed to lower level functions.

Details

No details

Value

No value returned

Author(s)

FSDA team

Examples

## Not run: 

n <- 100
y <- rnorm(n)
X <- matrix(rnorm(n*4), nrow=n)

out <- fsreg(y~X, method="LTS")
out <- fsreg(y~X, method="FS", bsb=out$bs, monitoring=TRUE)
mdrplot(out)

## End(Not run)