mgraph function

Mining graph function

Mining graph function

Plots a graph given a mining list, list of several mining lists or given the pair y - target and x - predictions.

mgraph(y, x = NULL, graph, leg = NULL, xval = -1, PDF = "", PTS = -1, 
       size = c(5, 5), sort = TRUE, ranges = NULL, data = NULL,
       digits = NULL, TC = -1, intbar = TRUE, lty = 1, col = "black",
       main = "", metric = "MAE", baseline = FALSE, Grid = 0, 
       axis = NULL, cex = 1)

Arguments

  • y: if there are predictions (!is.null(x)), y should be a numeric vector or factor with the target desired responses (or output values).

    Else, y should be a list returned by the mining function or a vector list with several mining lists.

  • x: the predictions (should be a numeric vector if task="reg", matrix if task="prob" or factor if task="class" (use if y is not a list).

  • graph: type of graph. Options are:

    • ROC -- ROC curve (classification);
    • LIFT -- LIFT accumulative curve (classification);
    • IMP -- relative input importance barplot;
    • REC -- REC curve (regression);
    • VEC -- variable effect curve;
    • RSC -- regression scatter plot;
    • REP -- regression error plot;
    • REG -- regression plot;
    • DLC -- distance line comparison (for comparing errors in one line);

  • leg: legend of graph:

    • if NULL -- not used;
    • if -1 and graph="ROC" or "LIFT" -- the target class name is used;
    • if -1 and graph="REG" -- leg=c("Target","Predictions");
    • if -1 and graph="RSC" -- leg=c("Predictions");
    • if vector with "character" type (text) -- the text of the legend;
    • if is list -- $leg = vector with the text of the legend and $pos is the position of the legend (e.g. "top" or c(4,5));

  • xval: auxiliary value, used by some graphs:

    • VEC -- if -1 means perform several 1-D sensitivity analysis VEC curves, one for each attribute, if >0 means the attribute index (e.g. 1).
    • ROC or LIFT or REC -- if -1 then xval=1. For these graphs, xval is the maximum x-axis value.
    • IMP -- xval is the x-axis value for the legend of the attributes.
    • REG -- xval is the set of plotted examples (e.g. 1:5), if -1 then all examples are used.
    • DLC -- xval is the val of the mmetric function.

  • PDF: if "" then the graph is plotted on the screen, else the graph is saved into a pdf file with the name set in this argument.

  • PTS: number of points in each line plot. If -1 then PTS=11 (for ROC, REC or LIFT) or PTS=6 (VEC).

  • size: size of the graph, c(width,height), in inches.

  • sort: if TRUE then sorts the data (works only for some graphs, e.g. VEC, IMP, REP).

  • ranges: matrix with the attribute minimum and maximum ranges (only used by VEC).

  • data: the training data, for plotting histograms and getting the minimum and maximum attribute ranges if not defined in ranges (only used by VEC).

  • digits: the number of digits for the axis, can also be defined as c(x-axis digits,y-axis digits) (only used by VEC).

  • TC: target class (for multi-class classification class) from 1 to Nc, where Nc is the number of classes. If multi-class and TC==-1 then TC is set to the index of the last class.

  • intbar: if 95% confidence interval bars (according to t-student distribution) should be plotted as whiskers.

  • lty: the same lty argument of the par function.

  • col: color, as defined in the par function.

  • main: the title of the graph, as defined in the plot function.

  • metric: the error metric, as defined in mmetric (used by DLC).

  • baseline: if the baseline should be plotted (used by ROC and LIFT).

  • Grid: if >1 then there are GRID light gray squared grid lines in the plot.

  • axis: Currently only used by IMP: numeric vector with the axis numbers (1 -- bottom, 3 -- top). If NULL then axis=c(1,3).

  • cex: label font size

Details

Plots a graph given a mining list, list of several mining lists or given the pair y - target and x - predictions.

Returns

A graph (in screen or pdf file).

References

  • To check for more details about rminer and for citation purposes:

    P. Cortez.

    Data Mining with Neural Networks and Support Vector Machines Using the R/rminer Tool.

    In P. Perner (Ed.), Advances in Data Mining - Applications and Theoretical Aspects 10th Industrial Conference on Data Mining (ICDM 2010), Lecture Notes in Artificial Intelligence 6171, pp. 572-583, Berlin, Germany, July, 2010. Springer. ISBN: 978-3-642-14399-1.

    @Springer: https://link.springer.com/chapter/10.1007/978-3-642-14400-4_44

    http://www3.dsi.uminho.pt/pcortez/2010-rminer.pdf

  • This tutorial shows additional code examples:

    P. Cortez.

    A tutorial on using the rminer R package for data mining tasks.

    Teaching Report, Department of Information Systems, ALGORITMI Research Centre, Engineering School, University of Minho, Guimaraes, Portugal, July 2015.

    http://hdl.handle.net/1822/36210

Author(s)

Paulo Cortez http://www3.dsi.uminho.pt/pcortez/

Note

See also http://hdl.handle.net/1822/36210 and http://www3.dsi.uminho.pt/pcortez/rminer.html

See Also

fit, predict.fit, mining, mmetric, savemining and Importance.

Examples

### regression
y=c(1,5,10,11,7,3,2,1);x=rnorm(length(y),0,1.0)+y
mgraph(y,x,graph="RSC",Grid=10,col=c("blue"))
mgraph(y,x,graph="REG",Grid=10,lty=1,col=c("black","blue"),
       leg=list(pos="topleft",leg=c("target","predictions")))
mgraph(y,x,graph="REP",Grid=10)
mgraph(y,x,graph="REP",Grid=10,sort=FALSE)
x2=rnorm(length(y),0,1.2)+y;x3=rnorm(length(y),0,1.4)+y;
L=vector("list",3); pred=vector("list",1); test=vector("list",1);
pred[[1]]=y; test[[1]]=x; L[[1]]=list(pred=pred,test=test,runs=1)
test[[1]]=x2; L[[2]]=list(pred=pred,test=test,runs=1)
test[[1]]=x3; L[[3]]=list(pred=pred,test=test,runs=1)
# distance line comparison graph:
mgraph(L,graph="DLC",metric="MAE",leg=c("x1","x2","x3"),main="MAE errors")

# new REC multi-curve single graph with NAREC (normalized Area of REC) values
# for maximum tolerance of val=0.5 (other val values can be used)
e1=mmetric(y,x,metric="NAREC",val=5)
e2=mmetric(y,x2,metric="NAREC",val=5)
e3=mmetric(y,x3,metric="NAREC",val=5)
l1=paste("x1, NAREC=",round(e1,digits=2))
l2=paste("x2, NAREC=",round(e2,digits=2))
l3=paste("x3, NAREC=",round(e3,digits=2))
mgraph(L,graph="REC",leg=list(pos="bottom",leg=c(l1,l2,l3)),main="REC curves")

### regression example with mining
## Not run:

data(sin1reg)
M1=mining(y~.,sin1reg[,c(1,2,4)],model="mr",Runs=5)
M2=mining(y~.,sin1reg[,c(1,2,4)],model="mlpe",nr=3,maxit=50,size=4,Runs=5,feature="simp")
L=vector("list",2); L[[1]]=M2; L[[2]]=M1
mgraph(L,graph="REC",xval=0.1,leg=c("mlpe","mr"),main="REC curve")
mgraph(L,graph="DLC",metric="TOLERANCE",xval=0.01,
       leg=c("mlpe","mr"),main="DLC: TOLERANCE plot")
mgraph(M2,graph="IMP",xval=0.01,leg=c("x1","x2"),
       main="sin1reg Input importance",axis=1)
mgraph(M2,graph="VEC",xval=1,main="sin1reg 1-D VEC curve for x1")
mgraph(M2,graph="VEC",xval=1,
       main="sin1reg 1-D VEC curve and histogram for x1",data=sin1reg)
## End(Not run)

### classification example
## Not run:

data(iris)
M1=mining(Species~.,iris,model="rpart",Runs=5) # decision tree (DT)
M2=mining(Species~.,iris,model="ksvm",Runs=5) # support vector machine (SVM)
L=vector("list",2); L[[1]]=M2; L[[2]]=M1
mgraph(M1,graph="ROC",TC=3,leg=-1,baseline=TRUE,Grid=10,main="ROC")
mgraph(M1,graph="ROC",TC=3,leg=-1,baseline=TRUE,Grid=10,main="ROC",intbar=FALSE)
mgraph(L,graph="ROC",TC=3,leg=c("SVM","DT"),baseline=TRUE,Grid=10,
       main="ROC for virginica")
mgraph(L,graph="LIFT",TC=3,leg=list(pos=c(0.4,0.2),leg=c("SVM","DT")),
       baseline=TRUE,Grid=10,main="LIFT for virginica")
## End(Not run)
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