Query for finding out which components are in use

Hi,
I had a query once to check, if specific components are in use. Theres some internal view but i just can't remember. Can someone help me with this?
EDIT: Damn it wrong board...
Edited by: torgum on Nov 2, 2009 10:51 AM

Key binding may vary as you switch between programs. I have some set to be "universal" but some applications manage to override those settings even though the key shortcuts in the application's menu show my programmed shortcuts.
In your case I'd try disabling applications you suspect may potentially be effecting problems and see if you can track down which are affecting your other applications.

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    > library(randomForest) # reference the library
    > data(mtcars) # load motor cars data that ships with 
    #train a random forest 
    > mtcars.rf <- randomForest(mpg ~ ., data=mtcars, ntree=1000,keep.forest=FALSE, importance=TRUE)
    > importance(mtcars.rf)
    > importance(mtcars.rf)
    #You will see an output like below
           %IncMSE IncNodePurity
    cyl  16.168645     169.96741
    disp 18.672188     260.08722
    hp   17.584375     184.95007
    drat  6.948743      63.54528
    wt   17.818509     254.30347
    qsec  4.772889      33.25546
    vs    5.303058      24.39064
    am    5.210181      17.36626
    gear  4.619161      21.55450
    carb  8.577037      28.46715
    # or plot the importance as follows
    > varImpPlot(mtcars.rf)
    What this tells you is how important predictors/features/variables like horsepower(hp), weight(wt), no. of cylinders (cyl) is in predicting miles per gallon (mpg).
    If you are wondering how the data looks like. Try this
    head(mtcars) # gives first few lines of the data set.
     mpg cyl disp  hp drat   wt ... 
    Mazda RX4     21.0   6  160 110 3.90 2.62 ... 
    Mazda RX4 Wag 21.0   6  160 110 3.90 2.88 ... 
    Datsun 710    22.8   4  108  93 3.85 2.32 ... 
    . Try this
    Here is a few useful resources:
    Random Forest documentation (Check out the pages on variable importance and variable importance plot)
    http://cran.r-project.org/web/packages/randomForest/randomForest.pdf 
    Motor cars data: http://stat.ethz.ch/R-manual/R-devel/library/datasets/html/mtcars.html

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