Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

R


RA RB RC RD RE RF RG RH RI RJ RK RL RM RN RO RP RQ RR RS RT RU RV RW RX RY RZ
randbin
computes random numbers based on the binomial distribution
randmix2exp
generates pseudo random variables with mixture of two exponentials distribution.
randomize
Sets the seed of the pseudorandom number generator.
randomize2
Sets the seed of the pseudo-random number generator
randomnumbers
computes n p-dimensional independent random vectors
randx
randx generates a vector of pseudo random variables with extreme value and generalized Pareto distribution.
ranexp2
Ranexp2 generates arrays up to eight dimensions of pseudo random variables with the standard exponential distribution.
rank
Computes the rank vector of a given vector.
rankcorr
computes rank correlation coefficients according to Spearman and Kendall. In the case of ties, corrected versions are computed.
rankm
Computes the rank r of a matrix x.
ranpois
generates arrays (up to 8 dimensions) of pseudo random variables with Poisson distribution.
rbfinfo
shows information about the given network
rbfload
loads a saved RBF network from a file
rbfpredict
predicts the output of given RBF neural network
rbfsave
saves given radial basis function network into the given file
rbftest
tests the given rbfnet network
rbftrain
trains a radial basis function neural network
rbftrain2
trains a radial basis function neural network
Rdenbest
evaluates a kernel estimate of an integrated squared density (derivative) using the normal kernel for a (vector of) bandwidth(s) h. This quantlet is a variation of Rdenxest and uses linearly prebinned data for faster computation.
Rdenxest
evaluates a kernel estimate of an integrated squared density (derivative) using the normal kernel for a (vector of) bandwidth(s) h.
rdl1
Computes RDL1 estimate --- a weighted L1-estimator of y on on continuous variables x and binary variables xdum with weights min(1, p/(RD^2)); RD contains the robust distances obtained by the MVE estimator for x.
read
read is a command to read data from a file. Each column of the file will be interpreted as a vector of numbers.
readascii
readascii is a command to read ASCII data from a file.
readcomponent
internal routine for readlist
readcond
reads the data according to a condition which is explicitly stated as a string.
readcsv
reads numerical data from a csv file
readcsvm
readcsvm reads mixed data from a CSV file
readevent
readevent reads a key- or a mouse- event while a program is running. An "event" is a stroke of a key or a click of a mouse button. readevent will be mainly useful for letting XploRe know whether such an event has occured and to get some special information like the coordinates where the mouse click
readlist
Reads a composed object as ASCII data from a set of files. All elements of the composed object have to be numerical matrices or textvectors !
readm
readm reads mixed data from a file.
readshow
shows the visualization of a feedforward neural network
readshow2
shows the visualization of a feedforward neural network
readvalue
asks for one or more input values via a dialog box and reads them.
readxls
reads numerical data from a MS Excel file
readxlsm
reads mixed data from a MS Excel file
reca
RECA (REgression CAlibration) is a method in which replacing the unobserved x by its expected value E(x|w,z) and then to perform a standard analysis.
recode
allocates categories 1,2,...,L to intervals of categories. The upper bounds of the intervals have to be specified. It is an useful tool to join classes and hence to collaps contingency tables.
reduce
Deletes all dimensions with only a single component.
redun
calculates a single redundance and a redundance vector for dpls quantlet as a measure of goodness.
regbwcrit
determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice
regbwsel
interactive tool for bandwidth selection in univariate kernel regression estimation.
regcb
computes uniform confidence bands with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator. The computation uses WARPing.
regci
computes pointwise confidence intervals with prespecified confidence level for univariate regression using the Nadaraya-Watson estimator. The computation uses WARPing.
regest
computes the Nadaraya-Watson estimator for univariate regression. The computation uses WARPing.
regestp
Nadaraya-Watson estimator for multivariate regression. The computation uses WARPing.
regxbwcrit
determines the optimal from a range of bandwidths by one using the resubstitution estimator with one of the following penalty functions: Shibata's penalty function (shi), Generalized Cross Validation (gcv), Akaike's Information Criterion (aic), Finite Prediction Error (fpe), Rice's T function (rice
regxbwsel
interactive tool for bandwidth selection in univariate kernel regression estimation.
regxcb
computes uniform confidence bands with a pre-specified confidence level for univariate regression using the Nadaraya-Watson estimator.
regxci
computes pointwise confidence intervals with a pre-specified confidence level for univariate regression using the Nadaraya-Watson estimator.
regxest
computes the Nadaraya-Watson estimator for univariate regression.
regxestp
computes the Nadaraya-Watson estimator for multivariate regression.
relation
Computes the relation coefficients (chi^2, contingency, corrected contingency, spearman rank, bravais-pearson) for the data x.
relationchi2
Computes the Chi^2 coefficients for discrete data.
relationcont
Computes the contingency coefficient for discrete data.
relationcorr
Computes the bravais-pearson correlation for metric data.
relationcorrcont
Computes the corrected contingency coefficient for discrete data.
relationrank
Computes the rank correlation of spearman for ordinal data.
repa
computes the radial symmetric epanechnikov kernel
replace
Replaces values by other values.
replicdata
replicdata reduces a matrix x to its distinct rows and gives the number of replications of each rows in the original dataset. An optional second matrix y can be given, the rows of y are summed up accordingly. replicdata does in fact the same as discrete but provides an additional index vector to id
resclass
shows the residuals in case of the classification
resclass2
shows the residuals in case of the classification
reshape
reshape transforms an array into a new one with given dimensions.
residuen
calculates residuals for VAR models
resplots2
auxiliary quantlet for spdest2, plots the residuals.
resreg
shows the residuals in case of the regression
resreg2
shows the residuals in case of the regression
rev
reverts the order of the rows of the input matrix
rgb2hls
converts an RGB colour vector into an HLS vector.
rgb2lab
converts an RGB colour vector into an L*a*b* vector.
rgenss
generates the restriction matrix for Subset VAR
rICfil
Calculates a filtered time serie (uni- or multivariate) using a robust, recursive Filter based on LS-optimality, the rLS-filter. Additionally to the Kalman-Filter one needs to specify the degree of robustness one wants to achieve; this is done either by specifying a clipping height or by specifying
rici
auxiliary quantlet for cointegration
rint
rint gives the next nearest integer value of the elements of an array.
rkernpq
Computes the radial kernel of the form: C (1-r^q)^p.
rlogspline
random samples from a logspline density - auxiliary quantlet for logspline density estimation
rlsbnorm
Auxiliary routine for rlsfil: solves E [ |X-MYw_b(MY)|^2]=(1+e)E [ |X-MY|^2] - if possible - by MC-integration for X ~ N_n(0,Sigt), v ~ N_m(0,Q) indep. M = Sigt H'(Q+HSigt H')^{-1} Y = HX+v, w_b(x)=min(1,b/|x|)
rlsbnorm1
Auxiliary routine for rlsfil: solves E [ |X-MYw_b(MY)|^2]=(1+e)E [ |X-MY|^2] - if possible - by numerical integration for X ~ N(0,Sigt), v ~ N(0,Q) indep. M=Sigt H'(Q+HSigt H')^{-1} Y=HX+v, w_b(x)=min(1,b/|x|)
rlsfil
Calculates a filtered time serie (uni- or multivariate) using a robust, recursive Filter based on LS-optimality, the rLS-filter. additionally to the Kalman-Filter one needs to specify the degree of robustness one wants to achieve; this is done either by specifying a clipping height or by specifying
rmed
rmed computes the running median of y using the optimal median smoothing algorithm of Haerdle and Steiger (1990).
rndBurr
generates a vector of pseudo random variables with Burr distribution.
rndexp
generates a vector of pseudo random variables with exponential distribution.
rndgamma
generates a vector of pseudo random variables with gamma distribution.
rndgengamma
generates a vector or matrix of pseudo random variables with generalized gamma distribution.
rndgeom
generates a vector or matrix of pseudo random variables with geometric distribution.
rndhyp
generates arrays up to eight dimensions of pseudo random variables with hyperbolic (HYP) distribution.
rndln
generates a vector of pseudo random variables with lognormal distribution.
rndmixexp
generates a vector of pseudo random variables with mixture of exponentials distributions.
rndnig
generates arrays up to eight dimensions of pseudo random variables with Normal Inverse Gaussian (NIG) distribution.
rndPareto
generates a vector of pseudo random variables with Pareto distribution.
rndsstab
generates arrays up to eight dimensions of pseudo-random variables with symmetric stable distribution.
rndstab
generates arrays up to eight dimensions of pseudo-random variables with stable distribution.
rndtrbeta
generates a vector or matrix of pseudo random variables with transformed beta distribution.
rndWeibull
generates a vector of pseudo random variables with Weibull distribution.
roblm
Semiparametric average periodogram estimator of the degree of long memory of a time series. The first argument of the quantlet is the series, the second optional argument is a strictly positive constant q, which is also strictly less than one. The third optional argument is the bandwidth vector m.
robmest
calculates M-estimators in linear model
robtechmain
Main routine of robtech library.
robwhittle
Semiparametric Gaussian estimator of the degree of long memory of a time series, based on the Whittle estimator. The first argument is the series, the second argument is the vector of bandwidths, i.e., the number of frequencies after zero that are considered. By default, the bandwidth vector m = n/
rootsci
calculates characteristic roots of VAR operator
rot2mat
Computes an orthonormal matrix from a set of Givens rotations.
rotationmatrix
auxiliary quantlet for plotgt, rotates the rotation cosinus matrix of the graphic
round
Rounds to a given precision. If the precision is omitted the nearest integer is displayed.
rows
rows returns the number of rows in an array.
rqfit
Performs quantile regression of y on x using the original simplex approach of Barrodale-Roberts/Koenker-d'Orey.
rqua
computes the radial quartic kernel
rrstest
Computes the regression rankscore test of a linear hypothesis based on the dual quantile regression process. It tests the hypothesis that b1 = 0 in the quantile regression model y = x0'b0 + x1'b1 + u. Test statistic is asymptotically Chi-squared with rank(x1) degrees of freedom.
rtri
computes the radial symmetric triweight kernel
rtrian
computes the radial symmetric triangular kernel
runcv
runs a cross validation and estimates the generalization error
runcv2
runs a cross validation and estimates the generalization error
runi
computes the radial symmetric uniform kernel
runinit
initializes the training andtest dataset, the errors and the weights in the network
runinit2
initializes the training andtest dataset, the errors and the weights in the network
runnet
runs a network with prespecified optimization method
runnet2
runs a network with prespecified optimization method
runnew
optimize a neural network by a quadratic approximation
runnew2
optimize a neural network by a quadratic approximation
runqsa
optimizes a neural network by a stochastic search
runqsa2
optimizes a neural network by a stochastic search
runsa
optimizes a neural network by Boltzman annealing
runsa2
optimizes a neural network by Boltzman annealing
runshow
visualizes a neural network during optimization
runshow2
visualizes a neural network during optimization
rvlm
Calculation of the rescaled variance test for I(0) against long-memory alternatives. The statistic is the centered kpss statistic based on the deviation from the mean. The limit distribution of this statistic is a Brownian bridge whose distribution is related to the distribution of the Kolmogorov s

Keywords - Function Groups - @ A B C D E F G H I J K L M N O P Q R S T U V W X Y Z

(C) MD*TECH Method and Data Technologies, 05.02.2006Impressum