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

randbin - readevent - redun - regressionstatistic - relationcorr - resreg - rot2mat - rpcstopclient - runnet - rvlm
randbin computes random numbers based on the binomial distribution
randomize Sets the seed of the pseudorandom number generator.

randomsample selects a random sample according approximatively to a specified percentage of the dataset using a uniform random generator. The exact percentage of extracted rows is given in the output window.
randx randx generates a vector of pseudo random variables with extreme value and generalized Pareto 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 comptuted.
rankm Computes the rank r of a matrix 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
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 happened or a key code. readevent will "record" the relevant event if it ocurred previous to the moment when readevent is called. readevent can therefore be used to "tell" the program that the event has happened. Sometimes it is usefull to call at first setmode(...., 2) to disable default event handling.
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.
readmatrix Reads a matrix with mixed text and number columns as ASCII data from a file.
readshow shows the visualization of a feedforward neural network
readvalue asks for one or more input values via a dialog box and reads them.

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 intervalls of categories. The upper bounds of the intervals have to be specified. It is an useful tool in order to join classes and hence to collaps contingency tabels.
recodeista recodes the selected variables into binary variables with with the most frequent value as the reference value.
reduce Deletes all dimensions with only a single component.
redun calculating single redundance and redundance vector for dpls macro as measure for 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). The computation uses WARPing.
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.
regressionplots shows different plots after performing a regression analysis (linear regression or neuronal nets) and saving the appropriate variables
regressionsave saves different variables after performing a regression analysis (linear regression or neuronal nets)
regressionselection selection of different regression methods (enter, forward, backward, stepwise) for the chosen X and Y variables.
regressionstatistic computes different statistics after performing a linear regression analysis
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 prespecified confidence level for univariate regression using the Nadaraya-Watson estimator.
regxci computes pointwise confidence intervals with prespecified 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.
relations Computes the relation coefficients (chi^2,contingency, corrected contingency, spearman rank, bravais-pearson) for selected variables. It is possible to compute the coefficients interactively or non-interactively. In the interactive mode you have to choose one of the coefficients. Then you will get a menu sorted after the largest coefficients. If you click on the coefficient you will get some more information to the corresponding variables.
repa repa computes the multivariate 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 identify the reduced data with the original. It takes a little longer due an additonal sort.
resclass 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
resreg shows the residuals in case of the regression
rev reverts the order of the rows of the input matrix
rgb2hls Generates HLS-colors from the RGB color model.
rgenss generates the restriction matrix for Subset VAR
rici auxiliary macro for cointegration
rint rint gives the next nearest integer value of the elements of an array.
rmed rmed computes the running median of y using the optimal median smoothing algorithm od Haerdle ans Steiger (1990).
roblm Semiparametric average periodogram estimator of the degree of long memory of a time series. The first argument of the macro 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. By default q is set to 0.5 and the bandwidth vector is equal to m = n/4, n/8, n/16. If q and m contain several elements, the estimator is evaluated for all the combinations of q and m. The quantlet returns in the first column the estimated degree of long-memory, in the second column the number of frequencies considered, in the third column the value of q.
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/4, n/8, n/16, where n is the sample size. This quantlet displays the estimated parameter d, with the number of frequencies considered.
rootsci calculates characteristic roots of VAR operator
rot2mat Computes an orthonormal matrix from a set of Givens rotations.
round Rounds to a given precision. If the precision is omitted the nearest integer is given back.
rows rows returns the number of rows in an array.
rpclibmain program that will be executed on each call of library("rpclib")
rpclibtest test program for the library rpclib
rpclink rpclink links an XploRe display with an external RPC client.
rpcsendrequest rpcsendrequest sends a request to a given client.
rpcstartclient rpcstartclient starts an RPC client using a given port number.
rpcstartserver rpcstartserver starts an RPC server using a given portnumber. Only one server can be active at a time. rpcstartserver can only be called again after rpcstopserver has been called.
rpcstarttimer rpcstarttimer starts a timer that checks for incoming RPC requests from external clients.
rpcstopclient rpcstopclient stops an RPC client using a given handle.
rpcstopserver rpcstopserver stops the active RPC server.
rpcstoptimer rpcstoptimer stops a timer.
rqua rqua computes the multivariate radial symmetric quartic kernel

rqua computes the rescaled Gaussian kernel ngau(u) = 5.*gau(5.*u), multivariate.
rtri rtri computes the multivariate radial symmetric triweight kernel

rtrian rtrian computes the multivariate radial symmetric triangle kernel

runcv runs a cross validation and estimates the generalization error
runi runi computes the multivariate radial symmetric uniform kernel

runinit initializes the training andtest dataset, the errors and the weights in the network
runnet runs a network with prespecified optimization method
runnew optimize a neural network by a quadratic approximation
runqsa optimizes a neural network by a stochastic search
runsa optimizes a neural network by Boltzman annealing
runshow 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 statistic. This statistic can also be used for detecting long-memory in ARCH models. The first argument of the quantlet is the series, the second optional argument is the vector of truncation lags for the spectral based autocorrelation consistent estimator of the variance. If this optional argument is not provided, the default vector of truncation lags used by Kwiatkowski, Phillips, Schmidt and Shin is used. The quantlet returns the order of the truncation lag, the rescaled variance statistic, with the 95% critical value.

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, 28.6.1999