randbin | computes random numbers based on the binomial distribution |
randomize |
Sets the seed of the pseudorandom number generator.
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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.
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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
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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
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rqua | computes the rescaled Gaussian kernel ngau(u) = 5.*gau(5.*u), multivariate. |
rtri |
rtri computes the multivariate radial symmetric
triweight kernel
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rtrian |
rtrian computes the multivariate radial symmetric
triangle kernel
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runcv | runs a cross validation and estimates the generalization error |
runi |
runi computes the multivariate radial symmetric
uniform kernel
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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. |