SA SB SC SD SE SF SG SH SI SJ SK SL SM SN SO SP SQ SR SS ST SU SV SW SX SY SZ

- samplelevf
- returns the values of the sample limited expected value for a data set.
- samplemef
- returns the values of the sample mean excess function for a data set.
- second
- returns the second as a string
- selec
- selects rows from the matrix mat
- select
- select calculates semiparametric estimates of the intercept and slope coefficients in the "outcome" or "level" equation of a self-selection model. It is the second step of the two-step estimator of these models. It combines the procedures in the quantlets powell (slope estimator) and andrews (inter
- select0item
- select0item is an item selector for glm. It allows exactly at most one item to be selected.
- select1item
- select1item is an item selector for glm. It allows exactly one item to be selected.
- selectitem
- opens a self defined menu box to ask for a choice. The choice may have to be confirmed or not (mode="single").
- seq
- Estimates a simultaneous equations model by 3-stage least squares
- setenv
- setenv sets one of the environment variables described below to a desired value
- setfractions
- Changes the proportions of parts of a display
- setgopt
- Controls the layout of a display. First, the display should be created and shown. Then call setgopt to change its headline, the labels of its axes, limits, etc.
- setheadline
- Changes the headline of plot.
- setmask
- front-end for the setting of mask vectors that allows easy definitions for points, lines (polygons), surfaces and text.
- setmaskl
- Connects certain data points by lines, which can be defined separately.
- setmaskp
- color, graphical representation (symbol) and size of each point of a data matrix can be specified.
- setmaskt
- Assigns labels to the points of a data set.
- setmode
- setmode sets the mouse mode of a plot. 0=ZOOM and INDEX 1=BRUSH 2=defines your own actions using readevent
- setsize
- Sets the size (in pixels) of a display for all the plots created afterwards.
- settime
- settime generates from a set of vectors a vector of time points. The parameter yr (year) is necessary, the parameters mo (month), dy (day of month), hr (hour), mn (minute), sc (second) are optional. The granulation of the measurement can be changed with the command settimegran. Each value in t
- settimegran
- settimegran changes the granulation. Possible values for newgran are 1...7 (Seconds, minutes, hour, days, weeks, month, years)
- settimezero
- settimezero changes the fixed time point.
- setxaxis
- Changes the layout of the horizontal axis.
- setyaxis
- Changes the layout of the vertical axis.
- sfcoeff
- estimates standard errors of parameter estimates
- sfvonbss
- standard errors of parameters for Subset VAR
- sfvonmw
- standard errors for mean in VAR models
- shiftr
- Shifts the rows of a matrix
- show
- Used to show graphical objects (such as a data matrix) in a display.
- sigma1
- auxiliary quantlet for full VAR model analysis
- sign
- Computes the sign function (0, -1, 1) for zeros, negative or positive values.
- sijci
- auxiliary quantlet for cointegration
- simdep
- Computes the simplicial depth estimate of location
- simex
- SIMEX (SIMulation EXtrapolation) is a simulation-based method of estimating and reducing bias due to measurement error. simex is applicable to general estimation methods, for example, least-squares, maximum likelihood, quasi-likelihood, etc.
- simGBM
- Simulation of discrete observations of a Geometric Brownian Motion (GBM) via direct integration (method=1) or Euler scheme (method=2). The process follows the stochastic differential equation: dX(t) = mu X(t) dt + sigma X(t) dW(t).
- simgOU
- Simulation of discrete observations of a generalized Ornstein-Uhlenbeck process via Euler scheme. The process follows the stochastic differential equation: dX(t) = beta (L - X(t)) dt + sigma (X(t)^gamma) dW(t).
- simHeston
- Simulation of the spot price and volatility processes in the Heston stochastic volatility model: dS(t) = mu S(t) dt + v^0.5 S(t) dW1(t) dv(t) = kappa (theta - v(t)) dt + sigma (v(t)^0.5) dW2(t) Cov[dW1(t), dW2(t)] = rho dt
- simHPP
- generates homogeneous Poisson process with intensity lambda.
- simHPPALP
- generates aggregate loss process driven by the homogeneous Poisson process.
- simHPPRP
- generates risk process driven by the homogeneous Poisson process.
- simNHPP
- generates non-homogeneous Poisson process.
- simNHPPALP
- generates aggregate loss process driven by the non-homogeneous Poisson process.
- simNHPPRP
- generates risk process driven by the non-homogeneous Poisson process.
- simNHPPRPedf
- generates risk process driven by the non-homogeneous Poisson process with claims generated from empirical distribution function.
- simNHPPRPedfRT
- generates real-life trajectory of the risk process from given data with premium corresponding to the non-homogeneous Poisson process and incorporating emprirical mean loss.
- simNHPPRPmeanloss
- generates risk process driven by non-homogeneous Poisson process with given mean losss value incorporated in the premium.
- simNHPPRPmeanlossRT
- plots real-life trajectory of the risk process from given data with the premium corresponding to non-homogeneous Poisson process and incorporating given mean loss value.
- simNHPPRPRT
- plots real-life trajectory of the risk process from given data with the premium corresponding to non-homogeneous Poisson process.
- simou
- Simulation of discrete observations of an Ornstein-Uhlenbeck process via its transition probability law. The simulated process follows the stochastic differential equation: dX(t) = aX(t) dt + s dW(t).
- simpsonint
- simpsonint computes the integral of a given realvalued function about a d-dimensional cube [a_1,b_1] x ... x [a_d,b_d]. The method of simpson is used.
- simRP
- generates renewal process.
- simRPRP
- generates risk process driven by the renewal process.
- simvar
- computes a multidimensional autoregressive time series.
- sin
- Returns the sine in radian of the elements of an array.
- sinh
- Returns the hyperbolic sinus of the elements of an array.
- sir
- Calculates the effective dimension-reduction (edr) directions by Sliced Inverse Regression (Li, 1991)
- sir1
- the taylored Sliced Inverse Regression method for the "sssm" quantlet.
- sir2
- Calculates the effective dimension-reduction (edr) directions by Sliced Inverse Regression II (Li, 1991)
- size
- gives the number of elements contained in a list or array
- sker
- sker computes a direct kernel estimate without binning from scatter plot data.
- skewness
- Computes the skewness for a given vector.
- sknn
- sknn computes the k-nearest neighbour smooth regression from scatter plot data. As inputs you have to specify the explanatory variable x, the dependent variable y and the smoothing parameter k.
- slidevalue
- asks for one or more input values via a dialog box with sliders ; and returns them.
- smoothermain
- loads the kernels needed by the smoother lib functions
- smoothertest
- smoothertest tests all the aforementioned quantlets of the smoother.lib
- softauto
- Softthresholds the mother wavelet coefficients b1 and b2 automatically by sqrt(2 sigma n). To compute the threshold value only b1 and x is used.
- softthres
- Softthresholds the mother wavelet coefficients b1 and b2 interactively. The user is a threshold offered by sqrt(2 sigma n). To compute the threshold value only b1 and x is used.
- sort
- sort sorts the rows of a matrix. If column c1 is specified the matrix will be sorted with respect to column c1. That is, the rows of the matrix will be arranged in order that elements of column c1 are in ascending (descending) order.
- spatialmain
- Loads the dll needed for the quantlets in spatial.
- spatialSPKRtest
- tests the functionality of the spatial interpolation, smoothing and kriging quantlets that have been adapted from Venables and Ripley (1999).
- spatialSPPPtest
- tests the functionality of the spatial point process analysis quantlets that have been adapted from Venables and Ripley (1999).
- spccusum1
- graphical representation of a one-sided CUSUM chart for given data, critical value c and reference value k.
- spccusum1ad
- Computes the Average Delays of a one-sided CUSUM chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
- spccusum1arl
- Computes the Average Run Lengths of a one-sided CUSUM chart for a given critical value and for various expected values mu. The data are normally distributed with variance 1.
- spccusum1c
- Computes the critical value of a one-sided CUSUM chart for a given in-control Average Run Length. The data is normally distributed with variance 1.
- spccusum1pmf
- Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the one-sided CUSUM chart run length for given i. The data are normally distributed with variance 1.
- spccusum1pmfm
- Computes the probability mass function (PMF, P(L=i))and the cumulated distribution function (CDF, P(L<=i)) of the one-sided CUSUM chart run length up to a given i. The data are normally distributed with variance 1.
- spccusum2
- graphical representation of a two-sided CUSUM chart for given data, critical value and reference value k
- spccusum2ad
- Computes the Average Delays of a two-sided CUSUM chart for a given critical value and for various expected values mu. The data are normally distributed with variance 1.
- spccusum2arl
- Computes the Average Run Lengths of a two-sided CUSUM chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
- spccusum2c
- Computes the critical value of a two-sided CUSUM chart for a given in-control Average Run Length. The data are normally distributed with variance 1.
- spccusum2pmf
- Computes the probability mass function (PMF, (P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the two-sided CUSUM chart run length for given i. The data are normally distributed with variance 1.
- spccusum2pmfm
- Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the two-sided CUSUM chart run length up to a given i. The data are normally distributed with variance 1.
- spccusumC
- graphical representation of a Crosier CUSUM chart for given data, critical value and reference value k.
- spccusumCad
- Computes the Average Delays of the Crosier-CUSUM chart for a given critical value and for various expected values mu. The data are normally distributed with variance 1.
- spccusumCarl
- Computes the Average Run Lengths of the Crosier-CUSUM chart for a given critical value and for various expected values mu. The data are normally distributed with variance 1.
- spccusumCc
- Computes the critical value of the Crosier-CUSUM chart for given in-control Average Run Length. The data is normally distributed with variance 1.
- spccusumCpmf
- Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the Crosier CUSUM chart run length for given i. The data are normally distributed with variance 1.
- spccusumCpmfm
- Computes the probability mass function (PMF P(L=i)) and the cumulative distribution function (CDF P(L<=i)) of the Crosier CUSUM chart run length up to a given i. The data are normally distributed with a variance of 1.
- spcewma1
- graphical representation of a one-sided EWMA chart for given data, critical value and smoothing parameter lambda
- spcewma1ad
- Computes the Average Delays of a one-sided EWMA chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
- spcewma1arl
- Computes the Average Run Lengths of a one-sided EWMA chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
- spcewma1c
- Computes the critical value of a one-sided EWMA chart for a given in-control Average Run Length. The data is normally distributed with variance 1.
- spcewma1pmf
- Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of a one-sided EWMA chart run length for given i. The data are normally distributed with variance 1.
- spcewma1pmfm
- Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the one-sided EWMA chart run length up to a given i. The data are normally distributed with variance 1.
- spcewma2
- graphical representation of a two-sided EWMA chart for given data, critical value and smoothing parameter lambda
- spcewma2ad
- Computes the Average Delays of a two-sided EWMA chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
- spcewma2arl
- Computes the Average Run Lengths of a two-sided EWMA chart for a given critical value and for various expected values mu. The data is normally distributed with variance 1.
- spcewma2c
- Computes the critical value of a two-sided EWMA chart for a given in-control Average Run Length. The data is normally distributed with variance 1.
- spcewma2pmf
- Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the two-sided EWMA chart run length for given i. The data are normally distributed with variance 1.
- spcewma2pmfm
- Computes the probability mass function (PMF, P(L=i)) and the cumulative distribution function (CDF, P(L<=i)) of the two-sided EWMA chart run length up to a given i. The data are normally distributed with variance 1.
- spcmain
- Quantlet for loading the shared library *.dll/so
- spdbl
- Uses the Breeden and Litzenberger (1978) method and a semiparametric specification of the Black-Scholes option pricing function to calculate the empirical State Price Density. The analytic formula uses an estimate of the volatility smile and its first and second derivative to calculate the State-pr
- spdbs
- Using the assumptions of the Black-Scholes call-option pricing formula this quantlet calculates the Black- Scholes State-Price Density, Delta and Gamma from call-options data
- spdest2
- estimates the state price density from the prices of European call and put options.
- SPDlp
- computes the State-Price Density for European options using the result of Breeden and Litzenberger.
- spdopt
- defines a list with optional parameters in spd functions. Note that spdopt does accept _any_ values for the parameters without checking validity.
- spec
- estimates and plots the spectral density of a time series
- spfill
- spfill fills places of sparsity with interpolated observations to avoid the need of oversmoothing.
- SPKRcorrelogram
- computes spatial correlograms of spatial data or residuals. Initially, it divides the range of the data into nint bins, computes the covariance for pairs with separation in each bin, then divides by the variance. It returns results only for bins with 6 or more pairs.
- SPKRexpcov
- spatial covariance function for use with SPKRsurfgls
- SPKRgaucov
- spatial covariance function for use with SPKRsurfgls
- SPKRmultcontours
- draws multiple contour lines of a spatial object of type "trmat", "prmat", or "semat"
- SPKRprmat
- evaluates a Kriging surface over a grid
- SPKRsemat
- evaluates a Kriging standard error of prediction surface over a grid
- SPKRsphercov
- spatial covariance function for use with SPKRsurfgls
- SPKRsurfgls
- fits a trend surface by generalized least squares
- SPKRsurfls
- fits a trend surface, i.e., a polynomial regression surface, by least squares
- SPKRtrmat
- evaluates a trend surface over a grid
- SPKRvariogram
- computes spatial (semi-)variograms of spatial data or residuals. Initially, it divides the range of the data into nint bins, and computes the average squared difference for pairs with separation in each bin. It returns results only for bins with 6 or more pairs.
- spline
- spline fits a cubic spline to input data.
- SPPPgetregion
- retrieves the rectangular spatial domain that previously has been set by SPPPinit or SPPPsetregion
- SPPPinit
- creates a point process object and calls SPPPsetregion to set the rectangular spatial domain
- SPPPinitrandom
- resets the random number generator for point processes
- SPPPkaver
- computes average of simulations of K-fns
- SPPPkenvl
- computes envelope (upper and lower limits) and average of simulations of K-fns
- SPPPkfn
- computes K-fn of a point pattern. Actually, it computes L = sqrt(K / pi). Note that SPPPinit or SPPPsetregion must have been called before.
- SPPPpsim
- simulates a Binomial (Poisson) spatial point process. Note that SPPPinit or SPPPsetregion must have been called before to set the domain. To be able to reproduce results, reset the random number generator for point processes by calling SPPPinitrandom first.
- SPPPsetregion
- sets the rectangular spatial domain for spatial point pattern analysis
- SPPPssi
- simulates a SSI (sequential spatial inhibition) point process. Note that SPPPinit or SPPPsetregion must have been called before to set the domain. To be able to reproduce results, reset the random number generator for point processes by calling SPPPinitrandom first. Note that this quantlet will not
- SPPPstrauss
- simulates a Strauss spatial point process. It uses a spatial birth-and-death process for (4 n) steps (or for (40 n) steps when starting from a binomial pattern on the first call from another function). Note that SPPPinit or SPPPsetregion must have been called before to set the domain. To be able to
- sptest
- Additive component analysis in additive separable models using wavelet estimation. An additive component can be tested against a given polynomial form with degree p, e.g. when p is set to zero we test for significant influence of that component. The procedure is presented in Haerdle, Sperlich,
- spur
- computes the trace of the matrix
- sqrt
- sqrt computes the square root of the elements of an array.
- sssm
- computes the estimates of the slope vectors in the outcome equation and in the selection equation for a semiparametric sample selection model (SSSM).
- sstern1
- auxiliary quantlet for mcmillan
- sstern2
- auxiliary quantlet for mcmillan
- stabcull
- returns parameter estimates of stable distribution using McCulloch's method
- stabmom
- returns parameter estimates of stable distribution using the method of moments
- stabreg
- returns parameter estimates of stable distribution using regression method
- stack
- Joins two arrays along the third dimension.
- StandardNormalCharf
- computes the characteristic function of a one-dimensional normally distributed random variable.
- station
- test for structural change (for VAR models)
- statsmain
- sets defaults for library stats.
- statstest
- executes some tests for the quantlets defined in stats.lib. Is invoked by vertestl().
- stein
- Stein computes the optimal threshold for a vector of data plus noise so that the mean squared error is minimized. Stein uses Stein's unbiased risk estimator for the risk. The quantlet sure uses stein to threshold the father and mother wavelet coefficients.
- steps4plot
- produces a matrix of points for plotting a left continuous step function.
- stockest
- estimates for a given dataset of a random process the parameters of the following two models: a Wiener Process (model 1) and a compounded Poisson Jump Process mixed with a Wiener Process (model 2)
- stockestsim
- using the given data stockestsim estimates the parameters for the following models: model 1, a Wiener Process, and model 2, a Wiener Process with jumps which are following a compounded Poisson Jump Process; after that both models are compared with the real dataset by a simulation.
- stocksim
- simulates random processes for a stock price by three different ways: 1. using a Wiener Process, 2. using a compounded Poisson Jump Process with a log normal distribution of jump height and 3. using a mixture of both.
- stointp
- Auxiliary routine for rICfil: calculates for dimension p>(=)2 diag(E[ YY' u min(b/|aIhY|,u) ]) and diag(E[ YY' min(b/|aIhY|,u)^2 ]) for u square root of a Chi^2_p-variable, and Y~ufo(S_2) indep of u by using a polar representation of Lambda:= I^{1/2} Y u, u = | I^{-1/2} Lambda |, Y=I^{
- stointpm
- Auxiliary routine for rICfil: calculates for dimension p>(=)2 (E[ YY' u min(b/|aIhY|,u) ]) and (E[ YY' min(b/|aIhY|,u)^2 ]) for u square root of a Chi^2_p-variable, and Y~ufo(S_2) indep of u by using a polar representation of Lambda:= I^{1/2} Y u, u = | I^{-1/2} Lambda |, Y=I^{-1/2} La
- stree
- constructs and plots a survival tree - a nonparametric regression model for censored survival data
- streeoutput
- an auxiliary quantlet for stree, provides the text output and prepares output variables
- string
- converts a set of vectors through a format string in a string.
- strlen
- strlen returns the length of a string, i.e. the number of characters in the string.
- strtok
- splits a string into tokens. Therefore a set of delimiters has to be specified, i.e., characters that separate the tokens, which are not returned themselves.
- strucbru
- auxiliary quantlet for multi
- substr
- substr extracts a substring from a string.
- sum
- sum computes the sum of the elements of an array regarding a given dimension.
- sumex
- an extended form of the sum function - NaN and all values contained in excl are excluded from computation
- summarize
- provides a short summary table (min, max, mean, median standard error) for all columns of a data matrix. An additional vector of name strings can be given to identify columns by names.
- summarizex
- provides a summary table (containing: N, Nmiss, min, max, mean, standard error, 1%, 10%, 25%, 50%, 75%, 90%, 99% quantiles) for all columns of a data matrix. Missings values are omitted. An additional vector of name strings can be given to identify the columns by names.
- supsmo
- calculates the super smoother
- sur
- Estimates a seemingly unrelated regression system by feasible generalized least squares
- sure
- Sure denoises wavelet coefficients so that the mean squared error is minimized. MSE is estimated by Stein's unbiased risk estimator based on the variance of the coefficients. Sure computes the optimal threshold for the father wavelets and each level of mother wavelets. The input arrays can be obtai
- sure2d
- Sure denoises wavelet coefficients. If the stein procedure is chosen, the mean squared error is minimized. MSE is estimated by Stein's unbiased risk estimator based on the variance of the coefficients. Sure computes then the optimal threshold for the father wavelets and each level of mother wavelet
- SurfaceInterpol
- Given a surface on a regular 2-dimensional grid, SurfaceInterpol computes the surface value at unknown points by a local polynomial interpolation
- svd
- computes the singular value decomposition of an n x p matrix x (n >= p). The singular value decomposition finds matrices u, l, v such that x = u*l*v', where u and v are orthogonal matrices and l is a diagonal matrix.
- svm
- returns the vector of scores for the objects represented in AC. AT is a training set where the last column describes the class of an object (must be +1 or -1).
- svmplot
- support quantlet providing graphical output for svm.xpl.
- svmplotcol
- support quantlet providing graphical output for svm.xpl.
- switch
- switch allows the selection of one or more alternatives of many. Each alternative is introduced by a case statement. Similar to if-endif it controls, whether the following block is processed or not. The keyword break serves as end marker of case and leaves the switch block at the position of endsw.
- symroot
- Calculates the symmetric root of a symmetric positive semidefinite matrix,(s.p.s.d.) ie. symroot(x)=symroot(x)' and symroot(x)*symroot(x) = x. uesful for simulation of multivariate normal variates with a given Covariance Structure
- symweigh
- computes the symmetrical weights for a selected kernel

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