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

gamfit - genarma - getenv - givenrot - glminvlink - glmout - gplmbilobiased - gplmopt - grash - grcolorscheme - grlinreg - grqqu - grxline - gryline
gamfit gamfit provides an interactive tool for fitting additive models
gammaci auxiliary macro for cointegration
gammain sets defaults for library gam
gamopt gamopt defines a list with optional parameters in gam macros. The list is either created or new options are appended to an existing list. Note that gamopt does accept _any_ values for the parameters without validity.
gamout creates a nice output for GAM. ! auxiliary macro !
gamtest tests all abovementioned macros of gam.lib
gau gau computes the gaussian kernel, multivariate
gauder gauder evaluates derivatives of the Gaussian kernel rescaled by a bandwidth h, to be used for density estimation bandwidth selection.
genar genar generates an autoregressive process.
genarch generates a GARCH process with Gaussian innovations
genarma generates an autoregressive moving average process with mean zero
genbil generates a bilinear process x(t)=sum phi(i)x(t-i) + sum theta(j)e(t-j) + sum sum gamma(i,j)x(t-i)e(t-j)
genbilo genbilo generates data from a logit model, i.e. y from Binomial(m,p) with P(y=1)=1./(1+exp(-x*b))
genexpar generates an exponential AR process
genmultlo genmultlo generates data according to a multinomial logit model with P( Y = j | Xa , Xi) proportional to exp( Xa * ba + Xi * bi[j] ). Here, Xi denotes the part of the explanatory variables which merely depends on the individuals, Xa covers variables which may vary with the alternatives j. Either part, Xa or Xi, can be omitted.
gennet generates interactively a feedforward network
gennorm Generates observations from a multivariate normal distribution with given mean vector and covariance matrix.
gentar generates a Threshold AR process
genvub genvub computes the volume of unit ball from dimiension 1 up to 15 and put the results as global
getdata getdata gets a data from a plot.
The data set must be previous shown in this plot using show oder adddata.
getenv getenv reads the content of an environment variables (xpl4home, xpl4data, xpl4help, xpl4prog, xpl4backup, format) format contains the output format used by the printf of the C library.
getglobal getglobal reads a global variable

getgopt getgopt gets the layout of a plot. It is usually used to copy some of all layout components from one plot to another one.

getlocalnow getlocalnow returns the actual date and time with correction for the time zone, daylight savings and so on. The corrections are machine dependend.
getnow getnow returns the actual date and time in Greenwich time.
getxgobiinfo
gform
gintest estimation of the univariate additive functions in a separable generalized additive model using Nad.Watson, local linear or local quadratic
gintestpl gintestpl fits an additive generalized partially linear model E[y|x,t] = G(x*b + m(t)). This macro offers a convenient interface for GPLM estimation. A preparation of data is performed (inclusive sorting).
ginv Calculates a pseudo-inverse of x, such that x*ginv(x)*x = x.
givenrot Decomposes an orthonormal matrix into a set of rotations by Givens rotation
glmbackward glmbackward performs a backward model selection by searching the best of all subset models w.r.t. the AIC or BIC criterion. Optionally, a number of columns can be given, which are always included in the submodels.
glmbilo glmbilo fits a generalized linear model where y|x is binomial distributed and E[y|x] and x*b are linked via the logistic function (canonical link)
glmbipro glmbipro fits a generalized linear model where y|x is binomial distributed and E[y|x] and x*beta are linked via the gaussian cdf (non canonical link)
glmcore fits a generalized linear model E[y|x] = G(x*b). This is the core macro for GLM estimation. It assumes that all input variables are given in the right manner. No preparation of data is performed. A more convenient way to estimate a GLM is to call the function glmest.
glmdiagh glmdiagh calculates the diagonal elements of the 'hat' matrix
glmest glmest fits a generalized linear model E[y|x] = G(x*b). This macro offers a convenient interface for GLM estimation. A check of the data is performed.
glmfit helper macro for doglm.
glmforward glmforward performs a forward model selection by searching the best of all subset models w.r.t. the AIC or BIC criterion. Optionally, a number of columns can be given, which are always included in the submodels.
glminit glminit checks the validity of input and performs the initial calculations for an GLM fit. The output is ready to be used with glmcore.
glminvlink glminvlink computes the inverse link function.
glmlink glmlink computes the link function.
glmll glmll computes the individual log-likelihood.
glmlld glmlld computes the first and second derivative of the individual log-likelihood in dependence of the linear index eta and y.
glmlrtest glmlrtest performs a likelihood ratio test of two nested GLM.
glmmain sets defaults for library glm.
glmmultlo glmmultlo fits a multinomial/conditional logit model where the response Y is multinomial distributed. This means, P( Y = j | Xa , Xi) is proportional to exp( Xa * ba + Xi * bi[j] ). Here, Xi denotes that part of the explanatory variables which merely depends on the individuals and Xa covers variables which may vary with the alternatives j. Either part, Xa or Xi, can be omitted.
glmmultshape reshapes data from panel format to matrix format and vice versa. The matrix format is needed for glmmultlo.
glmnoid glmnoid fits a generalized linear model where y|x is normally distributed and E[y|x] and x*beta are linked via the identity function (canonical link)
glmopt glmopt defines a list with optional parameters in glm macros. The list is either created or new options are appended to an existing list. Note that glmopt does accept _any_ values for the parameters without validity.
glmout glmout creates a nice output display for GLM.
glmplot glmplot creates a display and plots for a one-dimensional explanatory variable: the distribution, a scatterplot of the marginal influence versus the response and a scatterplot of the variabel versus the response.
glmscatter glmscatter computes a scatterplot to explain the marginal influence of a variable on the response.
glmselect glmselect performs a model selection by searching the best of all subset models w.r.t. the AIC or BIC criterion. Optionally a number of columns can be given, which are always included in the submodels.
glmstat glmstat provides some statistics for a fitted GLM.
glmtest executes some tests for the macros defined in glm.lib. Is invoked by vertestl().
gls Computes the Generalized Least Squares estimate for the coefficients of a linear model when the errors have as covariance matrix sigma^2 * om.
gp1me gp1me evaluates the mean excess function of the Pareto (GP1) distribution with shape parameter alpha for all elements of a vector.
gph Estimation of the degree of long memory of a time series by using a log-periodogram regression
gplmbilo GPLM (logit) -- gplmbilo fits a generalized partially linear model where y|x,t is binomial distributed and E[y|x,t] and x*b + m(t) are linked via the logistic function (canonical link).
gplmbilobiased biased GLM (logit) -- gplmbilobiased computes the biased generalized linear model for the test of a GLM (logit) versus a GPLM (logit).
gplmbilobootstraptest Bootstrap test (using as. normality) GLM (logit) vs. GPLM (logit) -- by default gplmnoidtest tests the generalized "truly" linear model E[y|x,t] = G(x*b + t*g + c) versus the genralized partially linear model E[y|x,t] = G(x*b + m(t)), where G is the logistic link function. Optional, an alternative design matrix can be specified for t or parametric estimates for b and m can be given directly.
gplmbilotest Test (using as. normality) GLM (logit) vs. GPLM (logit) -- by default gplmnoidtest tests the generalized "truly" linear model E[y|x,t] = G(x*b + t*g + c) versus the genralized partially linear model E[y|x,t] = G(x*b + m(t)), where G is the logistic link function. Optional, an alternative design matrix can be specified for t or parametric estimates for b and m can be given directly.
gplmcore gplmcore fits a generalized partially linear model E(y|x,t) = G(x*b + m(t)). This is the core macro for GPLM estimation. It assumes that all input variables are given in the right manner. No preparation of data is performed. A more convenient way to estimate a GPLM is to call the function gplmest.
gplmest gplmest fits a generalized partially linear model E[y|x,t] = G(x*b + m(t)). This macro offers a convenient interface for GPLM estimation. A preparation of data is performed (inclusive sorting).
gplminit gplminit checks the validity of input and performs the initial calculations for an GPLM fit (inclusive sorting). The output is ready to be used with gplmcore.
gplmmain loads everything necessary for library gplm.
gplmnoid PLM -- gplmnoid fits a generalized partially linear model where y|x,t is normally distributed and E[y|x,t] = x*b + m(t) (canonical link).
gplmnoidbiased biased LM -- gplmnoidbiased computes the biased linear model for the test of a linear model versus a PLM. This is a fast routine using the command sker to obtain kernel estimates.
gplmnoidtest Test (using as. normality) linear vs. partially linear -- by default gplmnoidtest tests the "truly" linear model E[y|x,t] = x*b + t*g + c versus the partially linear model E[y|x,t] = x*b + m(t). Optional, an alternative design matrix can be specified for t or parametric estimates for b and m can be given directly.
gplmopt gplmopt defines a list with optional parameters in gplm macros. The list is either created or new options are appended to an existing list. Note that gplmopt does accept _any_ values for the parameters without validity.
gplmout gplmout creates a nice output display for gplm.
gplmstat gplmstat provides some statistics for a fitted GPLM.
gplmtest gplmtest verifies the GPLM macros.
gpme gpme evaluates the mean excess function of the GP distribution with shape parameter gamma for all elements of a vector.
gpplot returns the Grassberger-Procaccia plot for time series
gpsigmaest estimator for scale parameter within GP models
grandrews Generates an Andrews plot.
graphicmain Generates graphical constants and loads all libraries necessary for the graphics.
graphictest Tests the macros of the graphic library.
grash Generates an averaged shifted histogram.
grbar Generates a barchart.
grbinomial generates a graphical object which represents the probability function of a binomial distribution B(n,p)
grbiplot Generates a graphical object containing the coordinates for the biplot of a given matrix.
grbox Generates a boxplot with mean line and median line. Outliers outside the intervall [Q_25-3*IQR, Q_75+3*IQR] will be plotted as crosses, outliers ouside the intervall [Q_25-1.5*IQR, Q_75+1.5*IQR] will be plotted as circles.
grboxgrouped Generates a boxplot for grouped data with mean line (dotted) and median line (solid).
grboxmean Generates a boxplot with the mean line. The box borders are plus/minus one standard deviation of the mean line and the whiskers are plus/minus two standard deviations.
grboxmedian Generates a boxplot with the median line. The box borders are the percentiles which are equivalent to the mean plus/minus one standard deviation in the normal case (16% and 84% percentile) and the whiskers are equivalent to to the mean plus/minus two standard deviations in the normal case (2.5% and 97.5% percentile).
grcircle Generates a circle or ellipse as a graphical object. The circle is centered at (0,0) and has the given radius.
grcirclesector Generates a sector of a circle.
grcolorscheme Returns a vector of rgb colors.
grcontour2 Generates a contour plot from a 3-dimensional dataset x.
grcontour3 generates a contour plot from a 4-dimensional dataset x.
grcube Generates a 3-D cube with labels at the axes surrounding a 3-dimensional dataset.
grdot Generates a dotplot.
grdotd Generates a dotplot as a density plot.
grdotdl Generates a dotplot as a density line.
greeks calculates and displays the different indices which are used for trading with options, in particular delta (the partial derivative of an option with respect to the price S of the underlying asset), gamma (2 x p .d.w.r.t. price S of underlying asset), eta (delta x S / option price), delta-K (p.d.w.r.t. exercise price K), vega (p.d.w.r.t. the volatility of the asset), theta (p.d.w.r.t. the time to expiration), rho (p.d.w.r.t. the domestic interest rate), rho-b (p.d. w.r.t. the costs of carry (in percent of the value of the underlying object).
grhist Generates a histogram from the data.
grid This command generates a grid with origin x and stepwidth h with respect to each dimension, n indicating the number of gridpoints in the respective dimension.
grlinreg Generates a graphical object which contains a linear regression line from the data.
grlinreg2 generates a graphical object which contains a linear regression plane from the data. The plane is computed on a rectangular grid with n^2 meshes.
grmove Moves a graphical object.
groupcol Decomposes a (color) vector into single groups.
grpcp Generates a parallel coordinates plot.
grpie Generates a pie chart from the data.
grpp generates a probability-probability-plot to compare two variables
grppn generates a probability-probability-plot to compare a variable with a normal distribution
grqq Generates a quantile-quantile-plot to compare two variables.
grqqn Generates a quantile-quantile-plot to compare a variable with a normal distribution.
grqqu Generates a quantile-quantile-plot to compare a variable with a uniform distribution.
grrot Rotates a graphical object.
grscale scales a graphical object
grstar Generates a star diagram.
grsunflower Generates a sunflower plot.
grsurface generates a surface plot from a 3-dimensional dataset.
gruppenscatter creates according the selected variables and group variables the appropriate parameters d, c, r and title to build the displays in showd for a scatterplot
gruppenscatterp creates according the selected variables the appropriate parameters d, c, r and title to build the displays in showd for a scatterplot for the saved variables after regression analysis (no group variable possible)
gruppenvariable creates according the selected variables and group variables the appropriate parameters d, c, r and title to build the displays in showd
gruppenvariable2 creates according the selected variables and group variables the appropriate parameters d, c, r and title to build the displays in showd for barcharts, boxplots, dotplots
grxline Generates a vertical line as a graphical object.
gryline Generates a horizontal line as a graphical object.

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