Marco Riani, Professor of Statistics

      Univ. of Parma (ITALY)

GAUSS ROUTINES

WARNING: THIS PAGE IS NOT MAINTAINED ANYMORE. I STOPPED USING GAUSS IN 2010

Addel.g = deletion diagnostics for linear or quadratic discriminant analysis

addt.g = t test for additional explanatory variable

addvar.g = added variable plot using constructed variable coming
from transformation

addvarw.g = added variable plot using additional real variable

andrews.g = Andrews' curves

anova.g = to test equality of multivariate means

boxplot.g = univariate boxplot

boxplotb.g = to superimpose a bivariate boxplot on a scatter diagram

conflr.g = sign sqrt of lik. ratio for transformations (expansion around a set of values of \lambda)

conflrad.g = sign sqrt of lik. ratio for transformations in discriminant analysis

elms.g = enumerate all subsamples (n choose p)
Sampling without replacement

elmsr.g = enumerate all subsamples
Sampling with replacement

eqvar.g = to test homogeneity of covariances in different groups

flr.g = forward version of the likelihood ratio test for transformation

flrld.g = forward version of the likelihood ratio test for transformation in linear discriminant analysis

flrqd.g = forward version of the likelihood ratio test for transformation in quadratic discriminant analysis

fwdbsb.g = in each step of the forward search in multivariate analysis the units forming subset are stored

fldell.g = plots confidence ellipses in selected steps of the forward search

fwdglm.g = forward search for Generalized Linear Models

fwdlda.g = forward search in linear discriminant analysis

fwdmle.g = estimates of the transformation parameters in each step of the forward search

fwdmleld.g = estimates of the transformation parameters in each step of the forward search in linear discriminant analysis

fwdmleqd.g = estimates of the transformation parameters in each step of the forward search in quadratic discriminant analysis

fwdmles.g = estimates of the transformation parameter in each step of the forward search imposing a common value of lambda for all the variables

fwdols.g = forward search in regression

fwdolsmdr.g = simplified version of fwdols.g. This routine returns only the forward estimates of the minimum deletion residual, s^2 and the regression coefficients

fwdolsst.g= estimates of the forward deletion t-statistics

fwdpca.g= Forward search in principal component analysis

fwdqda.g= Forward search in quadratic discriminant analysis

glm.g = to fit a generalized linear model

glmdel = deletion diagnostics for generalized linear models
hull.g = convex hull peeling

inputbox.g= Compute necessary values to create a univariate boxplot 

lda.g = linear discriminant analysis

ldasimpl.g = simplified version of lda.g

likla.g = likelihood and score test for different values of the
transformation parameter λ in linear regression models

liklabs.g = likelihood and score test for different values of the
transformation parameter \lambda (both sides of the equation
are transformed) in linear regression models

liklag.g = to calculate the score test for different values
of the transformation parameter \lambda in linear regression models.
Both response and explanatory can be transformed

lms.g = to compute least median of squares (or least trimmed of aquares) estimator

lmsbs.g = to calculate the least median of squares estimator when
both sides of a model are transformed

lmsg.g = to compute the least median of squares estimator when both
response and explanatory are transformed

lmsglm.g = least median of squared in generalized linear models

lmsnls.g = least median of squares in non linear regression models

lraddel.g = deletion diagnostic based on likelihood ratio test for
transformation parameters in linear and quadratic
discriminant analysis

lrdel.g = deletion diagnostic based on likelihood ratio test for
transformation parameters in multivariate analysis

medb.g = to produce univariate or bivariate medians

multout.g = to monitor particular distances (e.g. max. distance inside subset, min. distance outside subset) in each step of the forward search


multsimp.g = simplified version of routine multout.g


nls.g = non linear least squares

norm.g = to test multivariate normality

outc.g = to detect the units which lie outside a B-spline curve

pca.g = principal component analysis

predglm.g = to produce reiduals in generalized linear models
given an input vector of beta coefficients

prednls.g = to produce residuals in non linear models given
an input vector of beta coefficients

qda.g = quadratic discriminant analysis

qdasimpl.g = simplified version of qda.g

qqnorm.g = qqnorm plot

quelplot.g = to produce the inputs to draw a bivariate ellipse

regressi.g = linear regression models

rflr.g = fwd search for lik. ratio for transformation in multivariate analysis
H0: λ=λ0 when matrix Y has a regression structure

rfwdmle.g = fwd serach for maximum likelihood estimates of
transformation parameters of the columns of a data matrix Y, when
Y has a regression structure

rfwdmles.g = fdw search for a common maximum likelihood estimate of a transformation
parameter of a data matrix Y, when Y has a regression structure

rob.g = robust methods for estimating regression coefficients
using routine optmum


scatter.g = scatter plot matrix with univariate boxplots on the main diagonal
It also enables you to specifiy different groups

scatterb.g = scatter plot matrix with superimposed bivariate boxplots.
It also enables you to specifiy different groups

scglm.g = goodness of link test in generalized linear models

scodel.g = deletion diagnostic for transformations in linear regression models

scom.g = multivariate version of the score test for linear regression models.
The additional variables are costructed automatically from transformation
parameters vector λ

scomR.g = multivariate version of the score test for linear regression models
The additional variables are supplied by the user

simenv.g = simulation envelopes for qqplots

splinem.g = to superimpose a B-spline curve on a polygon

stand.g = to standardize the data

unibiv.g = to detect univariate and bivariate outliers from a multivariate data matrix.
It superimposes robust ellipses in each scatter diagram and counts the number of times units fall outside the outer contuors for each pair of variables.

vardec.g = to decompose total deviance inside groups and between groups

vcxm.g = max lik. var-covar matrix from data matrix

wilks.g = deletion diagnostics in multivariate analysis using ratio
between determinants

without.g =sample without replacement

 

All files in .zip format