WARNING: THIS PAGE IS NOT MAINTAINED ANYMORE. I STOPPED USING GAUSS IN 2010
Addel.g = deletion diagnostics for linear or quadratic discriminant analysis
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
multout.g = to monitor
particular distances (e.g. max. distance inside subset, min.
distance outside subset) in each step of the forward search
rflr.g = fwd search for lik.
ratio for transformation in multivariate 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
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
H0:
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