public class CategoricalGenLinModel extends Object
Reweighted least squares is used to compute (extended) maximum likelihood estimates in some generalized linear models involving categorized data. One of several models, including probit, logistic, Poisson, logarithmic, and negative binomial models, may be fit for input point or interval observations. (In the usual case, only point observations are observed.)
Let
be the linear response where is a design column vector obtained from a row of is the column vector of coefficients to be estimated, and is a fixed parameter that may be input inx
. When some of the
are infinite at the supremum of the
likelihood, then extended maximum likelihood estimates are computed.
Extended maximum likelihood are computed as the finite (but nonunique)
estimates that optimize the likelihood
containing only the observations with finite . These estimates, when combined with the set of indices of the
observations such that is infinite at
the supremum of the likelihood, are called extended maximum estimates. When
none of the optimal are infinite,
extended maximum likelihood estimates are identical to maximum likelihood
estimates. Extended maximum likelihood estimation is discussed in more
detail by Clarkson and Jennrich (1991). In
CategoricalGenLinModel
, observations with potentially infinite
are detected and removed from the likelihood if
infin
= 0. See below.
The models available in CategoricalGenLinModel are:
Model Name  Parameterization  Response PDF 
MODEL0 (Poisson)  
MODEL1 (Negative Binomial)  
MODEL2 (Logarithmic)  
MODEL3 (Logistic)  
MODEL4 (Probit)  
MODEL5 (Loglog) 
Here denotes the cumulative normal
distribution, N and S are known parameters specified for each
observation via column ipar
of x
, and w is
an optional fixed parameter specified for each observation via column
ifix
of x
. (By default N is
taken to be 1 for model
= 0, 3, 4 and 5 and S is taken
to be 1 for model
= 1. By default w
is taken to be 0.) Since the loglog model (model
= 5)
probabilities are not symmetric with respect to 0.5, quantitatively, as well
as qualitatively, different models result when the definitions of "success"
and "failure" are interchanged in this distribution. In this model and all
other models involving , is
taken to be the probability of a "success."
Note that each row vector in the data matrix can represent a single
observation; or, through the use of column ifrq
of the matrix
x
, each vector can represent several observations.
For interval observations, the probability of the observation is computed
by summing the probability distribution function over the range of values in
the observation interval. For rightinterval observations, is computed as a sum based upon the equality . Derivatives are computed similarly.
CategoricalGenLinModel
allows three types of
interval observations. In full interval observations, both the lower and the
upper endpoints of the interval must be specified. For rightinterval
observations, only the lower endpoint need be given while for leftinterval
observations, only the upper endpoint is given.
The computations proceed as follows:
ifrq
of the
data matrix x
. In binomial distribution models, the
frequency is taken as the product of n = x[i][ipar]
and x[i][ifrq]
. In all cases these values default to 1.
Means are computed as
init
= 0, initial estimates of the coefficients are
obtained (based upon the observation intervals) as multiple
regression estimates relating transformed observation probabilities
to the observation design vector. For example, in the binomial
distribution models, for point
observations may be estimated as
and, when model
= 3, the linear relationship is given
by
while if model
= 4,
For bounded interval observations, the midpoint of the interval is
used for x[i][irt]
. Rightinterval observations are not
used in obtaining initial estimates when the distribution has
unbounded support (since the midpoint of the interval is not
defined). When computing initial estimates, standard modifications
are made to prevent illegal operations such as division by zero.
Regression estimates are obtained at this point, as well as later, by use of linear regression.
eps
or when the relative change in the loglikelihood
from one iteration to the next is less than eps
/100.
Convergence is also assumed after maxIterations
or when
step halving leads to a step size of less than .0001 with no
increase in the loglikelihood.setInfiniteEstimateMethod
set to 0, then the methods
of Clarkson and Jennrich (1991) are used to check for the existence
of infinite estimates in
As an example of a situation in which infinite estimates can occur,
suppose that observation j is right censored with in a logistic model. If design matrix x
is is such that and for all , then the
optimal estimate of occurs at
leading to an infinite estimate of both
and . In
CategoricalGenLinModel
, such estimates may be
"computed."
In all models fit by CategoricalGenLinModel
, infinite estimates can only occur when the optimal
estimated probability associated with the left or rightcensored
observation is 1. If setInfiniteEstimateMethod
set to 0,
left or right censored observations that have estimated probability
greater than 0.995 at some point during the iterations are excluded
from the loglikelihood, and the iterations proceed with a loglikelihood
based upon the remaining observations. This allows convergence of
the algorithm when the maximum relative change in the estimated
coefficients is small and also allows for the determination of
observations with infinite
When setInfiniteEstimateMethod
is set to 1, no
observations are eliminated during the iterations. In this case, when
infinite estimates occur, some (or all) of the coefficient estimates
will become large, and it is likely
that the Hessian will become (numerically) singular prior to convergence.
When infinite estimates for the are detected, linear regression (see Chapter 2, Regression;) is used at the convergence of the algorithm to obtain unique estimates . This is accomplished by regressing the optimal or the observations with finite against , yielding a unique (by setting coefficients that are linearly related to previous coefficients in the model to zero). All of the final statistics relating to are based upon these estimates.
Following Cook and Weisberg (1982), we take the influence of the ith observation to be
This quantity is a onestep approximation to the change in the estimates when the ith observation is deleted. Here, the partial derivatives are with respect to .setClassificationVariableColumn
. Indicator or dummy
variables are created for the classification variables.setModelIntercept
is set to 1 and
(number of observations)  (number of rows in x
missing
one or more values) 1. In doing so, the
sample means of the design variables are subtracted from each
observation prior to its inclusion in the model. On convergence the
intercept, its variance and its covariance with the remaining
estimates are transformed to the uncentered estimate values.x[i][ifrq]
contains the
frequency of the observation while x[i][irt]
is 0 or 1
depending upon whether the observation is a success or failure. In
this case, N = x[i][ipar]
is always 1. The model
is treated as repeated Bernoulli trials, and interval observations
are not possible.A second method for specifying binomial models is to use x[i][irt]
to represent the number of successes in the x[i][ipar]
trials. In this case, x[i][ifrq]
will usually be 1, but it may
be greater than 1, in which case interval observations are possible.
Note that the solve
method must be called prior to calling
the "get" member functions, otherwise a null
is returned.
Modifier and Type  Class and Description 

static class 
CategoricalGenLinModel.ClassificationVariableException
The ClassificationVariable vector has not been initialized.

static class 
CategoricalGenLinModel.ClassificationVariableLimitException
The Classification Variable limit set by the user through
setUpperBound has been exceeded. 
static class 
CategoricalGenLinModel.ClassificationVariableValueException
The number of distinct values for each Classification Variable must be
greater than 1.

static class 
CategoricalGenLinModel.DeleteObservationsException
The number of observations to be deleted (set by
setObservationMax )
has grown too large. 
static class 
CategoricalGenLinModel.RankDeficientException
The model has been determined to be rank deficient.

Modifier and Type  Field and Description 

static int 
MODEL0
Indicates an exponential function is used to model the distribution
parameter.

static int 
MODEL1
Indicates a logistic function is used to model the distribution
parameter.

static int 
MODEL2
Indicates a logistic function is used to model the distribution
parameter.

static int 
MODEL3
Indicates a logistic function is used to model the distribution
parameter.

static int 
MODEL4
Indicates a probit function is used to model the distribution parameter.

static int 
MODEL5
Indicates a loglog function is used to model the distribution
parameter.

Constructor and Description 

CategoricalGenLinModel(double[][] x,
int model)
Constructs a new
CategoricalGenLinModel . 
Modifier and Type  Method and Description 

double[][] 
getCaseAnalysis()
Returns the case analysis.

int[] 
getClassificationVariableCounts()
Returns the number of values taken by each classification variable.

double[] 
getClassificationVariableValues()
Returns the distinct values of the classification variables in ascending
order.

double[][] 
getCovarianceMatrix()
Returns the estimated asymptotic covariance matrix of the coefficients.

double[] 
getDesignVariableMeans()
Returns the means of the design variables.

int[] 
getExtendedLikelihoodObservations()
Returns a vector indicating which observations are included in the
extended likelihood.

double[][] 
getHessian()
Returns the Hessian computed at the initial parameter estimates.

double[] 
getLastParameterUpdates()
Returns the last parameter updates (excluding step halvings).

int 
getNRowsMissing()
Returns the number of rows of data in
x that contain
missing values in one or more specific columns of x . 
double 
getOptimizedCriterion()
Returns the optimized criterion.

double[][] 
getParameters()
Returns the parameter estimates and associated statistics.

double[] 
getProduct()
Returns the inverse of the Hessian times the gradient vector computed at
the input parameter estimates.

void 
setCensorColumn(int icen)
Sets the column number in
x which contains the interval
type for each observation. 
void 
setClassificationVariableColumn(int[] indcl)
Initializes an index vector to contain the column numbers in
x
that are classification variables. 
void 
setConvergenceTolerance(double eps)
Set the convergence criterion.

void 
setEffects(int[] indef,
int[] nvef)
Initializes an index vector to contain the column numbers in
x
associated with each effect. 
void 
setExtendedLikelihoodObservations(int[] iadds)
Initializes a vector indicating which observations are to be included in
the extended likelihood.

void 
setFixedParameterColumn(int ifix)
Sets the column number in
x that contains a fixed parameter
for each observation that is added to the linear response prior to
computing the model parameter. 
void 
setFrequencyColumn(int ifrq)
Sets the column number in
x that contains the frequency of
response for each observation. 
void 
setInfiniteEstimateMethod(int infin)
Sets the method to be used for handling infinite estimates.

void 
setInitialEstimates(int init,
double[] estimates)
Sets the initial parameter estimates option.

void 
setLowerEndpointColumn(int irt)
Sets the column number in
x that contains the lower
endpoint of the observation interval for full interval and right
interval observations. 
void 
setMaxIterations(int maxIterations)
Set the maximum number of iterations allowed.

void 
setModelIntercept(int intcep)
Sets the intercept option.

void 
setObservationMax(int nmax)
Sets the maximum number of observations that can be handled in the
linear programming.

void 
setOptionalDistributionParameterColumn(int ipar)
Sets the column number in
x that contains an optional
distribution parameter for each observation. 
void 
setTolerance(double tol)
Initializes the tolerance used in determining linear dependence.

void 
setUpperBound(int maxcl)
Sets the upper bound on the sum of the number of distinct values taken
on by each classification variable.

void 
setUpperEndpointColumn(int ilt)
Sets the column number in
x that contains the upper
endpoint of the observation interval for full interval and left interval
observations. 
double[][] 
solve()
Returns the parameter estimates and associated statistics for a
CategoricalGenLinModel object.

public static final int MODEL0
public static final int MODEL1
public static final int MODEL2
public static final int MODEL3
public static final int MODEL4
public static final int MODEL5
public CategoricalGenLinModel(double[][] x, int model)
CategoricalGenLinModel
.x
 A double
input matrix containing the data where
the number of rows in the matrix is equal to the number of
observations.model
 An int
scalar which specifies the
distribution of the response variable and the function
used to model the distribution parameter. Use one of the
class members from the following table. The lower bound
given in the table is the minimum possible value of the
response variable:
Model  Distribution  Function  Lowerbound 
0  Poisson  Exponential  0 
1  Negative Binomial  Logistic  0 
2  Logarithmic  Logistic  1 
3  Binomial  Logistic  0 
4  Binomial  Probit  0 
5  Binomial  Loglog  0 
Name  Function 
Exponential  
Logistic  
Probit  (where is the normal cdf) 
Loglog 
public double[][] getCaseAnalysis()
double
matrix containing the case analysis or
null
if solve
has not been called. The
matrix is where nobs
is the number of observations. The matrix contains:
Column  Statistic 
0  Prediction. 
1  The residual. 
2  The estimated standard error of the residual. 
3  The estimated influence of the observation. 
4  The standardized residual. 
Model  Prediction 
0  The predicted mean for the observation. 
14  The probability of a success on a single trial. 
public int[] getClassificationVariableCounts() throws CategoricalGenLinModel.ClassificationVariableException
int
array of length nclvar containing the
number of values taken by each classification variable where
nclvar is the number of classification variables or
null
if solve
has not been called.CategoricalGenLinModel.ClassificationVariableException
 is thrown when the number of
values taken by each classification variable has been
set by the user to be less than or equal to 1public double[] getClassificationVariableValues() throws CategoricalGenLinModel.ClassificationVariableException
double
array of length containing the
distinct values of the classification variables in ascending
order where nclvar is the number of classification
variables and nclval[i] is the number of values taken by
the ith classification variable. A null
is
returned if solve
has not been called prior to calling
this method.CategoricalGenLinModel.ClassificationVariableException
 is thrown when the number of
values taken by each classification variable has been
set by the user to be less than or equal to 1public double[][] getCovarianceMatrix()
double
matrix containing the estimated asymptotic
covariance matrix of the coefficients or null
if solve
has not been called. The covariance matrix is
nCoef by nCoef where nCoef is the number of
coefficients in the model.public double[] getDesignVariableMeans()
double
array of length nCoef containing
the means of the design variables where nCoef is the
number of coefficients in the model or null
if solve
has not been called.public int[] getExtendedLikelihoodObservations()
int
array of length nobs indicating which
observations are included in the extended likelihood where
nobs is the number of observations. The values within the
array are interpreted as:
Value  Status of observation 

0  Observation i is in the likelihood. 
1  Observation i cannot
be in the likelihood because it contains at least one
missing value in x . 
2  Observation i is not in the likelihood. Its estimated parameter is infinite. 
null
is returned if solve
has not been called
prior to calling this method.public double[][] getHessian() throws CategoricalGenLinModel.ClassificationVariableException, CategoricalGenLinModel.ClassificationVariableLimitException, CategoricalGenLinModel.ClassificationVariableValueException, CategoricalGenLinModel.DeleteObservationsException, CategoricalGenLinModel.RankDeficientException, SVD.DidNotConvergeException
double
matrix containing the Hessian computed at
the input parameter estimates. The Hessian matrix is nCoef
by nCoef where nCoef is the number of
coefficients in the model. This member function will call solve
to get the Hessian if the Hessian has not already been computed.CategoricalGenLinModel.ClassificationVariableException
 is thrown when the number of
values taken by each classification variable has been
set by the user to be less than or equal to 1CategoricalGenLinModel.ClassificationVariableLimitException
 is thrown when the
sum of the number of distinct values taken on by each
classification variable exceeds the maximum allowed,
maxcl
CategoricalGenLinModel.DeleteObservationsException
 is thrown if the number of
observations to be deleted has grown too largeCategoricalGenLinModel.ClassificationVariableValueException
CategoricalGenLinModel.RankDeficientException
SVD.DidNotConvergeException
public double[] getLastParameterUpdates()
double
array of length nCoef containing
the last parameter updates (excluding step halvings) or null
if solve
has not been called.public int getNRowsMissing()
x
that contain
missing values in one or more specific columns of x
.int
scalar representing the number of rows of
data in x
that contain missing values in one or
more specific columns of x
or null
if
solve
has not been called. The columns of x
included in the count are the columns containing the
upper or lower endpoints of full interval, left interval, or
right interval observations. Also included are the columns
containing the frequency responses, fixed parameters, optional
distribution parameters, and interval type for each observation.
Columns containing classification variables and columns
associated with each effect in the model are also included.public double getOptimizedCriterion()
double
scalar representing the optimized
criterion or null
if solve
has not been
called. The criterion to be maximized is a constant plus the
loglikelihood.public double[][] getParameters()
nCoef
row by 4 column double
matrix containing the parameter
estimates and associated statistics or null
if
solve
has not been called. Here, nCoef
is
the number of coefficients in the model. The statistics returned
are as follows:
Column  Statistic 
0  Coefficient estimate. 
1  Estimated standard deviation of the estimated coefficient. 
2  Asymptotic normal score for testing that the coefficient is zero. 
3   value associated with the normal score in column 2. 
public double[] getProduct() throws CategoricalGenLinModel.ClassificationVariableException, CategoricalGenLinModel.ClassificationVariableLimitException, CategoricalGenLinModel.ClassificationVariableValueException, CategoricalGenLinModel.DeleteObservationsException, CategoricalGenLinModel.RankDeficientException, SVD.DidNotConvergeException
double
array of length nCoef containing
the inverse of the Hessian times the gradient vector computed at
the input parameter estimates. nCoef is the number of
coefficients in the model. This member function will call solve
to get the product if the product has not already been computed.CategoricalGenLinModel.ClassificationVariableException
 is thrown when the number of
values taken by each classification variable has been
set by the user to be less than or equal to 1CategoricalGenLinModel.ClassificationVariableLimitException
 is thrown when the
sum of the number of distinct values taken on by each
classification variable exceeds the maximum allowed,
maxcl
CategoricalGenLinModel.DeleteObservationsException
 is thrown if the number of
observations to be deleted has grown too largeCategoricalGenLinModel.ClassificationVariableValueException
CategoricalGenLinModel.RankDeficientException
SVD.DidNotConvergeException
public void setCensorColumn(int icen)
x
which contains the interval
type for each observation.icen
 An int
scalar which indicates the column
number x
which contains the interval
type code for each observation. The valid codes are
interpreted as:
x[i][icen] 
Censoring 
0  Point observation.
The response is unique and is given by
x[i][irt] . 
1  Right interval. The
response is greater than or equal to
x[i][irt] and less than or equal to the
upper bound, if any, of the distribution. 
2  Left interval. The
response is less than or equal to
x[i][ilt] and greater than or equal to
the lower bound of the distribution. 
3  Full interval. The
response is greater than or equal to
x[i][irt] but less than or equal to
x[i][ilt]. 
IllegalArgumentException
 is thrown when icen
is less than 0 or greater than or equal to the number of
columns of x
public void setClassificationVariableColumn(int[] indcl)
x
that are classification variables.indcl
 An int
vector which contains the column
numbers in x
that are classification
variables. By default this vector is not referenced.IllegalArgumentException
 is thrown when an element of
indcl
is less than 0 or greater than or
equal to the number of columns of x
public void setConvergenceTolerance(double eps)
eps
 A double
scalar specifying the convergence
criterion. Convergence is assumed when the maximum relative
change in any coefficient estimate is less than eps
from one iteration to the next or when the relative
change in the loglikelihood, getOptimizedCriterion
, from one
iteration to the next is less than eps
/100. eps
must be greater than 0. If this member function is not called,
eps
= .001 is assumed.IllegalArgumentException
 is thrown if eps
is
or equal to 0public void setEffects(int[] indef, int[] nvef)
x
associated with each effect.indef
 An int
vector of length where
nef is the number of effects in the model.
indef
contains the column numbers in
x
that are associated with each effect.
Member function setEffects(int [], nvef [])
sets the number of variables associated with each effect
in the model. The first nvef[0]
elements of indef
give the column numbers
of the variables in the first effect. The next
nvef[0]
elements give the column numbers of the
variables in the second effect, etc. By default this
vector is not referenced.nvef
 An int
vector of length nef
where nef is the number of effects in the
model. nvef
contains the number of
variables associated with each effect in the model. By
default this vector is not referenced.IllegalArgumentException
 is thrown when an element of
indef
is less than 0 or greater than or
equal to the number of columns of x
or if
an element of nvef
is less than or equal to
0public void setExtendedLikelihoodObservations(int[] iadds)
iadds
 An int
array of length nobs
indicating which observations are included in the
extended likelihood where nobs is the number of
observations. The values within the array are
interpreted as:
Value  Status of observation 

0  Observation i is in the likelihood. 
1  Observation i
cannot be in the likelihood because it
contains at least one missing value in x
. 
2  Observation i is not in the likelihood. Its estimated parameter is infinite. 
iadds
is set to all zeroes.IllegalArgumentException
 is thrown when an element of
iadds
is not in the range [0,2]public void setFixedParameterColumn(int ifix)
x
that contains a fixed parameter
for each observation that is added to the linear response prior to
computing the model parameter.ifix
 An int
scalar which indicates the column
number in x
that contains a fixed parameter
for each observation that is added to the linear
response prior to computing the model parameter. The
"fixed" parameter allows one to test hypothesis about
the parameters via the loglikelihoods. By default the
fixed parameter is assumed to be zero.IllegalArgumentException
 is thrown when ifix
is less than 0 or greater than or equal to the number of
columns of x
public void setFrequencyColumn(int ifrq)
x
that contains the frequency of
response for each observation.ifrq
 An int
scalar which indicates the column
number in x
that contains the frequency of
response for each observation. By default a frequency of
1 for each observation is assumed.IllegalArgumentException
 is thrown when ifrq
is less than 0 or greater than or equal to the number of
columns of x
public void setInfiniteEstimateMethod(int infin)
infin
 An int
scalar which indicates the method to
be used for handling infinite estimates. The method
value is interpreted as follows:
infin  Method 
0  Remove a right or
leftcensored observation from the
loglikelihood whenever the probability of the
observation exceeds 0.995. At convergence, use
linear programming to check that all removed
observations actually have an estimated linear
response that is infinite. Set iadds[i]
for observation i to 2 if the
linear response is infinite. If not all removed
observations have infinite linear response,
recompute the estimates based upon the
observations with estimated linear response that
is finite. This option is valid only for
censoring codes 1 and 2. 
1  Iterate without checking for infinite estimates. 
infin
= 1.IllegalArgumentException
 is thrown when infin
is less than 0 or greater than 1public void setInitialEstimates(int init, double[] estimates)
init
 An input int
indicating the desired
initialization method for the initial estimates of the
parameters. If this method is not called, init
is set to 0.
init  Action 
0  Unweighted linear regression is used to obtain initial estimates. 
1  The nCoef,
number of coefficients, elements of estimates contain
initial estimates of the parameters. Use of this
option requires that the user know nCoef
beforehand. 
estimates
 An input double
array of length
nCoef containing the initial estimates of the
parameters where nCoef is the number of
estimated coefficients in the model. (Used if
init
= 1.) If this member function is not
called, unweighted linear regression is used to
obtain the initial estimates.IllegalArgumentException
 is thrown when init
is not in the range [0,1]public void setLowerEndpointColumn(int irt)
x
that contains the lower
endpoint of the observation interval for full interval and right
interval observations.irt
 An int
scalar which indicates the column number
in x
that contains the lower endpoint of the
observation interval for full interval and right interval
observations. By default all observations are treated as
"point" observations and x[i][irt]
contains the
observation point. If this member function is not called,
the last column of x
is assumed to contain the
"point" observations.IllegalArgumentException
 is thrown when irt
is less than 0 or greater than or equal to the number of
columns of x
public void setMaxIterations(int maxIterations)
maxIterations
 An int
specifying the maximum
number of iterations allowed. maxIterations
must be greater than 0. If this member function is
not called, the maximum number of iterations is set to 30.IllegalArgumentException
 is thrown if maxIterations
is
less than or equal to 0public void setModelIntercept(int intcep)
intcep
 An int
scalar which indicates whether or
not the model has an intercept. Input intcep
is interpreted as follows:
Value  Action 
0  No intercept is in the model (unless otherwise provided for by the user). 
1  Intercept is automatically included in the model. 
intcep
= 1.IllegalArgumentException
 is thrown when intcep
is less than 0 or greater than 1public void setObservationMax(int nmax)
nmax
 An int
scalar which sets the maximum number
of observations that can be handled in the linear
programming. An illegal argument exception is thrown if
nmax
is less than 0. If this member
function is not called, nmax
is set to the
number of observations.IllegalArgumentException
 is thrown when nmax
is less than 0public void setOptionalDistributionParameterColumn(int ipar)
x
that contains an optional
distribution parameter for each observation.ipar
 An int
scalar which indicates the column
number in x
that contains an optional
distribution parameter for each observation. The
distribution parameter values are interpreted as follows
depending on the model chosen:
Model  Meaning of x[i][ipar]


0  The Poisson parameter is given by . 
1  The number of
successes required in the negative binomial is
given by x[i][ipar]. 
2  x[i][ipar] is
not used. 
35  The number of
trials in the binomial distribution is given by
x[i][ipar]. 
IllegalArgumentException
 is thrown when ipar
is
less than 0 or greater than or equal to the number of
columns of x
public void setTolerance(double tol)
tol
 An double
value used in determining linear
dependence. When linear dependence is detected, a
RankDeficientException is thrown and no results
are computed. Computations for a rank deficient
model can be forced to continue by specifying a
negative tolerance. If tol
is negative,
the absolute value of tol
will be used
to determine linear dependence, but computations will
proceed with warning RankDeficientWarning. In this case
the results should be carefully inspected and used with
caution. If this member function is not called, tol
will be set to .22204460492503130808e14.public void setUpperBound(int maxcl)
maxcl
 An int
scalar specifying the upper bound on
the sum of the number of distinct values taken on by
each classification variable. If this member function is
not called, an upper bound of 1 is used if method
setClassificationVariableColumn
has not been
referenced. Otherwise, the default upper bound is set to
nobs * nclvar
where nobs
is the
number of observations and nclvar
is the number
of classification variables.IllegalArgumentException
 is thrown when maxcl
is less than 1 and the number of classification
variables is greater than 0public void setUpperEndpointColumn(int ilt)
x
that contains the upper
endpoint of the observation interval for full interval and left interval
observations.ilt
 An int
scalar which indicates the column number
in x
that contains the upper endpoint of the
observation interval for full interval and left interval
observations. By default all observations are treated as
"point" observations.IllegalArgumentException
 is thrown when ilt
is less than
0 or greater than or equal to the number of columns of
x
public double[][] solve() throws CategoricalGenLinModel.ClassificationVariableException, CategoricalGenLinModel.ClassificationVariableLimitException, CategoricalGenLinModel.ClassificationVariableValueException, CategoricalGenLinModel.DeleteObservationsException, CategoricalGenLinModel.RankDeficientException, SVD.DidNotConvergeException
nCoef
row by 4 column double
matrix containing the parameter
estimates and associated statistics. Here, nCoef
is the number of coefficients
in the model. The statistics returned are as follows:
Column  Statistic 
0  Coefficient estimate. 
1  Estimated standard deviation of the estimated coefficient. 
2  Asymptotic normal score for testing that the coefficient is zero. 
3   value associated with the normal score in column 2. 
CategoricalGenLinModel.ClassificationVariableException
 is thrown when the number of
values taken by each classification variable has been
set by the user to be less than or equal to 1CategoricalGenLinModel.ClassificationVariableLimitException
 is thrown when the
sum of the number of distinct values taken on by each
classification variable exceeds the maximum allowed,
maxcl
CategoricalGenLinModel.DeleteObservationsException
 is thrown if the number of
observations to be deleted has grown too largeCategoricalGenLinModel.ClassificationVariableValueException
CategoricalGenLinModel.RankDeficientException
SVD.DidNotConvergeException
Copyright © 19702015 Rogue Wave Software
Built October 13 2015.