CategoricalGenLinModel Class

Analyzes categorical data using logistic, probit, Poisson, and other linear models.

Inheritance Hierarchy

System.Object
Imsl.Stat.CategoricalGenLinModel

Namespace: Imsl.Stat
Assembly: ImslCS (in ImslCS.dll) Version: 6.5.2.0

Syntax

C++

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[SerializableAttribute]
public class CategoricalGenLinModel

<SerializableAttribute>
Public Class CategoricalGenLinModel

[SerializableAttribute]
public ref class CategoricalGenLinModel

[<SerializableAttribute>]
type CategoricalGenLinModel =  class end

The CategoricalGenLinModel type exposes the following members.

Constructors

	Name	Description
	CategoricalGenLinModel	Constructs a new CategoricalGenLinModel.

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Methods

	Name	Description
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	SetEffects	Initializes an index vector to contain the column numbers in x associated with each effect.
	SetInitialEstimates	Sets the initial parameter estimates option.
	Solve	Returns the parameter estimates and associated statistics for a CategoricalGenLinModel object.
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Properties

	Name	Description
	CaseAnalysis	The case analysis.
	CensorColumn	The column number in x which contains the interval type for each observation.
	ClassificationVariableColumn	An index vector to contain the column numbers in x that are classification variables.
	ClassificationVariableCounts	The number of values taken by each classification variable.
	ClassificationVariableValues	The distinct values of the classification variables in ascending order.
	ConvergenceTolerance	The convergence criterion.
	CovarianceMatrix	The estimated asymptotic covariance matrix of the coefficients.
	DesignVariableMeans	The means of the design variables.
	ExtendedLikelihoodObservations	A vector indicating which observations are included in the extended likelihood.
	FixedParameterColumn	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.
	FrequencyColumn	The column number in x that contains the frequency of response for each observation.
	Hessian	The Hessian computed at the initial parameter estimates.
	InfiniteEstimateMethod	Specifies the method used for handling infinite estimates.
	LastParameterUpdates	The last parameter updates (excluding step halvings).
	LowerEndpointColumn	The column number in x that contains the lower endpoint of the observation interval for full interval and right interval observations.
	MaxIterations	The maximum number of iterations allowed.
	ModelIntercept	The intercept option.
	NRowsMissing	The number of rows of data in x that contain missing values in one or more specific columns of x.
	ObservationMax	The maximum number of observations that can be handled in the linear programming.
	OptimizedCriterion	The optimized criterion.
	OptionalDistributionParameterColumn	The column number in x that contains an optional distribution parameter for each observation.
	Parameters	Parameter estimates and associated statistics.
	Product	The inverse of the Hessian times the gradient vector computed at the input parameter estimates.
	Tolerance	The tolerance used in determining linear dependence.
	UpperBound	Defines the upper bound on the sum of the number of distinct values taken on by each classification variable.
	UpperEndpointColumn	The column number in x that contains the upper endpoint of the observation interval for full interval and left interval observations.

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Remarks

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

${\gamma}_i=w_i+x_i^T\beta=w_i+\eta_i$

be the linear response where

is a design column vector obtained from a row of $x,\beta$ is the column vector of coefficients to be estimated, and

is a fixed parameter that may be input in x. When some of the ${\gamma}_i$ are infinite at the supremum of the likelihood, then extended maximum likelihood estimates are computed. Extended maximum likelihood is computed as the finite (but nonunique) estimates $\hat{\beta}$ that optimize the likelihood containing only the observations with finite ${\hat{ \gamma}}_i$ . These estimates, when combined with the set of indices of the observations such that ${\hat{\gamma}}_i$ is infinite at the supremum of the likelihood, are called extended maximum estimates. When none of the optimal ${\hat{ \gamma}}_i$ 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

${\hat{\eta}}_i = x_i^T\hat{\beta}$

are detected and removed from the likelihood if InfiniteEstimateMethod = 0. See below.

The models available in CategoricalGenLinModel are:

Model Name	Parameterization	Response PDF
Model0 (Poisson)	$\lambda=N \times{e^{w+\eta}}$	$f(y)=\lambda^{y}e^{ -\lambda}/y!$
Model1 (Negative Binomial)	$\theta=\frac{e^{w+\eta}}{1+e^{w+\eta}}$	$f(y)=\left(\begin{array}{rr}S+y-1\\y-1\end{array}\right)\theta^S(1- \theta)^y$
Model2 (Logarithmic)	$\theta= \frac{e^{w+\eta}}{1+e^{w+\eta}}$	$f(y)=(1 -\theta)^y/(y\ln\theta)$
Model3 (Logistic)	$\theta=\frac {e^{w +\eta}}{1+e^{w+\eta}}$	$f(y)=\left(\begin{array}{rr}N\\y\end{array}\right)\theta^y(1-\theta )^{N-y}$
Model4 (Probit)	$\theta=\Phi(w+ \eta)$	$f(y)=\left(\begin{array}{rr}N\\y \end{array}\right)\theta^y(1-\theta)^{N-y}$
Model5 (Log-log)	$\theta=1-e^{- e^{w+\eta}}$	$f(y)=\left(\begin{array} {rr}N\\y\end{array}\right)\theta^y(1-\theta)^{N-y}$

Here $\Phi$ denotes the cumulative normal distribution, N and S are known parameters specified for each observation via column OptionalDistributionParameterColumn of x, and w is an optional fixed parameter specified for each observation via column FixedParameterColumn 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 log-log 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 $\theta$ , $\theta$ 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 FrequencyColumn of the matrix x, each vector can represent several observations.

Computational Details

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 right-interval observations, $\Pr(Y\ge{y})$ is computed as a sum based upon the equality $\Pr(Y\ge{y})=1-\Pr(Y\lt{y})$ . Derivatives are similarly computed. 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 right-interval observations, only the lower endpoint need be given while for left-interval observations, only the upper endpoint is given.

The computations proceed as follows:

The input parameters are checked for consistency and validity.
Estimates of the means of the "independent" or design variables are computed. The frequency of the observation in all but the binomial distribution model is taken from column FrequencyColumn of the data matrix x. In binomial distribution models, the frequency is taken as the product of n = x[i,OptionalDistributionParameterColumn] and x[i,FrequencyColumn]. In all cases these values default to 1. Means are computed as
$\bar{x}=\frac{\Sigma_if_ix_i}{\Sigma_if_i}$
If 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, $\theta$ for point observations may be estimated as
$\hat{\theta}=x[i,LowerEndpointColumn]/x[i, OptionalDistributionParameterColumn]$
and, when model = 3, the linear relationship is given by
$\left(\ln(\hat{\theta}/(1-\hat{\theta})) \approx x\beta\right)$
while if model = 4,
$\left(\Phi^{-1}(\hat{\theta})=x\beta\right)$
For bounded interval observations, the midpoint of the interval is used for x[i,LowerEndpointColumn]. Right-interval 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.
Newton-Raphson iteration for the maximum likelihood estimates is implemented via iteratively reweighted least squares. Let
$\Psi(x^T_i\beta)$
denote the log of the probability of the i-th observation for coefficients $\beta$ . In the least-squares model, the weight of the i-th observation is taken as the absolute value of the second derivative of
$\Psi(x^T_i\beta)$
with respect to
$\gamma_i=x^T_i\beta$
(times the frequency of the observation), and the dependent variable is taken as the first derivative $\Psi$ with respect to $\gamma_i$ , divided by the square root of the weight times the frequency. The Newton step is given by
$\Delta\beta=\left(\sum_{i}|\Psi^{''}(\gamma_i) |x_ix_i^T \right)^{-1} \sum_{i}\Psi^{'}(\gamma_i)x_i$
where all derivatives are evaluated at the current estimate of $\gamma$ , and $\beta_{n+1}=\beta_n-\Delta \beta$ . This step is computed as the estimated regression coefficients in the least-squares model. Step halving is used when necessary to ensure a decrease in the criterion.
Convergence is assumed when the maximum relative change in any coefficient update from one iteration to the next is less than ConvergenceTolerance or when the relative change in the log-likelihood from one iteration to the next is less than ConvergenceTolerance/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 log-likelihood.
For interval observations, the contribution to the log-likelihood is the log of the sum of the probabilities of each possible outcome in the interval. Because the distributions are discrete, the sum may involve many terms. The user should be aware that data with wide intervals can lead to expensive (in terms of computer time) computations.
If InfiniteEstimateMethod is set to 0, then the methods of Clarkson and Jennrich (1991) are used to check for the existence of infinite estimates in
$\eta_i=x_i^T\beta$
As an example of a situation in which infinite estimates can occur, suppose that observation j is right censored with $t_j\gt{15}$ in a logistic model. If design matrix x is such that $x_{jm}=1$ and $x_{im}=0$ for all $i\neq{j}$ , then the optimal estimate of $\beta_m$ occurs at
$\hat{\beta_m}=\infty$
leading to an infinite estimate of both $\beta_m$ and $\eta_j$ . 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 right-censored observation is 1. If InfiniteEstimateMethod is 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 log-likelihood, and the iterations proceed with a log-likelihood 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
$\eta_i=x_i^T\beta$
At convergence, linear programming is used to ensure that the eliminated observations have infinite $\eta_i$ . If some (or all) of the removed observations should not have been removed (because their estimated $\eta_{i's}$ must be finite), then the iterations are restarted with a log-likelihood based upon the finite $\eta_i$ observations. See Clarkson and Jennrich (1991) for more details.
When InfiniteEstimateMethod 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 $\hat{\beta}$ will become large, and it is likely that the Hessian will become (numerically) singular prior to convergence.
When infinite estimates for the $\hat{\eta_i}$ are detected, linear regression (see Chapter 2, Regression;) is used at the convergence of the algorithm to obtain unique estimates $\hat{ \beta}$ . This is accomplished by regressing the optimal $\hat{\eta_i}$ or the observations with finite $\eta$ against $x\beta$ , yielding a unique $\hat{\beta}$ (by setting coefficients $\hat{\beta}$ that are linearly related to previous coefficients in the model to zero). All of the final statistics relating to $\hat{\beta}$ are based upon these estimates.
Residuals are computed according to methods discussed by Pregibon (1981). Let $\ell_i(\gamma_i)$ denote the log-likelihood of the i-th observation evaluated at $\gamma_i$ . Then, the standardized residual is computed as
$r_i=\frac{\ell_i^{'}(\hat{\gamma_i})}{ \sqrt{\ell_i^{''}(\hat{\gamma_i})}}$
where $\hat{\gamma_i}$ is the value of $\gamma_i$ when evaluated at the optimal $\hat{\beta}$ and the derivatives here (and only here) are with respect to $\gamma$ rather than with respect to $\beta$ . The denominator of this expression is used as the "standard error of the residual" while the numerator is the "raw" residual.
Following Cook and Weisberg (1982), we take the influence of the i-th observation to be
$\ell_i^{'}(\hat{\gamma_i})^T\ell^{''}(\hat {\gamma})^{-1}\ell^{'}(\hat{\gamma_i})$
This quantity is a one-step approximation to the change in the estimates when the i-th observation is deleted. Here, the partial derivatives are with respect to $\beta$ .

Programming Notes

Classification variables are specified via ClassificationVariableColumn. Indicator or dummy variables are created for the classification variables.
To enhance precision "centering" of covariates is performed if ModelIntercept 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.
Two methods for specifying a binomial distribution model are possible. In the first method, x[i,FrequencyColumn] contains the frequency of the observation while x[i,LowerEndpointColumn] is 0 or 1 depending upon whether the observation is a success or failure. In this case, N = x[i,OptionalDistributionParameterColumn] 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,LowerEndpointColumn] to represent the number of successes in the x[i,OptionalDistributionParameterColumn] trials. In this case, x[i,FrequencyColumn] 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 before using any property as a right operand, otherwise the value is null.

Reference

Imsl.Stat Namespace

Other Resources

Example 1

Example 2