The following formulas are shown for each population and for all populations combined.
Source 
Formula 
Dimension 


Probability Estimates 

jth response 



ith population 



all populations 



Variance of Probability Estimates 

ith population 



all populations 



Response Functions 

ith population 



all populations 



Derivative of Function with Respect to Probability Estimates 

ith population 



all populations 



Variance of Functions 

ith population 



all populations 



Inverse Variance of Functions 

ith population 



all populations 


In the following table, let be a vector of functions of , and let denote , which is the first derivative matrix of with respect to :
Function 

Derivative 

Multiply matrix 


Logarithm 


Exponential 


Add constant 


In the following table, subscripts i for the population are suppressed. Also denote for for each population .
Formula 


Inverse of Response Functions for a Population 



Form of F and Derivative for a Population 



Covariance Results for a Population 


The following calculations are shown for each population and then for all populations combined:
Source 
Formula 
Dimension 


Design Matrix 

ith population 



all populations 



Crossproduct of Design Matrix 

ith population 



all populations 


In the following table, is the 100pth percentile of the standard normal distribution:
Formula 
Dimension 


Crossproduct of Design Matrix with Function 




Weighted Least Squares Estimates 




Covariance of Weighted Least Squares Estimates 




Wald Confidence Limits for Parameter Estimates 




Predicted Response Functions 




Covariance of Predicted Response Functions 




Residual ChiSquare 

RSS 


ChiSquare for 

Q 

Let be the Hessian matrix and be the gradient of the loglikelihood function (both functions of and the parameters ). Let denote the vector containing the first sample proportions from population i, and let denote the corresponding vector of probability estimates from the current iteration. Starting with the least squares estimates of (if you use the ML and WLS options; with the ML option alone, the procedure starts with ), the probabilities are computed, and is calculated iteratively by the NewtonRaphson method until it converges (see the EPSILON= option). The factor is a stephalving factor that equals one at the start of each iteration. For any iteration in which the likelihood decreases, PROC CATMOD uses a series of subiterations in which is iteratively divided by two. The subiterations continue until the likelihood is greater than that of the previous iteration. If the likelihood has not reached that point after 10 subiterations, then convergence is assumed, and a warning message is displayed.
Sometimes, infinite parameters are present in the model, either because of the presence of one or more zero frequencies or because of a poorly specified model with collinearity among the estimates. If an estimate is tending toward infinity, then PROC CATMOD flags the parameter as infinite and holds the estimate fixed in subsequent iterations. PROC CATMOD regards a parameter to be infinite when two conditions apply:
The absolute value of its estimate exceeds five divided by the range of the corresponding variable.
The standard error of its estimate is at least three times greater than the estimate itself.
The estimator of the asymptotic covariance matrix of the maximum likelihood predicted probabilities is given by Imrey, Koch, and Stokes (1981, eq. 2.18).
The following equations summarize the method:

where









The algorithm used by PROC CATMOD for iterative proportional fitting is described in Bishop, Fienberg, and Holland (1975); Haberman (1972); Agresti (2002). To illustrate the method, consider the observed threedimensional table for the variables X
, Y
, and Z
, and the following hierarchical model:

The following statements request that PROC CATMOD use IPF to fit the preceding model:
model X*Y*Z = _response_ / ml=ipf; loglin XYZ@2;
Begin with a table of initial cell estimates . PROC CATMOD produces the initial estimates by setting the structural zero cells to 0 and all other cells to , where n is the total weight of the table and is the total number of cells in the table. Iteratively adjust the estimates at step to the observed marginal tables specified in the model by cycling through the following threestage process to produce the estimates at step s:









The subscript “” indicates summation over the missing subscript. The loglikelihood is estimated at each step s by

When the function is less than , the iterations terminate. You can change the comparison value with the EPSILON= option, and you can change the convergence criterion with the CONVCRIT= option. The option CONVCRIT=CELL uses the maximum cell difference

as the criterion while the option CONVCRIT=MARGIN computes the maximum difference of the margins
