
ABSCONV=r
ABSTOL=r

specifies an absolute function convergence criterion. For minimization, the termination criterion is , where is the vector of parameters in the optimization and is the objective function. The default value of r is the negative square root of the largest doubleprecision value, which serves only as a protection against overflows.

ABSFCONV=r <n>
ABSFTOL=r<n>

specifies an absolute function difference convergence criterion. For all techniques except NMSIMP, the termination criterion
is a small change of the function value in successive iterations:
Here, denotes the vector of parameters that participate in the optimization, and is the objective function. The same formula is used for the NMSIMP technique, but is defined as the vertex with the lowest function value, and is defined as the vertex with the highest function value in the simplex. The default value is r=0. The optional integer value n specifies the number of successive iterations for which the criterion must be satisfied before the process can be terminated.

ABSGCONV=r <n>
ABSGTOL=r<n>

specifies an absolute gradient convergence criterion. The termination criterion is a small maximum absolute gradient element:
Here, denotes the vector of parameters that participate in the optimization, and is the gradient of the objective function with respect to the jth parameter. This criterion is not used by the NMSIMP technique. The default value is r=1E–5. The optional integer value n specifies the number of successive iterations for which the criterion must be satisfied before the process can be terminated.

COMPONENTINFO
COMPINFO
CINFO

produces a table with additional details about the fitted model components.

COV

produces the covariance matrix of the parameter estimates. For maximum likelihood estimation, this matrix is based on the
inverse (projected) Hessian matrix. For Bayesian estimation, it is the empirical covariance matrix of the posterior estimates.
The covariance matrix is shown for all parameters, even if they did not participate in the optimization or sampling.

COVI

produces the inverse of the covariance matrix of the parameter estimates. For maximum likelihood estimation, the covariance
matrix is based on the inverse (projected) Hessian matrix. For Bayesian estimation, it is the empirical covariance matrix
of the posterior estimates. This matrix is then inverted by sweeping, and rows and columns that correspond to linear dependencies
or singularities are zeroed.

CORR

produces the correlation matrix of the parameter estimates. For maximum likelihood estimation this matrix is based on the
inverse (projected) Hessian matrix. For Bayesian estimation, it is based on the empirical covariance matrix of the posterior
estimates.

CRITERION=keyword
CRIT=keyword

specifies the criterion by which the FMM procedure ranks models when multiple models are evaluated during maximum likelihood
estimation. You can choose from the following keywords to rank models:
 AIC

based on Akaike’s information criterion
 AICC

based on the biascorrected AIC criterion
 BIC

based on the Bayesian information criterion
 GRADIENT

based on the largest element of the gradient (in absolute value)
 LOGL  LL

based on the mixture log likelihood
 PEARSON

based on the Pearson statistic
The default is CRITERION=BIC.

DATA=SASdataset

names the SAS data set to be used by PROC FMM. The default is the most recently created data set.

EXCLUSION=NONE  ANY  ALL
EXCLUDE=NONE  ANY  ALL

specifies how the FMM procedure handles support violations of observations. For example, in a mixture of two Poisson variables,
negative response values are not possible. However, in a mixture of a Poisson and a normal variable, negative values are possible,
and their likelihood contribution to the Poisson component is zero. An observation that violates the support of one component
distribution of the model might be a valid response with respect to one or more other component distributions. This requires
some nuanced handling of support violations in mixture models.
The default exclusion technique, EXCLUSION=ALL, removes an observation from the analysis only if it violates the support of
all component distributions. The other extreme, EXCLUSION=NONE, permits an observation into the analysis regardless of support
violations. EXCLUSION=ANY removes observations from the analysis if the response violates the support of any component distributions.
In the singlecomponent case, EXCLUSION=ALL and EXCLUSION=ANY are identical.

FCONV=r<n>
FTOL=r<n>

specifies a relative function convergence criterion that is based on the relative change of the function value. For all techniques
except NMSIMP, PROC FMM terminates when there is a small relative change of the function value in successive iterations:
Here, denotes the vector of parameters that participate in the optimization, and is the objective function. The same formula is used for the NMSIMP technique, but is defined as the vertex with the lowest function value, and is defined as the vertex with the highest function value in the simplex.
The default is , where FDIGITS is by default , and is the machine precision. The optional integer value n specifies the number of successive iterations for which the criterion must be satisfied before the process terminates.

FCONV2=r<n>
FTOL2=r<n>

specifies a relative function convergence criterion that is based on the predicted reduction of the objective function. For
all techniques except NMSIMP, the termination criterion is a small predicted reduction
of the objective function. The predicted reduction
is computed by approximating the objective function f by the first two terms of the Taylor series and substituting the Newton step:
For the NMSIMP technique, the termination criterion is a small standard deviation of the function values of the simplex vertices , ,
where . If there are boundary constraints active at , the mean and standard deviation are computed only for the unconstrained vertices.
The default value is r = 1E–6 for the NMSIMP technique and r = 0 otherwise. The optional integer value n specifies the number of successive iterations for which the criterion must be satisfied before the process terminates.

FITDETAILS

requests that the “Optimization Information,” “Iteration History,” and “Fit Statistics” tables be produced for all optimizations when models with different number of components are evaluated. For example, the
following statements fit a binomial regression model with up to three components and produces fit and optimization information
for all three:
proc fmm fitdetails;
model y/n = x / kmax=3;
run;
Without the FITDETAILS option, only the “Fit Statistics” table for the selected model is displayed.

GCONV=r<n>
GTOL=r<n>

specifies a relative gradient convergence criterion. For all techniques except CONGRA and NMSIMP, the termination criterion
is a small normalized predicted function reduction:
Here, denotes the vector of parameters that participate in the optimization, is the objective function, and is the gradient. For the CONGRA technique (where a reliable Hessian estimate is not available), the following criterion is used:
This criterion is not used by the NMSIMP technique. The default value is r=1E–8. The optional integer value n specifies the number of successive iterations for which the criterion must be satisfied before the process can terminate.

HESSIAN

displays the Hessian matrix of the model. This option is not available for Bayesian estimation.

INVALIDLOGL=r

specifies the value assumed by the FMM procedure if a log likelihood cannot be computed (for example, because the value of
the response variable falls outside of the response distribution’s support). The default value is –1E20.

ITDETAILS

adds parameter estimates and gradients to the “Iteration History” table. If the FMM procedure centers or scales the model variables (or both), the parameter estimates and gradients reported
during the iteration refer to that scale. You can suppress centering and scaling with the NOCENTER option.

MAXFUNC=n
MAXFU=n

specifies the maximum number of function calls in the optimization process. The default values are as follows, depending on
the optimization technique:
The optimization can terminate only after completing a full iteration. Therefore, the number of function calls that are actually
performed can exceed the number that is specified by the MAXFUNC= option. You can choose the optimization technique with the
TECHNIQUE= option.

MAXITER=n
MAXIT=n

specifies the maximum number of iterations in the optimization process. The default values are as follows, depending on the
optimization technique:
These default values also apply when n is specified as a missing value. You can choose the optimization technique with the TECHNIQUE= option.

MAXTIME=r

specifies an upper limit of r seconds of CPU time for the optimization process. The default value is the largest floatingpoint double representation of
your computer. The time specified by the MAXTIME= option is checked only once at the end of each iteration. Therefore, the
actual running time can be longer than that specified by the MAXTIME= option.

MINITER=n
MINIT=n

specifies the minimum number of iterations. The default value is 0. If you request more iterations than are actually needed
for convergence to a stationary point, the optimization algorithms can behave strangely. For example, the effect of rounding
errors can prevent the algorithm from continuing for the required number of iterations.

NAMELEN=number

specifies the length to which long effect names are shortened. The default and minimum value is 20.

NOCENTER

requests that regressor variables not be centered or scaled. By default the FMM procedure centers and scales columns of the
matrix if the models contain intercepts. If NOINT options in MODEL statements are in effect, the columns of are scaled but not centered. Centering and scaling can help with the stability of estimation and sampling algorithms. The
FMM procedure does not produce a table of the centered and scaled coefficients and provides no user control over the type
of centering and scaling that is applied. The NOCENTER option turns any centering and scaling off and processes the raw values
of the continuous variables.

NOCLPRINT<=number>

suppresses the display of the “Class Level Information” table if you do not specify number. If you specify number, the values of the classification variables are displayed for only those variables whose number of levels is less than number. Specifying a number helps to reduce the size of the “Class Level Information” table if some classification variables have a large number of levels.

NOITPRINT

suppresses the display of the “Iteration History Information” table.

NOPRINT

suppresses the normal display of tabular and graphical results. The NOPRINT option is useful when you want to create only
one or more output data sets with the procedure. This option temporarily disables the Output Delivery System (ODS); see Chapter 20: Using the Output Delivery System, for more information.

ORDER=ordertype

specifies the sort order for the levels of CLASS variables. This ordering determines which parameters in the model correspond to each level in the data.
You can specify the following values for ordertype:

DATA

sorts the levels by order of appearance in the input data set.

FORMATTED

sorts the levels by external formatted value, except for numeric variables with no explicit format, which are sorted by their
unformatted (internal) value.

FREQ

sorts the levels by descending frequency count; levels with the most observations come first in the order.

INTERNAL

sorts the levels by unformatted value.

FREQDATA

sorts the levels by order of descending frequency count, and within counts by order of appearance in the input data set when
counts are tied.

FREQFORMATTED

sorts the levels by order of descending frequency count, and within counts by formatted value (as above) when counts are tied.

FREQINTERNAL

sorts the levels by order of descending frequency count, and within counts by unformatted value when counts are tied.
When the default ORDER=FORMATTED is in effect for numeric variables for which you have supplied no explicit format, the levels
are ordered by their internal values. To order numeric class levels with no explicit format by their BEST12. formatted values,
you can specify this format explicitly for the CLASS variables.
When FORMATTED and INTERNAL values are involved, the sort order is machinedependent.
When the response variable appears in a CLASS statement, the ORDER= option in the PROC FMM statement applies to its sort order. For example, in the following statements
the sort order of the wheeze
variable is determined by the order of appearance in the input data set because the response variable appears in the CLASS statement:
proc fmm order=data;
class city wheeze;
model wheeze = city age / dist=binary s;
run;
However, in the following statements the sort order of the wheeze
variable is determined by the formatted value (the default responseoption in the MODEL statement):
proc fmm order=data;
class city;
model wheeze = city age / dist=binary s;
run;
The ORDER= option in the PROC FMM statement has no effect on the sort order of the wheeze
variable because it does not appear in the CLASS statement.
When you specify a responseoption in the MODEL statement, it overrides the ORDER= option in the PROC FMM statement.
For more information about sort order, see the chapter on the SORT procedure in the
Base SAS Procedures Guide and the discussion of BYgroup processing in
SAS Language Reference: Concepts.

PARMSTYLE=EFFECT  LABEL

specifies the display style for parameters and effects. The FMM procedure can display parameters in two styles:

The EFFECT style (which is used by the MIXED and GLIMMIX procedure, for example) identifies a parameter with an “Effect” column and adds separate columns for the CLASS variables in the model.

The LABEL style creates one column, named Parameter, that combines the relevant information about a parameter into a single
column. If your model contains multiple CLASS variables, the LABEL style might use space more economically.
The EFFECT style is the default for models that contain effects; otherwise the LABEL style is used (for example, in homogeneous
mixtures). You can change the display style with the PARMSTYLE= option. Regardless of the display style, ODS output data sets
that contain information about parameter estimates contain columns for both styles.

PARTIAL=variable
MEMBERSHIP=variable

specifies a variable in the input data set that identifies component membership. You can specify missing values for observations
whose component membership is undetermined; this is known as a partial classification (McLachlan and Peel, 2000, p. 75). For observations with known membership, the likelihood contribution is no longer a mixture. If observation i is known to be a member of component m, then its log likelihood contribution is
Otherwise, if membership is undetermined, it is
The variable
specified in the PARTIAL= option can be numeric or character. In case of a character variable, the variable must appear in
the CLASS statement. If the PARTIAL= variable appears in the CLASS statement, the membership assignment is made based on the levelized values of the variable, as shown in the “Class Level Information” table. Invalid values of the PARTIAL= variable are ignored.
In a model in which label switching is a problem, the switching can sometimes be avoided by assigning just a few observations
to categories. For example, in a threecomponent model, switches might be prevented by assigning the observation with the
smallest response value to the first component and the observation with the largest response value to the last component.

PLOTS <(globalplotoptions)> <= plotrequest <(options)>>
PLOTS <(globalplotoptions)> <= (plotrequest <(options)> <... plotrequest <(options)>>)>

controls the plots produced through ODS Graphics.
ODS Graphics must be enabled before plots can be requested. For example:
ods graphics on;
proc fmm data=yeast seed=12345;
model count/n = / k=2;
freq f;
performance cpucount=2;
bayes;
run;
ods graphics off;
For more information about enabling and disabling ODS Graphics, see the section Enabling and Disabling ODS Graphics in Chapter 21: Statistical Graphics Using ODS.
Global Plot Options
The globalplotoptions apply to all relevant plots generated by the FMM procedure. The globalplotoptions supported by the FMM procedure are as follows:

UNPACKPANEL
UNPACK

displays each graph separately. (By default, some graphs can appear together in a single panel.)

ONLY

produces only the specified plots. This option is useful if you do not want the procedure to generate all default graphics,
but only the ones specified.
Specific Plot Options
The following listing describes the specific plots and their options.

ALL

requests that all plots appropriate for the analysis be produced.

NONE

requests that no ODS graphics be produced.

DENSITY <(densityoptions)>

requests a plot of the data histogram and mixture density function. This graphic is a default graphic in models without effects
in the MODEL statements and is available only in these models. Furthermore, all distributions involved in the mixture must be continuous.
You can specify the following densityoptions to modify the plot:

CUMULATIVE
CDF

displays the histogram and densities in cumulative form.

NBINS=n
BINS=n

specifies the number of bins in the histogram; n is greater than or equal to 0. By default, the FMM procedure computes a suitable bin width and number of bins, based on the
range of the response and the number of usable observations. The option has no effect for binary data.

NOCOMPONENTS
NOCOMP

suppresses the component densities from the plot. If the component densities are displayed, they are scaled so that their
sum equals the mixture density at any point on the graph. In singlecomponent models, this option has no effect.

NODENSITY
NODENS

suppresses the computation of the mixture density (and the component densities if the COMPONENTS suboption is specified).
If you specify the NOHISTOGRAM and the NODENSITY option, no graphic is produced.

NOLABEL

suppresses the component identification with labels. By default, the FMM procedure labels component densities in the legend
of the plot. If you do not specify a model label with the LABEL= option in the MODEL statement, an identifying label is constructed from the parameter estimates that are associated with the component. In this
case the parameter values are not necessarily the mean and variance of the distribution; the values used to identify the densities
on the plot are chosen to simplify linking between graphical and tabular results.

NOHISTOGRAM
NOHIST

suppresses the computation of the histogram of the raw values. If you specify the NOHISTOGRAM and the NODENSITY option, no
graphic is produced.

NPOINTS=n
N=n

specifies the number of values used to compute the density functions; n is greater than or equal to 0. The default is N=200.

WIDTH=value
BINWIDTH=value

specifies the bin width for the histogram. The value is specified in units of the response variable and must be positive. The option has no effect for binary data.

TRACE <(tadpaneloptions)>

requests a trace panel with posterior diagnostics for a Bayesian analysis. If a BAYES statement is present, the trace panel plots are generated by default, one for each sampled parameter. You can specify the
following tadpaneloptions to modify the graphic:

BOX
BOXPLOT

replaces the autocorrelation plot with a box plot of the posterior sample.

SMOOTH=NONE  MEAN  SPLINE

adds a reference estimate to the trace plot. By default, SMOOTH=NONE. SMOOTH=MEAN uses the arithmetic mean of the trace as
the reference. SMOOTH=SPLINE adds a penalized Bspline.

REFERENCE= referencestyle

adds vertical reference lines to the density plot, trace plot, and box plot. The available options for the referencestyle are:
 NONE

suppresses the reference lines
 EQT

requests equaltail intervals
 HPD

requests intervals of highest posterior density. The level for the credible or HPD intervals is chosen based on the “Posterior Interval Statistics” table.
 PERCENTILES

(or PERC) for percentiles. Up to three percentiles can be displayed, as based on the “Posterior Summary Statistics” table.
The default is REFERENCE=CREDIBLE.

UNPACK

unpacks the panel graphic and displays its elements as separate plots.

SEED=n

determines the random number seed for analyses that depend on a random number stream. If you do not specify a seed or if you
specify a value less than or equal to zero, the seed is generated from reading the time of day from the computer clock. The
largest possible value for the seed is . The seed value is reported in the “Model Information” table.
You can use the SYSRANDOM and SYSRANEND macro variables after a PROC FMM run to query the initial and final seed values. However,
using the final seed value as the starting seed for a subsequent analysis does not continue the random number stream where
the previous analysis left off. The SYSRANEND macro variable provides a mechanism to pass on seed values to ensure that the
sequence of random numbers is the same every time you run an entire program.
Analyses that use the same (nonzero) seed are not completely reproducible if they are executed with a different number of
threads since the random number streams in separate threads are independent. You can control the number of threads used by
the FMM procedure with system options or through the PERFORMANCE statement in the FMM procedure.

SINGCHOL=number

tunes the singularity criterion in Cholesky decompositions. The default is 1E4 times the machine epsilon; this product is
approximately 1E–12 on most computers.

SINGRES=number

sets the tolerance for which the residual variance or scale parameter is considered to be zero. The default is 1E4 times the
machine epsilon; this product is approximately 1E–12 on most computers.

SINGULAR=number

tunes the general singularity criterion applied by the FMM procedure in sweeps and inversions. The default is 1E4 times the
machine epsilon; this product is approximately 1E–12 on most computers.

TECHNIQUE=keyword
TECH=keyword

specifies the optimization technique to obtain maximum likelihood estimates. You can choose from the following techniques
by specifying the appropriate keyword:
 CONGRA

performs a conjugategradient optimization.
 DBLDOG

performs a version of doubledogleg optimization.
 NEWRAP

performs a NewtonRaphson optimization combining a linesearch algorithm with ridging.
 NMSIMP

performs a NelderMead simplex optimization.
 NONE

performs no optimization.
 NRRIDG

performs a NewtonRaphson optimization with ridging.
 QUANEW

performs a dual quasiNewton optimization.
 TRUREG

performs a trustregion optimization.
The default is TECH=QUANEW.
For more details about these optimization methods, see the section Choosing an Optimization Algorithm of Chapter 19: Shared Concepts and Topics.