Mx Quick Reference


This document outlines the basic features and syntax of Mx. It is an abbreviated version of the Mx manual.

This HTML version was created by Wayne Hadady from a hardcopy edition. This HTML version is now and will likely remain more up-to-date than the hardcopy version.

An HTML-version of the main manual is in work.


Mx is public domain. You may download versions for most systems from http://www.vcu.edui/mx which has executables for most platforms along with documentation and examples. The latter have been extended to include all the appendices from the Neale & Cardon book.

World Wide Web

There is world-wide-web access to Mx: is the Mx home page. With a suitable browser, you can obtain the program, documentation and examples, send comments, and see the latest developments. Email bug reports, requests for further information, and most important your comments and suggestions for improvements to


Mx development is supported by NIH grant RR08123. We are very grateful to all those who made comments and suggestions for improving the program. Please keep the comments coming!

refer to the Mx manual as:

Neale, M.C. (1994). Mx: Statistical Modeling. Box 710 MCV, Richmond, VA 23298: Department of Psychiatry. 2nd edition.

Technical Support

For technical support, please use one of the following methods of contact, listed in order of preference:

email: (internet)
804 828 6577
804 828 8590
snail mail:
Michael C. Neale
Department of Psychiatry
Box 710 MCV
Richmond VA 23298-0710, USA

Using Mx under different Operating Systems

Currently Mx is available for MSDOS/ WINDOWS 3.1 (or higher) for IBM compatible 386 PCs and above, several unix systems (e.g. Vax BSD4.3, Sun-OS, IBM (RS-6000) AIX, Dec Ultrix, OSF/1, HP), and VAX VMS.


We recommend that input files have the naming convention where cutename is a name of your choice. To run Mx on a PC, create an input script and type:

mx {cutename.mxo}

if you are running DOS. If you use WINDOWS, you can either associate files with the .mx extension with the mx.exe file and double click the input file or launch the input file on the Mx icon. Feedback of function evaluations is printed on the screen and an outputfile cutename.mxo is automatically created.


Mx may be used very simply in UNIX by typing:

mx <inputfile >outputfile &

where the parameter & is to run the job in batch. Other command-line interfaces may be available. Refer to the notes in the distribution for details.


Mx can be run either interactively or as a batch job under VMS with:

$ mx cutename

With this syntax, the output will be in a file called cutename.mxo

Outline of Mx Scripts


Mx reads three things: keywords, parameters and numbers. Keywords must start on a new line. Parameters pass information to commands, e.g. filenames or numbers. Comments can be put anywhere following !. Characters after column 1200 and blank lines are ignored. The syntax described for commands follows these conventions:

alternatives are represented by /
optional parameters or keywords are enclosed by { and }
items to be substituted according to the specific application are enclosed by < and >

Job Structure

Mx has been written for multiple groups, since genetically informative data generally comprise information on different types of relatives which form distinct groups. Several Mx programs can be run in one file. Minimum specifications per group are capitalized:

for reference
indicate group type: data/calculation/constraint
Read, select any observed data, labels
define at least one matrix
define matrix formula: covariances/means/threshold/compute/algebra
Specify numbers and parameters, starting values, equality constraints
Request fit functions, statistical output, optimization options, multiple fit mode, save matrices and job specifications
signifies end of group

#Define Command

#define <name> <number>

In multivariate modeling it is quite common that the same matrix dimensions are used in many different parts of a script. #define statements can minimize the number of changes required.

Matrix Declaration Section

Begin matrices;
<matrix name> <type> <r> <c>
End matrices;

After declaring matrices, the user is free to enter values and parameter specifications for the matrices prior to giving a matrix formula for covariance structure or means.

Multistatement Matrix Algebra

Begin Algebra;
B = (I-A)~ ; ! for example
C = B*S*B' ; ! for example
End Algebra;

In many cases breaking up a complicated matrix algebra expression into smaller parts can improve readability or efficiency or both. The begin algebra... end algebra declares new matrices as matrix functions. Each matrix that appears on the left hand side of the = sign is newly defined in this group (it must not have been previously defined).

Reading Data

Title Line

The title line is purely for the user's reference, it is printed when Mx prints the parameter specifications and the parameter estimates for a group. The title is recognized by its location rather than a keyword at the start of a line.

Group Type

Data/Calculation/Constraint {NGroups=n NInput_vars=n NObservations=n}

The group-type line specifies type of group: Data for data group, Calculation for calculation group; Constraint for constraint group. The first group must specify number of groups NG. Other parameters are number of input variables NI and number of observations NO.

Covariance and Correlation Matrices

CMatrix/KMatrix/PMatrix {Full} {File=<filename>}

Several different commands are used to read summary statistics from either the input script or an external file, if File= appears. Covariance matrices (CM) and correlation matrices (KM or PM for matrices of polyserial or polychoric correlations) are by default symmetric. Full indicates that a full matrix is supplied.

Asymptotic Variances and Covariances

Acov/AVar/AInv {File=<filename>}

To use asymptotic or diagonally weighted least squares, it is necessary to read a weight matrix. Mx expects a PRELIS output type matrix of asymptotic covariances (AC) or asymptotic variances (AV). The inverse of the asymptotic matrix (AI) could be used also.

Rectangular and Variable Length Data

Rectangular/VLength {File=<filename>}

Two types of raw data can be read: a rectangular matrix of balanced raw data (RE) using . (dots) as missing value indicators. Unbalanced data with many missing values may be read with a variable length file (VL). A variable length file contains, per vector, the number of input variables k (on a separate line) and identification codes and observed data for k variables.

Definition variables

Definition_variable <label>

This feature allows new types of analysis with VL or rectangular data files. Essentially, some of the variables may be assigned as definition variables which may subsequently be used as case-specific parameters of the model. Mx uses the Definition keyword to identify variables that are to be used in the model; they are extracted from the data set so modeling is restricted to the other variables. Definition variables are referenced as -1, -2 etc. in Specification statements. A new covariance matrix is computed for each case, and then the usual raw data log-likelihood function is computed.

Contingency Tables

CTable <r> <c> {File=<filename>}

Frequency data are read in a two-way contingency table (CT) which the requires number of rows and columns on same line. Mx will automatically handle incomplete ascertainment (when flagged by a negative number for cells that are not ascertained).

Mean Vector

Means {File=<filename>}

In the data-reading section (before Matrices command) the Means keyword reads a vector of observed means.

Labels and Select commands

Labels <label list>
Select <label list>/<numlist> /
Labels Row/Col <matrix name> <label list>

Labels may be read for the input variables, and either labels or variable numbers may be used to select variables. A / must end the select command. After matrices has been declared, either within the Matrices section or in an Algebra section, labels may be provided for the rows or columns of defined matrices.

Identification Codes

ICodes <numlist>
<numlist> is a number list of length NI.

The ICodes command may be used with the RM fit function to specify a non-standard structure of the expected covariance matrix.

Building Models with Matrices

Matrices command

<matrix name> <type> <r> <c> {Free}

Matrices are defined by their name, type and dimensions (rows & columns) for use in current or subsequent groups. Possible types, structure, shape and number of usable elements are given.

Matrix Types

Type    Structure           Shape     # of Usable
Zero    Null                Any       0
Unit    Unit                Any       0
Iden    Identity            Square    0
IZero   Identity|Zero       Any       0
ZIden   Zero|Identity       Any       0
Diag    Diagonal            Square    r
SDiag   Subdiagonal         Square    r(r-1)/2
Stand   Standardized        Square    r(r-1)/2
Symm    Symmetric           Square    r(r+1)/2
Lower   Lower triangular    Square    r(r+1)/2
Full    Full                Any       rxc

r is the number of rows and c the number of columns of the matrix.

Number of free elements indicates how many elements should be supplied. All usable elements of matrices are initialized at zero and are fixed parameters, unless the Free keyword is used, in which case each usable element is specified as a different free parameter.

Equating Some Matrices across groups

<matrix name> <type> <r> <c> {= <matrix name> / <symbol> <group>} /
Symbol   Matrix Quantity              Dimensions
%On      Observed covariance/data     NIn x NIn
%En      Expected covariance matrix   NIn x NIn
%Mn      Expected mean vector           1 x NIn
%Pn      Expected proportions         NRn x NCn
%Fn      Function value                 1 x 1

NIn: number of input variables in group n following any selection

NR and NC: number of rows and columns in a contingency table

Special codes exist for constraining a matrix to equal one previously computed or defined in group n. Note that none of the equalities may refer to groups that appear after the current group.

Equating Matrices to Computed Matrices

<matrix name> computed {<r> <c>} = <matrix name> <group number>

When matrices are declared with the Matrices command, a special type, computed, may be used to equate to a matrix which was defined within the Algebra section of a previous group. Row and column dimensions are set to those of the previously calculated matrix, and may be omitted when declaring a matrix as computed.

Equating All Matrices across groups

Matrices = Group <number>

This command equates all matrices in a group to those of a previous group.

Matrix Formulae

Matrix Functions

Keyword     Function                     Restric-  Result
                                         tions     Size
\tr()       Trace                        r=c       1x1
\det()      Determinant                  r=c       1x1

\sum()      Sum                          None      1x1
\prod()     Product                      None      1x1
\max()      Maximum                      None      1x1
\min()      Minimum                      None      1x1
\abs()      Absolute value               None      rxc

\cos()      Cosine                       None      rxc
\cosh()     Hyperbolic cosine            None      rxc
\sin()      Sin                          None      rxc
\sinh()     Hyperbolic sin               None      rxc
\tan()      Tan                          None      rxc
\tanh()     Hyperbolic tan               None      rxc

\exp()      Exponent (e**A)              None      rxc
\ln()       Natural logarithm            None      rxc
\sqrt()     Square root                  None      rxc

\d2v()      Diagonal to Vector           None      min(r,c)x1
\v2d()      Vector to Diagonal           r=1 or    max(r,c)x
                                         c=1       max(r,c)
\m2v()      Matrix to Vector             None      rcx1
\vec()      Matrix to Vector*            None      rcx1
\vech()     Lower triangle to Vector     None      rcx1
\stnd()     Standardize matrix           r=c       rxc

\eval()     Real eigenvalues             r=c       rxc
\evec()     Real eigenvectors            r=c       rxr
\ival()     Imaginary eigenvalues        r=c       rx1
\ivec()     Imaginary eigenvectors       r=c       rxr

\mean()     Mean of columns              None      1xc
\cov()      Covariance of columns        None      cxc
\mnor()     Multivariate normal integral r=c+3     1x1
\momnor()   Moments of multiv normal     r=c+3     1x1

\aorder()   Ascending sort order         rx1       rx1
\dorder()   Descending sort order        rx1       rx1
\sortr()    Row sort                     None      rxmax
\sortc()    Column sort                  None      max
\part()     Extract part of matrix**     None      Variable

*vec: vectorizes by columns, whereas m2v vectorizes by rows.

**part: part(A,B) takes two arguments. Elements of the 1x4 matrix B define a rectangle in A to be extracted.

Matrix functions are defined by a keyword starting with a backslash and followed by an argument enclosed in parentheses. This argument may be a single matrix name or a complex matrix formula. The expression within parentheses will be evaluated prior to the function evaluation, e.g., \tr(A*B)

Matrix Operators

Symbol  Function            Example  Priority
  ~     Inversion             A~       1
  '     Transposition         A'       1

  ^     Element powering      A^B      2

  *     Multiplication        A*B      3
  .     Dot product           A.B      3
  @     Kronecker product     A@B      3
  &     Quadratic product     A&B      3
  %     Element division      A%B      3

  +     Addition              A+B      4
  -     Subtraction           A-B      4
  |     Horizontal adhesion   A|B      4
  _     Vertical adhesion     A_B      4

Unary and binary matrix operators are used to perform operations on or between matrices, declared by the Matrices command. Operations with lower priority are evaluated first, equal priority operations are carried out from left to right. Parentheses may be used to change order of evaluation.

Covariances/Compute command

Covariance/Compute {funct} <matrix name> {operator <matrix name> ..} /

The Covariance command may be formed with any syntactically correct combination of matrices (specified by Matrices command), operators and functions. The command may extend over several lines and must end in a /. Compute is the recommended keyword for calculation groups, to make reading scripts easier for humans.

Means command

Means <matrix name> .. /

A matrix formula for the means may be supplied after the Matrices command. Means will be used in covariance analysis if both a means model and observed means are supplied. Raw data analysis requires a model for means.

Threshold command

Threshold <matrix name> .. /

Thresholds can be used only when fitting to contingency table data. Special restrictions apply to the dimensions of the matrix calculated in the threshold command. The resulting matrix must have 2 rows and at least d columns where d=max ((r-1),(c-1)) and r and c are the rows and columns of the contingency table. The elements are the predicted row and column thresholds.

Matrix command

Matrix <matrix name> {File=<filename>} <numlist>
<numlist> is a free format list of numbers.

The list of numbers must be equal to the number of usable elements of that matrix.

Start and Value commands

Start/Value <value><elemlist>/All
<elemlist> consists of matrix elements and may include the TO keyword

The TO keyword operates differently for start and value, otherwise the commands are synonymous.

Free keyword

<matrix name> <type> <r> <c> {Free}

When a matrix is declared in the Matrices section, the Free keyword sets all usable elements free.

Specification command

Specification <matrix name> <numlist>
<numlist> contains not necessarily distinct integers.

Specify is a convenient method of defining constraints between parameters. If two elements are given the same value, then the same free parameter is assigned to both elements. A zero indicates that the element does not have a free parameter.

Pattern command

Pattern <matrix name> {File=<filename>} <numlist>
<numlist> is a list of 1's and 0's

The pattern command requests a different free parameter for every element with a 1. A zero fixes the corresponding matrix element.

Free, Fix and Equate commands

Free/Fix/Equate <element list>
<elemlist> is a list of matrix elements

Matrix elements are referred to by group, row and column. Fix makes a parameter fixed (if it was free before) and Free makes an element a free parameter to be estimated. Equate passes the value and the parameter specification of the first matrix element in the list to the remaining elements in the list. Elements of matrices in the current group may be specified with 2 subscripts; elements in previous groups must be specified with 3 subscripts. For large models with many constraints it may be more convenient to use the Specification command. See also the Pattern command.

Boundary command

Bound low high <parlist> /All
<parlist> is a list of parameter names or a list of parameter specification numbers.

Free parameters in the list will be bounded to lie between low and high. Negative parameter numbers will bound non-linear constraints.

Output Options

Options command

Options {options}

The Options command takes a wide variety of keywords to control the use of non-default fit functions, the amount of statistical output, optimization parameters, filenames for result matrices, etc.

Fit Functions

The default fit function for a group is set according to the type of data that are read. Note that the method may change between groups.

Input data                              Default fit
CMatrix, Kmatrix or PMatrix             ML
CMatrix, Kmatrix or Pmatrix with Acov   AWLS
CMatrix, Kmatrix or Pmatrix with Avar   DWLS
Rawdata, Vlength or Rectangular         RM
Ctable                                  MLn

ML - maximum likelihood
AWLS - asymptotic weighted least squares
DWLS - diagonal weighted least squares, RM raw maximum likelihood
MLn - maximum likelihood assuming bivariate normal liability.

The fit function for a group may be user defined. For this, the User-defined keyword must appear on the Options line, and the matrix expression given as the model (Constraint or Covariance command) must evaluate to a scaler. There are no other rules. Use matrix functions and operators to suit. Any of the pre-defined fit functions LS, ML, AWLS, etc., could be specified as user-defined functions, but it is generally less efficient to do so. User-defined functions are recommended only when the default functions are not suitable.

  !User-defined fit function
  !Fit to a correlation matrix by least squares
  Data NInput_vars=3 NGroups=1
  CMatrix Symm
  .2 1
  .3 .4 1
  A Symm 3 3 = %01
  B Stan 3 3 Free
  Covariances \tr((A-B)*(A-B))/
  Option User RSiduals

Analyzing Raw Data

When VL or Rectangular files are read, Mx calculates twice the negative log-likelihood of the data for each case. When there are missing data , the appropriate mean vector and covariance matrix is automatically created by Mx for each observation.

Contingency Table Analysis

Mx estimates thresholds and polychoric correlations from 2-way contingency tables. The covariance structure is necessarily limited to two variables. For an r by c contingency table, there are r-1 row thresholds and c-1 column thresholds that separate the observed categories of individuals. The fit function is based on twice the log-likelihood of the observed frequency data.


Option NDecimals=n or Width=m
n is the number of decimal places, and m is the number of columns

By default, Mx will print most numbers with three decimal places, or use exponential format. With NDecimals=n, Mx will print most numbers with n decimal places. By default, Mx prints up to 80 columns of output. With Width=m, this may be changed to m columns of output. At this time, NDecimals and Width may not be used together.

Suppressing Output

Option NO_output

Before describing ways in which Mx output can be increased, we note the valuable keyword NO_Output which prevents printing of all output for a group.


Option RSiduals

The RSiduals keyword requests that the observed matrix, the expected matrix, and the residuals (O-E) be printed.

Adjusting Degrees of Freedom

Option DFreedom=n
n is the adjusted number of degrees of freedom

If a correlation matrix is read instead of a covariance matrix, the number of statistics provided is usually less than when variances are also given. This option can be used to correct such problems.

Power Calculations

Option Power=alpha,df
alpha is the probability level of the test, and df are the degrees of freedom associated with it

Power calculations are useful in experimental design and getting grants. If the function values computed by Mx may be considered as a Chi-squared, then the Power command will compute the power of the study to reject the hypothesis at significance level alpha for the given number of degrees of freedom (df). In addition, the program very kindly works out the total sample size that would be required, given the current proportion of subjects in each group, to reject the hypothesis at various power levels.

Confidence Intervals

Option CInterval=n
n is the desired percentage of the confidence interval; e.g. CI=90 will give 90% confidence intervals

The difference in fit between two competing models may be assessed by examining the confidence limits on their Chi-squared statistics. These are computed using the inverse non-central Chi-squared distribution.

Standard Errors

Option SErrors

The numerical estimates of the hessian matrix of the parameters to provide approximate standard errors on the parameters.

Randomizing Starting Values

Option THard=n
n is a positive or a negative integer

If the parameter THard is set using TH=n where n is a positive integer, Mx will generate random starting values for all parameters and attempt to fit the model again n times. THard can be very useful when exploring the identification of structural equation models. THard with a negative integer requests repeat fits from the solution, resetting the covariance matrix to an identity matrix.

Jiggling Parameter Starting Values

Option Jiggle

Sometimes used with the randomizing option described above, parameter start values can be jiggled. This option can be useful to nudge Mx away from a saddle point.

Fit indices

Option Null=,df

Users may supply the results of fitting a null model (usually a simple diagonal model of variances) which will extend the output with other fit indices.

Check Identification of Model

Option Check

By default, Mx does not test identification of models via examination of the rank of the hessian matrix of parameter estimates. Option check does this, but it can give either false positives or false negatives. Mx computes the eigenvalues and eigenvectors of the hessian matrix, and uses this information to assess potential areas of underidentification.


Mx uses NPSOL to perform numerical optimization in the presence of general linear, non-linear and boundary constraints, obtained from Walter Murray and Philip Gill. The default optimization parameters are suitable for most problems. Examples:

NAG=n creates output file (NAGDUMP.OUT) if n>0

Iterations=n alters maximum numbers of iterations

Multiple Fit Option

Option Multiple

The multiple keyword may be given in the last group of an Mx script. Following optimization, the program is in multiple fit mode, and will accept commands to alter the model specification. Parameters of the model not changed will restart estimation at the previous solution. The only commands that may be used in multiple fit mode to modify matrices are SP, PA, MA, VA, ST, EQ, FI, FR. An End line is necessary to end the group. A number identifying the group in which a matrix is to be found must be placed directly after the keywords SP, PA and MA, before the letter indicating the matrix.

Changing the model

#group <number>

It is possible to change matrix formulae and other characteristics of a group. Options or matrix formulae supplied after this command would apply to that group.

Drop command

Drop {@value} <parlist>
<parlist> is a list of parameter numbers and {@value} is an optional value to fix at.

Drop fixes all occurrences of a parameter with that number to zero or to a specified value.

Save and Get command

Save <filename>
Get <filename>
<filename> is the name of the file to be saved or retrieved;the extension .mxs is recommended.

When the multiple fit option is implemented, the entire input job specification, date, and estimates may be stored in binary format for rapid retrieval and estimation in subsequent fitting of submodels.

Mx command

MXa= <filename>
a is the single-letter name of the matrix to be written, or one of %E %M %P %V.

Mx will write matrices to files, including %E, expected covariance matrices, %M, expected mean matrices, %P, a series of columns of information about the likelihood of individual data vectors, and %V, a variable length file.

Draw command


If you specify a RAM model with matrices A, S and F, RAMpath graphics commands may be written to a file for later input for RAMpath to draw a path diagram.

End command


The End command signifies the end of a group.

Error Messages

When Mx runs into something it doesn't understand, it tries to tell you as soon as possible. Sometimes this will be after the error itself, so check the earlier input for warnings & mistakes if the cause is not immediately obvious. The Just WHAT is this command... message usually comes when Mx has been given too many numbers or labels for a matrix. You may get a "non-numeric characters" warning if you supply too few numbers, or Mx may run into the end of the file in the vain search for enough numbers. By and large, the error messages are supposed to inform and amuse a little to make them less user-hostile.

Examples of Mx Scripts

Single Group Example

  TITLE: Factor Model
  DATA NGroups=1 NInput_vars=3 NObs=100
  Cmatrix Symmetric
   .5  .8
   .4  .2  .7
  A Full 3 1
  U Diag 3 3 Free
  Specification A
  4 5 0
  Start .5 A 1 1 - A 2 1
  Options ML

Model Fitting To Covariances

  Double cholesky model
  !This group calculates, no fitting
  Calculation NGroups=4
  Begin Matrices;
  H Lower 2 2 Free
  U Lower 2 2 Free
  End Matrices;
  Begin Algebra;
  A= H*H' /  ! Additive Genetic
  E= U*U' /  ! Specific Environment
  End Algebra;

  !  Now get to actual data
  Group 2: Unmatched twins
  Data NInput_vars=2 NObservations=449
   KMatrix Symmetric 
   -.22 1.
   ACov File=unm.asy
  Matrices= Group 1
  Covariances (A + E) /
  Group 3: MZ twins with cotwins
  DAta NInput_vars=6 NObservations=456
  CMatrix File=mz.cov
  Select 1 2 4 5/
  Matrices= Group 1
  Covariances (A+E | A_
               A   | A+E) /
  Group 4: DZ twins with cotwins
  Data NInput_vars=6 NObservations=357
  CMatrix File=dz.cov
  Labels Ex1 Ne1 De1 Ex2 Ne2 De2
  Select ex1 ne1 ex2 ne2 /
  Matrices= Group 1
  H Diag 1 1
  Covariances (A+E | H@A _
               H@A | A+E) /
  Matrix Half .5
  Start .5 All
  Boundary -1 1 All
  Options RSiduals Iterations=200

Categorical Data Analysis

  PACE MODEL:  MZ twins
  Data Ninput_vars=2 NGroups=3
  CTable 2 2
  30  20
  19  60
  A Full 2 6
  B Full 2 2
  I Iden 2 2
  P Symm 6 6
  T Full 2 1
  Thresholds T /
  Covariances (I-B)~*A*P*A'*(I-B)~/
  Specify A
  1 2 3 0 0 0
  0 0 0 1 2 3
  Labels Row A At1 Ct1 Et1 A2 C2 E2
  Labels Col A Pt1 Pt2
  Start .6 A 1 1 - A 2 6
  Specification T 4 5
  Pattern B
  0 1 1 0
  Equate B 2 1 B 1 2
  Boundary -.99 .99 6
  Matrix P
  0 1
  0 0 1
  1 0 0 1
  0 1 0 0 1
  0 0 0 0 0 1
  Options RSidual
  DZ twins in PACE model
  Data Ninput_vars=2
  CTable 2 2
  30  30
  29  50
  A Full 2 6 =A1
  B Full 2 2 =B1
  I Iden 2 2 
  P Stand 6 6
  T Full 2 1 =T1
  Thresholds T /
  Covariances  (I-B)~* A*P*A'* (I-B)~ /
  Value .5 P 4 1
  Value 1 P 5 2
  Option RSiduals
  Constraint group: a*a+c*c+e*e=1
  Data Constraint Ninput=1
  S Full 1 3
  I Identity 1 1
  Constraint I - S*S' /
  Specification S
  1 2 3
  Options Multiple Iterations=300
  Save pace.mxs
  ! No common environment
  Drop 2
  ! No interaction
  Get pace.mxs
  Value 0 B 1 1 1 - B 1 2 2

Missing Data ML Example

  Estimate means and cholesky
  Data NInput_vars=3 NGroups=1
  VLength File=[neale]unbalanced.raw
  M Full 1 3 Free
  S Lower 3 3 Free
  Means M /  ! Means model
  Covariances S*S' /
  Start .7 s 1 1 - s 3 3
  Options RM MX%E=unbal.cov THard=-1
  First few lines of unbalanced.raw
  1 2   0.5550   -1.1114  
  1 2 3   1.6442   -0.1728  3.69
  1 3  -0.2145   5.01