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Archive for September 2011

## Multilevel random effects in xtmixed and sem — the long and wide of it

xtmixed was built from the ground up for dealing with multilevel random effects — that is its raison d’être. sem was built for multivariate outcomes, for handling latent variables, and for estimating structural equations (also called simultaneous systems or models with endogeneity). Can sem also handle multilevel random effects (REs)? Do we care?

This would be a short entry if either answer were “no”, so let’s get after the first question.

Can sem handle multilevel REs?

A good place to start is to simulate some multilevel RE data. Let’s create data for the 3-level regression model

where the classical multilevel regression assumption holds that and are distributed normal and are uncorrelated.

This represents a model of nested within nested within . An example would be students nested within schools nested within counties. We have random intercepts at the 2nd and 3rd levels — , . Because these are random effects, we need estimate only the variance of , , and .

For our simulated data, let’s assume there are 3 groups at the 3rd level, 2 groups at the 2nd level within each 3rd level group, and 2 individuals within each 2nd level group. Or, , , and . Having only 3 groups at the 3rd level is silly. It gives us only 3 observations to estimate the variance of . But with only observations, we will be able to easily see our entire dataset, and the concepts scale to any number of 3rd-level groups.

First, create our 3rd-level random effects — .

. set obs 3
. gen k = _n
. gen Uk = rnormal()

There are only 3 in our dataset.

I am showing the effects symbolically in the table rather than showing numeric values. It is the pattern of unique effects that will become interesting, not their actual values.

Now, create our 2nd-level random effects — — by doubling this data and creating 2nd-level effects.

. expand 2
. by k, sort: gen j = _n
. gen Vjk = rnormal()

We have 6 unique values of our 2nd-level effects and the same 3 unique values of our 3rd-level effects. Our original 3rd-level effects just appear twice each.

Now, create our 1st-level random effects — — which we typically just call errors.

. expand 2
. by k j, sort: gen i = _n
. gen Eijk = rnormal()

There are still only 3 unique in our dataset, and only 6 unique .

Finally, we create our regression data, using ,

. gen xijk = runiform()
. gen yijk = 2 * xijk + Uk + Vjk + Eijk

We could estimate our multilevel RE model on this data by typing,

. xtmixed yijk xijk || k: || j:

xtmixed uses the index variables k and j to deeply understand the multilevel structure of the our data. sem has no such understanding of multilevel data. What it does have is an understanding of multivariate data and a comfortable willingness to apply constraints.

Let’s restructure our data so that sem can be made to understand its multilevel structure.

First some renaming so that the results of our restructuring will be easier to interpret.

. rename Uk U
. rename Vjk V
. rename Eijk E
. rename xijk x
. rename yijk y

We reshape to turn our multilevel data into multivariate data that sem has a chance of understanding. First, we reshape wide on our 2nd-level identifier j. Before that, we egen to create a unique identifier for each observation of the two groups identified by j.

. egen ik = group(i k)
. reshape wide y x E V, i(ik) j(j)

We now have a y variable for each group in j (y1 and y2). Likewise, we have two x variables, two residuals, and most importantly two 2nd-level random effects V1 and V2. This is the same data, we have merely created a set of variables for every level of j. We have gone from multilevel to multivariate.
We still have a multilevel component. There are still two levels of i in our dataset. We must reshape wide again to remove any remnant of multilevel structure.

. drop ik
. reshape wide y* x* E*, i(k) j(i)

I admit that is a microscopic font, but it is the structure that is important, not the values. We now have 4 y’s, one for each combination of 2nd- and 3rd-level identifiers — i and j. Likewise for the x’s and E’s.

We can think of each xji yji pair of columns as representing a regression for a specific combination of j and i — y11 on x11, y12 on x12, y21 on x21, and y22 on x22. Or, more explicitly,

So, rather than a univariate multilevel regression with 4 nested observation sets, () * (), we now have 4 regressions which are all related through and each of two pairs are related through . Oh, and all share the same coefficient . Oh, and the all have identical variances. Oh, and the also have identical variances. Luckily both the sem command and the SEM Builder (the GUI for sem) make setting constraints easy.

There is one other thing we haven’t addressed. xtmixed understands random effects. Does sem? Random effects are just unobserved (latent) variables and sem clearly understands those. So, yes, sem does understand random effects.

Many SEMers would represent this model in a path diagram by drawing.

There is a lot of information in that diagram. Each regression is represented by one of the x boxes being connected by a path to a y box. That each of the four paths is labeled with means that we have constrained the regressions to have the same coefficient. The y21 and y22 boxes also receive input from the random latent variable V2 (representing our 2nd-level random effects). The other two y boxes receive input from V1 (also our 2nd-level random effects). For this to match how xtmixed handles random effects, V1 and V2 must be constrained to have the same variance. This was done in the path diagram by “locking” them to have the same variance — S_v. To match xtmixed, each of the four residuals must also have the same variance — shown in the diagram as S_e. The residuals and random effect variables also have their paths constrained to 1. That is to say, they do not have coefficients.

We do not need any of the U, V, or E variables. We kept these only to make clear how the multilevel data was restructured to multivariate data. We might “follow the money” in a criminal investigation, but with simulated multilevel data is is best to “follow the effects”. Seeing how these effects were distributed in our reshaped data made it clear how they entered our multivariate model.

Just to prove that this all works, here are the results from a simulated dataset ( rather than the 3 that we have been using). The xtmixed results are,

. xtmixed yijk xijk || k: || j: , mle var

(log omitted)

Mixed-effects ML regression                     Number of obs      =       400

-----------------------------------------------------------
|   No. of       Observations per Group
Group Variable |   Groups    Minimum    Average    Maximum
----------------+------------------------------------------
k |      100          4        4.0          4
j |      200          2        2.0          2
-----------------------------------------------------------

Wald chi2(1)       =     61.84
Log likelihood = -768.96733                     Prob > chi2        =    0.0000

------------------------------------------------------------------------------
yijk |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
xijk |   1.792529   .2279392     7.86   0.000     1.345776    2.239282
_cons |    .460124   .2242677     2.05   0.040     .0205673    .8996807
------------------------------------------------------------------------------

------------------------------------------------------------------------------
Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
k: Identity                  |
var(_cons) |   2.469012   .5386108      1.610034    3.786268
-----------------------------+------------------------------------------------
j: Identity                  |
var(_cons) |   1.858889    .332251      1.309522    2.638725
-----------------------------+------------------------------------------------
var(Residual) |   .9140237   .0915914      .7510369    1.112381
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(2) =   259.16   Prob > chi2 = 0.0000

Note: LR test is conservative and provided only for reference.


The sem results are,

sem (y11 <- x11@bx _cons@c V1@1 U@1)
(y12 <- x12@bx _cons@c V1@1 U@1)
(y21 <- x21@bx _cons@c V2@1 U@1)
(y22 <- x22@bx _cons@c V2@1 U@1) ,
covstruct(_lexog, diagonal) cov(_lexog*_oexog@0)
cov( V1@S_v V2@S_v  e.y11@S_e e.y12@S_e e.y21@S_e e.y22@S_e)

(notes omitted)

Endogenous variables

Observed:  y11 y12 y21 y22

Exogenous variables

Observed:  x11 x12 x21 x22
Latent:    V1 U V2

(iteration log omitted)

Structural equation model                       Number of obs      =       100
Estimation method  = ml
Log likelihood     = -826.63615

(constraint listing omitted)
------------------------------------------------------------------------------
|                 OIM             |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
Structural   |
y11 <-     |
x11 |   1.792529   .2356323     7.61   0.000     1.330698     2.25436
V1 |          1   7.68e-17  1.3e+16   0.000            1           1
U |          1   2.22e-18  4.5e+17   0.000            1           1
_cons |    .460124    .226404     2.03   0.042     .0163802    .9038677
-----------+----------------------------------------------------------------
y12 <-     |
x12 |   1.792529   .2356323     7.61   0.000     1.330698     2.25436
V1 |          1   2.00e-22  5.0e+21   0.000            1           1
U |          1   5.03e-17  2.0e+16   0.000            1           1
_cons |    .460124    .226404     2.03   0.042     .0163802    .9038677
-----------+----------------------------------------------------------------
y21 <-     |
x21 |   1.792529   .2356323     7.61   0.000     1.330698     2.25436
U |          1   5.70e-46  1.8e+45   0.000            1           1
V2 |          1   5.06e-45  2.0e+44   0.000            1           1
_cons |    .460124    .226404     2.03   0.042     .0163802    .9038677
-----------+----------------------------------------------------------------
y22 <-     |
x22 |   1.792529   .2356323     7.61   0.000     1.330698     2.25436
U |          1  (constrained)
V2 |          1  (constrained)
_cons |    .460124    .226404     2.03   0.042     .0163802    .9038677
-------------+----------------------------------------------------------------
Variance     |
e.y11 |   .9140239    .091602                        .75102    1.112407
e.y12 |   .9140239    .091602                        .75102    1.112407
e.y21 |   .9140239    .091602                        .75102    1.112407
e.y22 |   .9140239    .091602                        .75102    1.112407
V1 |   1.858889   .3323379                      1.309402    2.638967
U |   2.469011   .5386202                      1.610021    3.786296
V2 |   1.858889   .3323379                      1.309402    2.638967
-------------+----------------------------------------------------------------
Covariance   |
x11        |
V1 |          0  (constrained)
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
x12        |
V1 |          0  (constrained)
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
x21        |
V1 |          0  (constrained)
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
x22        |
V1 |          0  (constrained)
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
V1         |
U |          0  (constrained)
V2 |          0  (constrained)
-----------+----------------------------------------------------------------
U          |
V2 |          0  (constrained)
------------------------------------------------------------------------------
LR test of model vs. saturated: chi2(25)  =     22.43, Prob > chi2 = 0.6110


And here is the path diagram after estimation.

The standard errors of the two estimation methods are asymptotically equivalent, but will differ in finite samples.

Sidenote: Those familiar with multilevel modeling will be wondering if sem can handle unbalanced data. That is to say a different number of observations or subgroups within groups. It can. Simply let reshape create missing values where it will and then add the method(mlmv) option to your sem command. mlmv stands for maximum likelihood with missing values. And, as strange as it may seem, with this option the multivariate sem representation and the multilevel xtmixed representations are the same.

Do we care?

You will have noticed that the sem command was, well, it was really long. (I wrote a little loop to get all the constraints right.) You will also have noticed that there is a lot of redundant output because our SEM model has so many constraints. Why would anyone go to all this trouble to do something that is so simple with xtmixed? The answer lies in all of those constraints. With sem we can relax any of those constraints we wish!

Relax the constraint that the V# have the same variance and you can introduce heteroskedasticity in the 2nd-level effects. That seems a little silly when there are only two levels, but imagine there were 10 levels.

Add a covariance between the V# and you introduce correlation between the groups in the 3rd level.

What’s more, the pattern of heteroskedasticity and correlation can be arbitrary. Here is our path diagram redrawn to represent children within schools within counties and increasing the number of groups in the 2nd level.

We have 5 counties at the 3rd level and two schools within each county at the 2nd level — for a total of 10 dimensions in our multivariate regression. The diagram does not change based on the number of children drawn from each school.

Our regression coefficients have been organized horizontally down the center of the diagram to allow room along the left and right for the random effects. Taken as a multilevel model, we have only a single covariate — x. Just to be clear, we could generalize this to multiple covariates by adding more boxes with covariates for each dependent variable in the diagram.

The labels are chosen carefully. The 3rd-level effects N1, N2, and N3 are for northern counties, and the remaining second level effects S1 and S2 are for southern counties. There is a separate dependent variable and associated error for each school. We have 4 public schools (pub1 pub2, pub3, and pub4); three private schools (prv1 prv2, and prv3); and 3 church-sponsored schools (chr1 chr2, and chr3).

The multivariate structure seen in the diagram makes it clear that we can relax some constraints that the multilevel model imposes. Because the sem representation of the model breaks the 2nd level effect into an effect for each county, we can apply a structure to the 2nd level effect. Consider the path diagram below.

We have correlated the effects for the 3 northern counties. We did this by drawing curved lines between the effects. We have also correlated the effects of the two southern counties. xtmixed does not allow these types of correlations. Had we wished, we could have constrained the correlations of the 3 northern counties to be the same.

We could also have allowed the northern and southern counties to have different variances. We did just that in the diagram below by constraining the northern counties variances to be N and the southern counties variances to be S.

In this diagram we have also correlated the errors for the 4 public schools. As drawn, each correlation is free to take on its own values, but we could just as easily constrain each public school to be equally correlated with all other public schools. Likewise, to keep the diagram readable, we did not correlate the private schools with each other or the church schools with each other. We could have done that.

There is one thing that xtmixed can do that sem cannot. It can put a structure on the residual correlations within the 2nd level groups. xtmixed has a special option, residuals(), for just this purpose.

With xtmixed and sem you get,

• robust and cluster-robust SEs
• survey data

With sem you also get

• endogenous covariates
• estimation by GMM
• missing data — MAR (also called missing on observables)
• heteroskedastic effects at any level
• correlated effects at any level
• easy score tests using estat scoretests
• are the coefficients truly are the same across all equations/levels, whether effects?
• are effects or sets of effects uncorrelated?
• are effects within a grouping homoskedastic?

Whether you view this rethinking of multilevel random-effects models as multivariate structural equation models (SEMs) as interesting, or merely an academic exercise, depends on whether your model calls for any of the items in the second list.

Categories: Statistics Tags:

I’m still recycling my talk called “Mata, The Missing Manual” at user meetings, a talk designed to make Mata more approachable. One of the things I say late in the talk is, “Unless you already know what pointers are and know you need them, ignore them. You don’t need them.” And here I am writing about, of all things, pointers. Well, I exaggerated a little in my talk, but just a little.

Before you take my previous advice and stop reading, let me explain: Mata serves a number of purposes and one of them is as the primary langugage we at StataCorp use to implement new features in Stata. I’m not referring to mock ups, toys, and experiments, I’m talking about ready-to-ship code. Stata 12’s Structural Equation Modeling features are written in Mata, so is Multiple Imputation, so is Stata’s optimizer that is used by nearly all estimation commands, and so are most features. Mata has a side to it that is exceedingly serious and intended for use by serious developers, and every one of those features are available to users just as they are to StataCorp developers. This is one of the reasons there are so many user-written commands are available for Stata. Even if you don’t use the serious features, you benefit.

So every so often I need to take time out and address the concerns of these user/developers. I knew I needed to do that now when Kit Baum emailed a question to me that ended with “I’m stumped.” Kit is the author of An Introduction to Stata Programming which has done more to make Mata approachable to professional researchers than anything StataCorp has done, and Kit is not often stumped.

I have a certain reptutation about how I answer most questions. “Why do you want to do that?” I invariably reply, or worse, “You don’t want to do that!” and then go on to give the answer to the question I wished they had asked. When Kit asks a question, however, I just answer it. Kit asked a question about pointers by setting up an artificial example and I have no idea what his real motivation was, so I’m not even going to try to motivate the question for you. The question is interesting in and of itself anyway.

Here is Kit’s artificial example:

real function x2(real scalar x) return(x^2)

real function x3(real scalar x) return(x^3)

void function tryit()
{
pointer(real scalar function) scalar fn
string rowvector                     func
real scalar                          i

func = ("x2", "x3")
for(i=1;i<=length(func);i++) {
fn = &(func[i])
(*fn)(4)
}
}


Kit is working with pointers, and not just pointers to variables, but pointers to functions. A pointer is the memory address, the address where the variable or function is stored. Real compilers translate names into memory addresses which is one of the reasons real compilers produce code that runs fast. Mata is a real compiler. Anyway, pointers are memory addresses, such as 58, 212,770, 427,339,488, except the values are usually written in hexadecimal rather than decimal. In the example, Kit has two functions, x2(x) and x3(x). Kit wants to create a vector of the function addresses and then call each of the functions in the vector. In the artificial example, he's calling each with an argument of 4.

The above code does not work:

: tryit()
tryit():  3101  matrix found where function required
<istmt>:     -  function returned error


The error message is from the Mata compiler and it's complaining about the line

        (*fn)(4)


but the real problem is earlier in the tryit() code.

One corrected version of tryit() would read,

void function tryit()
{
pointer(real scalar function) scalar fn
pointer(real scalar function) vector func     // <---
real scalar                          i

func = (&x2(), &x3())                         // <---
for(i=1;i<=length(func);i++) {
fn = func[i]                          // <---
(*fn)(4)
}
}


If you make the three changes I marked, tryit() works:

: tryit()
16
64


I want to explain this code and alternative ways the code could have been fixed. It will be easier if we just work interactively, so let's start all over again:

: real scalar x2(x) return(x^2)

: real scalar x3(x) return(x^3)

: func = (&x2(), &x3())


Let's take a look at what is in func:

: func
1            2
+---------------------------+
1 |  0x19551ef8   0x19552048  |
+---------------------------+


Those are memory addresses. When we typed &x2() and &x3() in the line

: func = (&x2(), &x3())


functions x2() and x3() were not called. &x2() and &x3() instead evaluate to the addresses of the functions named x2() and x3(). I can demonstrate this:

: &x2()
0x19551ef8


0x19551ef8 is the memory address of where the function x2() is stored. 0x19551ef8 may not look like a number, but that is only because it is presented in base 16. 0x19551ef8 is in fact the number 425,008,888, and the compiled code for the function x2() starts at the 425,008,888th byte of memory and continues thereafter.

Let's assign to fn the value of the address of one of the functions, say x2(). I could do that by typing

: fn = func[1]


or by typing

: fn = &x2()


and either way, when I look at fn, it contains a memory address:

: fn
0x19551ef8


Let's now call the function whose address we have stored in fn:

: (*fn)(2)
4


When we call a function and want to pass 2 as an argument, we normally code f(2). In this case, we substitute (*fn) for f because we do not want to call the function named f(), we want to call the function whose address is stored in variable fn. The operator * usually means multiplication, but when * is used as a prefix, it means something different, in much the same way the minus operator - can be subtract or negate. The meaning of unary * is "the contents of". When we code *fn, we mean not the value 425,008,888 stored in fn, we mean the contents of the memory address 425,008,888, which happens to be the function x2().

We type (*fn)(2) and not *fn(2) because *fn(2) would be interpreted to mean *(fn(2)). If there were a function named fn(), that function would be called with argument 2, the result obtained, and then the star would take the contents of that memory address, assuming fn(2) returned a memory address. If it didn't, we'd get a type mismatch error.

The syntax can be confusing until you understand the reasoning behind it. Let's start with all new names. Consider something named X. Actually, there could be two different things named X and Mata would not be confused. There could be a variable named X and there could be a function named X(). To Mata, X and X() are different things, or said in the jargon, have different name spaces. In Mata, variables and functions can have the same names. Variables and functions having the same names in C is not allowed -- C has only one name space. So in C, you can type

fn = &x2


to obtain the address of variable x2 or function x2(), but in Mata, the above means the address of the variable x2, and if there is no such variable, that's an error. In Mata, to obtain the address of function x2(), you type

fn = &x2()


The syntax &x2() is a definitional nugget; there is no taking it apart to understand its logic. But we can take apart the logic of the programmer who defined the syntax. & means "address of" and &thing means to take the address of thing. If thing is a name -- &name -- that means to look up name in the variable space and return its address. If thing is name(), that means look up name in the function space and return its address. They way we formally write this grammar is

 &thing, where

thing  :=   name
name()
exp


There are three possibilities for thing; it's a name or it's a name followed by () or it's an expression. The last is not much used. &2 creates a literal 2 and then tells you the address where the 2 is stored, which might be 0x195525d8. &(2+3) creates 5 and then tells you where the 5 is stored.

But let's get back to Kit's problem. Kit coded,

func = ("x2", "x3")


and I said no, code instead

func = (&x2(), &x3())


You do not use strings to obtain pointers, you use the actual name prefixed by ampersand.

There's a subtle difference in what Kit was trying to code and what I did code, however. In what Kit tried to code, Kit was seeking "run-time binding". I, however, coded "compile-time binding". I'm about to explain the difference and show you how to achieve run-time binding, but before I do, let me tell you that

1. You probably want compile-time binding.
2. Compile-time binding is faster.
3. Run-time binding is sometimes required, but when persons new to pointers think they need run-time binding, they usually do not.

Let me define compile-time and run-time binding:

1. Binding refers to establishing addresses corresponding to names and names(). The names are said to be bound to the address.

2. In compile-time binding, the addresses are established at the time the code is compiled.

More correctly, compile-time binding does not really occur at the time the code is compiled, it occurs when the code is brought together for execution, an act called linking and which happens automatically in Mata. This is a fine and unimportant distiction, but I do not want you to think that all the functions have to be compiled at the same time or that the order in which they are compiled matters.

In compile-time binding, if any functions are missing when the code is brought together for execution, and error message is issued.

3. In run-time binding, the addresses are established at the time the code is executed (run), which happens after compilation, and after linking, and is an explicit act performed by you, the programmer.

To obtain the address of a variable or function at run-time, you use built-in function findexternal(). findexternal() takes one argument, a string scalar, containing the name of the object to be found. The function looks up that name and returns the address corresponding to it, or it returns NULL if the object cannot be found. NULL is the word used to mean invalid memory address and is in fact defined as equaling zero.

findexternal() can be used only with globals. The other variables that appear in your program might appear to have names, but those names are used solely by the compiler and, in the compiled code, these "stack-variables" or "local variables" are referred to by their addresses. The names play no other role and are not even preserved, so findexternal() cannot be used to obtain their addresses. There would be no reason you would want findexternal() to find their addresses because, in all such cases, the ampersand prefix is a perfect substitute.

Functions, however, are global, so we can look up functions. Watch:

: findexternal("x2()")
0x19551ef8


Compare that with

: &x2()
0x19551ef8


It's the same result, but they were produced differently. In the findexternal() case, the 0x19551ef8 result was produced after the code was compiled and assembled. The value was obtained, in fact, by execution of the findexternal() function.

In the &x2() case, the 0x19551ef8 result was obtained during the compile/assembly process. We can better understand the distinction if we look up a function that does not exist. I have no function named x4(). Let's obtain x4()'s address:

: findexternal("x4()")
0x0

: &x4()


I may have no function named x4(), but that didn't bother findexternal(). It merely returned 0x0, another way of saying NULL.

In the &x4() case, the compiler issued an error. The compiler, faced with evaluating &x4(), could not, and so complained.

Anyway, here is how we could write tryit() with run-time binding using the findexternal() function:

void function tryit()
{
pointer(real scalar function) scalar fn
pointer(real scalar function) vector func
real scalar                          i

func = (findexternal("x2()"), findexternal("x3()")

for(i=1;i<=length(func);i++) {
fn = func[i]
(*fn)(4)
}
}


To obtain run-time rather than compile-time bindings, all I did was change the line

        func = (&x2(), &x3())


to be

        func = (findexternal("x2()"), findexternal("x3()")


Or we could write it this way:

void function tryit()
{
pointer(real scalar function) scalar fn
string vector                        func
real scalar                          i

func = ("x2()", "x3()")

for(i=1;i<=length(func);i++) {
fn = findexternal(func[i])
(*fn)(4)
}
}


In this variation, I put the names in a string vector just as Kit did originally. Then I changed the line that Kit wrote,

        fn = &(func[i])


        fn = findexternal(func[i])


Either way you code it, when performing run-time binding, you the programmer should deal with what is to be done if the function is not found. The loop

for(i=1;i<=length(func);i++) {
fn = findexternal(func[i])
(*fn)(4)
}


for(i=1;i<=length(func);i++) {
fn = findexternal(func[i])
if (fn!=NULL) {
(*fn)(4)
}
else {
...
}
}


Unlike C, if you do not include the code for the not-found case, the program will not crash if the function is not found. Mata will give you an "invalid use of NULL pointer" error message and a traceback log.

If you were writing a program in which the user of your program was to pass to you a function you were to use, such as a likelihood function to be maximized, you could write your program with compile-time binding by coding,

function myopt(..., pointer(real scalar function) scalar f, ...)
{
...
... (*f)(...) ...
...
}


and the user would call you program my coding myopt(..., &myfunc(), ...), or you could use run-time binding by coding

function myopt(..., string scalar fname, ...)
{
pointer(real scalar function) scalar f
...

f = findexternal(fname)
if (f==NULL) {