楼主最好把完整的程序提供出来，这样才能正确的判断 。
目前，根据你提供的信息，
~  horizontal concatenation 从右侧合并两个矩阵，ones（n，1 ）是 产生n*1的列向量，所以
xx = ones(n,1)~edu~urb~minor~age~expp~kids~kids5~lofinc  应该是 xx=ones(n,1) edu urb minor age expp kids kids5 lofinc 的合并后的矩阵。
vnx 应该是一个字符型的变量， 后门是变量的相关内容。

第二句，invpd returns the inverse of a symmetric, positive definite matrix. 就很简单了。

lz应该是在做一个包含教育， 年龄，孩子，民族， 经验等的一个数据表吧？

原程序如下：
/*
** Probit MLE Estimation
*/

new ;

@ LOADING DATA @


load dat[1962,25] = mwpsid82.db ; @ Data on married women from PSID 82 @

@ OPEN OUTPUT FILE @


output file = probit.out reset ;

@ DEFINE VARIABLES @


nlf
= dat[.,1]
; @ dummy variable for non-labor-force
@

emp
= dat[.,2]
; @ dummy variable for employed workers @

wrate
= dat[.,3]
; @ hourly wage rate @

lrate
= dat[.,4]
; @ log of (hourly wage rate ($)+1) @

edu
= dat[.,5]
; @ years of schooling @

urb
= dat[.,6]
; @ dummy variable for urban resident @

minor
= dat[.,7]
; @ dummy variable for minority race @

age
= dat[.,8]
; @ age @

tenure
= dat[.,9]
; @ # of months under current employer @

expp
= dat[.,10] ; @ years of experience since age 18 @

regs
= dat[.,11] ; @ dummy variable for South @

occw
= dat[.,12] ; @ dummy variable for white collar @

occb
= dat[.,13] ; @ dummy variable for blue collar @

indumg
= dat[.,14] ; @ dummy variable for manufacturing industry @

indumn
= dat[.,15] ; @ dummy variable for non-manufacturing industry @

unionn
= dat[.,16] ; @ dummy variable for union membership @

unempr
= dat[.,17] ; @ local unemployment rate @

ofinc
= dat[.,18] ; @ other family income in 1980 ($) @

lofinc
= dat[.,19] ; @ log of (other family income + 1) @

kids
= dat[.,20] ; @ # of children of age <= 17 @

wune80
= dat[.,21] ; @ UNKNOWN @

hwork
= dat[.,22] ; @ hours of house work per week @

uspell
= dat[.,23] ; @ # of unemployed weeks in 1980 @

search
= dat[.,24] ; @ # of weeks looking for job in 1980 @

kids5
= dat[.,25] ; @ # of children of age <= 5 @


@ DEFINE # of OBSERVATIONS @



n = rows(dat) ;

@
DEFINE DEPENDENT VARIABLE AND REGRESSORS @


@ Dependent variable @


yy = emp ;

vny = {"emp"};


@ Regressors @


xx = ones(n,1)~edu~urb~minor~age~expp~kids~kids5~lofinc ;
       vnx = {"cons", "edu", "urb", "minor", "age", "expp",

"kids", "kids5", "lofinc" };

/*
**
DO NOT CHANGE FROM HERE
*/

@
DEFINE INITIAL VALUE @


bb = invpd(xx'xx)*(xx'yy) ;

library optmum;
#include optmum.ext;
optset ;

proc
f(b) ;
local ltt
;

接上：
ltt = yy.*ln( cdfn(xx*b) ) + (1-yy).*ln( 1-cdfn(xx*b) ) ;

RETP(
-sumc(ltt)
) ;
ENDP
;

b0 = bb ;
__title = "Probit MLE ";
_opgtol = 1e-4;
_opstmth = "newton, helf";
__output = 0 ;

{b,func,grad,retcode} = optmum(&f,b0) ;

@ value of likelihood function @


logl = -func ;

@ likelihood ratio test @


rlogl = sumc(yy)*ln(sumc(yy)/n) + (n-sumc(yy))*ln(1-sumc(yy)/n) ;

lrt
= 2*(logl-rlogl);

lrdf
= cols(xx)-1 ;
      
@ Covariance matrix by Hessian @


covhess = invpd(hessp(&f,b));

@ Covariance matrix by BHHH @


proc
ff(b) ;

local ltt
;


ltt = yy.*ln(cdfn(xx*b)) + (1-yy).*ln(1-cdfn(xx*b));


RETP(
ltt
) ;

ENDP
;


gtt = gradp(&ff,b);

covbhhh = invpd(gtt'gtt) ;

@ Robust covariance matrix @


covrob = covhess*invpd(covbhhh)*covhess ;

@ Computing Standard errors @


sehess = sqrt(diag(covhess));

sebhhh = sqrt(diag(covbhhh));

serob
= sqrt(diag(covrob)) ;

stathess = b~sehess~(b./sehess);
statbhhh = b~sebhhh~(b./sebhhh);
statrob
= b~serob~(b./serob);
yyhess = vnx~stathess;
yybhhh = vnx~statbhhh;
yyrob
= vnx~statrob;

let mask[1,4] = 0 1 1 1;
let fmt[4,3] =

"-*.*s" 8 8

"*.*lf" 10 4

"*.*lf" 10 4

"*.*lf" 10 4;
这部分是什么意思？

format /rd 10,4 ;
"" ;
"Probit Estimation Result" ;
"------------------------" ;
" dependent variable: " $vny ;
"" ;
" log likelihood:
" logl ;
"" ;
" Pseudo R-square:
" (1-logl/rlogl) ;
"" ;
" LR test, df, p-val: " lrt lrdf cdfchic(lrt,lrdf) ;
"" ;
" Using Hessian ";
"" ;
"variable
coeff.
std. err.
t-st " ;
yyprin = printfm(yyhess,mask,fmt);
"" ;
" Using BHHH ";
"" ;
"variable
coeff.
std. err.
t-st " ;
yyprin = printfm(yybhhh,mask,fmt);
"" ;
" Using Robust ";
"" ;
"variable
coeff.
std. err.
t-st " ;
yyprin = printfm(yyrob,mask,fmt);

output off ;

xx = ones(n,1)~edu~urb~minor~age~expp~kids~kids5~lofinc ;解释变量
vnx = {"cons", "edu", "urb", "minor", "age", "expp","kids", "kids5", "lofinc" };工具变量
bb = invpd(xx'xx)*(xx'yy) ; OLS系数估计