/* nag_ml_mixed_regsn (g02jbc) Example Program.
*
* NAGPRODCODE Version.
*
* Copyright 2016 Numerical Algorithms Group.
*
* Mark 26, 2016.
*/
#include <stdio.h>
#include <nag.h>
#include <nag_stdlib.h>
#include <nagg02.h>
int main(void)
{
/* Scalars */
double like, tol;
Integer cwid, df, exit_status, fint, i, j, k, l, lb, maxit, n, ncol, nff,
nfv;
Integer nrf, nrv, nvpr, tddat, rint, svid, warnp, yvid, fnlevel, rnlevel;
Integer lgamma, fl;
/* Nag types */
NagError fail;
/* Arrays */
double *b = 0, *dat = 0, *gamma = 0, *se = 0;
Integer *fvid = 0, *levels = 0, *rvid = 0, *vpr = 0;
#define DAT(I, J) dat[(I-1)*tddat + J - 1]
exit_status = 0;
INIT_FAIL(fail);
printf("nag_ml_mixed_regsn (g02jbc) Example Program Results\n\n");
/* Skip heading in data file */
scanf("%*[^\n] ");
/* Read in the problem size information */
scanf("%" NAG_IFMT "%" NAG_IFMT "%" NAG_IFMT "%" NAG_IFMT "%" NAG_IFMT
"%*[^\n] ", &n, &ncol, &nfv, &nrv, &nvpr);
/* Check problem size */
if (n < 0 || ncol < 0 || nfv < 0 || nrv < 0 || nvpr < 0) {
printf("Invalid problem size, at least one of n, ncol, nfv, "
"nrv or nvpr is < 0\n");
exit_status = 1;
goto END;
}
/* Allocate memory first lot of memory */
if (!(levels = NAG_ALLOC(ncol, Integer)) ||
!(fvid = NAG_ALLOC(nfv, Integer)) ||
!(rvid = NAG_ALLOC(nrv, Integer)) || !(vpr = NAG_ALLOC(nrv, Integer)))
{
printf("Allocation failure\n");
exit_status = -1;
goto END;
}
/* Read in number of levels for each variable */
for (i = 1; i <= ncol; ++i) {
scanf("%" NAG_IFMT "", &levels[i - 1]);
}
scanf("%*[^\n] ");
/* Read in model information */
scanf("%" NAG_IFMT "", &yvid);
for (i = 1; i <= nfv; ++i) {
scanf("%" NAG_IFMT "", &fvid[i - 1]);
}
for (i = 1; i <= nrv; i++) {
scanf("%" NAG_IFMT "", &rvid[i - 1]);
}
scanf("%" NAG_IFMT "%" NAG_IFMT "%" NAG_IFMT "%" NAG_IFMT "%*[^\n] ", &svid,
&cwid, &fint, &rint);
/* Read in the variance component flag */
for (i = 1; i <= nrv; ++i) {
scanf("%" NAG_IFMT "", &vpr[i - 1]);
}
scanf("%*[^\n] ");
/* If no subject specified, then ignore rint */
if (svid == 0) {
rint = 0;
}
/* Count the number of levels in the fixed parameters */
for (i = 1, fnlevel = 0; i <= nfv; ++i) {
fl = levels[fvid[i - 1] - 1] - 1;
fnlevel += (fl < 1) ? 1 : fl;
}
if (fint == 1) {
fnlevel++;
}
/* Count the number of levels in the random parameters */
for (i = 1, rnlevel = 0; i <= nrv; ++i) {
rnlevel += levels[rvid[i - 1] - 1];
}
if (rint) {
rnlevel++;
}
/* Calculate the sizes of the output arrays */
if (rint == 1) {
lgamma = nvpr + 2;
}
else {
lgamma = nvpr + 1;
}
if (svid) {
lb = fnlevel + levels[svid - 1] * rnlevel;
}
else {
lb = fnlevel + rnlevel;
}
tddat = ncol;
/* Allocate remaining memory */
if (!(dat = NAG_ALLOC(n * ncol, double)) ||
!(gamma = NAG_ALLOC(lgamma, double)) ||
!(b = NAG_ALLOC(lb, double)) || !(se = NAG_ALLOC(lb, double)))
{
printf("Allocation failure\n");
exit_status = -1;
goto END;
}
/* Read in the Data matrix */
for (i = 1; i <= n; ++i) {
for (j = 1; j <= ncol; ++j) {
scanf("%lf", &DAT(i, j));
}
}
/* Read in the initial values for GAMMA */
for (i = 1; i < lgamma; ++i) {
scanf("%lf", &gamma[i - 1]);
}
/* Read in the maximum number of iterations */
scanf("%" NAG_IFMT "%*[^\n] ", &maxit);
/* Run the analysis */
tol = 0.;
warnp = 0;
/* nag_ml_mixed_regsn (g02jbc).
* Linear mixed effects regression using Maximum Likelihood
* (ML)
*/
nag_ml_mixed_regsn(n, ncol, dat, tddat, levels, yvid, cwid, nfv, fvid, fint,
nrv, rvid, nvpr, vpr, rint, svid, gamma, &nff, &nrf, &df,
&like, lb, b, se, maxit, tol, &warnp, &fail);
/* Report the results */
if (fail.code == NE_NOERROR) {
/* Output results */
if (warnp != 0) {
printf("Warning: At least one variance component was ");
printf("estimated to be negative and then reset to zero\n");
}
printf("Fixed effects (Estimate and Standard Deviation)\n\n");
k = 1;
if (fint == 1) {
printf("Intercept %10.4f%10.4f\n", b[k - 1], se[k - 1]);
++k;
}
for (i = 1; i <= nfv; ++i) {
for (j = 1; j <= levels[fvid[i - 1] - 1]; ++j) {
if (levels[fvid[i - 1] - 1] != 1 && j == 1)
continue;
printf("Variable%4" NAG_IFMT " Level%4" NAG_IFMT "%10.4f%10.4f\n",
i, j, b[k - 1], se[k - 1]);
++k;
}
}
printf("\n");
printf("Random Effects (Estimate and Standard Deviation)\n");
if (svid == 0) {
for (i = 1; i <= nrv; ++i) {
for (j = 1; j <= levels[rvid[i - 1] - 1]; ++j) {
printf("%s%4" NAG_IFMT "%s%4" NAG_IFMT "%10.4f%10.4f\n",
"Variable", i, " Level", j, b[k - 1], se[k - 1]);
++k;
}
}
}
else {
for (l = 1; l <= levels[svid - 1]; ++l) {
if (rint == 1) {
printf("%s%4" NAG_IFMT "%s%10.4f%10.4f\n",
"Intercept for Subject Level", l, " ",
b[k - 1], se[k - 1]);
++k;
}
for (i = 1; i <= nrv; ++i) {
for (j = 1; j <= levels[rvid[i - 1] - 1]; ++j) {
printf("%s%4" NAG_IFMT "%s%4" NAG_IFMT "%s%4" NAG_IFMT ""
"%10.4f%10.4f\n", "Subject Level", l,
" Variable", i, " Level", j, b[k - 1], se[k - 1]);
++k;
}
}
}
}
printf("\n");
printf("%s\n", " Variance Components");
for (i = 1; i <= nvpr + rint; ++i) {
printf("%4" NAG_IFMT "%10.4f\n", i, gamma[i - 1]);
}
printf("%s%10.4f\n\n", "SIGMA^2 = ", gamma[nvpr + rint]);
printf("%s%10.4f\n\n", "-2LOG LIKE = ", like);
printf("%s%" NAG_IFMT "\n", "DF = ", df);
}
else {
printf("Routine nag_ml_mixed_regsn (g02jbc) failed, with error"
" message:\n%s\n", fail.message);
}
END:
NAG_FREE(b);
NAG_FREE(dat);
NAG_FREE(gamma);
NAG_FREE(se);
NAG_FREE(fvid);
NAG_FREE(levels);
NAG_FREE(rvid);
NAG_FREE(vpr);
return exit_status;
}