// -*- c -*- // // PSPP - a program for statistical analysis. // Copyright (C) 2005, 2006, 2009 Free Software Foundation, Inc. // // This program is free software: you can redistribute it and/or modify // it under the terms of the GNU General Public License as published by // the Free Software Foundation, either version 3 of the License, or // (at your option) any later version. // // This program is distributed in the hope that it will be useful, // but WITHOUT ANY WARRANTY; without even the implied warranty of // MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the // GNU General Public License for more details. // // You should have received a copy of the GNU General Public License // along with this program. If not, see . operator NEG (x) = -x; operator ADD (a, b) = a + b; operator SUB (a, b) = a - b; absorb_miss operator MUL (a, b) = (a == 0. || b == 0. ? 0. : a == SYSMIS || b == SYSMIS ? SYSMIS : a * b); absorb_miss operator DIV (a, b) = (a == 0. ? 0. : a == SYSMIS || b == SYSMIS ? SYSMIS : a / b); absorb_miss operator POW (a, b) = (a == SYSMIS ? (b == 0. ? 1. : a) : b == SYSMIS ? (a == 0. ? 0. : SYSMIS) : a == 0. && b <= 0. ? SYSMIS : pow (a, b)); absorb_miss boolean operator AND (boolean a, boolean b) = (a == 0. ? 0. : b == 0. ? 0. : b == SYSMIS ? SYSMIS : a); absorb_miss boolean operator OR (boolean a, boolean b) = (a == 1. ? 1. : b == 1. ? 1. : b == SYSMIS ? SYSMIS : a); boolean operator NOT (boolean a) = (a == 0. ? 1. : a == 1. ? 0. : SYSMIS); // Numeric relational operators. boolean operator EQ (a, b) = a == b; boolean operator GE (a, b) = a >= b; boolean operator GT (a, b) = a > b; boolean operator LE (a, b) = a <= b; boolean operator LT (a, b) = a < b; boolean operator NE (a, b) = a != b; // String relational operators. boolean operator EQ_STRING (string a, string b) = compare_string (&a, &b) == 0; boolean operator GE_STRING (string a, string b) = compare_string (&a, &b) >= 0; boolean operator GT_STRING (string a, string b) = compare_string (&a, &b) > 0; boolean operator LE_STRING (string a, string b) = compare_string (&a, &b) <= 0; boolean operator LT_STRING (string a, string b) = compare_string (&a, &b) < 0; boolean operator NE_STRING (string a, string b) = compare_string (&a, &b) != 0; // Unary functions. function ABS (x) = fabs (x); extension function ACOS (x >= -1 && x <= 1) = acos (x); function ASIN (x >= -1 && x <= 1) = asin (x); function ATAN (x) = atan (x); extension function ARCOS (x >= -1 && x <= 1) = acos (x); function ARSIN (x >= -1 && x <= 1) = asin (x); function ARTAN (x) = atan (x); function COS (x) = cos (x); function EXP (x) = check_errno (exp (x)); function LG10(x) = check_errno (log10 (x)); function LN (x) = check_errno (log (x)); function LNGAMMA (x >= 0) = gsl_sf_lngamma (x); function MOD10 (x) = fmod (x, 10); function RND (x) = x >= 0. ? floor (x + .5) : -floor (-x + .5); function SIN (x) = sin (x); function SQRT (x >= 0) = sqrt (x); function TAN (x) = check_errno (tan (x)); function TRUNC (x) = x >= 0. ? floor (x) : -floor (-x); absorb_miss function MOD (n, d) { if (d != SYSMIS) return n != SYSMIS ? fmod (n, d) : SYSMIS; else return n != 0. ? SYSMIS : 0.; } // N-ary numeric functions. absorb_miss boolean function ANY (x != SYSMIS, a[n]) { int sysmis = 0; size_t i; for (i = 0; i < n; i++) if (a[i] == x) return 1.; else if (a[i] == SYSMIS) sysmis = 1; return sysmis ? SYSMIS : 0.; } boolean function ANY (string x, string a[n]) { size_t i; for (i = 0; i < n; i++) if (!compare_string (&x, &a[i])) return 1.; return 0.; } function CFVAR.2 (a[n]) { double mean, variance; moments_of_doubles (a, n, NULL, &mean, &variance, NULL, NULL); if (mean == SYSMIS || mean == 0 || variance == SYSMIS) return SYSMIS; else return sqrt (variance) / mean; } function MAX.1 (a[n]) { double max; size_t i; max = -DBL_MAX; for (i = 0; i < n; i++) if (a[i] != SYSMIS && a[i] > max) max = a[i]; return max; } string function MAX (string a[n]) { struct substring *max; size_t i; max = &a[0]; for (i = 1; i < n; i++) if (compare_string (&a[i], max) > 0) max = &a[i]; return *max; } function MEAN.1 (a[n]) { double mean; moments_of_doubles (a, n, NULL, &mean, NULL, NULL, NULL); return mean; } function MIN.1 (a[n]) { double min; size_t i; min = DBL_MAX; for (i = 0; i < n; i++) if (a[i] != SYSMIS && a[i] < min) min = a[i]; return min; } string function MIN (string a[n]) { struct substring *min; size_t i; min = &a[0]; for (i = 1; i < n; i++) if (compare_string (&a[i], min) < 0) min = &a[i]; return *min; } absorb_miss function NMISS (a[n]) { size_t i; size_t missing_cnt = 0; for (i = 0; i < n; i++) missing_cnt += a[i] == SYSMIS; return missing_cnt; } absorb_miss function NVALID (a[n]) { size_t i; size_t valid_cnt = 0; for (i = 0; i < n; i++) valid_cnt += a[i] != SYSMIS; return valid_cnt; } absorb_miss boolean function RANGE (x != SYSMIS, a[n*2]) { size_t i; int sysmis = 0; for (i = 0; i < n; i++) { double w = a[2 * i]; double y = a[2 * i + 1]; if (w != SYSMIS && y != SYSMIS) { if (w <= x && x <= y) return 1.0; } else sysmis = 1; } return sysmis ? SYSMIS : 0.; } boolean function RANGE (string x, string a[n*2]) { int i; for (i = 0; i < n; i++) { struct substring *w = &a[2 * i]; struct substring *y = &a[2 * i + 1]; if (compare_string (w, &x) <= 0 && compare_string (&x, y) <= 0) return 1.; } return 0.; } function SD.2 (a[n]) { double variance; moments_of_doubles (a, n, NULL, NULL, &variance, NULL, NULL); return sqrt (variance); } function SUM.1 (a[n]) { double sum; size_t i; sum = 0.; for (i = 0; i < n; i++) if (a[i] != SYSMIS) sum += a[i]; return sum; } function VARIANCE.2 (a[n]) { double variance; moments_of_doubles (a, n, NULL, NULL, &variance, NULL, NULL); return variance; } // Time construction & extraction functions. function TIME.HMS (h, m, s) { if ((h > 0. || m > 0. || s > 0.) && (h < 0. || m < 0. || s < 0.)) { msg (SW, _("TIME.HMS cannot mix positive and negative arguments.")); return SYSMIS; } else return H_S * h + MIN_S * m + s; } function TIME.DAYS (days) = days * DAY_S; function CTIME.DAYS (time) = time / DAY_S; function CTIME.HOURS (time) = time / H_S; function CTIME.MINUTES (time) = time / MIN_S; function CTIME.SECONDS (time) = time; // Date construction functions. function DATE.DMY (d, m, y) = expr_ymd_to_date (y, m, d); function DATE.MDY (m, d, y) = expr_ymd_to_date (y, m, d); function DATE.MOYR (m, y) = expr_ymd_to_date (y, m, 1); function DATE.QYR (q, y) = expr_ymd_to_date (y, q * 3 - 2, 1); function DATE.WKYR (w, y) = expr_wkyr_to_date (w, y); function DATE.YRDAY (y, yday) = expr_yrday_to_date (y, yday); function YRMODA (y, m, d) = expr_yrmoda (y, m, d); // Date extraction functions. function XDATE.TDAY (date) = floor (date / DAY_S); function XDATE.HOUR (date) = fmod (floor (date / H_S), DAY_H); function XDATE.MINUTE (date) = fmod (floor (date / H_MIN), H_MIN); function XDATE.SECOND (date) = fmod (date, MIN_S); function XDATE.DATE (date) = floor (date / DAY_S) * DAY_S; function XDATE.TIME (date) = fmod (date, DAY_S); function XDATE.JDAY (date >= DAY_S) = calendar_offset_to_yday (date / DAY_S); function XDATE.MDAY (date >= DAY_S) = calendar_offset_to_mday (date / DAY_S); function XDATE.MONTH (date >= DAY_S) = calendar_offset_to_month (date / DAY_S); function XDATE.QUARTER (date >= DAY_S) = (calendar_offset_to_month (date / DAY_S) - 1) / 3 + 1; function XDATE.WEEK (date >= DAY_S) = (calendar_offset_to_yday (date / DAY_S) - 1) / 7 + 1; function XDATE.WKDAY (date >= DAY_S) = calendar_offset_to_wday (date / DAY_S); function XDATE.YEAR (date >= DAY_S) = calendar_offset_to_year (date / DAY_S); // Date arithmetic functions. no_abbrev function DATEDIFF (date2 >= DAY_S, date1 >= DAY_S, string unit) = expr_date_difference (date1, date2, unit); no_abbrev function DATESUM (date, quantity, string unit) = expr_date_sum (date, quantity, unit, ss_cstr ("closest")); no_abbrev function DATESUM (date, quantity, string unit, string method) = expr_date_sum (date, quantity, unit, method); // String functions. string function CONCAT (string a[n]) expression e; { struct substring dst; size_t i; dst = alloc_string (e, MAX_STRING); dst.length = 0; for (i = 0; i < n; i++) { struct substring *src = &a[i]; size_t copy_len; copy_len = src->length; if (dst.length + copy_len > MAX_STRING) copy_len = MAX_STRING - dst.length; memcpy (&dst.string[dst.length], src->string, copy_len); dst.length += copy_len; } return dst; } function INDEX (string haystack, string needle) { if (needle.length == 0) return SYSMIS; else { int limit = haystack.length - needle.length + 1; int i; for (i = 1; i <= limit; i++) if (!memcmp (&haystack.string[i - 1], needle.string, needle.length)) return i; return 0; } } function INDEX (string haystack, string needles, needle_len_d) { if (needle_len_d <= INT_MIN || needle_len_d >= INT_MAX || (int) needle_len_d != needle_len_d || needles.length == 0) return SYSMIS; else { int needle_len = needle_len_d; if (needle_len < 0 || needle_len > needles.length || needles.length % needle_len != 0) return SYSMIS; else { int limit = haystack.length - needle_len + 1; int i, j; for (i = 1; i <= limit; i++) for (j = 0; j < needles.length; j += needle_len) if (!memcmp (&haystack.string[i - 1], &needles.string[j], needle_len)) return i; return 0; } } } function RINDEX (string haystack, string needle) { if (needle.length == 0) return SYSMIS; else { int limit = haystack.length - needle.length + 1; int i; for (i = limit; i >= 1; i--) if (!memcmp (&haystack.string[i - 1], needle.string, needle.length)) return i; return 0; } } function RINDEX (string haystack, string needles, needle_len_d) { if (needle_len_d <= INT_MIN || needle_len_d >= INT_MAX || (int) needle_len_d != needle_len_d || needles.length == 0) return SYSMIS; else { int needle_len = needle_len_d; if (needle_len < 0 || needle_len > needles.length || needles.length % needle_len != 0) return SYSMIS; else { int limit = haystack.length - needle_len + 1; int i, j; for (i = limit; i >= 1; i--) for (j = 0; j < needles.length; j += needle_len) if (!memcmp (&haystack.string[i - 1], &needles.string[j], needle_len)) return i; return 0; } } } function LENGTH (string s) { return s.length; } string function LOWER (string s) { int i; for (i = 0; i < s.length; i++) s.string[i] = tolower ((unsigned char) s.string[i]); return s; } function MBLEN.BYTE (string s, idx) { if (idx < 0 || idx >= s.length || (int) idx != idx) return SYSMIS; else return 1; } string function UPCASE (string s) { int i; for (i = 0; i < s.length; i++) s.string[i] = toupper ((unsigned char) s.string[i]); return s; } absorb_miss string function LPAD (string s, n) expression e; { if (n < 0 || n > MAX_STRING || (int) n != n) return empty_string; else if (s.length >= n) return s; else { struct substring t = alloc_string (e, n); memset (t.string, ' ', n - s.length); memcpy (&t.string[(int) n - s.length], s.string, s.length); return t; } } absorb_miss string function LPAD (string s, n, string c) expression e; { if (n < 0 || n > MAX_STRING || (int) n != n || c.length != 1) return empty_string; else if (s.length >= n) return s; else { struct substring t = alloc_string (e, n); memset (t.string, c.string[0], n - s.length); memcpy (&t.string[(int) n - s.length], s.string, s.length); return t; } } absorb_miss string function RPAD (string s, n) expression e; { if (n < 0 || n > MAX_STRING || (int) n != n) return empty_string; else if (s.length >= n) return s; else { struct substring t = alloc_string (e, n); memcpy (t.string, s.string, s.length); memset (&t.string[s.length], ' ', n - s.length); return t; } } absorb_miss string function RPAD (string s, n, string c) expression e; { if (n < 0 || n > MAX_STRING || (int) n != n || c.length != 1) return empty_string; else if (s.length >= n) return s; else { struct substring t = alloc_string (e, n); memcpy (t.string, s.string, s.length); memset (&t.string[s.length], c.string[0], n - s.length); return t; } } string function LTRIM (string s) { while (s.length > 0 && s.string[0] == ' ') { s.length--; s.string++; } return s; } string function LTRIM (string s, string c) { if (c.length == 1) { while (s.length > 0 && s.string[0] == c.string[0]) { s.length--; s.string++; } return s; } else return empty_string; } string function RTRIM (string s) { while (s.length > 0 && s.string[s.length - 1] == ' ') s.length--; return s; } string function RTRIM (string s, string c) { if (c.length == 1) { while (s.length > 0 && s.string[s.length - 1] == c.string[0]) s.length--; return s; } else return empty_string; } function NUMBER (string s, ni_format f) { union value out; data_in (ss_head (s, f->w), LEGACY_NATIVE, f->type, f->d, 0, 0, NULL, &out, 0); return out.f; } absorb_miss string function STRING (x, no_format f) expression e; { union value v; struct substring dst; char *s; v.f = x; assert (!fmt_is_string (f->type)); s = data_out (&v, LEGACY_NATIVE, f); dst = alloc_string (e, strlen (s)); strcpy (dst.string, s); free (s); return dst; } absorb_miss string function SUBSTR (string s, ofs) expression e; { if (ofs >= 1 && ofs <= s.length && (int) ofs == ofs) return copy_string (e, &s.string[(int) ofs - 1], s.length - ofs + 1); else return empty_string; } absorb_miss string function SUBSTR (string s, ofs, cnt) expression e; { if (ofs >= 1 && ofs <= s.length && (int) ofs == ofs && cnt >= 1 && cnt <= INT_MAX && (int) cnt == cnt) { int cnt_max = s.length - (int) ofs + 1; return copy_string (e, &s.string[(int) ofs - 1], cnt <= cnt_max ? cnt : cnt_max); } else return empty_string; } absorb_miss no_opt no_abbrev string function VALUELABEL (var v) expression e; case c; { const char *label = var_lookup_value_label (v, case_data (c, v)); if (label != NULL) return copy_string (e, label, strlen (label)); else return empty_string; } // Artificial. operator SQUARE (x) = x * x; boolean operator NUM_TO_BOOLEAN (x) { if (x == 0. || x == 1. || x == SYSMIS) return x; else { msg (SE, _("A number being treated as a Boolean in an " "expression was found to have a value other than " "0 (false), 1 (true), or the system-missing value. " "The result was forced to 0.")); return 0.; } } operator BOOLEAN_TO_NUM (boolean x) = x; // Beta distribution. function PDF.BETA (x >= 0 && x <= 1, a > 0, b > 0) = gsl_ran_beta_pdf (x, a, b); function CDF.BETA (x >= 0 && x <= 1, a > 0, b > 0) = gsl_cdf_beta_P (x, a, b); function IDF.BETA (P >= 0 && P <= 1, a > 0, b > 0) = gsl_cdf_beta_Pinv (P, a, b); no_opt function RV.BETA (a > 0, b > 0) = gsl_ran_beta (get_rng (), a, b); function NCDF.BETA (x >= 0, a > 0, b > 0, lambda > 0) = ncdf_beta (x, a, b, lambda); function NPDF.BETA (x >= 0, a > 0, b > 0, lambda > 0) = npdf_beta (x, a, b, lambda); // Bivariate normal distribution. function CDF.BVNOR (x0, x1, r >= -1 && r <= 1) = cdf_bvnor (x0, x1, r); function PDF.BVNOR (x0, x1, r >= -1 && r <= 1) = gsl_ran_bivariate_gaussian_pdf (x0, x1, 1, 1, r); // Cauchy distribution. function CDF.CAUCHY (x, a, b > 0) = gsl_cdf_cauchy_P ((x - a) / b, 1); function IDF.CAUCHY (P > 0 && P < 1, a, b > 0) = a + b * gsl_cdf_cauchy_Pinv (P, 1); function PDF.CAUCHY (x, a, b > 0) = gsl_ran_cauchy_pdf ((x - a) / b, 1) / b; no_opt function RV.CAUCHY (a, b > 0) = a + b * gsl_ran_cauchy (get_rng (), 1); // Chi-square distribution. function CDF.CHISQ (x >= 0, df > 0) = gsl_cdf_chisq_P (x, df); function IDF.CHISQ (P >= 0 && P < 1, df > 0) = gsl_cdf_chisq_Pinv (P, df); function PDF.CHISQ (x >= 0, df > 0) = gsl_ran_chisq_pdf (x, df); no_opt function RV.CHISQ (df > 0) = gsl_ran_chisq (get_rng (), df); function NCDF.CHISQ (x >= 0, df > 0, c) = unimplemented; function NPDF.CHISQ (x >= 0, df > 0, c) = unimplemented; function SIG.CHISQ (x >= 0, df > 0) = gsl_cdf_chisq_Q (x, df); // Exponential distribution. function CDF.EXP (x >= 0, a > 0) = gsl_cdf_exponential_P (x, 1. / a); function IDF.EXP (P >= 0 && P < 1, a > 0) = gsl_cdf_exponential_Pinv (P, 1. / a); function PDF.EXP (x >= 0, a > 0) = gsl_ran_exponential_pdf (x, 1. / a); no_opt function RV.EXP (a > 0) = gsl_ran_exponential (get_rng (), 1. / a); // Exponential power distribution. extension function PDF.XPOWER (x, a > 0, b >= 0) = gsl_ran_exppow_pdf (x, a, b); no_opt extension function RV.XPOWER (a > 0, b >= 0) = gsl_ran_exppow (get_rng (), a, b); // F distribution. function CDF.F (x >= 0, df1 > 0, df2 > 0) = gsl_cdf_fdist_P (x, df1, df2); function IDF.F (P >= 0 && P < 1, df1 > 0, df2 > 0) = idf_fdist (P, df1, df2); function PDF.F (x >= 0, df1 > 0, df2 > 0) = gsl_ran_fdist_pdf (x, df1, df2); no_opt function RV.F (df1 > 0, df2 > 0) = gsl_ran_fdist (get_rng (), df1, df2); function NCDF.F (x >= 0, df1 > 0, df2 > 0, lambda >= 0) = unimplemented; function NPDF.F (x >= 0, df1 > 0, df2 > 0, lmabda >= 0) = unimplemented; function SIG.F (x >= 0, df1 > 0, df2 > 0) = gsl_cdf_fdist_Q (x, df1, df2); // Gamma distribution. function CDF.GAMMA (x >= 0, a > 0, b > 0) = gsl_cdf_gamma_P (x, a, 1. / b); function IDF.GAMMA (P >= 0 && P <= 1, a > 0, b > 0) = gsl_cdf_gamma_Pinv (P, a, 1. / b); function PDF.GAMMA (x >= 0, a > 0, b > 0) = gsl_ran_gamma_pdf (x, a, 1. / b); no_opt function RV.GAMMA (a > 0, b > 0) = gsl_ran_gamma (get_rng (), a, 1. / b); // Half-normal distribution. function CDF.HALFNRM (x, a, b > 0) = unimplemented; function IDF.HALFNRM (P > 0 && P < 1, a, b > 0) = unimplemented; function PDF.HALFNRM (x, a, b > 0) = unimplemented; no_opt function RV.HALFNRM (a, b > 0) = unimplemented; // Inverse Gaussian distribution. function CDF.IGAUSS (x > 0, a > 0, b > 0) = unimplemented; function IDF.IGAUSS (P >= 0 && P < 1, a > 0, b > 0) = unimplemented; function PDF.IGAUSS (x > 0, a > 0, b > 0) = unimplemented; no_opt function RV.IGAUSS (a > 0, b > 0) = unimplemented; // Landau distribution. extension function PDF.LANDAU (x) = gsl_ran_landau_pdf (x); no_opt extension function RV.LANDAU () = gsl_ran_landau (get_rng ()); // Laplace distribution. function CDF.LAPLACE (x, a, b > 0) = gsl_cdf_laplace_P ((x - a) / b, 1); function IDF.LAPLACE (P > 0 && P < 1, a, b > 0) = a + b * gsl_cdf_laplace_Pinv (P, 1); function PDF.LAPLACE (x, a, b > 0) = gsl_ran_laplace_pdf ((x - a) / b, 1) / b; no_opt function RV.LAPLACE (a, b > 0) = a + b * gsl_ran_laplace (get_rng (), 1); // Levy alpha-stable distribution. no_opt extension function RV.LEVY (c, alpha > 0 && alpha <= 2) = gsl_ran_levy (get_rng (), c, alpha); // Levy skew alpha-stable distribution. no_opt extension function RV.LVSKEW (c, alpha > 0 && alpha <= 2, beta >= -1 && beta <= 1) = gsl_ran_levy_skew (get_rng (), c, alpha, beta); // Logistic distribution. function CDF.LOGISTIC (x, a, b > 0) = gsl_cdf_logistic_P ((x - a) / b, 1); function IDF.LOGISTIC (P > 0 && P < 1, a, b > 0) = a + b * gsl_cdf_logistic_Pinv (P, 1); function PDF.LOGISTIC (x, a, b > 0) = gsl_ran_logistic_pdf ((x - a) / b, 1) / b; no_opt function RV.LOGISTIC (a, b > 0) = a + b * gsl_ran_logistic (get_rng (), 1); // Lognormal distribution. function CDF.LNORMAL (x >= 0, m > 0, s > 0) = gsl_cdf_lognormal_P (x, log (m), s); function IDF.LNORMAL (P >= 0 && P < 1, m > 0, s > 0) = gsl_cdf_lognormal_Pinv (P, log (m), s); function PDF.LNORMAL (x >= 0, m > 0, s > 0) = gsl_ran_lognormal_pdf (x, log (m), s); no_opt function RV.LNORMAL (m > 0, s > 0) = gsl_ran_lognormal (get_rng (), log (m), s); // Normal distribution. function CDF.NORMAL (x, u, s > 0) = gsl_cdf_gaussian_P (x - u, s); function IDF.NORMAL (P > 0 && P < 1, u, s > 0) = u + gsl_cdf_gaussian_Pinv (P, s); function PDF.NORMAL (x, u, s > 0) = gsl_ran_gaussian_pdf ((x - u) / s, 1) / s; no_opt function RV.NORMAL (u, s > 0) = u + gsl_ran_gaussian (get_rng (), s); function CDFNORM (x) = gsl_cdf_ugaussian_P (x); function PROBIT (P > 0 && P < 1) = gsl_cdf_ugaussian_Pinv (P); no_opt function NORMAL (s > 0) = gsl_ran_gaussian (get_rng (), s); // Normal tail distribution. function PDF.NTAIL (x, a > 0, sigma > 0) = gsl_ran_gaussian_tail_pdf (x, a, sigma); no_opt function RV.NTAIL (a > 0, sigma > 0) = gsl_ran_gaussian_tail (get_rng (), a, sigma); // Pareto distribution. function CDF.PARETO (x >= a, a > 0, b > 0) = gsl_cdf_pareto_P (x, b, a); function IDF.PARETO (P >= 0 && P < 1, a > 0, b > 0) = gsl_cdf_pareto_Pinv (P, b, a); function PDF.PARETO (x >= a, a > 0, b > 0) = gsl_ran_pareto_pdf (x, b, a); no_opt function RV.PARETO (a > 0, b > 0) = gsl_ran_pareto (get_rng (), b, a); // Rayleigh distribution. extension function CDF.RAYLEIGH (x, sigma > 0) = gsl_cdf_rayleigh_P (x, sigma); extension function IDF.RAYLEIGH (P >= 0 && P <= 1, sigma > 0) = gsl_cdf_rayleigh_Pinv (P, sigma); extension function PDF.RAYLEIGH (x, sigma > 0) = gsl_ran_rayleigh_pdf (x, sigma); no_opt extension function RV.RAYLEIGH (sigma > 0) = gsl_ran_rayleigh (get_rng (), sigma); // Rayleigh tail distribution. extension function PDF.RTAIL (x, a, sigma) = gsl_ran_rayleigh_tail_pdf (x, a, sigma); no_opt extension function RV.RTAIL (a, sigma) = gsl_ran_rayleigh_tail (get_rng (), a, sigma); // Studentized maximum modulus distribution. function CDF.SMOD (x > 0, a >= 1, b >= 1) = unimplemented; function IDF.SMOD (P >= 0 && P < 1, a >= 1, b >= 1) = unimplemented; // Studentized range distribution. function CDF.SRANGE (x > 0, a >= 1, b >= 1) = unimplemented; function IDF.SRANGE (P >= 0 && P < 1, a >= 1, b >= 1) = unimplemented; // Student t distribution. function CDF.T (x, df > 0) = gsl_cdf_tdist_P (x, df); function IDF.T (P > 0 && P < 1, df > 0) = gsl_cdf_tdist_Pinv (P, df); function PDF.T (x, df > 0) = gsl_ran_tdist_pdf (x, df); no_opt function RV.T (df > 0) = gsl_ran_tdist (get_rng (), df); function NCDF.T (x, df > 0, nc) = unimplemented; function NPDF.T (x, df > 0, nc) = unimplemented; // Type-1 Gumbel distribution. extension function CDF.T1G (x, a, b) = gsl_cdf_gumbel1_P (x, a, b); extension function IDF.T1G (P >= 0 && P <= 1, a, b) = gsl_cdf_gumbel1_P (P, a, b); extension function PDF.T1G (x, a, b) = gsl_ran_gumbel1_pdf (x, a, b); no_opt extension function RV.T1G (a, b) = gsl_ran_gumbel1 (get_rng (), a, b); // Type-2 Gumbel distribution. extension function CDF.T2G (x, a, b) = gsl_cdf_gumbel2_P (x, a, b); extension function IDF.T2G (P >= 0 && P <= 1, a, b) = gsl_cdf_gumbel2_P (P, a, b); extension function PDF.T2G (x, a, b) = gsl_ran_gumbel2_pdf (x, a, b); no_opt extension function RV.T2G (a, b) = gsl_ran_gumbel2 (get_rng (), a, b); // Uniform distribution. function CDF.UNIFORM (x <= b, a <= x, b) = gsl_cdf_flat_P (x, a, b); function IDF.UNIFORM (P >= 0 && P <= 1, a <= b, b) = gsl_cdf_flat_Pinv (P, a, b); function PDF.UNIFORM (x <= b, a <= x, b) = gsl_ran_flat_pdf (x, a, b); no_opt function RV.UNIFORM (a <= b, b) = gsl_ran_flat (get_rng (), a, b); no_opt function UNIFORM (b >= 0) = gsl_ran_flat (get_rng (), 0, b); // Weibull distribution. function CDF.WEIBULL (x >= 0, a > 0, b > 0) = gsl_cdf_weibull_P (x, a, b); function IDF.WEIBULL (P >= 0 && P < 1, a > 0, b > 0) = gsl_cdf_weibull_Pinv (P, a, b); function PDF.WEIBULL (x >= 0, a > 0, b > 0) = gsl_ran_weibull_pdf (x, a, b); no_opt function RV.WEIBULL (a > 0, b > 0) = gsl_ran_weibull (get_rng (), a, b); // Bernoulli distribution. function CDF.BERNOULLI (k == 0 || k == 1, p >= 0 && p <= 1) = k ? 1 : 1 - p; function PDF.BERNOULLI (k == 0 || k == 1, p >= 0 && p <= 1) = gsl_ran_bernoulli_pdf (k, p); no_opt function RV.BERNOULLI (p >= 0 && p <= 1) = gsl_ran_bernoulli (get_rng (), p); // Binomial distribution. function CDF.BINOM (k, n > 0 && n == floor (n), p >= 0 && p <= 1) = gsl_cdf_binomial_P (k, p, n); function PDF.BINOM (k >= 0 && k == floor (k) && k <= n, n > 0 && n == floor (n), p >= 0 && p <= 1) = gsl_ran_binomial_pdf (k, p, n); no_opt function RV.BINOM (p > 0 && p == floor (p), n >= 0 && n <= 1) = gsl_ran_binomial (get_rng (), p, n); // Geometric distribution. function CDF.GEOM (k >= 1 && k == floor (k), p >= 0 && p <= 1) = gsl_cdf_geometric_P (k, p); function PDF.GEOM (k >= 1 && k == floor (k), p >= 0 && p <= 1) = gsl_ran_geometric_pdf (k, p); no_opt function RV.GEOM (p >= 0 && p <= 1) = gsl_ran_geometric (get_rng (), p); // Hypergeometric distribution. function CDF.HYPER (k >= 0 && k == floor (k) && k <= c, a > 0 && a == floor (a), b > 0 && b == floor (b) && b <= a, c > 0 && c == floor (c) && c <= a) = gsl_cdf_hypergeometric_P (k, c, a - c, b); function PDF.HYPER (k >= 0 && k == floor (k) && k <= c, a > 0 && a == floor (a), b > 0 && b == floor (b) && b <= a, c > 0 && c == floor (c) && c <= a) = gsl_ran_hypergeometric_pdf (k, c, a - c, b); no_opt function RV.HYPER (a > 0 && a == floor (a), b > 0 && b == floor (b) && b <= a, c > 0 && c == floor (c) && c <= a) = gsl_ran_hypergeometric (get_rng (), c, a - c, b); // Logarithmic distribution. extension function PDF.LOG (k >= 1, p > 0 && p <= 1) = gsl_ran_logarithmic_pdf (k, p); no_opt extension function RV.LOG (p > 0 && p <= 1) = gsl_ran_logarithmic (get_rng (), p); // Negative binomial distribution. function CDF.NEGBIN (k >= 1, n == floor (n), p > 0 && p <= 1) = gsl_cdf_negative_binomial_P (k, p, n); function PDF.NEGBIN (k >= 1, n == floor (n), p > 0 && p <= 1) = gsl_ran_negative_binomial_pdf (k, p, n); no_opt function RV.NEGBIN (n == floor (n), p > 0 && p <= 1) = gsl_ran_negative_binomial (get_rng (), p, n); // Poisson distribution. function CDF.POISSON (k >= 0 && k == floor (k), mu > 0) = gsl_cdf_poisson_P (k, mu); function PDF.POISSON (k >= 0 && k == floor (k), mu > 0) = gsl_ran_poisson_pdf (k, mu); no_opt function RV.POISSON (mu > 0) = gsl_ran_poisson (get_rng (), mu); // Weirdness. absorb_miss boolean function MISSING (x) = x == SYSMIS || !finite (x); absorb_miss boolean function SYSMIS (x) = x == SYSMIS || !finite (x); no_opt boolean function SYSMIS (num_var v) case c; { return case_num (c, v) == SYSMIS; } no_opt boolean function VALUE (num_var v) case c; { return case_num (c, v); } no_opt operator VEC_ELEM_NUM (idx) vector v; case c; { if (idx >= 1 && idx <= vector_get_var_cnt (v)) { const struct variable *var = vector_get_var (v, (size_t) idx - 1); double value = case_num (c, var); return !var_is_num_missing (var, value, MV_USER) ? value : SYSMIS; } else { if (idx == SYSMIS) msg (SE, _("SYSMIS is not a valid index value for vector " "%s. The result will be set to SYSMIS."), vector_get_name (v)); else msg (SE, _("%g is not a valid index value for vector %s. " "The result will be set to SYSMIS."), idx, vector_get_name (v)); return SYSMIS; } } absorb_miss no_opt string operator VEC_ELEM_STR (idx) expression e; vector v; case c; { if (idx >= 1 && idx <= vector_get_var_cnt (v)) { struct variable *var = vector_get_var (v, (size_t) idx - 1); return copy_string (e, case_str (c, var), var_get_width (var)); } else { if (idx == SYSMIS) msg (SE, _("SYSMIS is not a valid index value for vector " "%s. The result will be set to the empty string."), vector_get_name (v)); else msg (SE, _("%g is not a valid index value for vector %s. " "The result will be set to the empty string."), idx, vector_get_name (v)); return empty_string; } } // Terminals. no_opt operator NUM_VAR () case c; num_var v; { double d = case_num (c, v); return !var_is_num_missing (v, d, MV_USER) ? d : SYSMIS; } no_opt string operator STR_VAR () case c; expression e; str_var v; { struct substring s = alloc_string (e, var_get_width (v)); memcpy (s.string, case_str (c, v), var_get_width (v)); return s; } no_opt perm_only function LAG (num_var v, pos_int n_before) dataset ds; { const struct ccase *c = lagged_case (ds, n_before); if (c != NULL) { double x = case_num (c, v); return !var_is_num_missing (v, x, MV_USER) ? x : SYSMIS; } else return SYSMIS; } no_opt perm_only function LAG (num_var v) dataset ds; { const struct ccase *c = lagged_case (ds, 1); if (c != NULL) { double x = case_num (c, v); return !var_is_num_missing (v, x, MV_USER) ? x : SYSMIS; } else return SYSMIS; } no_opt perm_only string function LAG (str_var v, pos_int n_before) expression e; dataset ds; { const struct ccase *c = lagged_case (ds, n_before); if (c != NULL) return copy_string (e, case_str (c, v), var_get_width (v)); else return empty_string; } no_opt perm_only string function LAG (str_var v) expression e; dataset ds; { const struct ccase *c = lagged_case (ds, 1); if (c != NULL) return copy_string (e, case_str (c, v), var_get_width (v)); else return empty_string; } no_opt operator NUM_SYS () case c; num_var v; { return case_num (c, v) == SYSMIS; } no_opt operator NUM_VAL () case c; num_var v; { return case_num (c, v); } no_opt operator CASENUM () case_idx idx; { return idx; }