/* PSPP - a program for statistical analysis.
- Copyright (C) 1997-9, 2000, 2007, 2009, 2010, 2011, 2012, 2013, 2014 Free Software Foundation, Inc.
+ Copyright (C) 1997-9, 2000, 2007, 2009, 2010, 2011, 2012, 2013, 2014,
+ 2020 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
moments1_calculate (mom_i, &n_i, NULL, &var_i, 0, 0);
moments1_calculate (mom_j, &n_j, NULL, &var_j, 0, 0);
- if ( n_i <= 1.0 || n_j <= 1.0)
+ if (n_i <= 1.0 || n_j <= 1.0)
return SYSMIS;
nom = pow2 (var_i/n_i + var_j/n_j);
static double sidak_pinv (double std_err, double alpha, double df, int k, const struct moments1 *mom_i UNUSED, const struct moments1 *mom_j UNUSED)
{
const double m = k * (k - 1) / 2;
- double lp = 1.0 - exp (log (1.0 - alpha) / m ) ;
+ double lp = 1.0 - exp (log (1.0 - alpha) / m) ;
return std_err * gsl_cdf_tdist_Pinv (1.0 - lp / 2.0, df);
}
static double tukey_pinv (double std_err, double alpha, double df, int k, const struct moments1 *mom_i UNUSED, const struct moments1 *mom_j UNUSED)
{
- if ( k < 2 || df < 2)
+ if (k < 2 || df < 2)
return SYSMIS;
return std_err / sqrt (2.0) * qtukey (1 - alpha, 1.0, k, df, 1, 0);
m = sqrt ((var_i/n_i + var_j/n_j) / 2.0);
- if ( k < 2 || df < 2)
+ if (k < 2 || df < 2)
return SYSMIS;
return m * qtukey (1 - alpha, 1.0, k, df, 1, 0);
int k = pvw->n_groups;
double df = ph->dff (pvw, dd_i->mom, dd_j->mom);
double ts = ph->tsf (k, dd_i->mom, dd_j->mom, std_err);
- if ( df == SYSMIS)
+ if (df == SYSMIS)
return SYSMIS;
return ph->p1f (ts, k - 1, df);
}
{
int k = pvw->n_groups;
double df = ph->dff (pvw, dd_i->mom, dd_j->mom);
- if ( df == SYSMIS)
+ if (df == SYSMIS)
return SYSMIS;
return ph->pinv (std_err, cmd->alpha, df, k, dd_i->mom, dd_j->mom);
ll_init (&oneway.contrast_list);
- if ( lex_match (lexer, T_SLASH))
+ if (lex_match (lexer, T_SLASH))
{
if (!lex_force_match_id (lexer, "VARIABLES"))
{
break;
}
}
- if ( method == false)
+ if (method == false)
{
if (lex_match_id (lexer, "ALPHA"))
{
- if ( !lex_force_match (lexer, T_LPAREN))
+ if (!lex_force_match (lexer, T_LPAREN))
goto error;
if (! lex_force_num (lexer))
goto error;
oneway.alpha = lex_number (lexer);
lex_get (lexer);
- if ( !lex_force_match (lexer, T_RPAREN))
+ if (!lex_force_match (lexer, T_RPAREN))
goto error;
}
else
}
else if (lex_match_id (lexer, "CONTRAST"))
{
- struct contrasts_node *cl = xzalloc (sizeof *cl);
+ struct contrasts_node *cl = XZALLOC (struct contrasts_node);
struct ll_list *coefficient_list = &cl->coefficient_list;
lex_match (lexer, T_EQUALS);
while (lex_token (lexer) != T_ENDCMD && lex_token (lexer) != T_SLASH)
{
- if ( lex_is_number (lexer))
+ if (lex_is_number (lexer))
{
struct coeff_node *cc = xmalloc (sizeof *cc);
cc->coeff = lex_number (lexer);
}
}
- if ( ll_count (coefficient_list) <= 0)
+ if (ll_count (coefficient_list) <= 0)
{
destroy_coeff_list (cl);
goto error;
struct oneway_workspace ws;
ws.actual_number_of_groups = 0;
- ws.vws = xzalloc (cmd->n_vars * sizeof (*ws.vws));
- ws.dd_total = xmalloc (sizeof (struct descriptive_data) * cmd->n_vars);
+ ws.vws = xcalloc (cmd->n_vars, sizeof (*ws.vws));
+ ws.dd_total = XCALLOC (cmd->n_vars, struct descriptive_data*);
for (v = 0 ; v < cmd->n_vars; ++v)
ws.dd_total[v] = dd_create (cmd->vars[v]);
const struct variable *v = cmd->vars[i];
const union value *val = case_data (c, v);
- if ( MISS_ANALYSIS == cmd->missing_type)
+ if (MISS_ANALYSIS == cmd->missing_type)
{
- if ( var_is_value_missing (v, val, cmd->exclude))
+ if (var_is_value_missing (v, val) & cmd->exclude)
continue;
}
casereader_destroy (reader);
reader = casereader_clone (input);
- for ( ; (c = casereader_read (reader) ); case_unref (c))
+ for (; (c = casereader_read (reader)); case_unref (c))
{
int i;
double w = dict_get_case_weight (dict, c, NULL);
const struct variable *v = cmd->vars[i];
const union value *val = case_data (c, v);
- if ( MISS_ANALYSIS == cmd->missing_type)
+ if (MISS_ANALYSIS == cmd->missing_type)
{
- if ( var_is_value_missing (v, val, cmd->exclude))
+ if (var_is_value_missing (v, val) & cmd->exclude)
continue;
}
casereader_destroy (reader);
reader = casereader_clone (input);
- for ( ; (c = casereader_read (reader) ); case_unref (c))
+ for (; (c = casereader_read (reader)); case_unref (c))
{
int i;
double w = dict_get_case_weight (dict, c, NULL);
const struct variable *v = cmd->vars[i];
const union value *val = case_data (c, v);
- if ( MISS_ANALYSIS == cmd->missing_type)
+ if (MISS_ANALYSIS == cmd->missing_type)
{
- if ( var_is_value_missing (v, val, cmd->exclude))
+ if (var_is_value_missing (v, val) & cmd->exclude)
continue;
}
const struct categoricals *cats = covariance_get_categoricals (pvw->cov);
const bool ok = categoricals_sane (cats);
- if ( ! ok)
+ if (! ok)
{
msg (MW,
_("Dependent variable %s has no non-missing values. No analysis for this variable will be done."),
{
const struct categoricals *cats = covariance_get_categoricals (ws.vws[v].cov);
- if ( ! categoricals_is_complete (cats))
+ if (! categoricals_is_complete (cats))
{
continue;
}
ll_for_each (cn, struct coeff_node, ll, cl)
sum += cn->coeff;
- if ( sum != 0.0 )
+ if (sum != 0.0)
msg (SW, _("Coefficients for contrast %zu do not total zero"), i);
}
show_contrast_tests (cmd, ws);
}
- if ( cmd->posthoc )
+ if (cmd->posthoc)
{
int v;
for (v = 0 ; v < cmd->n_vars; ++v)
{
const struct categoricals *cats = covariance_get_categoricals (ws->vws[v].cov);
- if ( categoricals_is_complete (cats))
+ if (categoricals_is_complete (cats))
show_comparisons (cmd, ws, v);
}
}
df_numerator = pow2 (df_numerator);
double std_error_contrast = sqrt (pvw->mse * coef_msq);
- double T = fabs (contrast_value / std_error_contrast);
+ double T = contrast_value / std_error_contrast;
double T_ne = contrast_value / sec_vneq;
double df_ne = df_numerator / df_denominator;
- double p_ne = gsl_cdf_tdist_P (T_ne, df_ne);
- double q_ne = gsl_cdf_tdist_Q (T_ne, df_ne);
struct entry
{
{ 1, 0, std_error_contrast },
{ 2, 0, T },
{ 3, 0, df },
- { 4, 0, 2 * gsl_cdf_tdist_Q (T, df) },
+ { 4, 0, 2 * gsl_cdf_tdist_Q (fabs(T), df) },
/* Do not assume equal. */
{ 0, 1, contrast_value },
{ 1, 1, sec_vneq },
{ 2, 1, T_ne },
{ 3, 1, df_ne },
- { 4, 1, 2 * (T > 0 ? q_ne : p_ne) },
+ { 4, 1, 2 * gsl_cdf_tdist_Q (fabs(T_ne), df_ne) },
};
for (size_t i = 0; i < sizeof entries / sizeof *entries; i++)
_("Multiple Comparisons (%s)"),
var_to_string (cmd->vars[v]))),
"Multiple Comparisons");
- table->omit_empty = true;
struct pivot_dimension *statistics = pivot_dimension_create (
table, PIVOT_AXIS_COLUMN, N_("Statistics"),