use strict;
use warnings;
my $F = [qw(F D)];
pp_add_exported(qw(binomial_test rtable which_id code_ivs
));
pp_addpm({At=>'Top'}, <<'EOD');
use strict;
use warnings;
use PDL::LiteF;
use Carp;
eval { require PDL::Core; require PDL::GSL::CDF; };
my $CDF = 1 if !$@;
=head1 NAME
PDL::Stats::Basic -- basic statistics and related utilities such as standard deviation, Pearson correlation, and t-tests.
=head1 DESCRIPTION
The terms FUNCTIONS and METHODS are arbitrarily used to refer to methods that are broadcastable and methods that are NOT broadcastable, respectively.
Does not have mean or median function here. see SEE ALSO.
=head1 SYNOPSIS
use PDL::LiteF;
use PDL::Stats::Basic;
my $stdv = $data->stdv;
or
my $stdv = stdv( $data );
=cut
EOD
pp_addhdr('
#include <math.h>
'
);
pp_def('stdv',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) sa = 0, a2 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
a2 += $a() * $a();
%}
if (N < 1) { $SETBAD(b()); continue; }
$GENERIC() var = a2 / N - (sa/N)*(sa/N);
if (var < 0) var = 0;
$b() = sqrt(var);
',
Doc => 'Sample standard deviation.',
);
pp_def('stdv_unbiased',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) sa = 0, a2 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
a2 += $a() * $a();
%}
if (N < 2) { $SETBAD(b()); continue; }
$GENERIC() var = a2/(N-1) - sa*sa/(N*(N-1));
if (var < 0) var = 0;
$b() = sqrt(var);
',
Doc => 'Unbiased estimate of population standard deviation.',
);
pp_def('var',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) a2 = 0, sa = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
a2 += $a() * $a();
%}
if (N < 1) { $SETBAD(b()); continue; }
$b() = a2 / N - sa*sa/(N*N);
',
Doc => 'Sample variance.',
);
pp_def('var_unbiased',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) a2 = 0, sa = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
a2 += $a() * $a();
sa += $a();
%}
if (N < 2) { $SETBAD(b()); continue; }
$b() = (a2 - sa*sa/N) / (N-1);
',
Doc => 'Unbiased estimate of population variance.',
);
pp_def('se',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) sa = 0, a2 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
a2 += $a() * $a();
%}
$GENERIC() se2 = (a2 - sa*sa/N) / (N*(N-1));
if (se2 < 0) se2 = 0;
$b() = sqrt(se2);
',
Doc => '
=for ref
Standard error of the mean. Useful for calculating confidence intervals.
=for example
# 95% confidence interval for samples with large N
$ci_95_upper = $data->average + 1.96 * $data->se;
$ci_95_lower = $data->average - 1.96 * $data->se;
',
);
pp_def('ss',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) sa = 0, a2 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
a2 += $a() * $a();
%}
if (N < 1) { $SETBAD(b()); continue; }
$b() = a2 - sa*sa/N;
',
Doc => 'Sum of squared deviations from the mean.',
);
pp_def('skew',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) sa = 0, m = 0, d=0, d2 = 0, d3 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
%}
if (N < 1) { $SETBAD(b()); continue; }
m = sa / N;
loop (n) %{
if ( $ISGOOD($a()) ) {
d = $a() - m;
d2 += d*d;
d3 += d*d*d;
}
%}
$b() = d3/N / pow(d2/N, 1.5);
',
Doc => 'Sample skewness, measure of asymmetry in data. skewness == 0 for normal distribution.',
);
pp_def('skew_unbiased',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) sa = 0, m = 0, d=0, d2 = 0, d3 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
%}
if (N < 3) { $SETBAD(b()); continue; }
m = sa / N;
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue;,)
d = $a() - m;
d2 += d*d;
d3 += d*d*d;
%}
$b() = sqrt(N*(N-1)) / (N-2) * d3/N / pow(d2/N, 1.5);
',
Doc => 'Unbiased estimate of population skewness. This is the number in GNumeric Descriptive Statistics.',
);
pp_def('kurt',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) sa = 0, m = 0, d=0, d2 = 0, d4 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
%}
if (N < 1) { $SETBAD(b()); continue; }
m = sa / N;
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue;,)
d = $a() - m;
d2 += d*d;
d4 += d*d*d*d;
%}
$b() = N * d4 / (d2*d2) - 3;
',
Doc => 'Sample kurtosis, measure of "peakedness" of data. kurtosis == 0 for normal distribution.',
);
pp_def('kurt_unbiased',
Pars => 'a(n); [o]b()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(b) sa = 0, m = 0, d=0, d2 = 0, d4 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
%}
if (N < 4) { $SETBAD(b()); continue; }
m = sa / N;
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue;,)
d = $a() - m;
d2 += d*d;
d4 += d*d*d*d;
%}
$b() = ((N-1)*N*(N+1) * d4 / (d2*d2) - 3 * (N-1)*(N-1)) / ((N-2)*(N-3));
',
Doc => 'Unbiased estimate of population kurtosis. This is the number in GNumeric Descriptive Statistics.',
);
pp_def('cov',
Pars => 'a(n); b(n); [o]c()',
GenericTypes => $F,
HandleBad => 1,
Code => '
$GENERIC(c) ab = 0, sa = 0, sb = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a()) || $ISBAD($b())) continue; N++;,)
ab += $a() * $b();
sa += $a();
sb += $b();
%}
if (N < 1) { $SETBAD(c()); continue; }
$c() = ab / N - (sa/N) * (sb/N);
',
Doc => 'Sample covariance. see B<corr> for ways to call',
);
pp_def('cov_table',
Pars => 'a(n,m); [o]c(m,m)',
HandleBad => 1,
RedoDimsCode => 'if ($SIZE(n) < 2) $CROAK("too few N");',
Code => '
$GENERIC(a) a_, b_;
PDL_Indx M = $SIZE(m), i, j;
for (i=0; i<M; i++) {
for (j=i; j<M; j++) {
$GENERIC(c) ab = 0, sa = 0, sb = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a(m=>i)) || $ISBAD($a(m=>j))) continue; N++;,)
sa += a_ = $a(m=>i);
sb += b_ = $a(m=>j);
ab += a_ * b_;
%}
if (N < 2) {
$SETBAD($c(m0=>i, m1=>j));
$SETBAD($c(m0=>j, m1=>i));
continue;
}
$GENERIC(c) cov = ab - (sa * sb) / N;
$c(m0=>i, m1=>j) =
$c(m0=>j, m1=>i) = cov / N;
}
}
',
Doc => '
=for ref
Square covariance table. Gives the same result as broadcasting using B<cov> but it calculates only half the square, hence much faster. And it is easier to use with higher dimension pdls.
=for example
Usage:
# 5 obs x 3 var, 2 such data tables
pdl> $a = random 5, 3, 2
pdl> p $cov = $a->cov_table
[
[
[ 8.9636438 -1.8624472 -1.2416588]
[-1.8624472 14.341514 -1.4245366]
[-1.2416588 -1.4245366 9.8690655]
]
[
[ 10.32644 -0.31311789 -0.95643674]
[-0.31311789 15.051779 -7.2759577]
[-0.95643674 -7.2759577 5.4465141]
]
]
# diagonal elements of the cov table are the variances
pdl> p $a->var
[
[ 8.9636438 14.341514 9.8690655]
[ 10.32644 15.051779 5.4465141]
]
for the same cov matrix table using B<cov>,
pdl> p $a->dummy(2)->cov($a->dummy(1))
',
);
pp_def('corr',
Pars => 'a(n); b(n); [o]c()',
GenericTypes => $F,
HandleBad => 1,
RedoDimsCode => 'if ($SIZE(n) < 2) $CROAK("too few N");',
Code => '
$GENERIC(c) ab, sa, sb, a2, b2, cov, va, vb;
ab=0; sa=0; sb=0; a2=0; b2=0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a()) || $ISBAD($b())) continue; N++;,)
ab += $a() * $b();
sa += $a();
sb += $b();
a2 += $a() * $a();
b2 += $b() * $b();
%}
if (N < 2) { $SETBAD(c()); continue; }
cov = ab - (sa * sb) / N;
va = a2 - sa*sa / N;
vb = b2 - sb*sb / N;
$c() = cov / sqrt( va * vb );
',
Doc => '
=for ref
Pearson correlation coefficient. r = cov(X,Y) / (stdv(X) * stdv(Y)).
=for example
Usage:
pdl> $a = random 5, 3
pdl> $b = sequence 5,3
pdl> p $a->corr($b)
[0.20934208 0.30949881 0.26713007]
for square corr table
pdl> p $a->corr($a->dummy(1))
[
[ 1 -0.41995259 -0.029301192]
[ -0.41995259 1 -0.61927619]
[-0.029301192 -0.61927619 1]
]
but it is easier and faster to use B<corr_table>.
',
);
pp_def('corr_table',
Pars => 'a(n,m); [o]c(m,m)',
HandleBad => 1,
RedoDimsCode => 'if ($SIZE(n) < 2) $CROAK("too few N");',
Code => '
$GENERIC(a) a_, b_;
$GENERIC(c) ab, sa, sb, a2, b2, cov, va, vb, r;
PDL_Indx M = $SIZE(m), i, j;
for (i=0; i<M; i++) {
for (j=i+1; j<M; j++) {
ab=0; sa=0; sb=0; a2=0; b2=0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a(m=>i)) || $ISBAD($a(m=>j))) continue; N++;,)
sa += a_ = $a(m=>i);
sb += b_ = $a(m=>j);
ab += a_ * b_;
a2 += a_ * a_;
b2 += b_ * b_;
%}
if (N < 2) {
$SETBAD($c(m0=>i, m1=>j));
$SETBAD($c(m0=>j, m1=>i));
continue;
}
cov = ab - (sa * sb) / N;
va = a2 - sa*sa / N;
vb = b2 - sb*sb / N;
r = cov / sqrt( va * vb );
$c(m0=>i, m1=>j) =
$c(m0=>j, m1=>i) = r;
}
PDL_IF_BAD(PDL_Indx N = 0;
loop (n) %{
if ($ISGOOD($a(m=>i)))
N ++;
if (N > 1)
break;
%}
if (N < 2) { $SETBAD($c(m0=>i, m1=>i)); continue; },)
$c(m0=>i, m1=>i) = 1.0;
}
',
Doc => '
=for ref
Square Pearson correlation table. Gives the same result as broadcasting using B<corr> but it calculates only half the square, hence much faster. And it is easier to use with higher dimension pdls.
=for example
Usage:
# 5 obs x 3 var, 2 such data tables
pdl> $a = random 5, 3, 2
pdl> p $a->corr_table
[
[
[ 1 -0.69835951 -0.18549048]
[-0.69835951 1 0.72481605]
[-0.18549048 0.72481605 1]
]
[
[ 1 0.82722569 -0.71779883]
[ 0.82722569 1 -0.63938828]
[-0.71779883 -0.63938828 1]
]
]
for the same result using B<corr>,
pdl> p $a->dummy(2)->corr($a->dummy(1))
This is also how to use B<t_corr> and B<n_pair> with such a table.
',
);
pp_def('t_corr',
Pars => 'r(); n(); [o]t()',
GenericTypes => $F,
HandleBad => 1,
Code => '
PDL_IF_BAD(
if ($ISBAD(r()) || $ISBAD(n()) ) { $SETBAD( $t() ); continue; }
if ($n() <= 2) { $SETBAD(t()); continue; }
,)
$t() = $r() / pow( (1 - $r()*$r()) / ($n() - 2) , .5);
',
Doc => '
=for ref
t significance test for Pearson correlations.
=for example
$corr = $data->corr( $data->dummy(1) );
$n = $data->n_pair( $data->dummy(1) );
$t_corr = $corr->t_corr( $n );
use PDL::GSL::CDF;
$p_2tail = 2 * (1 - gsl_cdf_tdist_P( $t_corr->abs, $n-2 ));
',
);
pp_def('n_pair',
Pars => 'a(n); b(n); indx [o]c()',
GenericTypes => [qw/L Q/],
HandleBad => 1,
Code => '
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
PDL_IF_BAD(loop(n) %{
if ($ISBAD($a()) || $ISBAD($b())) continue;
N++;
%},)
$c() = N;
',
Doc => 'Returns the number of good pairs between 2 lists. Useful with B<corr> (esp. when bad values are involved)',
);
pp_def('corr_dev',
Pars => 'a(n); b(n); [o]c()',
GenericTypes => $F,
HandleBad => 1,
RedoDimsCode => 'if ($SIZE(n) < 2) $CROAK("too few N");',
Code => '
$GENERIC(c) ab, a2, b2, cov, va, vb;
ab = 0; a2 = 0; b2 = 0;
PDL_Indx N = PDL_IF_BAD(0,$SIZE(n));
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a()) || $ISBAD($b())) continue; N++;,)
ab += $a() * $b();
a2 += $a() * $a();
b2 += $b() * $b();
%}
if (N < 2) { $SETBAD(c()); continue; }
cov = ab / N;
va = a2 / N;
vb = b2 / N;
$c() = cov / sqrt( va * vb );
',
Doc => 'Calculates correlations from B<dev_m> vals. Seems faster than doing B<corr> from original vals when data pdl is big',
);
pp_def('t_test',
Pars => 'a(n); b(m); [o]t(); [o]d()',
GenericTypes => $F,
HandleBad => 1,
RedoDimsCode => '
if ($SIZE(n) < 2) $CROAK("too few N");
if ($SIZE(m) < 2) $CROAK("too few M");
',
Code => '
$GENERIC(t) N = PDL_IF_BAD(0,$SIZE(n)), M = PDL_IF_BAD(0,$SIZE(m)), sa = 0, sb = 0, a2 = 0, b2 = 0;
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
a2 += $a() * $a();
%}
loop (m) %{
PDL_IF_BAD(if ($ISBAD($b())) continue;,)
sb += $b();
b2 += $b() * $b();
PDL_IF_BAD(M++;,)
%}
if (N < 2 || M < 2) {
$SETBAD($t());
$SETBAD($d());
continue;
}
$d() = N + M - 2;
$GENERIC(t) va = (a2 - sa*sa/N) / (N-1);
$GENERIC(t) vb = (b2 - sb*sb/M) / (M-1);
$GENERIC(t) sdiff = sqrt( (1/N + 1/M) * ((N-1)*va + (M-1)*vb) / $d() );
$t() = (sa/N - sb/M) / sdiff;
',
Doc => '
=for ref
Independent sample t-test, assuming equal var.
=for example
my ($t, $df) = t_test( $pdl1, $pdl2 );
use PDL::GSL::CDF;
my $p_2tail = 2 * (1 - gsl_cdf_tdist_P( $t->abs, $df ));
',
);
pp_def('t_test_nev',
Pars => 'a(n); b(m); [o]t(); [o]d()',
GenericTypes => $F,
HandleBad => 1,
RedoDimsCode => '
if ($SIZE(n) < 2) $CROAK("too few N");
if ($SIZE(m) < 2) $CROAK("too few M");
',
Code => '
$GENERIC(t) N = PDL_IF_BAD(0,$SIZE(n)), M = PDL_IF_BAD(0,$SIZE(m)), sa = 0, sb = 0, a2 = 0, b2 = 0;
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a())) continue; N++;,)
sa += $a();
a2 += $a() * $a();
%}
loop (m) %{
PDL_IF_BAD(if ($ISBAD($b())) continue; M++;,)
sb += $b();
b2 += $b() * $b();
%}
if (N < 2 || M < 2) {
$SETBAD($t());
$SETBAD($d());
continue;
}
$GENERIC(t) se_a_2 = (a2 - sa*sa/N) / (N*(N-1));
$GENERIC(t) se_b_2 = (b2 - sb*sb/M) / (M*(M-1));
$GENERIC(t) sdiff = sqrt( se_a_2 + se_b_2 );
$t() = (sa/N - sb/M) / sdiff;
$d() = (se_a_2 + se_b_2)*(se_a_2 + se_b_2)
/ ( se_a_2*se_a_2 / (N-1) + se_b_2*se_b_2 / (M-1) )
;
',
Doc => 'Independent sample t-test, NOT assuming equal var. ie Welch two sample t test. Df follows Welch-Satterthwaite equation instead of Satterthwaite (1946, as cited by Hays, 1994, 5th ed.). It matches GNumeric, which matches R.',
);
pp_def('t_test_paired',
Pars => 'a(n); b(n); [o]t(); [o]d()',
GenericTypes => $F,
HandleBad => 1,
RedoDimsCode => 'if ($SIZE(n) < 2) $CROAK("too few N");',
Code => '
$GENERIC(t) N = PDL_IF_BAD(0,$SIZE(n)), s_dif = 0, diff2 = 0;
loop (n) %{
PDL_IF_BAD(if ($ISBAD($a()) || $ISBAD($b())) continue; N++;,)
$GENERIC(t) diff = $a() - $b();
s_dif += diff;
diff2 += diff*diff;
%}
if (N < 2) {
$SETBAD($t());
$SETBAD($d());
continue;
}
$d() = N - 1;
$t() = s_dif / sqrt( ( N*diff2 - s_dif*s_dif ) / (N-1) );
',
Doc => 'Paired sample t-test.',
);
pp_addpm pp_line_numbers(__LINE__, <<'EOD');
=head2 binomial_test
=for Sig
Signature: (x(); n(); p_expected(); [o]p())
=for ref
Binomial test. One-tailed significance test for two-outcome distribution. Given the number of successes, the number of trials, and the expected probability of success, returns the probability of getting this many or more successes.
This function does NOT currently support bad value in the number of successes.
=for example
Usage:
# assume a fair coin, ie. 0.5 probablity of getting heads
# test whether getting 8 heads out of 10 coin flips is unusual
my $p = binomial_test( 8, 10, 0.5 ); # 0.0107421875. Yes it is unusual.
=cut
*binomial_test = \&PDL::binomial_test;
sub PDL::binomial_test {
my ($x, $n, $P) = @_;
carp 'Please install PDL::GSL::CDF.' unless $CDF;
carp 'This function does NOT currently support bad value in the number of successes.' if $x->badflag();
my $pdlx = pdl($x);
$pdlx->badflag(1);
$pdlx = $pdlx->setvaltobad(0);
my $p = 1 - PDL::GSL::CDF::gsl_cdf_binomial_P( $pdlx - 1, $P, $n );
$p = $p->setbadtoval(1);
$p->badflag(0);
return $p;
}
=head1 METHODS
=head2 rtable
=for ref
Reads either file or file handle*. Returns observation x variable pdl and var and obs ids if specified. Ids in perl @ ref to allow for non-numeric ids. Other non-numeric entries are treated as missing, which are filled with $opt{MISSN} then set to BAD*. Can specify num of data rows to read from top but not arbitrary range.
*If passed handle, it will not be closed here.
=for options
Default options (case insensitive):
V => 1, # verbose. prints simple status
TYPE => double,
C_ID => 1, # boolean. file has col id.
R_ID => 1, # boolean. file has row id.
R_VAR => 0, # boolean. set to 1 if var in rows
SEP => "\t", # can take regex qr//
MISSN => -999, # this value treated as missing and set to BAD
NROW => '', # set to read specified num of data rows
=for usage
Usage:
Sample file diet.txt:
uid height weight diet
akw 72 320 1
bcm 68 268 1
clq 67 180 2
dwm 70 200 2
($data, $idv, $ido) = rtable 'diet.txt';
# By default prints out data info and @$idv index and element
reading diet.txt for data and id... OK.
data table as PDL dim o x v: PDL: Double D [4,3]
0 height
1 weight
2 diet
Another way of using it,
$data = rtable( \*STDIN, {TYPE=>long} );
=cut
sub rtable {
# returns obs x var data matrix and var and obs ids
my ($src, $opt) = @_;
my $fh_in;
if ($src =~ /STDIN/ or ref $src eq 'GLOB') { $fh_in = $src }
else { open $fh_in, $src or croak "$!" }
my %opt = ( V => 1,
TYPE => double,
C_ID => 1,
R_ID => 1,
R_VAR => 0,
SEP => "\t",
MISSN => -999,
NROW => '',
);
if ($opt) { $opt{uc $_} = $opt->{$_} for keys %$opt; }
$opt{V} and print "reading $src for data and id... ";
local $PDL::undefval = $opt{MISSN};
my $id_c = []; # match declaration of $id_r for return purpose
if ($opt{C_ID}) {
chomp( $id_c = <$fh_in> );
my @entries = split $opt{SEP}, $id_c;
$opt{R_ID} and shift @entries;
$id_c = \@entries;
}
my ($c_row, $id_r, $data, @data) = (0, [], PDL->null, );
while (<$fh_in>) {
chomp;
my @entries = split /$opt{SEP}/, $_, -1;
$opt{R_ID} and push @$id_r, shift @entries;
# rudimentary check for numeric entry
for (@entries) { $_ = $opt{MISSN} unless defined $_ and m/\d\b/ }
push @data, pdl( $opt{TYPE}, \@entries );
$c_row ++;
last
if $opt{NROW} and $c_row == $opt{NROW};
}
# not explicitly closing $fh_in here in case it's passed from outside
# $fh_in will close by going out of scope if opened here.
$data = pdl $opt{TYPE}, @data;
@data = ();
# rid of last col unless there is data there
$data = $data->slice([0, $data->getdim(0)-2])->sever
unless ( nelem $data->slice(-1)->where($data->slice(-1) != $opt{MISSN}) );
my ($idv, $ido) = ($id_r, $id_c);
# var in columns instead of rows
$opt{R_VAR} == 0
and ($data, $idv, $ido) = ($data->inplace->transpose, $id_c, $id_r);
if ($opt{V}) {
print "OK.\ndata table as PDL dim o x v: " . $data->info . "\n";
$idv and print "$_\t$$idv[$_]\n" for 0..$#$idv;
}
$data = $data->setvaltobad( $opt{MISSN} );
$data->check_badflag;
return wantarray? (@$idv? ($data, $idv, $ido) : ($data, $ido)) : $data;
}
=head2 group_by
Returns pdl reshaped according to the specified factor variable. Most useful when used in conjunction with other broadcasting calculations such as average, stdv, etc. When the factor variable contains unequal number of cases in each level, the returned pdl is padded with bad values to fit the level with the most number of cases. This allows the subsequent calculation (average, stdv, etc) to return the correct results for each level.
Usage:
# simple case with 1d pdl and equal number of n in each level of the factor
pdl> p $a = sequence 10
[0 1 2 3 4 5 6 7 8 9]
pdl> p $factor = $a > 4
[0 0 0 0 0 1 1 1 1 1]
pdl> p $a->group_by( $factor )->average
[2 7]
# more complex case with broadcasting and unequal number of n across levels in the factor
pdl> p $a = sequence 10,2
[
[ 0 1 2 3 4 5 6 7 8 9]
[10 11 12 13 14 15 16 17 18 19]
]
pdl> p $factor = qsort $a( ,0) % 3
[
[0 0 0 0 1 1 1 2 2 2]
]
pdl> p $a->group_by( $factor )
[
[
[ 0 1 2 3]
[10 11 12 13]
]
[
[ 4 5 6 BAD]
[ 14 15 16 BAD]
]
[
[ 7 8 9 BAD]
[ 17 18 19 BAD]
]
]
ARRAY(0xa2a4e40)
# group_by supports perl factors, multiple factors
# returns factor labels in addition to pdl in array context
pdl> p $a = sequence 12
[0 1 2 3 4 5 6 7 8 9 10 11]
pdl> $odd_even = [qw( e o e o e o e o e o e o )]
pdl> $magnitude = [qw( l l l l l l h h h h h h )]
pdl> ($a_grouped, $label) = $a->group_by( $odd_even, $magnitude )
pdl> p $a_grouped
[
[
[0 2 4]
[1 3 5]
]
[
[ 6 8 10]
[ 7 9 11]
]
]
pdl> p Dumper $label
$VAR1 = [
[
'e_l',
'o_l'
],
[
'e_h',
'o_h'
]
];
=cut
*group_by = \&PDL::group_by;
sub PDL::group_by {
my $p = shift;
my @factors = @_;
if ( @factors == 1 ) {
my $factor = $factors[0];
my $label;
if (ref $factor eq 'ARRAY') {
$label = _ordered_uniq($factor);
$factor = code_ivs($factor);
} else {
my $perl_factor = [$factor->list];
$label = _ordered_uniq($perl_factor);
}
my $p_reshaped = _group_by_single_factor( $p, $factor );
return wantarray? ($p_reshaped, $label) : $p_reshaped;
}
# make sure all are arrays instead of pdls
@factors = map { ref($_) eq 'PDL'? [$_->list] : $_ } @factors;
my (@cells);
for my $ele (0 .. $#{$factors[0]}) {
my $c = join '_', map { $_->[$ele] } @factors;
push @cells, $c;
}
# get uniq cell labels (ref List::MoreUtils::uniq)
my %seen;
my @uniq_cells = grep {! $seen{$_}++ } @cells;
my $flat_factor = code_ivs( \@cells );
my $p_reshaped = _group_by_single_factor( $p, $flat_factor );
# get levels of each factor and reshape accordingly
my @levels;
for (@factors) {
my %uniq;
@uniq{ @$_ } = ();
push @levels, scalar keys %uniq;
}
$p_reshaped = $p_reshaped->reshape( $p_reshaped->dim(0), @levels )->sever;
# make labels for the returned data structure matching pdl structure
my @labels;
if (wantarray) {
for my $ifactor (0 .. $#levels) {
my @factor_label;
for my $ilevel (0 .. $levels[$ifactor]-1) {
my $i = $ifactor * $levels[$ifactor] + $ilevel;
push @factor_label, $uniq_cells[$i];
}
push @labels, \@factor_label;
}
}
return wantarray? ($p_reshaped, \@labels) : $p_reshaped;
}
# get uniq cell labels (ref List::MoreUtils::uniq)
sub _ordered_uniq {
my $arr = shift;
my %seen;
my @uniq = grep { ! $seen{$_}++ } @$arr;
return \@uniq;
}
sub _group_by_single_factor {
my $p = shift;
my $factor = shift;
$factor = $factor->squeeze;
die "Currently support only 1d factor pdl."
if $factor->ndims > 1;
die "Data pdl and factor pdl do not match!"
unless $factor->dim(0) == $p->dim(0);
# get active dim that will be split according to factor and dims to broadcast over
my @p_broadcastdims = $p->dims;
my $p_dim0 = shift @p_broadcastdims;
my $uniq = $factor->uniq;
my @uniq_ns;
for ($uniq->list) {
push @uniq_ns, which( $factor == $_ )->nelem;
}
# get number of n's in each group, find the biggest, fit output pdl to this
my $uniq_ns = pdl \@uniq_ns;
my $max = pdl(\@uniq_ns)->max->sclr;
my $badvalue = int($p->max + 1);
my $p_tmp = ones($max, @p_broadcastdims, $uniq->nelem) * $badvalue;
for (0 .. $#uniq_ns) {
my $i = which $factor == $uniq->slice($_);
$p_tmp->dice_axis(-1,$_)->squeeze->slice([0,$uniq_ns[$_]-1]) .= $p->slice($i);
}
$p_tmp->badflag(1);
return $p_tmp->setvaltobad($badvalue);
}
=head2 which_id
=for ref
Lookup specified var (obs) ids in $idv ($ido) (see B<rtable>) and return indices in $idv ($ido) as pdl if found. The indices are ordered by the specified subset. Useful for selecting data by var (obs) id.
=for usage
my $ind = which_id $ido, ['smith', 'summers', 'tesla'];
my $data_subset = $data( $ind, );
# take advantage of perl pattern matching
# e.g. use data from people whose last name starts with s
my $i = which_id $ido, [ grep { /^s/ } @$ido ];
my $data_s = $data($i, );
=cut
sub which_id {
my ($id, $id_s) = @_;
my %ind; @ind{ @$id } = (0 .. $#$id);
pdl grep defined, map $ind{$_}, @$id_s;
}
my %code_bad = map +($_=>1), '', 'BAD';
sub code_ivs {
my ($var_ref) = @_;
$var_ref = [ $var_ref->list ] if UNIVERSAL::isa($var_ref, 'PDL');
my @filtered = map !defined($_) || $code_bad{$_} ? undef : $_, @$var_ref;
my ($l, %level) = 0; $level{$_} //= $l++ for grep defined, @filtered;
my $pdl = pdl(map defined($_) ? $level{$_} : -1, @filtered)->setvaltobad(-1);
$pdl->check_badflag;
wantarray ? ($pdl, \%level) : $pdl;
}
=head1 SEE ALSO
PDL::Basic (hist for frequency counts)
PDL::Ufunc (sum, avg, median, min, max, etc.)
PDL::GSL::CDF (various cumulative distribution functions)
=head1 REFERENCES
Hays, W.L. (1994). Statistics (5th ed.). Fort Worth, TX: Harcourt Brace College Publishers.
=head1 AUTHOR
Copyright (C) 2009 Maggie J. Xiong <maggiexyz users.sourceforge.net>
All rights reserved. There is no warranty. You are allowed to redistribute this software / documentation as described in the file COPYING in the PDL distribution.
=cut
EOD
pp_done();