static apop_data *colmeans(apop_data *in){
Get_vmsizes(in); //maxsize
apop_data *sums = apop_data_summarize(in);
Apop_col_tv(sums, "mean", means);
apop_data *out = apop_matrix_to_data(apop_vector_to_matrix(means, 'r'));
apop_name_stack(out->names, in->names, 'c', 'c');
apop_data *cov = apop_data_add_page(out, apop_data_covariance(in), "<Covariance>");
gsl_matrix_scale(cov->matrix, 1/sqrt(maxsize));
return out;
}
static void make_covar(apop_model *est){
int size = est->parameters->vector->size;
//the trick where we turn the params into a p-vector
double * pv = est->parameters->vector->data;
int n = pv[0];
pv[0] = 1 - (apop_sum(est->parameters->vector)-n);
apop_data *cov = apop_data_add_page(est->parameters,
apop_data_alloc(size, size), "<Covariance>");
for (int i=0; i < size; i++){
double p = apop_data_get(est->parameters, i, -1);
apop_data_set(cov, i, i, n * p *(1-p));
for (int j=i+1; j < size; j++){
double pj = apop_data_get(est->parameters, j, -1);
double thiscell = -n*p*pj;
apop_data_set(cov, i, j, thiscell);
apop_data_set(cov, j, i, thiscell);
}
}
pv[0]=n;
}
int main(){
//bind together a Poisson and a Normal;
//make a draw producing a 2-element vector
apop_model *m1 = apop_model_set_parameters(apop_poisson, 3);
apop_model *m2 = apop_model_set_parameters(apop_normal, -5, 1);
apop_model *mm = apop_model_stack(m1, m2);
int len = 1e5;
gsl_rng *r = apop_rng_alloc(1);
apop_data *draws = apop_data_alloc(len, 2);
for (int i=0; i< len; i++){
Apop_row (draws, i, onev);
apop_draw(onev->data, r, mm);
assert((int)onev->data[0] == onev->data[0]);
assert(onev->data[1]<0);
}
//The rest of the test script recovers the parameters.
//First, set up a two-page data set: poisson data on p1, Normal on p2:
apop_data *comeback = apop_data_alloc();
Apop_col(draws, 0,fishdraws)
comeback->vector = apop_vector_copy(fishdraws);
apop_data_add_page(comeback, apop_data_alloc(), "p2");
Apop_col(draws, 1, meandraws)
comeback->more->vector = apop_vector_copy(meandraws);
//set up the un-parameterized stacked model, including
//the name at which to split the data set
apop_model *estme = apop_model_stack(apop_model_copy(apop_poisson), apop_model_copy(apop_normal));
Apop_settings_add(estme, apop_stack, splitpage, "p2");
apop_model *ested = apop_estimate(comeback, *estme);
//test that the parameters are as promised.
apop_model *m1back = apop_settings_get(ested, apop_stack, model1);
apop_model *m2back = apop_settings_get(ested, apop_stack, model2);
assert(fabs(apop_data_get(m1back->parameters, .col=-1) - 3) < 1e-2);
assert(fabs(apop_data_get(m2back->parameters, .col=-1) - -5) < 1e-2);
assert(fabs(apop_data_get(m2back->parameters, .col=-1, .row=1) - 1) < 1e-2);
}
/** Create a histogram from data by putting data into bins of fixed width.
\param indata The input data that will be binned. This is copied and the copy will be modified.
\param close_top_bin Normally, a bin covers the range from the point equal to its minimum to points strictly less than
the minimum plus the width. if \c 'y', then the top bin includes points less than or equal to the upper bound. This solves the problem of displaying histograms where the top bin is just one point.
\param binspec This is an \ref apop_data set with the same number of columns as \c indata.
If you want a fixed size for the bins, then the first row of the bin spec is the bin width for each column.
This allows you to specify a width for each dimension, or specify the same size for all with something like:
\param bin_count If you don't provide a bin spec, I'll provide this many evenly-sized bins. Default: \f$\sqrt(N)\f$. \code
Apop_data_row(indata, 0, firstrow);
apop_data *binspec = apop_data_copy(firstrow);
gsl_matrix_set_all(binspec->matrix, 10); //bins of size 10 for all dim.s
apop_data_to_bins(indata, binspec);
\endcode
The presumption is that the first bin starts at zero in all cases. You can add a second row to the spec to give the offset for each dimension. Default: NULL. if no binspec and no binlist, then a grid with offset equal to the min of the column, and bin size such that it takes \f$\sqrt{N}\f$ bins to cover the range to the max element.
\return A pointer to a binned \ref apop_data set. If you didn't give me a binspec, then I attach one to the output set as a page named \c \<binspec\>, so you can snap a second data set to the same grid using
\code
apop_data_to_bins(first_set, NULL);
apop_data_to_bins(second_set, apop_data_get_page(first_set, "<binspec>"));
\endcode
The text segment, if any, is not binned. I use \ref apop_data_pmf_compress as the final step in the binning,
and that does respect the text segment.
Here is a sample program highlighting the difference between \ref apop_data_to_bins and \ref apop_data_pmf_compress .
\include binning.c
*/
APOP_VAR_HEAD apop_data *apop_data_to_bins(apop_data *indata, apop_data *binspec, int bin_count, char close_top_bin){
apop_data *apop_varad_var(indata, NULL);
Apop_assert_c(indata, NULL, 1, "NULL input data set, so returning NULL output data set.");
apop_data *apop_varad_var(binspec, NULL);
char apop_varad_var(close_top_bin, 'n');
int apop_varad_var(bin_count, 0);
APOP_VAR_ENDHEAD
Get_vmsizes(indata); //firstcol, vsize, msize1, msize2
double binwidth, offset, max=0;
apop_data *out = apop_data_copy(indata);
apop_data *bs = binspec ? binspec
: apop_data_add_page(out,
apop_data_alloc(vsize? 2: 0, msize1? 2: 0, indata->matrix ? msize2: 0),
"<binspec>");
for (int j= firstcol; j< msize2; j++){
Apop_col(out, j, onecol);
if (binspec){
binwidth = apop_data_get(binspec, 0, j);
offset = ((binspec->vector && binspec->vector->size==2 )
||(binspec->matrix && binspec->matrix->size1==2)) ? apop_data_get(binspec, 1, j) : 0;
} else {
Apop_col(bs, j, abin);
max = gsl_vector_max(onecol);
offset = abin->data[1] = gsl_vector_min(onecol);
binwidth = abin->data[0] = (max - offset)/(bin_count ? bin_count : sqrt(onecol->size));
}
for (int i=0; i< onecol->size; i++){
double val = gsl_vector_get(onecol, i);
if (close_top_bin=='y' && val == max && val!=offset)
val -= 2*GSL_DBL_EPSILON;
gsl_vector_set(onecol, i, (floor((val -offset)/binwidth))*binwidth+offset);
}
}
apop_data_pmf_compress(out);
return out;
}
*/
#include "apop_internal.h"
#include <search.h> //lsearch; bsearch is in stdlib.
/** For many, it is a knee-jerk reaction to a parameter estimation to test whether each individual parameter differs from zero. This function does that.
\param est The \ref apop_model, which includes pre-calculated parameter estimates, var-covar matrix, and the original data set.
Returns nothing. At the end of the routine, <tt>est->info->more</tt> includes a set of t-test values: p value, confidence (=1-pval), t statistic, standard deviation, one-tailed Pval, one-tailed confidence.
*/
void apop_estimate_parameter_tests (apop_model *est){
Nullcheck_p(est, )
if (!est->data) return;
apop_data *ep = apop_data_add_page(est->info, apop_data_alloc(est->parameters->vector->size, 2), "<test info>");
apop_name_add(ep->names, "p value", 'c');
apop_name_add(ep->names, "confidence", 'c');
apop_name_stack(ep->names, est->parameters->names, 'r', 'r');
Get_vmsizes(est->data); //msize1, vsize
int df = msize1 ? msize1 : vsize;
df -= est->parameters->vector->size;
df = df < 1 ? 1 : df; //some models aren't data-oriented.
apop_data_add_named_elmt(est->info, "df", df);
apop_data *one_elmt = apop_data_calloc(1, 1);
gsl_vector *param_v = apop_data_pack(est->parameters);
for (size_t i=0; i< est->parameters->vector->size; i++){
Apop_settings_add_group(est, apop_pm, .index=i);
apop_model *m = apop_parameter_model(est->data, est);
