Ejemplo n.º 1
0
def _corr_to_table(table, Y, X, categories, levels, printXname=True, label=False,
                   pmax=None, nan=True):
    r, p, n = _corr(X, Y, categories)
    if (pmax is None) or (p <= pmax):
        if nan or (not np.isnan(r)):
            nstars = np.sum(p <= levels)
            if printXname:
                table.cell(X.name)
            else:
                table.cell()
            if label:
                table.cell(label)
            table.cells(fmtxt.texstr(r) + fmtxt.Stars(nstars, of=len(levels)), p, n)
        else:
            table._my_nan_count += 1
Ejemplo n.º 2
0
def test(Y, X=None, against=0, match=None, sub=None,
         par=True, corr='Hochberg',
         title='{desc}'):
    """
    One-sample tests.

    kwargs
    ------
    X: perform tests separately for all categories in X.
    Against: can be
             - value
             - string (category in X)

    """
    ct = celltable(Y, X, match, sub)

    if par:
        title_desc = "t-tests against %s" % against
        statistic_name = 't'
    else:
        raise NotImplementedError

    names = []; ts = []; dfs = []; ps = []

    if isinstance(against, str):
        k = len(ct.indexes) - 1
        assert against in ct.cells
        for id in ct.indexes:
            label = ct.cells[id]
            if against == label:
                baseline_id = id
                baseline = ct.data[id]

        for id in ct.indexes:
            if id == baseline_id:
                continue
            names.append(ct.cells[id])
            if (ct.within is not False) and ct.within[id, baseline_id]:
                t, p = scipy.stats.ttest_rel(baseline, ct.data[id])
                df = len(baseline) - 1
            else:
                data = ct.data[id]
                t, p = scipy.stats.ttest_ind(baseline, data)
                df = len(baseline) + len(data) - 2
            ts.append(t)
            dfs.append(df)
            ps.append(p)

    elif np.isscalar(against):
        k = len(ct.cells)

        for id in ct.indexes:
            label = ct.cells[id]
            data = ct.data[id]
            t, p = scipy.stats.ttest_1samp(data, against)
            df = len(data) - 1
            names.append(label); ts.append(t); dfs.append(df); ps.append(p)

    if corr:
        ps_adjusted = mcp_adjust(ps, corr)
    else:
        ps_adjusted = np.zeros(len(ps))
    stars = star(ps, out=str)  # , levels=levels, trend=trend, corr=corr
    if len(np.unique(dfs)) == 1:
        df_in_header = True
    else:
        df_in_header = False

    table = fmtxt.Table('l' + 'r' * (3 - df_in_header + bool(corr)))
    table.title(title.format(desc=title_desc))
    if corr:
        table.caption(_get_correction_caption(corr, k))

    # header
    table.cell("Effect")
    if df_in_header:
        table.cell([statistic_name,
                    fmtxt.texstr(dfs[0], property='_'),
                    ], mat=True)
    else:
        table.cell(statistic_name, mat=True)
        table.cell('df', mat=True)
    table.cell('p', mat=True)
    if corr:
        table.cell(fmtxt.symbol('p', df=corr))
    table.midrule()

    # body
    for name, t, mark, df, p, p_adj in zip(names, ts, stars, dfs, ps, ps_adjusted):
        table.cell(name)
        tex_stars = fmtxt.Stars(mark, of=3)
        tex_t = fmtxt.texstr(t, fmt='%.2f')
        table.cell([tex_t, tex_stars])
        if not df_in_header:
            table.cell(df)

        table.cell(fmtxt.p(p))
        if corr:
            table.cell(fmtxt.p(p_adj))
    return table
Ejemplo n.º 3
0
def pairwise(Y, X, match=None, sub=None,            # data in
             par=True, corr='Hochberg', trend=True, # stats
             title='{desc}', mirror=False,        # layout
             ):
    """
    pairwise comparison according to factor structure
    
    """
#    ct = celltable(Y, X, match=match, sub=sub)
    # test
    data, datalabels, names, within = _split_Y(Y, X, match=match, sub=sub)
    test = _pairwise(data, within=within, parametric=par, corr=corr, #levels=levels, 
                     trend=trend)
    # extract test results
    k = len(data)
    indexes = test['pw_indexes']
    statistic = test['statistic']
    _K = test[statistic]
    _P = test['p']
    if corr:
        _Pc = mcp_adjust(_P, corr)
    _df = test['df']
    _NStars = test['stars']
    symbols = test['symbols']
    
    # create TABLE
    table = textab.Table('l'+'l'*(k-1+mirror))
    title_desc = "Pairwise {0}".format(test['test'])
    table.title(title.format(desc=title_desc))
    table.caption(test['caption'])
    
    # headings
    table.cell()
    for name in names[1-mirror:]:
        table.cell(name)
    table.midrule()
    
    tex_peq = textab.texstr("p=")
    #tex_df = textab.Element(df, "_", digits=0)
    if corr and not mirror:
        subrows = range(3)
    else:
        subrows = range(2)
    
    for row in range(0, k-1+mirror):
        for subrow in subrows: # contains t/p
            # names column
            if subrow is 0:
                table.cell(names[row], r"\textbf")
            else:
                table.cell()
            # rows
            for col in range(1-mirror, k):
                if row == col:
                    table.cell()
                elif col > row:
                    index = indexes[(row, col)]
                    #(col-1) + ((k-2)*row) sum(range(k-1, k-1-row, -1))
                    K = _K[index]
                    p = _P[index]
                    df = _df[index]
#                    nstars = _NStars[index]
                    if subrow is 0:
                        tex_cell = textab.eq(statistic, K, df=df, 
                                             stars=symbols[index],   
                                             of=3+trend)
                    elif subrow is 1:
                        tex_cell = textab.texstr([tex_peq, texstr(p, fmt='%.3f')], 
                                                 mat=True)
                    elif subrow is 2:
                        tex_cell = textab.eq('p', _Pc[index], df='c', 
                                             fmt='%.3f', drop0=True)
                    table.cell(tex_cell)
                else:
                    if mirror and corr and subrow==0:
                        index = indexes[(col, row)]
                        p = _Pc[index]
                        table.cell(p, fmt='%.3f', drop0=True)
                    else:
                        table.cell()
    return table