def normality_test(self, var): # Проверка на нормальность распределения
if self.num_var == "2":
self.__scale_t = self.scale_type(var)
if (self.__scale_t == 'порядковая') or (self.__scale_t
== 'количественная'):
SW = stats.shapiro(self.df[var])
if SW[1] > self.p_val:
self.__norm = 1
print(
"Тест Шапиро-Уилка на нормальность распределения: W = {:.6}, p = {:f}. Вывод: распределение нормально."
.format(SW[0], SW[1]))
print(
'Среднее: {:.4} и 95% доверительный интервал: [{:.4}, {:.4}]'
.format(np.mean(self.df[var], axis=0),
zconfint(self.df[var])[0],
zconfint(self.df[var])[1]))
else:
self.__norm = 0
print(
"Тест Шапиро-Уилка на нормальность распределения: W = {:.6}, p = {:f}. Вывод: распределение НЕ нормально."
.format(SW[0], SW[1]))
else:
print(
"Данные не имеют количественной природы. Проверка на нормальность не требуется."
)
self.__norm = -1
return self.__norm
def conf_interval(field):
""""
Calculate confidence interval for given field
"""
# I've rounded numbers to integers because estimated values (likes, shares, ...) are integers themselves.
print(
"95% confidence interval for the EPH posts mean number of {:s}: ({z[0]:.0f}, {z[1]:.0f})"
.format(field, z=zconfint(park[field])))
print(
"95% confidence interval for the UCT posts mean number of {:s}: ({z[0]:.0f}, {z[1]:.0f})"
.format(field, z=zconfint(town[field])))
print(
"95% confidence interval for the FSZ posts mean number of {:s}: ({z[0]:.0f}, {z[1]:.0f})"
.format(field, z=zconfint(free[field])))
def test_normality_test(self):
with warnings.catch_warnings():
warnings.filterwarnings("ignore", category=PendingDeprecationWarning)
warnings.filterwarnings("ignore", category=DeprecationWarning)
self.EBM_work.df = pd.read_excel(self.EBM_work.file)
var = "ВОЗРАСТ"
self.EBM_work.scale_type(var)
assert self.EBM_work.normality_test(var) == 0 # распределение не нормально
assert round(stats.shapiro(self.EBM_work.df[var])[0], 4) == 0.9733
assert stats.shapiro(self.EBM_work.df[var])[1] < 0.05
var = "BF"
self.EBM_work.scale_type(var)
assert self.EBM_work.normality_test(var) == 0 # распределение не нормально
assert round(stats.shapiro(self.EBM_work.df[var])[0], 4) == 0.7668
assert stats.shapiro(self.EBM_work.df[var])[1] < 0.05
var = "N1"
self.EBM_work.scale_type(var)
assert self.EBM_work.normality_test(var) == 1 # распределение нормально
assert round(stats.shapiro(self.EBM_work.df[var])[0], 4) == 0.9970
assert stats.shapiro(self.EBM_work.df[var])[1] > 0.05
assert round(np.mean(self.EBM_work.df[var], axis=0), 2) == 0.02
assert round(np.std(self.EBM_work.df[var], axis=0), 2) == 2.18
assert round(zconfint(self.EBM_work.df[var])[0], 4) == -0.1875
assert round(zconfint(self.EBM_work.df[var])[1], 4) == 0.2353
var = "N2"
self.EBM_work.scale_type(var)
assert self.EBM_work.normality_test(var) == 1 # распределение нормально
assert stats.shapiro(self.EBM_work.df[var])[1] > 0.05
var = "ФО"
self.EBM_work.scale_type(var)
assert self.EBM_work.normality_test(var) == -1 # Проверка на нормальность не требуется
var = "Регион"
self.EBM_work.scale_type(var)
assert self.EBM_work.normality_test(var) == -1 # Проверка на нормальность не требуется
def z_confint(sample):
"""Доверительный интервал (95) основанный на нормальном распределении
Parameters
----------
sample : array_like
Массив наблюдений
Returns
-------
lower, upper : floats
Левая и правая граница доверительного интервала
"""
return zconfint(sample)
def test_ztost():
xfair = np.repeat([1, 0], [228, 762 - 228])
# comparing to SAS last output at
# http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_freq_sect028.htm
# confidence interval for tost
# generic ztost is moved to weightstats
from statsmodels.stats.weightstats import zconfint, ztost
ci01 = zconfint(xfair, alpha=0.1, ddof=0)
assert_almost_equal(ci01, [0.2719, 0.3265], 4)
res = ztost(xfair, 0.18, 0.38, ddof=0)
assert_almost_equal(res[1][0], 7.1865, 4)
assert_almost_equal(res[2][0], -4.8701, 4)
assert_array_less(res[0], 0.0001)
def test_ztost():
xfair = np.repeat([1,0], [228, 762-228])
# comparing to SAS last output at
# http://support.sas.com/documentation/cdl/en/procstat/63104/HTML/default/viewer.htm#procstat_freq_sect028.htm
# confidence interval for tost
# generic ztost is moved to weightstats
from statsmodels.stats.weightstats import zconfint, ztost
ci01 = zconfint(xfair, alpha=0.1, ddof=0)
assert_almost_equal(ci01, [0.2719, 0.3265], 4)
res = ztost(xfair, 0.18, 0.38, ddof=0)
assert_almost_equal(res[1][0], 7.1865, 4)
assert_almost_equal(res[2][0], -4.8701, 4)
assert_array_less(res[0], 0.0001)
def test(self):
x1, x2 = self.x1, self.x2
cm = self.cm
# tc : test cases
for tc in [
ztest_, ztest_smaller, ztest_larger, ztest_mu,
ztest_smaller_mu, ztest_larger_mu
]:
zstat, pval = ztest(x1,
x2,
value=tc.null_value,
alternative=alternatives[tc.alternative])
assert_allclose(zstat, tc.statistic, rtol=1e-10)
assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16)
zstat, pval = cm.ztest_ind(
value=tc.null_value, alternative=alternatives[tc.alternative])
assert_allclose(zstat, tc.statistic, rtol=1e-10)
assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16)
#overwrite nan in R's confint
tc_conf_int = tc.conf_int.copy()
if np.isnan(tc_conf_int[0]):
tc_conf_int[0] = -np.inf
if np.isnan(tc_conf_int[1]):
tc_conf_int[1] = np.inf
# Note: value is shifting our confidence interval in zconfint
ci = zconfint(x1,
x2,
value=0,
alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int, rtol=1e-10)
ci = cm.zconfint_diff(alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int, rtol=1e-10)
ci = zconfint(x1,
x2,
value=tc.null_value,
alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int - tc.null_value, rtol=1e-10)
# 1 sample test copy-paste
d1 = self.d1
for tc in [ztest_mu_1s, ztest_smaller_mu_1s, ztest_larger_mu_1s]:
zstat, pval = ztest(x1,
value=tc.null_value,
alternative=alternatives[tc.alternative])
assert_allclose(zstat, tc.statistic, rtol=1e-10)
assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16)
zstat, pval = d1.ztest_mean(
value=tc.null_value, alternative=alternatives[tc.alternative])
assert_allclose(zstat, tc.statistic, rtol=1e-10)
assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16)
#overwrite nan in R's confint
tc_conf_int = tc.conf_int.copy()
if np.isnan(tc_conf_int[0]):
tc_conf_int[0] = -np.inf
if np.isnan(tc_conf_int[1]):
tc_conf_int[1] = np.inf
# Note: value is shifting our confidence interval in zconfint
ci = zconfint(x1,
value=0,
alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int, rtol=1e-10)
ci = d1.zconfint_mean(alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int, rtol=1e-10)
#程序文件Pex4_14_3.py
import numpy as np
from statsmodels.stats.weightstats import zconfint
from scipy import stats
a = np.array([
506, 508, 499, 503, 504, 510, 497, 512, 514, 505, 493, 496, 506, 502, 509,
496
])
ci = zconfint(a)
print("置信区间为：", ci)
def test(self):
x1, x2 = self.x1, self.x2
cm = self.cm
# tc : test cases
for tc in [ztest_, ztest_smaller, ztest_larger,
ztest_mu, ztest_smaller_mu, ztest_larger_mu]:
zstat, pval = ztest(x1, x2, value=tc.null_value,
alternative=alternatives[tc.alternative])
assert_allclose(zstat, tc.statistic, rtol=1e-10)
assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16)
zstat, pval = cm.ztest_ind(value=tc.null_value,
alternative=alternatives[tc.alternative])
assert_allclose(zstat, tc.statistic, rtol=1e-10)
assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16)
# overwrite nan in R's confint
tc_conf_int = tc.conf_int.copy()
if np.isnan(tc_conf_int[0]):
tc_conf_int[0] = - np.inf
if np.isnan(tc_conf_int[1]):
tc_conf_int[1] = np.inf
# Note: value is shifting our confidence interval in zconfint
ci = zconfint(x1, x2, value=0,
alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int, rtol=1e-10)
ci = cm.zconfint_diff(alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int, rtol=1e-10)
ci = zconfint(x1, x2, value=tc.null_value,
alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int - tc.null_value, rtol=1e-10)
# 1 sample test copy-paste
d1 = self.d1
for tc in [ztest_mu_1s, ztest_smaller_mu_1s, ztest_larger_mu_1s]:
zstat, pval = ztest(x1, value=tc.null_value,
alternative=alternatives[tc.alternative])
assert_allclose(zstat, tc.statistic, rtol=1e-10)
assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16)
zstat, pval = d1.ztest_mean(value=tc.null_value,
alternative=alternatives[tc.alternative])
assert_allclose(zstat, tc.statistic, rtol=1e-10)
assert_allclose(pval, tc.p_value, rtol=1e-10, atol=1e-16)
# overwrite nan in R's confint
tc_conf_int = tc.conf_int.copy()
if np.isnan(tc_conf_int[0]):
tc_conf_int[0] = - np.inf
if np.isnan(tc_conf_int[1]):
tc_conf_int[1] = np.inf
# Note: value is shifting our confidence interval in zconfint
ci = zconfint(x1, value=0,
alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int, rtol=1e-10)
ci = d1.zconfint_mean(alternative=alternatives[tc.alternative])
assert_allclose(ci, tc_conf_int, rtol=1e-10)
print("Média dos filmes com pelo menos 10 votos", nota_media_dos_filmes_com_pelo_menos_10_votos.mean())
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(75243)
temp = nota_media_dos_filmes_com_pelo_menos_10_votos.sample(frac=1)
medias = [temp[0:i].mean() for i in range(1, len(temp))]
plt.plot(medias)
from statsmodels.stats.weightstats import zconfint
zconfint(nota_media_dos_filmes_com_pelo_menos_10_votos)
from statsmodels.stats.weightstats import DescrStatsW
descr_todos_com_10_votos = DescrStatsW(nota_media_dos_filmes_com_pelo_menos_10_votos)
descr_todos_com_10_votos.tconfint_mean()
"""# Vamos ver o filme 1..."""
filmes = pd.read_csv("movies.csv")
filmes.query("movieId==1")
notas1 = notas.query("movieId == 1")
notas1.head()
ax = sns.distplot(notas1.rating)
sumup_weak.append(tmin[(ids == i) & (scales_all == s) & (tmin <= -50)])
for i in strong:
# print(scales_all[(ids == i)])
sumup_strong.append(tmin[(ids == i) & (scales_all == s) &
(tmin <= -50)])
for i in uids:
# print(scales_all[(ids == i)])
sumup_mean.append(tmin[(ids == i) & (scales_all == s) & (tmin <= -50)])
weak_scales.append(np.nanmean(np.concatenate(sumup_weak)))
strong_scales.append(np.nanmean(np.concatenate(sumup_strong)))
mean_scales.append(np.nanmean(np.concatenate(sumup_mean)))
wupper.append(stats.zconfint(np.concatenate(sumup_weak))[1])
wlower.append(stats.zconfint(np.concatenate(sumup_weak))[0])
supper.append(stats.zconfint(np.concatenate(sumup_strong))[1])
slower.append(stats.zconfint(np.concatenate(sumup_strong))[0])
f = plt.figure()
plt.plot(uscales, np.array(weak_scales), label='Lowest Probability')
#plt.fill_between(uscales, wlower, wupper, alpha=0.3)
#plt.errorbar(uscales, weak_scales, xerr = w_std*2)
plt.plot(uscales,
np.array(strong_scales),
color='r',
label='Highest probability')
#plt.fill_between(uscales, slower, supper, color='r', alpha=0.3)
#plt.plot(uscales, mean_scales, color='g', label = 'Average distribution')
plt.xlabel('Scales (km)')
"""----------------------------------------------------------------------------
Z Test
Pressupõe normalidade
"""
diagnostico_m = df.query("diagnosis == 'M'")
diagnostico_b = df.query("diagnosis == 'B'")
# Efetuando o Zteste para a média (Comparando os resultados)
ztest(diagnostico_m['mean_radius'], value = diagnostico_m['mean_radius'].mean())
ztest(diagnostico_m['mean_radius'], value = diagnostico_b['mean_radius'].mean())
# Gerando o intervalo de confiança
zconfint(diagnostico_m['mean_radius'])
zconfint(diagnostico_b['mean_radius'])
"""----------------------------------------------------------------------------
T Test
"""
diagnostico_m = df.query("diagnosis == 'M'")
diagnostico_b = df.query("diagnosis == 'B'")
# Aplicando o teste
resultados_m = DescrStatsW(diagnostico_m['mean_radius'])
resultados_b = DescrStatsW(diagnostico_b['mean_radius'])
# Gerando o intervalo de confiança
resultados_m.tconfint_mean()
print(f'p-value Aventura: {p_av}')
if p_av < p_v: #Vamos testar a hipotese nula de que as distribuições são normais.
print(
"Rejeitamos a Hipotese nula para p_av, portanto não é uma distribuição normal"
)
else:
print("É uma distribuição normal")
# In[101]:
# solucao teste de normalidade ou justificativa para nao utiliza-lo
from scipy.stats import ranksums
from statsmodels.stats.weightstats import zconfint
print("Intervalo de confiança para Horror = ",
zconfint(votos_por_genero_por_filme.query("Horror > 0").Horror))
print("Intervalo de confiança para Aventura = ",
zconfint(votos_por_genero_por_filme.query("Adventure > 0").Adventure))
# In[102]:
from statsmodels.stats.weightstats import ztest
notas_horror = votos_por_genero_por_filme.query("Horror > 0").Horror
notas_aventura = votos_por_genero_por_filme.query("Adventure > 0").Adventure
# solução com o teste desejado
print(ranksums(notas_horror, notas_aventura))
# ### Solução (explique sua conclusão):
#
sumup_weak.append(tmin[(ids == i) & (scales_all == s) & (tmin <=-50)])
for i in strong:
# print(scales_all[(ids == i)])
sumup_strong.append( tmin[(ids == i) & (scales_all == s) & (tmin <=-50)])
for i in uids:
# print(scales_all[(ids == i)])
sumup_mean.append( tmin[(ids == i) & (scales_all == s) & (tmin <=-50)])
weak_scales.append(np.nanmean(np.concatenate(sumup_weak)))
strong_scales.append(np.nanmean(np.concatenate(sumup_strong)))
mean_scales.append(np.nanmean(np.concatenate(sumup_mean)))
wupper.append(stats.zconfint(np.concatenate(sumup_weak))[1])
wlower.append(stats.zconfint(np.concatenate(sumup_weak))[0])
supper.append(stats.zconfint(np.concatenate(sumup_strong))[1])
slower.append(stats.zconfint(np.concatenate(sumup_strong))[0])
f = plt.figure()
plt.plot(uscales, np.array(weak_scales), label = 'Lowest Probability')
#plt.fill_between(uscales, wlower, wupper, alpha=0.3)
#plt.errorbar(uscales, weak_scales, xerr = w_std*2)
plt.plot(uscales, np.array(strong_scales), color='r', label = 'Highest probability')
#plt.fill_between(uscales, slower, supper, color='r', alpha=0.3)
#plt.plot(uscales, mean_scales, color='g', label = 'Average distribution')
plt.xlabel('Scales (km)')
plt.ylabel('Tmean(power max)')
# Давайте вернёмся к данным выживаемости пациентов с лейкоцитарной лимфомой из видео про критерий знаков:
# Измерено остаточное время жизни с момента начала наблюдения (в неделях);
# звёздочка обозначает цензурирование сверху — исследование длилось 7 лет, и остаточное время жизни одного пациента, который дожил до конца наблюдения, неизвестно.
#%%
import numpy as np
from scipy import stats
from statsmodels.stats.weightstats import zconfint
life_times = np.array([49, 58, 75, 110, 112, 132, 151, 276, 281, 362]) #∗
print "95%% confidence interval for the life time: [%f, %f]" % zconfint(
life_times)
# Поскольку цензурировано только одно наблюдение, для проверки гипотезы H0:medX=200 на этих данных можно использовать критерий знаковых рангов — можно считать,
# что время дожития последнего пациента в точности равно 362, на ранг этого наблюдения это никак не повлияет.
# Критерием знаковых рангов проверьте эту гипотезу против двусторонней альтернативы, выведите достигаемый уровень значимости,
# округлённый до четырёх знаков после десятичной точки.
#%%
medX = 200
print "Wilcoxon criterion pvalue result: %.4f" % np.round(
stats.wilcoxon(life_times - medX).pvalue, 4)
#%%
# В ходе исследования влияния лесозаготовки на биоразнообразие лесов острова Борнео собраны данные о количестве видов деревьев в 12 лесах, где вырубка не ведётся:
no_cut_kinds = np.array([22, 22, 15, 13, 19, 19, 18, 20, 21, 13, 13, 15])
# и в 9 лесах, где идёт вырубка:
cut_kinds = np.array([17, 18, 18, 15, 12, 4, 14, 15, 10])
# Проверьте гипотезу о равенстве среднего количества видов в двух типах лесов против односторонней альтернативы о снижении биоразнообразия в вырубаемых лесах.
# Используйте ранговый критерий. Чему равен достигаемый уровень значимости? Округлите до четырёх знаков после десятичной точки.
#%%
print "Mann-Whitney criterion pvalue result: %.4f" % np.round(
stats.mannwhitneyu(no_cut_kinds, cut_kinds, alternative="greater").pvalue,
4)
# Доверительный z-интервал from statsmodels.stats.weightstats import zconfint x = [1, 2, 1, 1, 1] zint = zconfint(x, alpha=0.05, alternative='two-sided', ddof=1.0) # (0.8080072030919891, 1.5919927969080108) # Доверительный t-интервал для среднего from statsmodels.stats.weightstats import _tconfint_generic x = [1, 2, 0, 3, 1, 1, 2, 4, 5, 6] n = len(x) mean = x.mean() sigma = x.std(ddof=1)/math.sqrt(n) _tconfint_generic(mean, sigma, n-1, 0.05, 'two-sided') # (1.0994, 3.9006)) - 95% доверительный интервал для среднего # Доверительный интервал для доли from statsmodels.stats.proportion import proportion_confint normal_interval = proportion_confint(n_positive, n_all, alpha=0.05 method = 'normal') # 95% confident interval # Размер выборки для интервала заданной ширины from statsmodels.stats.proportion import samplesize_confint_proportion n_samples = samplesize_confint_proportion(random_sample.mean(), half_length=0.01, alpha=0.05) # 95% confident interval
df.ftypes
# Descrição dos dados
df.describe()
"""----------------------------------------------------------------------------
TESTE Z
Hzero = As medias sao iguais (mu_1 = mu_2)
"""
diagnostico_m = df.query("diagnosis == 'M'")
diagnostico_b = df.query("diagnosis == 'B'")
# Efetuando o Zteste para a comparacao entre as médias
ztest(diagnostico_m["mean_radius"], diagnostico_b["mean_radius"])
# Calculo do intervalo de confianca para a diferenca entre as medis
zconfint(diagnostico_m["mean_radius"], diagnostico_b["mean_radius"])
"""----------------------------------------------------------------------------
TESTE T
Hzero = As medias sao iguais (mu_1 = mu_2)
"""
diagnostico_m = df.query("diagnosis == 'M'")
diagnostico_b = df.query("diagnosis == 'B'")
# Efetuando o Zteste para a comparacao entre as médias
ztest(diagnostico_m["mean_radius"], diagnostico_b["mean_radius"])
# Calculo do intervalo de confianca para a diferenca entre as medis
descr_stats_m = DescrStatsW(diagnostico_m["mean_radius"])
descr_stats_b = DescrStatsW(diagnostico_b["mean_radius"])
resultado = descr_stats_m.get_compare(descr_stats_b)
print(resultado.summary(use_t=True))
kde_kws={'cumulative': True}
)
ax.set(xlabel="Tempo de duração", ylabel="% de filmes")
ax.set_title("Tempo de duração em filmes no TMDB 5000")
tmdb.query("runtime>0").runtime.dropna().quantile(0.8)
"""# Movielens: média dos filmes com pelo menos 10 votos"""
print("Média dos filmes com pelo menos 10 votos", nota_media_dos_filmes_com_pelo_menos_10_votos.mean())
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(75243)
temp = nota_media_dos_filmes_com_pelo_menos_10_votos.sample(frac=1)
medias = [temp[0:i].mean() for i in range(1, len(temp))]
plt.plot(medias)
from statsmodels.stats.weightstats import zconfint
zconfint(nota_media_dos_filmes_com_pelo_menos_10_votos)
from statsmodels.stats.weightstats import DescrStatsW
descr_todos_com_10_votos = DescrStatsW(nota_media_dos_filmes_com_pelo_menos_10_votos)
descr_todos_com_10_votos.tconfint_mean()
