Welch's t-test: Difference between revisions

ARRAY1_SIZE = len(array1)

elif ARRAY1_SIZE if ARRAY2_SIZE <= 1:

return 1.0

fmean1 = sum(array1)/ARRAY1_SIZE

fmean2 = sum(array2)/ARRAY2_SIZE

if fmean1 == fmean2:

return 1.0

unbiased_sample_variance1 = 0.0

unbiased_sample_variance2 = 0.0

for v in range(ARRAY1_SIZE):

unbiased_sample_variance1 += (array1[v] - fmean1) * (array1[v] - fmean1)

for v in range(ARRAY2_SIZE):

unbiased_sample_variance2 += (array2[v] - fmean2) * (array2[v] - fmean2)

unbiased_sample_variance1 = unbiased_sample_variance1/(ARRAY1_SIZE-1)

unbiased_sample_variance2 = unbiased_sample_variance2/(ARRAY2_SIZE-1)

WELCH_T_STATISTIC = (fmean1-fmean2)/math.sqrt(unbiased_sample_variance1/ARRAY1_SIZE+unbiased_sample_variance2/ARRAY2_SIZE)

DEGREES_OF_FREEDOM = pow((unbiased_sample_variance1/ARRAY1_SIZE+unbiased_sample_variance2/ARRAY2_SIZE),2.0) / ((unbiased_sample_variance1*unbiased_sample_variance1)/(ARRAY1_SIZE*ARRAY1_SIZE*(ARRAY1_SIZE-1))+ (unbiased_sample_variance2*unbiased_sample_variance2)/(ARRAY2_SIZE*ARRAY2_SIZE*(ARRAY2_SIZE-1))

a = DEGREES_OF_FREEDOM/2

value = DEGREES_OF_FREEDOM/(WELCH_T_STATISTIC*WELCH_T_STATISTIC+DEGREES_OF_FREEDOM)

beta = math.lgamma(a) + 0.57236494292470009-math.lgamma(a+0.5)

acu = 0.1E-14

if a <= 0.0:

return value

if value < 0.0 or 1.0 < value:

return value

if value == 0 or value == 1.0:

return value

psq = a + 0.5

cx = 1.0 - value

if a < (psq * value):

xx = cx

cx = value

pp = 0.5

qq = a

indx = 1

else:

xx = value

pp = a

qq = 0.5

indx = 0

term = 1.0

ai = 1.0

value = 1.0

ns = int(qq + cx*psq)

rx = xx/cx

temp = qq - ai

if ns == 0:

rx = xx

while (1True):

term = term * temp * rx / ( pp + ai )

value = value + term

temp = abs ( term )

if ((temp <= acu) and (temp <= acu * value)):

value = value * math.exp ( pp * math.log ( xx ) + ( qq - 1.0 ) * math.log ( cx ) - beta ) / pp

if indx:

value = 1.0 - value

break

ai = ai + 1.0

ns = ns - 1

if 0 <= ns:

temp = qq - ai

if ns == 0:

rx = xx

else:

temp = psq

psq = psq + 1.0

return value