def get_modindices(self, delta=0.1, alpha = 0.05, multigroup=True):
"""
Read the modification indices from the file,
and calculate the power of the score test.
"""
self.starting_string = re.compile("^MODEL MODIFICATION INDICES")
self.stopping_string = re.compile('^TECHNICAL \d OUTPUT')
in_block = False
critical = distributions.qchisq(1, alpha) # critical value for alpha level
statement_string = re.compile('^[\[]*([\w ]+\w+)[ \]]+([-]*\d+\.\d+)[ ]+([-]*\d+\.\d+)[ ]+([-]*\d+\.\d+)[ ]+([-]*\d+\.\d+)')
if multigroup:
class_num_string = re.compile('^Group ([A-Z_-]+)[ ]*[\r\n]')
else:
class_num_string = re.compile('^CLASS (\d+)[ ]*[\r\n]')
class_num = 1
statements = dict()
for line in self.f:
if ( self.starting_string.findall(line) ):
in_block = True
if ( self.stopping_string.findall(line) ):
in_block = False
if (in_block):
class_tmp = class_num_string.findall(line)
if (class_tmp):
class_num = str(class_tmp[0])
about_variable = statement_string.findall(line)
if (about_variable):
in_statement = about_variable[0][0]
in_statement = re.sub("[ ]+"," ", in_statement.strip())
values = about_variable[0][1:]
values = list(float(v) for v in values )
if (values[0] != 999.0 and abs(values[3]) > 0.0001 and \
abs(values[1])> 1e-6):
ncp = ( values[0] / values[1]**2 ) * delta**2
values.append(ncp)
values.append(distributions.pchisq(1, ncp, critical))
else:
values.extend([999.0,999.0]) # missing values
if in_statement not in statements:
statements[in_statement] = {class_num : values}
else:
statements[in_statement][class_num] = values
about_variable = []
self.f.seek(0) # rewind the file so it can be read again
return(statements)
def get_critical(self):
"Return critical value of 1df chi square test from field or cached value"
if not self.critical: # not cached
self.critical = distributions.qchisq(1, self.get_field_value('alpha'))
return self.critical
