本文整理汇总了Python中CGAT.Stats.getSignificance方法的典型用法代码示例。如果您正苦于以下问题：Python Stats.getSignificance方法的具体用法？Python Stats.getSignificance怎么用？Python Stats.getSignificance使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类CGAT.Stats的用法示例。
在下文中一共展示了Stats.getSignificance方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: main
# 需要导入模块: from CGAT import Stats [as 别名]
# 或者: from CGAT.Stats import getSignificance [as 别名]
#.........这里部分代码省略.........
## read data matrix
if options.input_filename:
lines = open(options.input_filename, "r").readlines()
else:
## note: this will not work for interactive viewing, but
## creating hardcopy plots works.
lines = sys.stdin.readlines()
lines = filter( lambda x: x[0] != "#", lines)
if len(lines) == 0:
if options.fail_on_empty:
raise IOError ( "no input" )
E.warn( "empty input" )
E.Stop()
return
matrix, headers, colours, legend = readTable( lines,
"matrix",
take_columns = options.columns,
headers=True,
colours=options.colours,
row_names = options.legend )
if options.input_filename2:
## read another matrix (should be of the same format.
matrix2, headers2, colours2, legend2 = readTable( lines,
"matrix2",
take_columns = options.columns,
headers=True,
colours=options.colours,
row_names = options.legend )
R.assign("headers", headers)
ndata = R( """length( matrix[,1] )""" )[0]
if options.loglevel >=1:
options.stdlog.write("# read matrix: %ix%i\n" % (len(headers),ndata) )
if colours:
R.assign("colours", colours)
for method in options.statistics:
if method == "correlation":
cor = R.cor(matrix, use="pairwise.complete.obs" )
writeMatrix( sys.stdout, cor, headers=headers, format = "%5.2f" )
elif method == "pearson":
options.stdout.write( "\t".join( ("var1",
"var2",
"coeff",
"passed",
"pvalue",
"n",
"method",
"alternative" )) + "\n" )
for x in range(len(headers)-1):
for y in range( x+1, len(headers)):
try:
result = R("""cor.test( matrix[,%i], matrix[,%i] )""" % (x + 1, y + 1))
except rpy.RPyException, msg:
E.warn( "correlation not computed for columns %i(%s) and %i(%s): %s" % (x, headers[x], y, headers[y], msg) )
options.stdout.write( "%s\t%s\t%s\t%s\t%s\t%i\t%s\t%s\n" % \
(headers[x], headers[y],
"na",
"na",
"na",
0,
"na",
"na" ))
else:
options.stdout.write( "%s\t%s\t%6.4f\t%s\t%e\t%i\t%s\t%s\n" % \
(headers[x], headers[y],
result.rx2('estimate').rx2('cor')[0],
Stats.getSignificance( float(result.rx2('p.value')[0]) ),
result.rx2('p.value')[0],
result.rx2('parameter').rx2('df')[0],
result.rx2('method')[0],
result.rx2('alternative')[0]) )
elif method == "spearman":
options.stdout.write( "\t".join( ("var1", "var2",
"coeff",
"passed",
"pvalue",
"method",
"alternative" )) + "\n" )
for x in range(len(headers)-1):
for y in range( x+1, len(headers)):
result = R("""cor.test( matrix[,%i], matrix[,%i], method='spearman' )""" % (x + 1, y + 1))
options.stdout.write( "%s\t%s\t%6.4f\t%s\t%e\t%i\t%s\t%s\n" % \
(headers[x], headers[y],
result['estimate']['rho'],
Stats.getSignificance( float(result['p.value']) ),
result['p.value'],
result['parameter']['df'],
result['method'],
result['alternative']))示例2: main
# 需要导入模块: from CGAT import Stats [as 别名]
# 或者: from CGAT.Stats import getSignificance [as 别名]
#.........这里部分代码省略.........
elif method == "pearson":
options.stdout.write("\t".join(("var1",
"var2",
"coeff",
"passed",
"pvalue",
"n",
"method",
"alternative")) + "\n")
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
try:
result = R(
"""cor.test( matrix[,%i], matrix[,%i] )""" % (x + 1, y + 1))
except rpy.RPyException as msg:
E.warn("correlation not computed for columns %i(%s) and %i(%s): %s" % (
x, headers[x], y, headers[y], msg))
options.stdout.write("%s\t%s\t%s\t%s\t%s\t%i\t%s\t%s\n" %
(headers[x], headers[y],
"na",
"na",
"na",
0,
"na",
"na"))
else:
options.stdout.write(
"%s\t%s\t%6.4f\t%s\t%e\t%i\t%s\t%s\n" %
(headers[x], headers[y],
result.rx2('estimate').rx2(
'cor')[0],
Stats.getSignificance(
float(result.rx2('p.value')[0])),
result.rx2('p.value')[0],
result.rx2('parameter').rx2(
'df')[0],
result.rx2('method')[0],
result.rx2('alternative')[0]))
elif method == "spearman":
options.stdout.write("\t".join(("var1", "var2",
"coeff",
"passed",
"pvalue",
"method",
"alternative")) + "\n")
for x in range(len(headers) - 1):
for y in range(x + 1, len(headers)):
result = R(
"""cor.test( matrix[,%i], matrix[,%i], method='spearman')""" % (x + 1, y + 1))
options.stdout.write(
"%s\t%s\t%6.4f\t%s\t%e\t%i\t%s\t%s\n" %
(headers[x], headers[y],
result['estimate']['rho'],
Stats.getSignificance(float(result['p.value'])),
result['p.value'],
result['parameter']['df'],
result['method'],
result['alternative']))
elif method == "count":
# number of shared elements > threshold
m, r, c = MatlabTools.ReadMatrix(open(options.input_filename, "r"),
take=options.columns,示例3: open
# 需要导入模块: from CGAT import Stats [as 别名]
# 或者: from CGAT.Stats import getSignificance [as 别名]
options.stdlog.write("# testing %s: ll=%f,df=%i versus %s:lnl=%f,df=%i\n" %\
(a,
lnl_complex,df_complex,
b, lnl_simple,
df_simple))
if lnl_complex < lnl_simple:
nerrors += 1
options.stdout.write( "\tna\tna" )
continue
lrt = Stats.doLogLikelihoodTest( lnl_complex, df_complex, lnl_simple, df_simple )
if lrt.mPassed: stats[(a,b)] += 1
options.stdout.write( "\t%s\t%5.2e" % (
Stats.getSignificance( lrt.mProbability),
lrt.mProbability ) )
options.stdout.write( "\n" )
noutput += 1
options.stdout.write( "npassed" )
for a, b in tests:
options.stdout.write( "\t%i\t%5.2f" % (stats[(a, b)], 100.0 * stats[(a,b)] / noutput ) )
options.stdout.write( "\n" )
if options.filename_graph:
outfile = open( options.filename_graph, 'w' )
for a, b in tests:
outfile.write( "%s\t%s\t%i\t%5.2f\n" % (a, b, stats[(a, b)], 100.0 * stats[(a,b)] / noutput ) )