@@ -62,9 +62,25 @@ def iterate_anova_tables(aov_table, aov_table_sqr):
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dd [row .Index ][col_name ] = getattr (row , col_name )
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return dd
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- def get_influence_parameters (anova_result , nrOfParameters ):
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- print (anova_result )
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- print (type (anova_result ))
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+ def get_influence_parameters (anova_result , alpha , nrOfParameters ):
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+
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+ print "##########"
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+ import pprint
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+ pp = pprint .PrettyPrinter (indent = 4 )
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+ pp .pprint (anova_result )
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+ print "##########"
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+ # now we want to select the most important factors out of anova result
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+
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+ significant_interactions = []
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+ for interaction_key in anova_result .keys ():
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+ pvalue = anova_result [interaction_key ]['PR(>F)' ]
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+ if pvalue < alpha :
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+ significant_interactions .append ((interaction_key , pvalue ))
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+
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+ sorted_significant_interactions = sorted ((value , key ) for (key , value ) in significant_interactions )
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+ print "!!!!!!!!!"
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+ pp .pprint (sorted_significant_interactions )
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+ print "!!!!!!!!!"
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return anova_result
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if __name__ == '__main__' :
@@ -73,5 +89,6 @@ def get_influence_parameters(anova_result, nrOfParameters):
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id = "a780bba9-a2c7-20a5-7be9-ede26d9c9b64"
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stage_ids = ["6dc62e9c-3625-85ca-657e-3b06cc269828#1" , "6dc62e9c-3625-85ca-657e-3b06cc269828#2" , "6dc62e9c-3625-85ca-657e-3b06cc269828#3" , "6dc62e9c-3625-85ca-657e-3b06cc269828#4" ]
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retrieved = db ().get_analysis (experiment_id = id , stage_ids = stage_ids , analysis_name = "two-way-anova" )
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- nrOfParameters = 2 # to be retrieved from experiment definition
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- get_influence_parameters (retrieved ["anova_result" ], nrOfParameters )
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+ nrOfParameters = 2 # to be retrieved from analysis definition
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+ alpha = 0.5 # to be retrieved from analysis definition
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+ get_influence_parameters (retrieved ["anova_result" ], alpha , nrOfParameters )
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