User Modules (from Phase-III Macro System): Unterschied zwischen den Versionen

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! Name  
 
! Name  
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! Function
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! Purpose
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! Description
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! Algorithm
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|- cellpadding = "15"
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| [[MACRO ROW BOOL|%ROW_BOOL()]]
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| '''Deliver PCT/count line dataset using 1 decode'''
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| Select single (‚true‘) value for categorial processing from *_freq and *_catv modules.
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|-
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| [[MACRO BLK BOOL|%BLK_BOOL()]]
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| '''Deliver PCT/count subt. dataset from N bool.vars'''
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| Loop with %row_bool over array of categorial values using same name prefix and  ‚true‘ value and output results as one block.
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|-
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| [[MACRO TAB BOOL|%TAB_BOOL()]]
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| '''Deliver PCT/count table dataset from N bool.blks'''
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| Loop with %blk_bool over groups of variables using diferent name prefixes.
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|-
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| [[MACRO BLK CATV|%BLK_CATV()]]
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| '''Deliver PCT/count subt. dataset from 1 cat. var'''
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| Use categorial processing from *_freq and *_catv modules with restricted paramater set.
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|-
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| [[MACRO TAB CATV|%TAB_CATV()]]
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| '''Deliver PCT/count table dataset fr. N cat. vars'''
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| Loop with %blk_catv over array of names using same processing parameters.
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|-
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| [[MACRO BLK CONV|%BLK_CONV()]]
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| '''Deliver univ. subtable dataset from 1 cont. var'''
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| Use continuous variable processing from *_univ and *_conv modules with restricted paramater set.
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|-
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| [[MACRO TAB CONV|%TAB_CONV()]]
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| '''Deliver univariate. Table dataset fr. N cont. vars'''
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| Loop with %blk_conv over array of names using same set of processing parameters.
 
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| [[MACRO TWO CATV|%TWO_CATV()]]  
 
| [[MACRO TWO CATV|%TWO_CATV()]]  
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| '''Deliver PCT/count table from 2 nested categorial variables.'''  
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| '''Deliver PCT/count table from 2 nested cat. vars'''  
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| Perform nested processing of two categorial variables looping the context variable from the row_* modules over the categories of the "outer" categories.
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| Perform nested processing of two categorial variables looping the context variable from the row_* modules over the categories of the ‚outer‘ categories.
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|-
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| [[MACRO TWO BOCA|%TWO_BOCA()]]
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| '''Deliver PCT/count table from bool./cat. vars'''
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| Perform nested processing with only one (‚true‘) value select from the outer category.
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|-
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| [[MACRO TWO BOBO|%TWO_BOBO()]]
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| '''Deliver PCT/count table from 2  nested bool vars'''
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| Perform nested processing with boolean (‚true value‘) selection from the outer category and an array of boolean selections inside like in %blk_bool. True values may be chosen for each inside variable separately.
 
|}
 
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Version vom 6. März 2014, 09:52 Uhr

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Vorwärts

User Modules generate datasets carrying subtables controlled by user-supplied parms.


Name Purpose Algorithm
%ROW_BOOL() Deliver PCT/count line dataset using 1 decode Select single (‚true‘) value for categorial processing from *_freq and *_catv modules.
%BLK_BOOL() Deliver PCT/count subt. dataset from N bool.vars Loop with %row_bool over array of categorial values using same name prefix and ‚true‘ value and output results as one block.
%TAB_BOOL() Deliver PCT/count table dataset from N bool.blks Loop with %blk_bool over groups of variables using diferent name prefixes.
%BLK_CATV() Deliver PCT/count subt. dataset from 1 cat. var Use categorial processing from *_freq and *_catv modules with restricted paramater set.
%TAB_CATV() Deliver PCT/count table dataset fr. N cat. vars Loop with %blk_catv over array of names using same processing parameters.
%BLK_CONV() Deliver univ. subtable dataset from 1 cont. var Use continuous variable processing from *_univ and *_conv modules with restricted paramater set.
%TAB_CONV() Deliver univariate. Table dataset fr. N cont. vars Loop with %blk_conv over array of names using same set of processing parameters.
%TWO_CATV() Deliver PCT/count table from 2 nested cat. vars Perform nested processing of two categorial variables looping the context variable from the row_* modules over the categories of the ‚outer‘ categories.
%TWO_BOCA() Deliver PCT/count table from bool./cat. vars Perform nested processing with only one (‚true‘) value select from the outer category.
%TWO_BOBO() Deliver PCT/count table from 2 nested bool vars Perform nested processing with boolean (‚true value‘) selection from the outer category and an array of boolean selections inside like in %blk_bool. True values may be chosen for each inside variable separately.
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