CRIT/ - Revision history

Tara: /* AUXILLIARY FUNCTIONS */

2011-01-24T20:05:17Z

AUXILLIARY FUNCTIONS

← Older revision		Revision as of 20:05, 24 January 2011
Line 93:		Line 93:
	===Checking against Threshold===		===Checking against Threshold===
	<pre>		<pre>
	#Returns a value to split on for the base case		Returns a value to split on for the base case
	getThreshold<-function(A, type=c("median","mean")) {		getThreshold<-function(A, type=c("median","mean")) {
	type <- match.arg(type)		type <- match.arg(type)
Line 107:		Line 107:
	===Return t-test Object===		===Return t-test Object===
	<pre>		<pre>
	#Returns only the t-test from the ttest object		Returns only the t-test from the ttest object
	getTfromStruct<-function(struct){		getTfromStruct<-function(struct){
	p<-unlist(lapply(struct, "[",1));		p<-unlist(lapply(struct, "[",1));
Line 116:		Line 116:
	===Return p Value Object===		===Return p Value Object===
	<pre>		<pre>
	#Returns only the pvalues from the ttest object		Returns only the pvalues from the ttest object
	getPvalfromStruct<-function(struct){		getPvalfromStruct<-function(struct){
	p<-unlist(lapply(struct, "[",3));		p<-unlist(lapply(struct, "[",3));

Tara: /* AUXILLIARY FUNCTIONS */

2011-01-24T19:33:38Z

AUXILLIARY FUNCTIONS

← Older revision		Revision as of 19:33, 24 January 2011
Line 91:		Line 91:
	==AUXILLIARY FUNCTIONS==		==AUXILLIARY FUNCTIONS==

			===Checking against Threshold===
			<pre>
	#Returns a value to split on for the base case		#Returns a value to split on for the base case
	getThreshold<-function(A, type=c("median","mean")) {		getThreshold<-function(A, type=c("median","mean")) {
Line 101:		Line 103:
	}		}

			</pre>

			===Return t-test Object===
			<pre>
	#Returns only the t-test from the ttest object		#Returns only the t-test from the ttest object
	getTfromStruct<-function(struct){		getTfromStruct<-function(struct){
Line 106:		Line 112:
	return(p)		return(p)
	}		}
			</pre>

			===Return p Value Object===
			<pre>
	#Returns only the pvalues from the ttest object		#Returns only the pvalues from the ttest object
	getPvalfromStruct<-function(struct){		getPvalfromStruct<-function(struct){
Line 112:		Line 121:
	return(p)		return(p)
	}		}
			</pre>

Tara at 19:32, 24 January 2011

2011-01-24T19:32:02Z

← Older revision		Revision as of 19:32, 24 January 2011
Line 89:		Line 89:


	===AUXILLIARY FUNCTIONS===		==AUXILLIARY FUNCTIONS==

	#Returns a value to split on for the base case		#Returns a value to split on for the base case

Tara at 19:31, 24 January 2011

2011-01-24T19:31:35Z

@@ Line 1: / Line 1: @@
 =Cross Pattern Identification Technique (CRIT)=
 ==Core Functions and Their Parameters==
@@ Line 9: / Line 11: @@
 Output: Vector assigning a label to every ROW of Column of A
 ===Labeler===
@@ Line 25: / Line 41: @@
 Output: Set of slices where slice is defined as a set of rows of B all containing the same label (in practice this is just the grouping variable as opposed to actual data slices)
 ===Discriminator===
@@ Line 35: / Line 61: @@
 Output: Value of test for every column of B
-#Label, Slice, Discriminate, Repeat
+<pre>
 #In practice this is implemented as a index
 #Additional discriminator functions that use KS test, hypergeometric
@@ Line 72: / Line 85: @@
+}
-#---------------AUXILLIARY FUNCTIONS---------------#
+===AUXILLIARY FUNCTIONS===
 #Returns a value to split on for the base case

Tara: Created page with '=Cross Pattern Identification Technique (CRIT)= ==Core Functions and Their Parameters== ===Initializer=== Initializer: Only run the first time to obtain some set of labels for …'

2011-01-24T19:27:44Z

Created page with '=Cross Pattern Identification Technique (CRIT)= ==Core Functions and Their Parameters== ===Initializer=== Initializer: Only run the first time to obtain some set of labels for …'

New page

=Cross Pattern Identification Technique (CRIT)=

==Core Functions and Their Parameters==

===Initializer===
Initializer: Only run the first time to obtain some set of labels for the rows of A. In all remaining steps, the discriminator generates the new label for propogation. Alternatively, this step can be skipped and a label can be supplied directly to the labeler function.

Required Arguments: Column of A, type of partitioning

Output: Vector assigning a label to every ROW of Column of A

===Labeler===
In implementation both labeler and slicer (labelSlicer) are integrated as the label is a simple column vector. We show the breakdown to make the connection between the algorithm design and implementation more transparent.

Labeler: Transfers labels on columns of previous datasets (A) to rows of new dataset (B)

Required Arguments: Matrix (B), vector assigning a label to every ROW of A

Output: Vector assigning a label to every COLUMN of B

===Slicer===
Slicer: Partition rows of N into "slices" based on labels from A

Required Arguments: Matrix (B), Vector assigning a label to every COLUMN of B

Output: Set of slices where slice is defined as a set of rows of B all containing the same label (in practice this is just the grouping variable as opposed to actual data slices)

===Discriminator===
Discriminator: Evaluates the discriminatory power of labels generated from B in the context of C. Set of slices where slice is defined as a set of rows of B all containing the same label. A random label would show no difference in the slices. (In practice this is implemented as an index instead of the literal data slice).

Required Arguments: Matrix (B), label

Optional Arguments: set to TRUE to compute the FDR

Output: Value of test for every column of B

#Label, Slice, Discriminate, Repeat

#---------------CORE FUNCTIONS---------------#

initializer<-function(A, type=c("median","mean")) {
#Create empty vector with the same number of rows of A
label<-matrix(0, length(A))
#Get value to threshold off of
t<-getThreshold(A, type=type)
#Create label
label<-labelSlicer(A ,t)
return (label)
}

#In practice this is implemented as a index
#Additional discriminator functions that use KS test, hypergeometric
#distribution, etc are possible
discriminator<-function(mat=B, testLabel=l, multCorrect=FALSE){

#Create empty list
ttest_struct<-list();

#Compute value of test for each row
for(i in 1:nrow(X)){
temp<-t.test(as.numeric(B[i,])~label)
ttest_struct[[i]]=temp;
}

#Extract pvalue from structure
all_pval<-getPvalfromStruct(ttest_struct);

#Test if we want to compute FDR
if (multcorrect) { all_pval<-p.adjust(all_pval, method="fdr") }

return (all_pval)
}

labelSlicer<-function(values, t){
#Set those strictly greater than threshold to 1
index<-which(values>=t)
label(index)<-1
return (label)
}

#---------------AUXILLIARY FUNCTIONS---------------#

#Returns a value to split on for the base case
getThreshold<-function(A, type=c("median","mean")) {
type <- match.arg(type)
threshold <- switch(type,
mean = mean(A),
median = median(A)
)
return (threshold)
}

#Returns only the t-test from the ttest object
getTfromStruct<-function(struct){
p<-unlist(lapply(struct, "[",1));
return(p)
}

#Returns only the pvalues from the ttest object
getPvalfromStruct<-function(struct){
p<-unlist(lapply(struct, "[",3));
return(p)
}