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                            ESTIMATOR OF THE INDIVIDUAL EFFECT SIZES
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DOWNLOAD:   
--------      
            
  The Individual Effect size estimator code is downloadable from the website http://www.people.vcu.edu/~jbukszar/

  The file 'ies_total.r' contains all of the codes necessary to run the individual effect size estimator. 

  


HOW TO USE THE CODE?
--------------------

 The syntax of the code is 

     ies <- ies_estimator(inp_x,MeinRice=TRUE,rule=1,delrange=c(0, 20))

 where
     
     THE OUTPUT 

         ies:      array containing the individual DETECTABILITY estimates. 

                   IMPORTANT: the individual effect size estimates can be obtained by dividing the individual detectability estimates 
                              by the square root of the sample size. The sample size is the number of alleles at the SNPs, i.e. two times
                              the number of individuals. The sample size may vary across the SNPs.  
                   

     THE INPUTS
 

         inp_x:     an array containing the test statistic values (which are assumed to follow chi-square d.f. 1 (e.g. Pearson's test stat value 
                    from allele-based case-control studies)   
     

         MeinRic:   if MeinRic is TRUE then Meinshausen-Rice method is used to estimate the number of positive effects, 
                    if MeinRic is FALSE then Conservative method is used to estimate the number of positive effects   
     
         rule:      the number added to the estimate of the number of positive effects in the stopping rule (should be either 1 or 0)

         delrange:  the range in which the effect sizes are searched



THE SIMPLEST WAY TO RUN THE CODE:
---------------------------------

 Type
        ies <- ies_estimator(x)

 where x is the array that contains the test statistic values.



HOW TO TEST THE CODE?
--------------------

  Two codes, tester and tester2, are provided to test ies_estimator.

--

  tester:   tests the individual effect size estimator 'ies_estimator' on randomly generated test statistic values
            that follow chi-square distribution with 1 degree of freedom.

            The code will display the mean of the individual effect size estimates (detectabilities) below
            the actual individual effect sizes (detectabilities, input argument delr) for comparison

  Syntax:   tester(m,delr,N,rule=1,blocksiz=5, blockval=0)

    where the INPUTS are

       m:         the number of markers

       delr:      array of detectabilities; the square of the detectabilities are the non-centrality parameters of 
                  the chi-square distribution under alternative hypothesis 

       N :        the number of simulation you wish to make 

       rule:      the number added to the estimate of the number of positive effects in the stopping rule (should be either 1 or 0)
                 
       blocksize: the size of the group of markers whose test statistics are correlated

       blockval:  the correlation between the test statistics of two markers within one block (they are independent when blockval is 0 (default)) 

  EXAMPLE: tester(100000,seq(6.6,3.8,length=10),N=5)

--

  tester2:   tests the individual effect size estimator 'ies_estimator' on Pearson's test statistic values calculated on
             randomly generated contingency tables of allele-based case-control study.

             The code will display the mean of the individual effect size estimates (detectabilities) below
             the actual individual effect sizes (detectabilities, input argument delr) for comparison

  Syntax:   tester2(maf_case,maf,num_nulls,nsample,N,rule=1,gamma=0.5)


    where the INPUTS are

       maf_case:  array of minor allele frequencies of the causal markers among the cases  

       maf:       array of minor allele frequencies of the causal markers among the controls  

       num_nulls:   the number of non-causal markers

       nsample:   sample size (number of alleles = two times the number of individuals

       N :        the number of simulation you wish to make 

       rule:      the number added to the estimate of the number of positive effects in the stopping rule (should be either 1 or 0)
                 
       gamma:     the proportion of cases among in the sammple 


  EXAMPLE:  m1<-10; tester2(seq(0.4,0.35,length=m1),rep(0.3,m1),100000-m1,4000,N=5)