#' t-test model class
 #'
 #' t-test model class. Calculate t-test for all features in a dataset
 #'
 #' @import struct
 #' @import stats
 #' @export ttest
 ttest<-setClass(
   "ttest",
   contains=c('method','stato'),
   slots=c(
     # INPUTS
     params.alpha='entity.stato',
     params.mtc='entity.stato',
     params.factor_names='entity',
     # OUTPUTS
     outputs.t_statistic='entity.stato',
     outputs.p_value='entity.stato',
     outputs.dof='entity.stato',
     outputs.significant='entity'
 
     # CHARTS
     # none
   ),
   prototype = list(name='t-test',
     description='Applies the t-test to each feature to indicate significance, with (optional)
                                 multiple-testing correction.',
     type="univariate",
     params=c('alpha','mtc'),
     outputs=c('t_statistic','p_value','rank'),
     charts=c('boxplots'),
     predicted='p_value',
     stato.id="STATO:0000304",
 
     params.factor_names=entity(name='Factor names',
       type='character',
       description='Names of sample_meta columns to use'
     ),
 
     params.alpha=entity.stato(name='Confidence level',
       stato.id='STATO:0000053',
       value=0.05,
       type='numeric',
       description='the p-value cutoff for determining significance.'
     ),
     params.mtc=entity.stato(name='Multiple Test Correction method',
       stato.id='OBI:0200089',
       value='fdr',
       type='numeric',
       description='The method used to adjust for multiple comparisons.'
     ),
     outputs.t_statistic=entity.stato(name='t-statistic',
       stato.id='STATO:0000176',
       type='numeric',
       description='the value of the calculate statistics which is converted to a p-value when compared to a t-distribution.'
     ),
     outputs.p_value=entity.stato(name='p value',
       stato.id='STATO:0000175',
       type='numeric',
       description='the probability of observing the calculated t-statistic.'
     ),
     outputs.rank=entity(name='rank',
       #stato.id='STATO:0000xxx',
       type='numeric',
       description='A relative status as compared to others within a group.'
     ),
     outputs.dof=entity.stato(name='degrees of freedom',
       stato.id='STATO:0000069',
       type='numeric',
       description='the number of degrees of freedom used to calculate the test statistic'
     ),
     outputs.significant=entity(name='Significant features',
       #stato.id='STATO:0000069',
       type='logical',
       description='TRUE if the calculated p-value is less than the supplied threhold (alpha)'
     )
   )
 )
 
 #' @export
 setMethod(f="method.apply",
   signature=c("ttest",'dataset'),
   definition=function(M,D)
   {
     X=dataset.data(D)
     y=dataset.sample_meta(D)[[M$factor_names]]
     L=levels(y)
     if (length(L)!=2) {
       stop('must have exactly two levels for this implmentation of t-statistic')
     }
     output=apply(X,2,function(x) {a=unlist(t.test(x[y==L[1]],x[y==L[2]])[c("statistic","p.value","parameter")])})
     output=as.data.frame(t(output),stringsAsFactors = FALSE)
     output$p.value=p.adjust(output$p.value,method = param.value(M,'mtc'))
     output.value(M,'t_statistic')=output$statistic.t
     output.value(M,'p_value')=output$p.value
     output.value(M,'dof')=output$parameter.df
     output.value(M,'significant')=output$p.value<param.value(M,'alpha')
 
     return(M)
   }
 )