seolmatrix: Correlations suite for jamovi

# This file is a generated template, your changes will not be overwritten

#' @importFrom R6 R6Class
#' @import jmvcore
#' @importFrom correlation correlation
#' @importFrom multilevel ICC1
#' @importFrom multilevel ICC2
#' @import correlation
#' @import ggplot2
#' @export


multilevelClass <- if (requireNamespace('jmvcore')) R6::R6Class(
    "multilevelClass",
    inherit = multilevelBase,
    private = list(
        
        #------------------------
        
        .init = function() {
            if (is.null(self$data) | is.null(self$options$vars)) {
                self$results$instructions$setVisible(visible = TRUE)
                
            }
            
            self$results$instructions$setContent(
                "<html>
            <head>
            </head>
            <body>
            <div class='instructions'>
            <p>____________________________________________________________________________________</p>
            <p>1. More than two dependent variables should be specified for Multilevel correlation.</p>
            <p>2. The rationale of Multilevel Correlation is described in the <a href='https://cran.r-project.org/web/packages/correlation/vignettes/multilevel.html' target = '_blank'>page.</a></p>
            <p>3. Feature requests and bug reports can be made on the <a href='https://github.com/hyunsooseol/seolmatrix/issues'  target = '_blank'>GitHub</a>.</p>
            <p>____________________________________________________________________________________</p>
            </div>
            </body>
            </html>"
                            )
            
            
            
            if (self$options$icc)
                self$results$icc$setNote(
                    "Note",
                    "ICC1= Individual-level variance that can be explained by group membership; ICC2= The reliability of the group means."
                    
                )
            
        },
        
        
        
        #---------------------------------
        
        .run = function() {

            
            if (length(self$options$facs)<1) return() 
            
       
            # get the data------
            
            data <- self$data 
            vars  <- self$options$vars
            facs <- self$options$facs
            
           
            
            if(self$options$multi==TRUE){ 
            
          
            # convert to appropriate data types
            
            for (i in seq_along(vars))
                data[[i]] <- jmvcore::toNumeric(data[[i]])
            
            
            #  data[[vars]] <- jmvcore::toNumeric(data[[vars]])
            
            for (fac in facs)
                data[[fac]] <- as.factor(data[[fac]])
            
            # data is now all of the appropriate type we can begin!
            
            data <- na.omit(data)
            
           # data <- jmvcore::select(data, self$options$vars)
            
          
           #############################################
            res<- correlation::correlation(data, multilevel = TRUE)
            #############################################

                v1 <- res$Parameter1
                v2 <- res$Parameter2
                r <- res$r
                low <- res$CI_low
                high <- res$CI_high
                t <- res$t
                df <- res$df
                p <- res$p
                n <- res$n_Obs

                results <-
                    list(
                        'v1' = v1,
                        'v2' = v2,
                        'r' = r,
                        'low' = low,
                        'high' = high,
                        't' = t,
                        'df' = df,
                        'p' = p,
                        'n' = n
                    )


                # populate multilevel table---

                table <- self$results$multi

                nrows <- ncol(combn(self$options$vars, 2))

                # for (i in seq_len(nrows)) {
                #     # add the rows in here
                # }


                for (i in seq_len(nrows)) {

                    row <- list()

                    row[['v1']] <- v1[i]
                    row[['v2']] <- v2[i]
                    row[['r']] <- r[i]
                    row[['low']] <- low[i]
                    row[['high']] <- high[i]
                    row[['t']] <- t[i]
                    row[['df']] <- df[i]
                    row[['p']] <- p[i]
                    row[['n']] <- n[i]

                    table$addRow(rowKey =i, values = row)
                }

                }


            ##Computing ICC###############################
            
                 #  for (var in self$options$vars) {
                 # 
                 #     dataA <- data.frame(
                 #         dep = data[[var]],
                 #         group = data[[facs]]
                 #     )
                 # 
                 # }

                    
                    ############################################
                 #   model <- stats::aov(dep ~ group, data=dataA)
                    ############################################

                    out <- NULL
                    
                    for(var in self$options$vars){
                        
                         # formula <- constructFormula(self$options$vars, self$options$facs)
                         # formula <- as.formula(formula)
                         # 
                        # results <- t.test(formula, self$data)
                         
                        dataA <- data.frame(
                            dep = data[[var]],
                            group = data[[facs]]
                        )   
                        
                        model<-stats::aov(dep ~ group,data=dataA)
                        
                        icc1<- multilevel::ICC1(model)
                        icc2 <- multilevel::ICC2(model)
                        
                        df <- data.frame(icc1,icc2) 
                         
                       
                        if (is.null(out)) {
                            out <- df
                        } else {
                            out <- rbind(out, df)
                        }
                    }
                    
                    out <- out #data frame
                    
                  #  self$results$text$setContent(out)
                    
                    
                    # Creating table----------
                    
                    table <- self$results$icc
                    
                    icc <- as.list(out) 
                    
                #  names <- dimnames(icc)[[1]]
                    
                    for (i in seq_along(self$options$vars)) {
                        
                        row <- list()
                        
                        row[["icc1"]] <-  icc[[1]][i]
                        row[["icc2"]] <-  icc[[2]][i]
                        
                        table$setRow(rowNo=i, values=row)
                        
                    }  
                    
                    
                    # icc1 <- multilevel::ICC1(model)
                    # icc2 <- multilevel::ICC2(model)
                    
                    #  self$results$text$setContent(icc1)
                    
                    # populate multilevel correlation---
                    
                    # table <- self$results$icc
                    # 
                    # row <- list()
                    # 
                    # row[['icc1']] <- icc1
                    # row[['icc2']] <- icc2
                    # 
                    # table$setRow(rowNo = 1, values = row)
                
            
            
        } 
        )
)