#'This function generates ROC and precision-recall curves

#'after fitting a Bayesian logit or probit model. 

#'@title ROC and Precision-Recall Curves using Bayesian MCMC estimates generalized

#'@description This function generates ROC and Precision-Recall curves 

#'after fitting a Bayesian logit or probit regression. For fast calculation for 

#'from an "rjags" object use \code{\link{mcmcRocPrc}}

#'@param modelmatrix model matrix, including intercept (if the intercept is among the

#'parameters estimated in the model). Create with model.matrix(formula, data).

#'Note: the order of columns in the model matrix must correspond to the order of columns 

#'in the matrix of posterior draws in the \code{mcmcout} argument. See the \code{mcmcout}

#'argument for more and Beger (2016) for background.

#'@param mcmcout posterior distributions of all logit coefficients, 

#'in matrix form. This can be created from rstan, MCMCpack, R2jags, etc. and transformed

#'into a matrix using the function as.mcmc() from the coda package for \code{jags} class

#'objects, as.matrix() from base R for \code{mcmc}, \code{mcmc.list}, \code{stanreg}, and 

#'\code{stanfit} class objects, and \code{object$sims.matrix} for \code{bugs} class objects.

#'Note: the order of columns in this matrix must correspond to the order of columns 

#'in the model matrix. One can do this by examining the posterior distribution matrix and sorting the 

#'variables in the order of this matrix when creating the model matrix. A useful function for sorting 

#'column names containing both characters and numbers as 

#'you create the matrix of posterior distributions is \code{mixedsort()} from the gtools package.

#'@param modelframe model frame in matrix form. Can be created using 

#'as.matrix(model.frame(formula, data))

#'@param curves logical indicator of whether or not to return values to plot the ROC or Precision-Recall 

#'curves. If set to \code{FALSE} (default), results are returned as a list without the extra

#'values. 

#'@param link type of generalized linear model; a character vector set to \code{"logit"} (default) or \code{"probit"}.

#'@param fullsims logical indicator of whether full object (based on all MCMC draws 

#'rather than their average) will be returned. Default is \code{FALSE}. Note:  If \code{TRUE}

#'is chosen, the function takes notably longer to execute.

#'@references Beger, Andreas. 2016. “Precision-Recall Curves.” Available at SSRN: 

#'https://ssrn.com/Abstract=2765419. http://dx.doi.org/10.2139/ssrn.2765419.

#'@return This function returns a list with 4 elements:

#'\itemize{

#'\item area_under_roc: area under ROC curve (scalar)

#'\item area_under_prc: area under precision-recall curve (scalar)

#'\item prc_dat: data to plot precision-recall curve (data frame)

#'\item roc_dat: data to plot ROC curve (data frame)

#'}

#'

#'@examples

#' \dontshow{.old_wd <- setwd(tempdir())}

#' \donttest{

#' # simulating data

#'

#' set.seed(123456)

#' b0 <- 0.2 # true value for the intercept

#' b1 <- 0.5 # true value for first beta

#' b2 <- 0.7 # true value for second beta

#' n <- 500 # sample size

#' X1 <- runif(n, -1, 1)

#' X2 <- runif(n, -1, 1)

#' Z <- b0 + b1 * X1 + b2 * X2

#' pr <- 1 / (1 + exp(-Z)) # inv logit function

#' Y <- rbinom(n, 1, pr) 

#' df <- data.frame(cbind(X1, X2, Y))

#' 

#' # formatting the data for jags

#' datjags <- as.list(df)

#' datjags$N <- length(datjags$Y)

#' 

#' # creating jags model

#' model <- function()  {

#'   

#'   for(i in 1:N){

#'     Y[i] ~ dbern(p[i])  ## Bernoulli distribution of y_i

#'     logit(p[i]) <- mu[i]    ## Logit link function

#'     mu[i] <- b[1] + 

#'       b[2] * X1[i] + 

#'       b[3] * X2[i]

#'   }

#'   

#'   for(j in 1:3){

#'     b[j] ~ dnorm(0, 0.001) ## Use a coefficient vector for simplicity

#'   }

#'   

#' }

#' 

#' params <- c("b")

#' inits1 <- list("b" = rep(0, 3))

#' inits2 <- list("b" = rep(0, 3))

#' inits <- list(inits1, inits2)

#' 

#' ## fitting the model with R2jags

#' set.seed(123)

#' fit <- R2jags::jags(data = datjags, inits = inits, 

#'                     parameters.to.save = params, n.chains = 2, n.iter = 2000, 

#'                     n.burnin = 1000, model.file = model)

#' 

#' # processing the data

#' mm <- model.matrix(Y ~ X1 + X2, data = df)

#' xframe <- as.matrix(model.frame(Y ~ X1 + X2, data = df))

#' mcmc <- coda::as.mcmc(fit)

#' mcmc_mat <- as.matrix(mcmc)[, 1:ncol(xframe)]

#' 

#' # using mcmcRocPrcGen

#' fit_sum <- mcmcRocPrcGen(modelmatrix = mm,

#'                       modelframe = xframe,

#'                       mcmcout = mcmc_mat,

#'                       curves = TRUE,

#'                       fullsims = FALSE)

#' }

#' 

#' \dontshow{setwd(.old_wd)}

#'@export

mcmcRocPrcGen <- function(modelmatrix,

                       mcmcout,

                       modelframe,

                       curves = FALSE,

                       link = "logit",

                       fullsims = FALSE){

  } else {

  }

  }

    # Observed y and x

    }

      # take out division by 0 for lowest threshold

    }

      # Results as a list

    }

      # Results as a list

    }

  }

      # run this function over each row (iteration) of the pred_prob matrix

      # y_pred <- apply(X = pred_prob, MARGIN = 2, FUN = function(x) median(x))

      # Observed y and x

      }

        # take out division by 0 for lowest threshold

      }

        # Results as a list

      }

        # Results as a list

      }

    }

      }

    }

      }

    }

  }

}