R/base.R

Defines functions plotMultOut scatterplot rgumbel.trunc qgumbel.trunc pgumbel.trunc dgumbel.trunc rnorm.trunc qnorm.trunc pnorm.trunc dnorm.trunc plotFG response extract tell ask

Documented in ask dgumbel.trunc dnorm.trunc extract pgumbel.trunc plotFG plotMultOut pnorm.trunc qgumbel.trunc qnorm.trunc rgumbel.trunc rnorm.trunc scatterplot tell

# Base functions of the sensitivity package
# Gilles Pujol, 2006 - 2008
# Modified by Bertrand Iooss.
# Modified by Frank Weber (2016): support model functions
# returning a matrix or a 3-dimensional array.

ask <- function(x, ...)
  UseMethod("ask")

tell <- function(x, y = NULL, ...)
  UseMethod("tell")

extract <- function(x, ...) # fct added in 2020 for sobolshap_knn()
  UseMethod("extract")

response <- function(x, loop = FALSE, other_types_allowed = FALSE, ...) {
  id <- deparse(substitute(x))
  
  if (class(x$model) == "function") {
    if (loop) {
      n <- nrow(x$X)
      y <- sapply(1:n, function(i){
        x$model(x$X[i,], ...)
      }, simplify = "array")
      if(is.matrix(y)){
        if(typeof(y) == "list"){
          stop("The model function returns a list when applied to a row of x$X")
        } else{
          # This means the model function returned vectors, so y is a 
          # (m times n)-matrix (with unknown m). For better consistency with the 
          # default case (that y is a vector of length n), y is transposed to a 
          # (n times m)-matrix.
          y <- t(y)
        }
      } else if(is.array(y) && length(dim(y)) == 3){
        # This means the model function returned matrices of the same sizes.
        # Let m be the number of rows and z be the number of columns.
        # For better consistency with the other cases (y is a vector or a 
        # matrix), y is transformed to an array of dimensions c(n, m, z).
        
        # Change the order of the dimensions:
        y <- aperm(y, perm = c(3, 1, 2))
      } else if(!is.numeric(y)){
        stop("x$model returned an object that can't be handled")
      }
    } else {
      y <- x$model(x$X, ...)
    }
  } else if (TRUE %in% (paste("predict.", class(x$model), sep="") %in% methods(predict))) {
    y <- predict(x$model, x$X, ...)
  } else {
    stop("The model isn't a function or does not have a predict method")
  }
  
  if(other_types_allowed){
#    if (!class(y)[1] %in% c("numeric", "matrix", "array") ||
    if ((!inherits(y, "numeric") && !inherits(y, "matrix") && !inherits(y, "array")) ||
        (is.array(y) && typeof(y) == "list")) {
      y <- as.numeric(y)
      warning("Conversion of the response to numeric")
    } else if(inherits(y, "array") && length(dim(y)) > 3){
      stop("If the model returns an array, it must not have more ",
           "than 3 dimensions")
    }
  } else{
    if(!inherits(y, "numeric")) {
      y <- as.numeric(y)
      warning("Conversion of the response to numeric")
    }
  }
  
  # Assign column names resp. dimnames if not existing:
  if(inherits(y, "matrix") && 
     (is.null(colnames(y)) || any(nchar(colnames(y)) == 0))){
    colnames(y) <- paste0("ycol", 1:ncol(y))
  } else if(inherits(y, "array")){
    if(is.null(dimnames(y))){
      dimnames(y) <- list(NULL, paste0("ycol", 1:dim(y)[2]), 
                          paste0("ydim3_", 1:dim(y)[3]))
    } else{
      if(is.null(dimnames(y)[[2]]) || any(nchar(dimnames(y)[[2]]) == 0)){
        dimnames(y)[[2]] <- paste0("ycol", 1:dim(y)[2])
      }
      if(is.null(dimnames(y)[[3]]) || any(nchar(dimnames(y)[[3]]) == 0)){
        dimnames(y)[[3]] <- paste0("ydim3_", 1:dim(y)[3])
      }
    } 
  }
  
  x$y <- y
  assign(id, x, parent.frame())
}

########################################
# function added in 2014 (for sobolTIIlo() and sobolTIIpf())

plotFG <- function(x) 
  UseMethod("plotFG")

########################################
# function added in 2016 (tests of PoincareConstant())

# *********************************************************************
# Functions for the Truncated Normal Distribution

# density
dnorm.trunc <- function(x, mean = 0, sd = 1, min = -1e6, max = 1e6){
  out = dnorm(x, mean, sd) / (pnorm(max, mean, sd) - pnorm(min, mean, sd))
  out[(x < min) | (x > max)] = 0
  return(out)
}

# distribution function
pnorm.trunc <- function(q, mean = 0, sd = 1, min = -1e6, max = 1e6){
  out = (pnorm(q, mean, sd) - pnorm(min, mean, sd)) /
    (pnorm(max, mean, sd) - pnorm(min, mean, sd))
  out[q < min] = 0
  out[q > max] = 1
  return(out)
}

# quantile function
qnorm.trunc <- function(p, mean = 0, sd = 1, min = -1e6, max = 1e6){
  return(qnorm((1 - p) * pnorm(min, mean, sd) + p * pnorm(max, mean, sd),
               mean, sd))
}

# random generation
rnorm.trunc <- function(n, mean = 0, sd = 1, min = -1e6, max = 1e6){
  return(qnorm.trunc(runif(n), mean, sd, min, max))
}

# ***************************************
# Functions for the Truncated Gumbel Distribution

# density
dgumbel.trunc <- function(x, loc = 0, scale = 1, min = -1e6, max = 1e6){
  out = evd::dgumbel(x, loc, scale) / (evd::pgumbel(max, loc, scale) - evd::pgumbel(min, loc, scale))
  out[(x < min) | (x > max)] = 0
  return(out)
}

# distribution function
pgumbel.trunc <- function(q, loc = 0, scale = 1, min = -1e6, max = 1e6){
  out = (evd::pgumbel(q, loc, scale) - evd::pgumbel(min, loc, scale)) /
    (evd::pgumbel(max, loc, scale) - evd::pgumbel(min, loc, scale))
  out[q < min] = 0
  out[q > max] = 1
  return(out)
}

# quantile function
qgumbel.trunc <- function(p, loc = 0, scale = 1, min = -1e6, max = 1e6){
  return(evd::qgumbel((1 - p) * evd::pgumbel(min, loc, scale) + p * evd::pgumbel(max, loc, scale),
                 loc, scale))
}

# random generation
rgumbel.trunc <- function(n, loc = 0, scale = 1, min = -1e6, max = 1e6){
  return(qgumbel.trunc(runif(n), loc, scale, min, max))
}

########################################
# function added in 2017 (for support())

scatterplot <- function(x, i, xprob = FALSE, p = NULL, p.arg = NULL, cex = 1, cex.lab = 1, ...)
  UseMethod("scatterplot")

########################################
# function added in 2020 (for sobolMultOut())

plotMultOut <- function(x, ylim = c(0, 1), ...) 
  UseMethod("plotMultOut")

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sensitivity documentation built on Sept. 5, 2021, 5:13 p.m.