JASP-LearnStats: Distributions (Beta) Module for JASP

#
# Copyright (C) 2019 University of Amsterdam
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#

LDgaussianunivariate <- function(jaspResults, dataset, options, state=NULL){
  options <- .recodeOptionsLDGaussianUnivariate(options)
  
  #### Show distribution section ----
  .ldIntroText(jaspResults, options, "Normal distribution")
  .ldGaussianParsSupportMoments(jaspResults, options)
   
  
  pdfContainer <- .ldGetPlotContainer(jaspResults, options, "plotPDF", "Probability Density Function", 3)
  .ldFillPDFContainer(pdfContainer, options, .ldFormulaGaussianPDF)
  
  cdfContainer <- .ldGetPlotContainer(jaspResults, options, "plotCDF", "Cumulative Distribution Function", 4)
  .ldFillCDFContainer(cdfContainer, options, .ldFormulaGaussianCDF)
  
  qfContainer  <- .ldGetPlotContainer(jaspResults, options, "plotQF", "Quantile Function", 5)
  .ldFillQFContainer(qfContainer,   options, .ldFormulaGaussianQF)
  
  #### Generate and Display data section ----
  # simulate and read data
  .simulateData(jaspResults, options)
  
  ready <- options[['variable']] != ""
  errors <- FALSE
  if(ready && is.null(dataset)){
    dataset <- .readDataSetToEnd(columns.as.numeric = options[['variable']])
    
    variable <- dataset[[.v(options[['variable']])]]
    variable <- variable[!is.na(variable)]
    errors <- .hasErrors(dataset, type = c("observations", "variance", "infinity", "limits"),
                         observations.amount = "<2",
                         limits.min = options$support$min, limits.max = options$support$max, 
                         exitAnalysisIfErrors = FALSE)
  }
  
  # overview of the data
  dataContainer <- .ldGetDataContainer(jaspResults, options, errors)

  readyDesc <- ready && (isFALSE(errors) || (is.null(errors$infinity) && is.null(errors$observations)))
  .ldSummaryContinuousTableMain(dataContainer, variable, options, readyDesc)
  .ldObservedMomentsTableMain  (dataContainer, variable, options, readyDesc)
  .ldPlotHistogram             (dataContainer, variable, options, readyDesc)
  .ldPlotECDF                  (dataContainer, variable, options, readyDesc)
  
  
  #### Fit data and assess fit ----
  
  readyFit <- ready && isFALSE(errors)
  #### Maximum Likelihood ----
  if(options$methodMLE){
    mleContainer <- .ldGetFitContainer(jaspResults, options, "mleContainer", "Maximum likelihood", 7, errors)
    
    # parameter estimates
    mleEstimatesTable  <- .ldEstimatesTable(mleContainer, options, TRUE, TRUE, "methodMLE")
    mleResults   <- .ldMLEResults(mleContainer, variable, options, readyFit, options$distNameInR,
                                  .ldGaussianMethodMLEStructureResults)
    .ldFillGaussianEstimatesTable(mleEstimatesTable, mleResults, options, readyFit)
    
    
    # fit assessment
    mleFitContainer    <- .ldGetFitContainer(mleContainer, options, "mleFitAssessment", "Fit Assessment", 8)
    
    # fit statistics
    mleFitStatistics   <- .ldFitStatisticsTable(mleFitContainer, options, "methodMLE")
    mleFitStatisticsResults <- .ldFitStatisticsResults(mleContainer, mleResults$fitdist, variable, options, readyFit)
    .ldFillFitStatisticsTable(mleFitStatistics, mleFitStatisticsResults, options, readyFit)
    # fit plots
    .ldFitPlots(mleFitContainer, mleResults$fitdist$estimate, options, variable, readyFit)
    
  }
  
  #### Method of moments ----
  
  #### Unbiased estimate ----
  
  return()
}

### options ----
.recodeOptionsLDGaussianUnivariate <- function(options){
  if(options$parametrization == "sigma2"){
    options$sd <- sqrt(options$varValue)
  } else if(options$parametrization == "sigma"){
    options$sd <- options$varValue
  } else if(options$parametrization == "tau2"){
    options$sd <- sqrt(1/options$varValue)
  } else if(options$parametrization == "tau"){
    options$sd <- 1/options$varValue
  }
  
  options[['parValNames']] <- c("mu", "varValue")
  
  options[['pars']]   <- list(mean = options[['mu']], sd = options[['sd']])
  options[['pdfFun']] <- dnorm
  options[['cdfFun']] <- pnorm
  options[['qFun']]   <- qnorm
  options[['rFun']]   <- rnorm
  options[['distNameInR']] <- "norm"
  
  options[['range_x']] <- c(options[['min_x']], options[['max_x']])
  
  if(options[['highlightType']] == "minmax"){
    options[['highlightmin']] <- options[['min']]
    options[['highlightmax']] <- options[['max']]
  } else if(options[['highlightType']] == "lower"){
    options[['highlightmin']] <- options[['range_x']][1]
    options[['highlightmax']] <- options[['lower_max']]
  } else if(options[['highlightType']] == "upper"){
    options[['highlightmin']] <- options[['upper_min']]
    options[['highlightmax']] <- options[['range_x']][2]
  } else{
    options[['highlightmin']] <- options[['highlightmax']] <- NULL
  }
  
  options$support <- list(min = -Inf, max = Inf)
  options$lowerBound <- c(-Inf, 0)
  options$upperBound <- c(Inf, Inf)
  
  options
}

### text fill functions -----
.ldGaussianParsSupportMoments <- function(jaspResults, options){
  if(options$parsSupportMoments && is.null(jaspResults[['parsSupportMoments']])){
    pars <- list()
    pars[[1]] <- gettext("mean: &mu; \u2208 \u211D")
    pars[[2]] <- switch(options[['parametrization']],
                        sigma2 = gettext("variance: &sigma;<sup>2</sup> \u2208 \u211D<sup>+</sup>"),
                        sigma  = gettext("standard deviation: &sigma; \u2208 \u211D<sup>+</sup>"),
                        tau2   = gettext("precision: &tau;<sup>2</sup> \u2208 \u211D<sup>+</sup>"),
                        tau    = gettext("square root of precision: &tau; \u2208 \u211D<sup>+</sup>"))
    
    support <- gettext("x \u2208 \u211D")
    
    moments <- list()
    moments$expectation <- gettext("&mu;")
    moments$variance <- switch(options[['parametrization']],
                               sigma2 = gettext("&sigma;<sup>2</sup>"),
                               sigma  = gettext("&sigma;<sup>2</sup>"),
                               gettext("1/&tau;<sup>2</sup>"))

    jaspResults[['parsSupportMoments']] <- .ldParsSupportMoments(pars, support, moments)
  }
}

.ldFormulaGaussianPDF <- function(options){
  if(options[['parametrization']] == "sigma2"){
    text <- "<MATH>
    f(x; <span style='color:red'>&mu;</span>, <span style='color:blue'>&sigma;&sup2;</span>) = 
(2&pi;<span style='color:blue'>&sigma;&sup2;</span>)<sup>-&frac12;</sup> 
exp[-(x-<span style='color:red'>&mu;</span>)&sup2; &frasl; 2<span style='color:blue'>&sigma;&sup2;</span>]
    </MATH>"
  } else if(options[['parametrization']] == "sigma"){
    text <- "<MATH>
    f(x; <span style='color:red'>&mu;</span>, <span style='color:blue'>&sigma;</span>) = 
    (2&pi;<span style='color:blue'>&sigma;</span>&sup2;)<sup>-&frac12;</sup> 
    exp[-(x-<span style='color:red'>&mu;</span>)&sup2; &frasl; 2<span style='color:blue'>&sigma;</span>&sup2;]
    </MATH>"
  } else if(options[['parametrization']] == "tau2"){
    text <- "<MATH>
    f(x; <span style='color:red'>&mu;</span>, <span style='color:blue'>&tau;&sup2;</span>) = 
    (<span style='color:blue'>&tau;&sup2;</span> &frasl; 2&pi;)<sup>&frac12;</sup> 
    exp[-(x-<span style='color:red'>&mu;</span>)&sup2; <span style='color:blue'>&tau;&sup2;</span> &frasl; 2]
    </MATH>"
  } else if(options[['parametrization']] == "tau"){
    text <- "<MATH>
    f(x; <span style='color:red'>&mu;</span>, <span style='color:blue'>&tau;</span>) = 
    <span style='color:blue'>&tau;</span> &frasl; (2&pi;)<sup>&frac12;</sup> 
    exp[-(x-<span style='color:red'>&mu;</span>)&sup2; <span style='color:blue'>&tau;</span>&sup2; &frasl; 2]
    </MATH>"
  }

  return(gsub(pattern = "\n", replacement = " ", x = text))
}

.ldFormulaGaussianCDF <- function(options){
  if(options$parametrization == "sigma2"){
    text <- "<MATH>
    F(x; <span style='color:red'>&mu;</span>, <span style='color:blue'>&sigma;&sup2;</span>)
    </MATH>"
  } else if(options$parametrization == "sigma"){
    text <- "<MATH>
    F(x; <span style='color:red'>&mu;</span>, <span style='color:blue'>&sigma;</span>)
    </MATH>"
  } else if(options$parametrization == "tau2"){
    text <- "<MATH>
    F(x; <span style='color:red'>&mu;</span>, <span style='color:blue'>&tau;&sup2;</span>)
    </MATH>"
  } else {
    text <- "<MATH>
    F(x; <span style='color:red'>&mu;</span>, <span style='color:blue'>&tau;</span>)
    </MATH>"
  }
  
  return(gsub(pattern = "\n", replacement = " ", x = text))
}

.ldFormulaGaussianQF <- function(options){
  if(options$parametrization == "sigma2"){
    text <- "<MATH>
    Q(p; <span style='color:red'>&mu;</span>, <span style='color:blue'>&sigma;&sup2;</span>)
    </MATH>"
  } else if(options$parametrization == "sigma"){
    text <- "<MATH>
    Q(p; <span style='color:red'>&mu;</span>, <span style='color:blue'>&sigma;</span>)
    </MATH>"
  } else if(options$parametrization == "tau2"){
    text <- "<MATH>
    Q(p; <span style='color:red'>&mu;</span>, <span style='color:blue'>&tau;&sup2;</span>)
    </MATH>"
  } else {
    text <- "<MATH>
    Q(p; <span style='color:red'>&mu;</span>, <span style='color:blue'>&tau;</span>)
    </MATH>"
  }
  
  return(gsub(pattern = "\n", replacement = " ", x = text))
}

#### Table functions ----

.ldFillGaussianEstimatesTable <- function(table, results, options, ready){
  if(!ready) return()
  if(is.null(results)) return()
  if(is.null(table)) return()

  par1 <- c(mu = "\u03BC")
  par2 <- c(sigma2 = "\u03C3\u00B2", sigma = "\u03C3", 
            tau2   = "\u03C4\u00B2", tau   = "\u03C4")[options$parametrization]
  res <- results$structured
  res <- res[res$par %in% names(c(par1, par2)),]
  res$parName <- c(par1, par2)
  
  if(results$fitdist$convergence != 0){
    table$addFootnote(gettext("The optimization did not converge, try adjusting the parameter values."), symbol = gettext("<i>Warning.</i>"))
  }
  if(!is.null(results$fitdist$optim.message)){
    table$addFootnote(results$fitdist$message, symbol = gettext("<i>Warning.</i>"))
  }
  
  table$setData(res)
  
  return()
}

.ldGaussianMethodMLEStructureResults <- function(fit, options){
  if(is.null(fit)) return()
  
  transformations <- c(mu = "mean", sigma2 = "sd^2", sigma = "sd", tau2 = "1/sd^2", tau = "1/sd")
  
  res <- sapply(transformations, function(tr) car::deltaMethod(fit$estimate, tr, fit$vcov, level = options$ciIntervalInterval))
  rownames(res) <- c("estimate", "se", "lower", "upper")
  res <- t(res)
  res <- cbind(par = rownames(res), res)
  res <- as.data.frame(res)
  
  return(res)
}

# old ----
# .ldGaussianMethodMomentsResults <- function(jaspResults, options, variable, ready){
#   if(!ready || !options[['methodMoments']])
#     return()
#   
#   
#   if(is.null(jaspResults[['methodMoments']][['results']]$object)){
#     jaspResults[['methodMoments']][['results']] <- createJaspState()
#     jaspResults[['methodMoments']][['results']]$dependOn(c("variable", "simulateNow"))
#     
#     results <- list()
#     results$par <- .computeObservedMoments(x = variable, max.moment = 2, about.mean = TRUE)
#     results$par[2] <- sqrt(results$par[2])
#     names(results$par) <- c("mean", "sd")
#     
#     results$table <- c(mu = results$par[[1]],
#                        sigma = results$par[[2]],
#                        sigma2 = results$par[[2]]^2,
#                        tau = results$par[[2]],
#                        tau2 = 1/results$par[[2]]^2)
#     jaspResults[['methodMoments']][['results']]$object <- results
#   }
#   
#   return()
# }
# 
# .ldGaussianMethodUnbiasedResults <- function(jaspResults, options, variable, ready){
#   if(!ready || !options[['methodUnbiased']])
#     return()
#   
#   
#   
#   if(is.null(jaspResults[['methodUnbiased']][['results']]$object)){
#     jaspResults[['methodUnbiased']][['results']] <- createJaspState()
#     jaspResults[['methodUnbiased']][['results']]$dependOn(c("variable", "simulateNow", "ciInterval"))
#     
#     results <- list()
#     results$par <- c(mean = mean(variable), sd = .sdGaussianUnbiased(variable))
#     names(results$par) <- c("mean", "sd")
#     
#     results$table <- c(mu = results$par[[1]],
#                        sigma = results$par[[2]],
#                        sigma2 = var(variable),
#                        tau = 1/results$par[[2]],
#                        tau2 = 1/var(variable))
#     
#     if(options[['ciInterval']]){
#       res <- t.test(variable, conf.level = options[['ciIntervalInterval']])
#       resvar <- ci.GaussianVar(variable, conf.level = options[['ciIntervalInterval']])
#       ressd  <- ci.GaussianSD (variable, conf.level = options[['ciIntervalInterval']])
#       
#       results$table <- c(results$table, mu.lower = res[['conf.int']][[1]], mu.upper = res[['conf.int']][[2]],
#                          sigma2.lower = resvar[1], sigma2.upper = resvar[2],
#                          sigma.lower  = ressd[1], sigma.upper = ressd[2],
#                          tau2.lower = 1/resvar[1], tau2.upper = 1/resvar[2],
#                          tau.lower = 1/ressd[1], tau.upper = 1/ressd[2])
#       
#     }
#     jaspResults[['methodUnbiased']][['results']]$object <- results
#   }
#   
#   return()
# }
# 
# .ldFitAssessment <- function(methodContainer, options, variable, ready){
#   if(is.null(methodContainer[['fitAssessment']])){
#     methodContainer[['fitAssessment']] <- createJaspContainer(title = "Fit Assessment")
#     methodContainer[['fitAssessment']]$dependOn(c("variable", "simulateNow"))
#   }
#   
#   
#   estParameters <- methodContainer[['results']]$object[['par']]
#   
#   .ldFillAssessmentTable(methodContainer, estParameters, options, variable, ready)
#   
#   
#   if(is.null(methodContainer[['fitAssessment']][['estPDF']]) && options$estPDF){
#     pdfplot <- createJaspPlot(title = "Histogram vs. Theoretical PDF")
#     pdfplot$dependOn(c("estPDF"))
#     pdfplot$position <- 2
#     methodContainer[['fitAssessment']][['estPDF']] <- pdfplot
#     
#     if(ready)
#       .ldFillEstPDFPlot(pdfplot, estParameters, options, variable)
#   }
#   
#   if(is.null(methodContainer[['fitAssessment']][['qqplot']]) && options$qqplot){
#     qqplot <- createJaspPlot(title = "Q-Q plot")
#     qqplot$dependOn(c("qqplot"))
#     qqplot$position <- 3
#     methodContainer[['fitAssessment']][['qqplot']] <- qqplot
#     
#     if(ready)
#       .ldFillQQPlot(qqplot, estParameters, options, variable)
#   }
#   
#   if(is.null(methodContainer[['fitAssessment']][['estCDF']]) && options$estCDF){
#     cdfplot <- createJaspPlot(title = "Empirical vs. Theoretical CDF")
#     cdfplot$dependOn(c("estCDF"))
#     cdfplot$position <- 4
#     methodContainer[['fitAssessment']][['estCDF']] <- cdfplot
#     
#     if(ready)
#       .ldFillEstCDFPlot(cdfplot, estParameters, options, variable)
#   }
#   
#   if(is.null(methodContainer[['fitAssessment']][['ppplot']]) && options$ppplot){
#     ppplot <- createJaspPlot(title = "P-P plot")
#     ppplot$dependOn(c("ppplot"))
#     ppplot$position <-5
#     methodContainer[['fitAssessment']][['ppplot']] <- ppplot
#     
#     if(ready)
#       .ldFillPPPlot(ppplot, estParameters, options, variable)
#   }
#   
#   return()
# }
# 
#### Helper functions ----
# .sdGaussianUnbiased <- function(x){
#   # https://en.wikipedia.org/wiki/Unbiased_estimation_of_standard_deviation
#   x <- na.omit(x)
#   n <- length(x)
#   logSDBiased <- log(sd(x))
#   
#   logCorrectionFactor <- 0.5*log(2) - 0.5*log(n-1) + lgamma(n/2) - lgamma((n-1)/2)
# 
#   logSDUnbiased <- logSDBiased - logCorrectionFactor
#   
#   return(exp(logSDUnbiased))
# }
# 
# 
# ci.GaussianVar <- function(x, conf.level = options[['ciIntervalInterval']]){
#   x <- na.omit(x)
#   df <- length(x) - 1
#   v <- var(x)
#   
#   alpha <- 1-conf.level
#   perc <- c(1-alpha/2, alpha/2)
#   res <- v * df / qchisq(p = perc, df = df)
#   
#   return(res)
# }
# 
# ci.GaussianSD <- function(variable, conf.level = options[['ciIntervalInterval']]){
#   sqrt(ci.GaussianVar(variable, conf.level))
# }
Kucharssim/JASP-LearnStats documentation built on Jan. 23, 2020, 3:47 a.m.
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Kucharssim/JASP-LearnStats
Distributions (Beta) Module for JASP

R/LDgaussianunivariate.R
In Kucharssim/JASP-LearnStats: Distributions (Beta) Module for JASP

Defines functions LDgaussianunivariate .recodeOptionsLDGaussianUnivariate .ldGaussianParsSupportMoments .ldFormulaGaussianPDF .ldFormulaGaussianCDF .ldFormulaGaussianQF .ldFillGaussianEstimatesTable .ldGaussianMethodMLEStructureResults

R Package Documentation

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Kucharssim/JASP-LearnStats Distributions (Beta) Module for JASP

R/LDgaussianunivariate.R In Kucharssim/JASP-LearnStats: Distributions (Beta) Module for JASP

Defines functions LDgaussianunivariate .recodeOptionsLDGaussianUnivariate .ldGaussianParsSupportMoments .ldFormulaGaussianPDF .ldFormulaGaussianCDF .ldFormulaGaussianQF .ldFillGaussianEstimatesTable .ldGaussianMethodMLEStructureResults

R Package Documentation

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We want your feedback!

Kucharssim/JASP-LearnStats
Distributions (Beta) Module for JASP

R/LDgaussianunivariate.R
In Kucharssim/JASP-LearnStats: Distributions (Beta) Module for JASP
