tests/testthat/testdfstructure.R

In Zadchow/concurve: Computes & Plots Compatibility (Confidence), Surprisal, & Likelihood Distributions

context("Dataframe Structure")
test_that("curve_mean", {

  # Produce random sample data
  GroupA <- runif(100, min = 0, max = 100)
  GroupB <- runif(100, min = 0, max = 100)

  RandomData <- data.frame(GroupA, GroupB)

  bob <- curve_mean(GroupA, GroupB, RandomData, method = "default")

  # Set sample dataframe.
  variable1 <- rnorm(100)
  variable2 <- rnorm(100)
  variable3 <- rnorm(100)
  variable4 <- rnorm(100)
  variable5 <- rnorm(100)
  variable6 <- rnorm(100)
  variable7 <- rnorm(100)

  sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5, variable6, variable7)

  columnnames <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")

  colnames(sampledf) <- columnnames

  expect_equivalent(str(bob[[1]]), str(sampledf))
})

test_that("curve_gen", {

  # Produce random sample data
  GroupA <- rnorm(50)
  GroupB <- rnorm(50)

  RandomData <- data.frame(GroupA, GroupB)

  rob <- glm(GroupA ~ GroupB, data = RandomData)
  bob <- curve_gen(rob, "GroupB", method = "glm")

  # Set sample dataframe.
  variable1 <- rnorm(100)
  variable2 <- rnorm(100)
  variable3 <- rnorm(100)
  variable4 <- rnorm(100)
  variable5 <- rnorm(100)
  variable6 <- rnorm(100)
  variable7 <- rnorm(100)

  sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5, variable6, variable7)

  columnnames <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")

  colnames(sampledf) <- columnnames

  expect_equivalent(str(bob[[1]]), str(sampledf))
})

test_that("curve_meta", {
  library(metafor)

  # Produce random sample data
  GroupAData <- runif(20, min = 0, max = 100)
  GroupAMean <- round(mean(GroupAData), digits = 2)
  GroupASD <- round(sd(GroupAData), digits = 2)

  GroupBData <- runif(20, min = 0, max = 100)
  GroupBMean <- round(mean(GroupBData), digits = 2)
  GroupBSD <- round(sd(GroupBData), digits = 2)

  GroupCData <- runif(20, min = 0, max = 100)
  GroupCMean <- round(mean(GroupCData), digits = 2)
  GroupCSD <- round(sd(GroupCData), digits = 2)

  GroupDData <- runif(20, min = 0, max = 100)
  GroupDMean <- round(mean(GroupDData), digits = 2)
  GroupDSD <- round(sd(GroupDData), digits = 2)

  # Combine the data

  StudyName <- c("Study1", "Study2")
  MeanTreatment <- c(GroupAMean, GroupCMean)
  MeanControl <- c(GroupBMean, GroupDMean)
  SDTreatment <- c(GroupASD, GroupCSD)
  SDControl <- c(GroupBSD, GroupDSD)
  NTreatment <- c(20, 20)
  NControl <- c(20, 20)

  metadf <- data.frame(StudyName, MeanTreatment, MeanControl, SDTreatment, SDControl, NTreatment, NControl)

  # Use metafor to calculate the standardized mean difference

  library(metafor)

  dat <- escalc(
    measure = "SMD", m1i = MeanTreatment, sd1i = SDTreatment, n1i = NTreatment,
    m2i = MeanControl, sd2i = SDControl, n2i = NControl, data = metadf
  )

  # Pool the data using a particular method. Here "FE" is the fixed-effects model

  res <- rma(yi, vi, data = dat, slab = paste(StudyName, sep = ", "), method = "FE", digits = 2)

  # Calculate the intervals using the metainterval function

  metaf <- curve_meta(res)

  # Set sample dataframe.
  variable1 <- rnorm(100)
  variable2 <- rnorm(100)
  variable3 <- rnorm(100)
  variable4 <- rnorm(100)
  variable5 <- rnorm(100)
  variable6 <- rnorm(100)
  variable7 <- rnorm(100)

  sampledf <- data.frame(variable1, variable2, variable3, variable4, variable5, variable6, variable7)

  columnnames <- c("lower.limit", "upper.limit", "intrvl.width", "intrvl.level", "cdf", "pvalue", "svalue")

  colnames(sampledf) <- columnnames

  expect_equivalent(str(metaf[[1]]), str(sampledf))
})