tests/testthat/_snaps/expression-vectorize.md

In poissonconsulting/mcmcderive: Derive MCMC Parameters

simple expr with iteration var replaced and for loop removed

Code
  expression_vectorize(rlang::expr(for (i in 1:nObs) {
    log(eCount[i]) <- b0
  }))
Output
  log(eCount) <- b0

avoiding double braces (#21)

Code
  expression_vectorize(rlang::expr({
    for (i in 1:10) {
      x[i] <- 1
      y[i] <- 2
    }
  }))
Output
  {
      x <- 1
      y <- 2
  }

iteration var replaced with squared term

Code
  expression_vectorize(rlang::expr(for (i in 1:length(LogLength)) {
    eWeightLength[i] <- bWeightLength + bDayte * Dayte[i] + bDayte2 * Dayte[i]^2
  }))
Output
  eWeightLength <- bWeightLength + bDayte * Dayte + bDayte2 * Dayte^2

iteration var replaced with prediction, fit and residual term

Code
  expression_vectorize(rlang::expr(for (i in 1:nObs) {
    log(prediction[i]) <- bWeight + eWeightLength[i] * LogLength[i]
    fit[i] <- log(prediction[i])
    residual[i] <- res_lnorm(Weight[i], fit[i], sWeight)
  }))
Output
  {
      log(prediction) <- bWeight + eWeightLength * LogLength
      fit <- log(prediction)
      residual <- res_lnorm(Weight, fit, sWeight)
  }

iteration var replaced and cbind added to arrays

Code
  expression_vectorize(rlang::expr(for (i in 1:nObs) {
    log(eCount[i]) <- b0 + bYear * Year[i] + bAnnual[Annual[i]] + bSiteAnnual[
      Site[i], Annual[i]]
    fit[i] <- eCount[i]
    residual[i] <- res_gamma_pois(Count[i], fit[i], sSiteAnnualQuadrat)
  }))
Output
  {
      log(eCount) <- b0 + bYear * Year + bAnnual[Annual] + bSiteAnnual[cbind(Site, 
          Annual)]
      fit <- eCount
      residual <- res_gamma_pois(Count, fit, sSiteAnnualQuadrat)
  }

expr with mutli lines

Code
  expression_vectorize(rlang::expr(for (i in 1:nObs) {
    log(eCount[i]) <- b0 + bYear * Year[i] + bKelpLine * KelpLine[i] + bSite[Site[
      i]] + bAnnual[Annual[i]] + bSiteAnnual[Site[i], Annual[i]]
    log(eCountKelpline[i]) <- b0 + bKelpLine + bYear * Year[i] + bAnnual[Annual[i]] +
      bSite[Site[i]] + bSiteAnnual[Site[i], Annual[i]]
    log(eCountBarren[i]) <- b0 + bYear * Year[i] + bAnnual[Annual[i]] + bSite[
      Site[i]] + bSiteAnnual[Site[i], Annual[i]]
  }))
Output
  {
      log(eCount) <- b0 + bYear * Year + bKelpLine * KelpLine + 
          bSite[Site] + bAnnual[Annual] + bSiteAnnual[cbind(Site, 
          Annual)]
      log(eCountKelpline) <- b0 + bKelpLine + bYear * Year + bAnnual[Annual] + 
          bSite[Site] + bSiteAnnual[cbind(Site, Annual)]
      log(eCountBarren) <- b0 + bYear * Year + bAnnual[Annual] + 
          bSite[Site] + bSiteAnnual[cbind(Site, Annual)]
  }

expr with non iteration

Code
  expression_vectorize(rlang::expr({
    max_age <- round(bA_max)
    age <- 1:max_age
    length <- bL_inf * (1 - exp(-bk * (age - ba0)))
    fecundity <- 10^(-5 + 3 * log(fl2tl(length), base = 2))
    survival <- c(1, rep(bS_J, 2), rep(bS_A, max_age - 1))
    survivorship <- cumprod(survival)
    maturity <- age^14 / (bAs^14 + age^14)
    eggs <- survivorship * maturity * fecundity * 0.5
    prediction <- 1 / sum(eggs)
  }))
Output
  {
      max_age <- round(bA_max)
      age <- 1:max_age
      length <- bL_inf * (1 - exp(-bk * (age - ba0)))
      fecundity <- 10^(-5 + 3 * log(fl2tl(length), base = 2))
      survival <- c(1, rep(bS_J, 2), rep(bS_A, max_age - 1))
      survivorship <- cumprod(survival)
      maturity <- age^14/(bAs^14 + age^14)
      eggs <- survivorship * maturity * fecundity * 0.5
      prediction <- 1/sum(eggs)
  }

sum() inside the expression leaves the for loop unchanged

Code
  expression_vectorize(rlang::expr(for (i in 1:length(Year)) {
    eGrowth[i] <- max(0, (bLinf - LengthAtRelease[i]) * (1 - exp(-sum(eK[Year[i]:
      (Year[i] + dYears[i] - 1)]))))
  }))
Output
  for (i in 1:length(Year)) {
      eGrowth[i] <- max(0, (bLinf - LengthAtRelease[i]) * (1 - 
          exp(-sum(eK[Year[i]:(Year[i] + dYears[i] - 1)]))))
  }

cbind with var and constant

Code
  expression_vectorize(rlang::expr(for (i in 1:nObs) {
    eDensity[i] <- bDensity[Island[i], Day[i]]
    ePopn[i] <- bPopn[Island[i], 1]
  }))
Output
  {
      eDensity <- bDensity[cbind(Island, Day)]
      ePopn <- bPopn[cbind(Island, 1)]
  }

code before for loop

Code
  expression_vectorize(rlang::expr({
    b0 <- 2
    for (i in 1:nObs) {
      log(eCount[i]) <- b0
    }
  }))
Output
  {
      b0 <- 2
      log(eCount) <- b0
  }

more than two dimensions

Code
  expression_vectorize(rlang::expr(for (i in 1:nObs) {
    log(eCount[i]) <- b0 + bKelpLine * KelpLine[i] + bYear * Year[i] + bSite[Site[
      i]] + bSiteAnnual[Site[i], Annual[i]] + bAnnual[Annual[i]]
    dpois(eCount[i] * bSiteAnnualQuadrat[Site[i], Annual[i], Quadrat[i]])
  }))
Output
  {
      log(eCount) <- b0 + bKelpLine * KelpLine + bYear * Year + 
          bSite[Site] + bSiteAnnual[cbind(Site, Annual)] + bAnnual[Annual]
      dpois(eCount * bSiteAnnualQuadrat[cbind(Site, Annual, Quadrat)])
  }

poissonconsulting/mcmcderive documentation built on Oct. 13, 2023, 4:24 p.m.