createData issue #436 for temporalAutocorrelation

Code/DHARMaIssues/436.R

@@ -0,0 +1,246 @@
+## Testing the createData with/without temporal autocorrelation
+# Melina Leite
+# 24 sept 2024
+
+
+# While doing unit testing for Mixed Models, I realized that if the model doesn't estiamte the correct variance of the group RE, this extra variability may end in the temporal autocorrelation becuase the way we create the column `time` in the dataset is sequential (which is also the same for creating groups), so the time is clustered in the groups.
+
+set.seed(123)
+testData <- createData(sampleSize = 200,
+                       overdispersion = 0, randomEffectVariance = 0.5,
+                       family = gaussian())
+head(testData)
+
+fittedModel <- lme4::lmer(observedResponse ~ Environment1 + (1|group),
+                          data = testData)
+summary(fittedModel)
+simulationOutput <- simulateResiduals(fittedModel, n=200)
+testTemporalAutocorrelation(simulationOutput = simulationOutput,
+                            time = testData$time)
+
+
+set.seed(123)
+testData <- createData(sampleSize = 200, temporalAutocorrelation = 1,
+                       overdispersion = 0, randomEffectVariance = 0.5,
+                       family = gaussian())
+head(testData)
+
+fittedModel <- lme4::lmer(observedResponse ~ Environment1 + (1|group),
+                          data = testData)
+summary(fittedModel)
+simulationOutput <- simulateResiduals(fittedModel, n=200)
+testTemporalAutocorrelation(simulationOutput = simulationOutput,
+                            time = testData$time)
+
+
+
+createData2 <- function(sampleSize = 100, intercept = 0, fixedEffects = 1,
+                       quadraticFixedEffects = NULL, numGroups = 10,
+                       randomEffectVariance = 1, overdispersion = 0,
+                       family = poisson(), scale = 1, cor = 0,
+                       roundPoissonVariance = NULL,  pZeroInflation = 0,
+                       binomialTrials = 1, temporalAutocorrelation = 0,
+                       spatialAutocorrelation = 0, factorResponse = FALSE,
+                       replicates = 1, hasNA = FALSE){
+
+  nPredictors = length(fixedEffects)
+
+  out = list()
+
+  time = sample(1:sampleSize, sampleSize) # change here
+  x = runif(sampleSize)
+  y = runif(sampleSize)
+
+  for (i in 1:replicates){
+
+    ########################################################################
+    # Create predictors
+
+    predictors = matrix(runif(nPredictors*sampleSize, min = -1), ncol = nPredictors)
+
+    if (cor != 0){
+      predTemp <- runif(sampleSize, min = -1)
+      predictors  = (1-cor) * predictors + cor * matrix(rep(predTemp, nPredictors), ncol = nPredictors)
+    }
+
+    colnames(predictors) = paste("Environment", 1:nPredictors, sep = "")
+
+    ########################################################################
+    # Create random effects
+
+    group = rep(1:numGroups, each = sampleSize/numGroups)
+    groupRandom = rnorm(numGroups, sd = sqrt(randomEffectVariance))
+
+    ########################################################################
+    # Creation of linear prediction
+
+    linearResponse = intercept + predictors %*% fixedEffects + groupRandom[group]
+
+    if(!is.null(quadraticFixedEffects)){
+      linearResponse = linearResponse + predictors^2 %*% quadraticFixedEffects
+    }
+
+    ########################################################################
+    # Overdispersion on linear predictor
+
+
+    if(is.numeric(overdispersion)) linearResponse = linearResponse + rnorm(sampleSize, sd = overdispersion)
+    if(is.function(overdispersion)) linearResponse = linearResponse + overdispersion(linearResponse)
+
+
+    ########################################################################
+    # Autocorrelation
+
+    if(!(temporalAutocorrelation == 0)){
+      t = 1:sampleSize             # INCLUDING CODE HERE
+      distMat <- as.matrix(dist(t))
+
+      invDistMat <- 1/distMat * 5000
+      diag(invDistMat) <- 0
+      invDistMat = sfsmisc::posdefify(invDistMat)
+
+      temporalError <- MASS::mvrnorm(n = 1, mu = rep(0,sampleSize), Sigma = invDistMat)
+
+      linearResponse = linearResponse + temporalAutocorrelation * temporalError[time] #
+    }
+
+
+    if(!(spatialAutocorrelation == 0)) {
+      distMat <- as.matrix(dist(cbind(x, y)))
+
+      invDistMat <- 1/distMat * 5000
+      diag(invDistMat) <- 0
+      invDistMat = sfsmisc::posdefify(invDistMat)
+
+      spatialError <- MASS::mvrnorm(n = 1, mu = rep(0,sampleSize), Sigma = invDistMat)
+
+      linearResponse = linearResponse + spatialAutocorrelation * spatialError
+    }
+
+
+    ########################################################################
+    # Link and distribution
+
+    linkResponse = family$linkinv(linearResponse)
+
+    if (family$family == "gaussian") observedResponse = rnorm(n = sampleSize, mean = linkResponse, sd = scale)
+    # need checking else if (family$family == "gamma") observedResponse = rgamma(n = sampleSize, shape = linkResponse / scale, scale = scale)
+    else if (family$family == "binomial"){
+      observedResponse = rbinom(n = sampleSize, binomialTrials, prob = linkResponse)
+      if (binomialTrials > 1) observedResponse = cbind(observedResponse1 = observedResponse, observedResponse0 = binomialTrials - observedResponse)
+    }
+    else if (family$family == "poisson") {
+      if(is.null(roundPoissonVariance)) observedResponse = rpois(n = sampleSize, lambda = linkResponse)
+      else observedResponse = round(rnorm(n = length(linkResponse), mean = linkResponse, sd = roundPoissonVariance))
+    }
+    else if (grepl("Negative Binomial",family$family)) {
+      theta = as.numeric(gsub("[\\(\\)]", "", regmatches(family$family, gregexpr("\\(.*?\\)", family$family))[[1]]))
+      observedResponse = MASS::rnegbin(linkResponse, theta = theta)
+    }
+    else stop("wrong link argument supplied")
+
+    ########################################################################
+    # Zero-inflation
+
+    if(pZeroInflation != 0){
+      artificialZeros = rbinom(n = length(observedResponse), size = 1, prob = 1-pZeroInflation)
+      observedResponse = observedResponse * artificialZeros
+    }
+
+
+    if(factorResponse) observedResponse = factor(observedResponse)
+
+    # add spatialError?
+
+    out[[i]] <- data.frame(ID = 1:sampleSize, observedResponse, predictors, group = as.factor(group), time, x, y)
+  }
+  if(length(out) == 1) out = out[[1]]
+
+  if(hasNA) out[1,3] = NA
+
+  return(out)
+}
+
+##############
+set.seed(123)
+testData2 <- createData2(sampleSize = 200,
+                       overdispersion = 0, randomEffectVariance = 0.5,
+                       family = gaussian())
+head(testData2)
+
+fittedModel <- lme4::lmer(observedResponse ~ Environment1 + (1|group),
+                          data = testData2)
+summary(fittedModel)
+simulationOutput <- simulateResiduals(fittedModel, n=200)
+testTemporalAutocorrelation(simulationOutput = simulationOutput,
+                            time = testData2$time)
+
+
+set.seed(123)
+testData2 <- createData2(sampleSize = 100, temporalAutocorrelation = 2,
+                       overdispersion = 0, randomEffectVariance = 0.5,
+                       family = gaussian())
+head(testData2)
+
+fittedModel <- lme4::lmer(observedResponse ~ Environment1 + (1|group),
+                          data = testData2)
+summary(fittedModel)
+simulationOutput <- simulateResiduals(fittedModel, n=200)
+testTemporalAutocorrelation(simulationOutput = simulationOutput,
+                            time = testData2$time)
+
+
+
+## copying test function:
+
+testTemporalAutocorrelation2 <- function(simulationOutput, time, alternative = c("two.sided", "greater", "less"), plot = TRUE){
+
+  simulationOutput = ensureDHARMa(simulationOutput, convert = T)
+
+  # actually not sure if this is neccessary for dwtest, but seems better to aggregate
+  if(any(duplicated(time))) stop("testing for temporal autocorrelation requires unique time values - if you have several observations per time value, either use the recalculateResiduals function to aggregate residuals per time step, or extract the residuals from the fitted object, and plot / test each of them independently for temporally repeated subgroups (typical choices would be location / subject etc.). Note that the latter must be done by hand, outside testTemporalAutocorrelation.")
+
+  alternative <- match.arg(alternative)
+
+  if(is.null(time)){
+    time = sample.int(simulationOutput$nObs, simulationOutput$nObs)
+    message("DHARMa::testTemporalAutocorrelation - no time argument provided, using random times for each data point")
+  }
+
+  # To avoid Issue #190
+  if (length(time) != length(residuals(simulationOutput))) stop("Dimensions of time don't match the dimension of the residuals")
+
+  out = lmtest::dwtest(simulationOutput$scaledResiduals ~ 1, order.by = time, alternative = alternative)
+
+  if(plot == T) {
+    oldpar <- par(mfrow = c(1,2))
+    on.exit(par(oldpar))
+
+    plot(simulationOutput$scaledResiduals[order(time)] ~ time[order(time)],
+         type = "l", ylab = "Scaled residuals", xlab = "Time", main = "Residuals vs. time", ylim = c(0,1))
+
+    abline(h=c(0.5))
+    abline(h=c(0,0.25,0.75,1), lty = 2 )
+
+    acf(simulationOutput$scaledResiduals[order(time)], main = "Autocorrelation", ylim = c(-1,1))
+    legend("topright",
+           c(paste(out$method, " p=", round(out$p.value, digits = 5)),
+             paste("Deviation ", ifelse(out$p.value < 0.05, "significant", "n.s."))),
+           text.col = ifelse(out$p.value < 0.05, "red", "black" ), bty="n")
+
+  }
+
+  return(out)
+}
+
+
+
+
+
+
+
+
+
+
+
+

DHARMa/R/createData.R

@@ -20,7 +20,14 @@
 #' @param hasNA should an NA be added to the environmental predictor (for test purposes).
 #' @export
 #' @example /inst/examples/createDataHelp.R
-createData <- function(sampleSize = 100, intercept = 0, fixedEffects = 1, quadraticFixedEffects = NULL, numGroups = 10, randomEffectVariance = 1, overdispersion = 0, family = poisson(), scale = 1, cor = 0, roundPoissonVariance = NULL,  pZeroInflation = 0, binomialTrials = 1, temporalAutocorrelation = 0, spatialAutocorrelation =0, factorResponse = FALSE, replicates=1, hasNA = FALSE){
+createData <- function(sampleSize = 100, intercept = 0, fixedEffects = 1,
+                       quadraticFixedEffects = NULL, numGroups = 10,
+                       randomEffectVariance = 1, overdispersion = 0,
+                       family = poisson(), scale = 1, cor = 0,
+                       roundPoissonVariance = NULL,  pZeroInflation = 0,
+                       binomialTrials = 1, temporalAutocorrelation = 0,
+                       spatialAutocorrelation = 0, factorResponse = FALSE,
+                       replicates = 1, hasNA = FALSE){
 
   nPredictors = length(fixedEffects)
 
@@ -77,7 +84,7 @@ createData <- function(sampleSize = 100, intercept = 0, fixedEffects = 1, quadra
       diag(invDistMat) <- 0
       invDistMat = sfsmisc::posdefify(invDistMat)
 
-      temporalError <- MASS::mvrnorm(n=1, mu=rep(0,sampleSize), Sigma=invDistMat)
+      temporalError <- MASS::mvrnorm(n = 1, mu = rep(0,sampleSize), Sigma = invDistMat)
 
       linearResponse = linearResponse + temporalAutocorrelation * temporalError
     }
@@ -90,7 +97,7 @@ createData <- function(sampleSize = 100, intercept = 0, fixedEffects = 1, quadra
       diag(invDistMat) <- 0
       invDistMat = sfsmisc::posdefify(invDistMat)
 
-      spatialError <- MASS::mvrnorm(n=1, mu=rep(0,sampleSize), Sigma=invDistMat)
+      spatialError <- MASS::mvrnorm(n = 1, mu = rep(0,sampleSize), Sigma = invDistMat)
 
       linearResponse = linearResponse + spatialAutocorrelation * spatialError
     }