lintr

tests/testthat/test-GLMMadaptive.R

@@ -387,29 +387,29 @@ test_that("detect custom families", {
   hurdle.lognormal <- function() {
     stats <- make.link("identity")
     log_dens <- function(y, eta, mu_fun, phis, eta_zi) {
-      sigma <- exp(phis)
+      scaleParameter <- exp(phis)
       # binary indicator for y > 0
       ind <- y > 0
       # non-zero part
       eta <- as.matrix(eta)
       eta_zi <- as.matrix(eta_zi)
       out <- eta
       out[ind, ] <- plogis(eta_zi[ind, ], lower.tail = FALSE, log.p = TRUE) +
-        dnorm(x = log(y[ind]), mean = eta[ind, ], sd = sigma, log = TRUE)
+        dnorm(x = log(y[ind]), mean = eta[ind, ], sd = scaleParameter, log = TRUE)
       # zero part
       out[!ind, ] <- plogis(eta_zi[!ind, ], log.p = TRUE)
       attr(out, "mu_y") <- eta
       out
     }
     score_eta_fun <- function(y, mu, phis, eta_zi) {
-      sigma <- exp(phis)
+      scaleParameter <- exp(phis)
       # binary indicator for y > 0
       ind <- y > 0
       # non-zero part
       eta <- as.matrix(mu)
       out <- eta
       out[!ind, ] <- 0
-      out[ind, ] <- (log(y[ind]) - eta[ind, ]) / sigma^2
+      out[ind, ] <- (log(y[ind]) - eta[ind, ]) / scaleParameter^2
       out
     }
     score_eta_zi_fun <- function(y, mu, phis, eta_zi) {
@@ -420,17 +420,17 @@ test_that("detect custom families", {
       out
     }
     score_phis_fun <- function(y, mu, phis, eta_zi) {
-      sigma <- exp(phis)
+      scaleParameter <- exp(phis)
       # binary indicator for y > 0
       ind <- y > 0
       # non-zero part
       eta <- as.matrix(mu)
       out <- eta
       out[!ind, ] <- 0
-      out[ind, ] <- -1 + (log(y[ind]) - eta[ind, ])^2 / sigma^2
+      out[ind, ] <- -1 + (log(y[ind]) - eta[ind, ])^2 / scaleParameter^2
       out
     }
-    simulate <- function(n, mu, phis, eta_zi) {
+    simulateResponses <- function(n, mu, phis, eta_zi) {
       y <- rlnorm(n = n, meanlog = mu, sdlog = exp(phis))
       y[as.logical(rbinom(n, 1, plogis(eta_zi)))] <- 0
       y
@@ -440,7 +440,7 @@ test_that("detect custom families", {
         family = "two-part log-normal", link = stats$name,
         linkfun = stats$linkfun, linkinv = stats$linkinv, log_dens = log_dens,
         score_eta_fun = score_eta_fun, score_eta_zi_fun = score_eta_zi_fun,
-        score_phis_fun = score_phis_fun, simulate = simulate
+        score_phis_fun = score_phis_fun, simulate = simulateResponses
       ),
       class = "family"
     )