Trying to fix mixsqp convergence issues for some ashr tests run on wi…

…nbuilder.

DESCRIPTION

@@ -13,8 +13,8 @@ Authors@R: c(person("Matthew","Stephens",role="aut",
              person("Jason","Willwerscheid",role="aut"),
              person("Nan","Xiao",role="aut"),
              person("Mazon","Zeng",role="ctb"))
-Version: 2.2-31
-Date: 2019-02-07
+Version: 2.2-32
+Date: 2019-02-18
 Title: Methods for Adaptive Shrinkage, using Empirical Bayes
 Description: The R package 'ashr' implements an Empirical Bayes
     approach for large-scale hypothesis testing and false discovery

tests/testthat/test_binom.R

@@ -1,14 +1,16 @@
 context("ashr with Binomial likelihoods")
 
-test_that("lik_binom (identity link) fitted g is close to true g",{
+test_that("lik_binom (identity link) fittced g is close to true g",{
 
   # Simulate a Binomial dataset
   set.seed(1)
   trueg = unimix(c(0.5,0.5),c(0.5,0.1),c(0.5,0.9)) # true prior g: 0.5*U(0.1,0.9)+0.5*delta(0.5)
   p = c(rep(0.5,500), runif(500,0.1,0.9)) # generate p from g
   n = rep(100,1000)
   x = rbinom(1000,n,p) # Binomial observations
-  ash.binom.out = ash(rep(0,length(x)),1,lik=lik_binom(x,n),g=trueg)
+  ash.binom.out = ash(rep(0,length(x)),1,lik = lik_binom(x,n),g = trueg,
+                      control = list(delta = 0,maxiter.sqp = 40,
+                                     verbose = TRUE))
 
   # Check if the estimated mixture proportion for components delta(0.5) and U(0.1,0.9)
   # is close to the true mixture proportion (0.5,0.5)
@@ -52,18 +54,21 @@ test_that("lik_binom (identity link) with big n gives similar answers to normal
 })
 
 test_that("lik_binom (logit link) fitted g is close to true g",{
+
   # Simulate a Binomial dataset
   set.seed(1)
   trueg = unimix(c(0.5,0.5),c(0,-3),c(0,3)) 
   logitp = c(rep(0,500), runif(500,-3,3))
   p = 1/(1+exp(-logitp))
   n = rep(1000,1000)
   x = rbinom(1000,n,p) # Binomial observations
-  ash.binom.out = ash(rep(0,length(x)),1,lik=lik_binom(x,n,link="logit"),
-                      g=trueg,prior="uniform")
+  ash.binom.out = ash(rep(0,length(x)),1,lik = lik_binom(x,n,link = "logit"),
+                      g = trueg,prior = "uniform",
+                      control = list(verbose = TRUE))
 
-  # Check if the estimated mixture proportion for components delta(0.5) and U(-3,3)
-  # is close to the true mixture proportion (0.5,0.5)
+  # Check if the estimated mixture proportion for components
+  # delta(0.5) and U(-3,3) is close to the true mixture proportion
+  # (0.5,0.5).
   expect_equal(ash.binom.out$fitted_g$pi, c(0.5,0.5), tolerance = 0.05)
 })
 

tests/testthat/test_pois.R

@@ -6,7 +6,8 @@ test_that("lik_pois (identity link) fitted g is close to true g",{
   trueg = unimix(c(0.5,0.5),c(1,1),c(1,5)) # true prior g: 0.5*U(1,5)+0.5*delta(1)
   lambda = c(rep(1,500), runif(500,1,5)) # generate lambda from g
   x = rpois(1000,lambda) # Poisson observations
-  ash.pois.out = ash(rep(0,length(x)),1,lik=lik_pois(x),g=trueg)
+  ash.pois.out = ash(rep(0,length(x)),1,lik=lik_pois(x),g=trueg,
+                     control = list(verbose = TRUE))
 
   # Check if the estimated mixture proportion for components delta(0.5) and U(0.1,0.9)
   # is close to the true mixture proportion (0.5,0.5)
@@ -53,7 +54,8 @@ test_that("lik_pois (log link) fitted g is close to true g",{
   loglambda = c(rep(0,800), runif(200,-3,3)) 
   lambda = exp(loglambda)
   x = rpois(1000,lambda) # Poisson observations
-  ash.pois.out = ash(rep(0,length(x)),1,lik=lik_pois(x,link="log"),g=trueg)
+  ash.pois.out = ash(rep(0,length(x)),1,lik = lik_pois(x,link="log"),
+                     g = trueg,control = list(verbose = TRUE))
 
   # Check if the estimated mixture proportion for components delta(0) and U(-3,3)
   # is close to the true mixture proportion (0.8,0.2)