detect cores - 1 added to the example, unintended warnings surpressed

Author: aliaksah

R/results.R

@@ -123,7 +123,7 @@ merge_results <- function (results, populations = NULL, complex.measure = NULL,
   # Generate mock data to compare features with
   uk <- 1 
   good.mock <- FALSE
-  while(!good.mock & uk < 10)
+  while(!good.mock & uk < 5)
   {
     uk <- uk + 1
     if (is.null(data)) mock.data <- list(x = matrix(runif((results[[1]]$ncov)^2, -100, 100), ncol = results[[1]]$ncov))
@@ -140,17 +140,8 @@ merge_results <- function (results, populations = NULL, complex.measure = NULL,
       break
     }
   }
-  if(uk == 10)
-    warning(
-      "Constant features detected in merge_results().\n",
-      " - If not already, provide the 'data' argument in the function call.\n",
-      " - If the warning persists, one or more features in your dataset are constant (no variation).\n",
-      "This should not affect results critically, but please:\n",
-      "   * check your input data, or\n",
-      "   * reconsider the chosen nonlinearities/features."
-    )
-  # Calculate the correlation to find equivalent features
-  cors <- cor(mock.data.precalc)
+ 
+  cors <- suppressWarnings(cor(mock.data.precalc))
 
   # A map to link equivalent features together,
   # row 1-3 are the simplest equivalent features based on three different complexity measures

tests_current/JSS_Script.R

@@ -83,7 +83,7 @@ result.P50 <- fbms(data = df.train, method = "gmjmcmc", transforms = transforms,
 set.seed(123)
 
 result_parallel <- fbms(data = df.train, method = "gmjmcmc.parallel", transforms = transforms,
-                          runs = 40, cores = 8, P = 25)
+                          runs = 40, cores = parallel::detectCores()-1, P = 25)
 
 
 ####################################################
@@ -455,7 +455,7 @@ c(time.lme4$callback_msg, time.inla$callback_msg, time.rtmb$callback_msg)
 
 
 result2a <- fbms(formula = z ~ 1+., data = df, N = 5000,
-                 method = "mjmcmc.parallel", runs = 40, cores = 8,
+                 method = "mjmcmc.parallel", runs = 40, cores = parallel::detectCores()-1,
                  family = "custom", loglik.pi = mixed.model.loglik.lme4,
                  model_prior = list(r = 1/dim(df)[1]), 
                  extra_params = list(dr = droplevels(Zambia$dr)))
@@ -476,7 +476,7 @@ params$feat$pop.max = 10
 
 result2b <- fbms(formula = z ~ 1+., data = df, transforms = transforms,
                  probs = probs, params = params, P=25, N = 100,
-                 method = "gmjmcmc.parallel", runs = 40, cores = 8,
+                 method = "gmjmcmc.parallel", runs = 40, cores = parallel::detectCores()-1,
                  family = "custom", loglik.pi = mixed.model.loglik.lme4,
                  model_prior = list(r = 1/dim(df)[1]), 
                  extra_params = list(dr = droplevels(Zambia$dr)))
@@ -496,7 +496,7 @@ params$feat$pop.max = 10
 
 result2c <- fbms(formula = z ~ 1+., data = df, transforms = transforms,
                  probs = probs, params = params, P=25, N = 100,
-                 method = "gmjmcmc.parallel", runs = 40, cores = 8,
+                 method = "gmjmcmc.parallel", runs = 40, cores = parallel::detectCores()-1,
                  family = "custom", loglik.pi = mixed.model.loglik.lme4,
                  model_prior = list(r = 1/dim(df)[1]), 
                  extra_params = list(dr = droplevels(Zambia$dr)))