pkg update

Author: philchalmers

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: SimDesign
 Title: Structure for Organizing Monte Carlo Simulation Designs
-Version: 2.13.10
+Version: 2.14
 Authors@R: c(person("Phil", "Chalmers", email = "[email protected]", role = c("aut", "cre"),
                     comment = c(ORCID="0000-0001-5332-2810")),
              person("Matthew", "Sigal", role = c("ctb")),

NEWS.md

@@ -4,7 +4,7 @@
 
 - `SimSolve()` function added to perform (stochastic) root-solving to estimate 
   specific criteria from simulation studies. Currently supports uni-root type 
-  problems for continuous or discrete variables via the the probabilistic 
+  problems for continuous or discrete variables via the probabilistic 
   bisection algorithm with bolstering and interpolations (ProBABLI), 
   Brent's method, and the classical bisection approach, the latter two of which
   can be problematic if the number of replications per iteration are too low
@@ -15,19 +15,20 @@
   via generalized linear models
 
 - `runSimulation(..., store_results = TRUE)` is now the default to automatically
-  store the results from `Analyse()` in the returned simulation. If RAM issues
-  are suspected then `save_results = TRUE` is still the recommended approach
+  store the results from `Analyse()` in the returned simulation object. 
+  If RAM issues are suspected then `save_results = TRUE` is still 
+  the recommended approach
 
 - `convertWarnings()` wrapper/post-hoc function added to convert specific 
   warning messages to errors during simulations. Useful when only a subset
   of warnings are known to be problematic, while other warning messages
   (whether known or not) are treated as provisionally innocuous
 
 - `control` gains a `print_RAM` logical argument to suppress printing the RAM
-  when `verbose = TRUE`. Disabling this can marginally improve execution speed
-  as the garbage collector (`gc()`) calls are avoided, which is used to extract
-  the current RAM state. Setting `verbose = FALSE` will automatically 
-  disable the RAM and `gc()` calls and their overhead as well
+  when `verbose = TRUE`. Disabling this can reduce execution time
+  as garbage collector (`gc()`) calls are avoided, which is required extract
+  the current RAM state. Setting `verbose = FALSE` will also automatically 
+  disable the RAM and `gc()` calls and their overhead
 
 - `Attach()` now accepts `matrix` input objects, and gains a `RStudio_flags` 
   argument to generate syntax that suppresses false positives about variables 

R/PBA.R

@@ -290,8 +290,8 @@ PBA <- function(f, interval, ..., p = .6,
 
         if(verbose){
             if(integer)
-                cat(sprintf("\rIter: %i; Median: %i", iter, med))
-            else cat(sprintf("\rIter: %i; Median: %.3f", iter, med))
+                cat(sprintf("\rIter: %i; Median = %i", iter, med))
+            else cat(sprintf("\rIter: %i; Median = %.3f", iter, med))
             cat(sprintf("; E(f(x)) = %.2f", abs(e.froot)))
             if(!is.null(FromSimSolve))
                 cat('; Total.reps =', sum(replications[1L:iter]))

R/SimSolve.R

@@ -233,16 +233,21 @@
 #'     # Must return a single number corresponding to f(x) in the
 #'     # root equation f(x) = b
 #'
-#'     ret <- EDR(results, alpha = condition$sig.level)
+#'     ret <- c(power = EDR(results, alpha = condition$sig.level))
 #'     ret
 #' }
 #'
 #' #~~~~~~~~~~~~~~~~~~~~~~~~
 #' #### Step 3 --- Optimize N over the rows in design
-#' # Initial search between N = [10,500] for each row using the default
-#' # integer solver (integer = TRUE)
 #'
-#' # In this example, b = target power
+#' # (For debugging) may want to see if simulation code works as intended first
+#' # for some given set of inputs
+#' runSimulation(design=createDesign(N=100, d=.8, sig.level=.05),
+#'               replications=10, generate=Generate, analyse=Analyse,
+#'               summarise=Summarise)
+#'
+#' # Initial search between N = [10,500] for each row using the default
+#'    # integer solver (integer = TRUE). In this example, b = target power
 #' solved <- SimSolve(design=Design, b=.8, interval=c(10, 500),
 #'                 generate=Generate, analyse=Analyse,
 #'                 summarise=Summarise)
@@ -259,21 +264,21 @@
 #'
 #' # verify with true power from pwr package
 #' library(pwr)
-#' pwr.t.test(d=.2, power = .8, sig.level = .05)
-#' pwr.t.test(d=.5, power = .8, sig.level = .05)
-#' pwr.t.test(d=.8, power = .8, sig.level = .05)
+#' pwr.t.test(d=.2, power = .8) # sig.level/alpha = .05 by default
+#' pwr.t.test(d=.5, power = .8)
+#' pwr.t.test(d=.8, power = .8)
 #'
 #' # use estimated N results to see how close power was
 #' N <- solved$N
-#' pwr.t.test(d=.2, n=N[1], sig.level = .05)
-#' pwr.t.test(d=.5, n=N[2], sig.level = .05)
-#' pwr.t.test(d=.8, n=N[3], sig.level = .05)
+#' pwr.t.test(d=.2, n=N[1])
+#' pwr.t.test(d=.5, n=N[2])
+#' pwr.t.test(d=.8, n=N[3])
 #'
 #' # with rounding
 #' N <- ceiling(solved$N)
-#' pwr.t.test(d=.2, n=N[1], sig.level = .05)
-#' pwr.t.test(d=.5, n=N[2], sig.level = .05)
-#' pwr.t.test(d=.8, n=N[3], sig.level = .05)
+#' pwr.t.test(d=.2, n=N[1])
+#' pwr.t.test(d=.5, n=N[2])
+#' pwr.t.test(d=.8, n=N[3])
 #'
 #' # failing analytic formula, confirm results with more precise
 #' #  simulation via runSimulation()
@@ -301,9 +306,9 @@
 #'
 #' # use estimated N results to see how close power was
 #' N <- solved_2min$N
-#' pwr.t.test(d=.2, n=N[1], sig.level = .05)
-#' pwr.t.test(d=.5, n=N[2], sig.level = .05)
-#' pwr.t.test(d=.8, n=N[3], sig.level = .05)
+#' pwr.t.test(d=.2, n=N[1])
+#' pwr.t.test(d=.5, n=N[2])
+#' pwr.t.test(d=.8, n=N[3])
 #'
 #' #------------------------------------------------
 #'
@@ -331,8 +336,8 @@
 #' # In this example, b = target power
 #' # note that integer = FALSE to allow smooth updates of d
 #' solved <- SimSolve(design=Design, b = .8, interval=c(.1, 2),
-#'                 generate=Generate, analyse=Analyse,
-#'                 summarise=Summarise, integer=FALSE)
+#'                    generate=Generate, analyse=Analyse,
+#'                    summarise=Summarise, integer=FALSE)
 #' solved
 #' summary(solved)
 #' plot(solved, 1)
@@ -346,14 +351,14 @@
 #'
 #' # verify with true power from pwr package
 #' library(pwr)
-#' pwr.t.test(n=100, power = .8, sig.level = .05)
-#' pwr.t.test(n=50, power = .8, sig.level = .05)
-#' pwr.t.test(n=25, power = .8, sig.level = .05)
+#' pwr.t.test(n=100, power = .8)
+#' pwr.t.test(n=50, power = .8)
+#' pwr.t.test(n=25, power = .8)
 #'
 #' # use estimated d results to see how close power was
-#' pwr.t.test(n=100, d = solved$d[1], sig.level = .05)
-#' pwr.t.test(n=50, d = solved$d[2], sig.level = .05)
-#' pwr.t.test(n=25, d = solved$d[3], sig.level = .05)
+#' pwr.t.test(n=100, d = solved$d[1])
+#' pwr.t.test(n=50, d = solved$d[2])
+#' pwr.t.test(n=25, d = solved$d[3])
 #'
 #' # failing analytic formula, confirm results with more precise
 #' #  simulation via runSimulation()