Parallel computation for confidence intervals

Only on Linux at the moment. Some more examples in the help page.
Remove the distribution argument for the quadratic method

1 files changed, 72 insertions, 7 deletions

diff --git a/man/confint.mkinfit.Rd b/man/confint.mkinfit.Rd
index b84facb8..99f5875c 100644
--- a/man/confint.mkinfit.Rd
+++ b/man/confint.mkinfit.Rd
@@ -7,7 +7,7 @@
 \method{confint}{mkinfit}(object, parm, level = 0.95, alpha = 1 -
   level, cutoff, method = c("profile", "quadratic"),
   transformed = TRUE, backtransform = TRUE,
-  distribution = c("student_t", "normal"), quiet = FALSE, ...)
+  cores = round(detectCores()/2), quiet = FALSE, ...)
 }
 \arguments{
 \item{object}{An \code{\link{mkinfit}} object}
@@ -36,11 +36,11 @@ applied to the likelihood based on the transformed parameters?}
 transformed parameters, should we backtransform the parameters with
 their confidence intervals?}
 
-\item{distribution}{For the quadratic approximation, should we use
-the student t distribution or assume normal distribution for
-the parameter estimate}
+\item{cores}{The number of cores to be used for multicore processing. This
+is only used when the \code{cluster} argument is \code{NULL}. On Windows
+machines, cores > 1 is not supported.}
 
-\item{quiet}{Should we suppress messages?}
+\item{quiet}{Should we suppress the message "Profiling the likelihood"}
 
 \item{\dots}{Not used}
 }
@@ -56,12 +56,77 @@ could likely be improved by using the method of Venzon and Moolgavkar (1988).
 \examples{
 f <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
 confint(f, method = "quadratic")
+
 \dontrun{
-  confint(f, method = "profile")
+confint(f, method = "profile")
+
+SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), quiet = TRUE)
+SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"),
+  use_of_ff = "max", quiet = TRUE)
+f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE)
+system.time(ci_profile <- confint(f_d_1, cores = 1, quiet = TRUE))
+# The following does not save much time, as parent_0 takes up most of the time
+# system.time(ci_profile <- confint(f_d_1, cores = 5))
+# system.time(ci_profile <- confint(f_d_1,
+#   c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = 1))
+# If we exclude parent_0 (the confidence of which is often of minor interest), we get a nice
+# performance improvement from about 30 seconds to about 12 seconds
+# system.time(ci_profile_no_parent_0 <- confint(f_d_1, c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = 4))
+ci_profile
+ci_quadratic_transformed <- confint(f_d_1, method = "quadratic")
+ci_quadratic_transformed
+ci_quadratic_untransformed <- confint(f_d_1, method = "quadratic", transformed = FALSE)
+ci_quadratic_untransformed
+# Against the expectation based on Bates and Watts (1988), the confidence
+# intervals based on the internal parameter transformation are less
+# congruent with the likelihood based intervals. Note the superiority of the
+# interval based on the untransformed fit for k_m1_sink
+rel_diffs_transformed <- abs((ci_quadratic_transformed - ci_profile)/ci_profile)
+rel_diffs_untransformed <- abs((ci_quadratic_untransformed - ci_profile)/ci_profile)
+rel_diffs_transformed
+rel_diffs_untransformed
+
+# Set the number of cores for further examples
+if (identical(Sys.getenv("NOT_CRAN"), "true")) {
+  n_cores <- parallel::detectCores() - 1
+} else {
+ n_cores <- 1
+}
+if (Sys.getenv("TRAVIS") != "") n_cores = 1
+if (Sys.info()["sysname"] == "Windows") n_cores = 1
+
+# Investigate a case with formation fractions
+f_d_2 <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE)
+ci_profile_ff <- confint(f_d_2, cores = n_cores)
+ci_profile_ff
+ci_quadratic_transformed_ff <- confint(f_d_2, method = "quadratic")
+ci_quadratic_transformed_ff
+ci_quadratic_untransformed_ff <- confint(f_d_2, method = "quadratic", transformed = FALSE)
+ci_quadratic_untransformed_ff
+rel_diffs_transformed_ff <- abs((ci_quadratic_transformed_ff - ci_profile_ff)/ci_profile_ff)
+rel_diffs_untransformed_ff <- abs((ci_quadratic_untransformed_ff - ci_profile_ff)/ci_profile_ff)
+# While the confidence interval for the parent rate constant is closer to
+# the profile based interval when using the internal parameter
+# transformation, the intervals for the other parameters are 'better
+# without internal parameter transformation.
+rel_diffs_transformed_ff
+rel_diffs_untransformed_ff
+
+# The profiling for the following fit does not finish in a reasonable time
+#m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
+#  M1 = mkinsub("SFO"),
+#  M2 = mkinsub("SFO"),
+#  use_of_ff = "max", quiet = TRUE)
+#DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
+#f_tc_2 <- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = "tc",
+#  error_model_algorithm = "direct", quiet = TRUE)
+#confint(f_tc_2, "parent_0")
 }
 }
 \references{
-Pawitan Y (2013) In all likelihood - Statistical modelling and
+Bates DM and Watts GW (1988) Nonlinear regression analysis & its applications
+
+  Pawitan Y (2013) In all likelihood - Statistical modelling and
   inference using likelihood. Clarendon Press, Oxford.
 
   Venzon DJ and Moolgavkar SH (1988) A Method for Computing