From 900790b4139dd672c7383a3ed6ad2c1e51d855b9 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 28 Oct 2019 16:39:14 +0100 Subject: 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 --- docs/reference/confint.mkinfit.html | 123 +++++++++++++++++++++++++++++++++--- 1 file changed, 113 insertions(+), 10 deletions(-) (limited to 'docs/reference/confint.mkinfit.html') diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html index fdbc9a3f..0053894b 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/reference/confint.mkinfit.html @@ -144,7 +144,7 @@ could likely be improved by using the method of Venzon and Moolgavkar (1988).

confint(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

@@ -192,14 +192,14 @@ transformed parameters, should we backtransform the parameters with their confidence intervals?

- - + + - + @@ -213,7 +213,8 @@ the parameter estimate

each parameter.

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 Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, @@ -224,11 +225,113 @@ the parameter estimate

confint(f, method="quadratic")
#> 2.5% 97.5% #> parent_0 71.8242430 93.1600766 #> k_parent_sink 0.2109541 0.4440528 -#> sigma 1.9778868 7.3681380
# \dontrun{ - confint(f, method = "profile")
#> Profiling the likelihood
#> 2.5% 97.5% +#> sigma 1.9778868 7.3681380
+# \dontrun{ +confint(f, method = "profile")
#> Profiling the likelihood
#> 2.5% 97.5% #> parent_0 73.0641834 92.1392181 #> k_parent_sink 0.2170293 0.4235348 -#> sigma 3.1307772 8.0628314
# } +#> sigma 3.1307772 8.0628314
+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))
#> User System verstrichen +#> 51.063 0.000 51.090
# 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
#> 2.5% 97.5% +#> parent_0 96.456003650 1.027703e+02 +#> k_parent_sink 0.040762501 5.549764e-02 +#> k_parent_m1 0.046786482 5.500879e-02 +#> k_m1_sink 0.003892605 6.702778e-03 +#> sigma 2.535612399 3.985263e+00
ci_quadratic_transformed <- confint(f_d_1, method = "quadratic") +ci_quadratic_transformed
#> 2.5% 97.5% +#> parent_0 96.403841649 1.027931e+02 +#> k_parent_sink 0.041033378 5.596269e-02 +#> k_parent_m1 0.046777902 5.511931e-02 +#> k_m1_sink 0.004012217 6.897547e-03 +#> sigma 2.396089689 3.854918e+00
ci_quadratic_untransformed <- confint(f_d_1, method = "quadratic", transformed = FALSE) +ci_quadratic_untransformed
#> 2.5% 97.5% +#> parent_0 96.403841653 102.79312450 +#> k_parent_sink 0.040485331 0.05535491 +#> k_parent_m1 0.046611581 0.05494364 +#> k_m1_sink 0.003835483 0.00668582 +#> sigma 2.396089689 3.85491806
# 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
#> 2.5% 97.5% +#> parent_0 0.0005407854 0.0002218012 +#> k_parent_sink 0.0066452394 0.0083795930 +#> k_parent_m1 0.0001833903 0.0020092090 +#> k_m1_sink 0.0307278240 0.0290580487 +#> sigma 0.0550252516 0.0327066836
rel_diffs_untransformed
#> 2.5% 97.5% +#> parent_0 0.0005407854 0.0002218011 +#> k_parent_sink 0.0067996407 0.0025717594 +#> k_parent_m1 0.0037382781 0.0011843074 +#> k_m1_sink 0.0146745610 0.0025299672 +#> sigma 0.0550252516 0.0327066836
+# 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)
#> Profiling the likelihood
ci_profile_ff
#> 2.5% 97.5% +#> parent_0 96.456003650 1.027703e+02 +#> k_parent 0.090911032 1.071578e-01 +#> k_m1 0.003892605 6.702778e-03 +#> f_parent_to_m1 0.471328495 5.611550e-01 +#> sigma 2.535612399 3.985263e+00
ci_quadratic_transformed_ff <- confint(f_d_2, method = "quadratic") +ci_quadratic_transformed_ff
#> 2.5% 97.5% +#> parent_0 96.403840123 1.027931e+02 +#> k_parent 0.090823791 1.072543e-01 +#> k_m1 0.004012216 6.897547e-03 +#> f_parent_to_m1 0.469118710 5.595960e-01 +#> sigma 2.396089689 3.854918e+00
ci_quadratic_untransformed_ff <- confint(f_d_2, method = "quadratic", transformed = FALSE) +ci_quadratic_untransformed_ff
#> 2.5% 97.5% +#> parent_0 96.403840057 1.027931e+02 +#> k_parent 0.090491932 1.069035e-01 +#> k_m1 0.003835483 6.685819e-03 +#> f_parent_to_m1 0.469113361 5.598386e-01 +#> sigma 2.396089689 3.854918e+00
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
#> 2.5% 97.5% +#> parent_0 0.0005408012 0.0002217857 +#> k_parent 0.0009596303 0.0009003981 +#> k_m1 0.0307277425 0.0290579163 +#> f_parent_to_m1 0.0046884178 0.0027782643 +#> sigma 0.0550252516 0.0327066836
rel_diffs_untransformed_ff
#> 2.5% 97.5% +#> parent_0 0.0005408019 0.0002217863 +#> k_parent 0.0046099989 0.0023730118 +#> k_m1 0.0146746451 0.0025300990 +#> f_parent_to_m1 0.0046997668 0.0023460293 +#> sigma 0.0550252516 0.0327066836
+# 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") +# }
distribution

For the quadratic approximation, should we use -the student t distribution or assume normal distribution for -the parameter estimate

cores

The number of cores to be used for multicore processing. This +is only used when the cluster argument is NULL. On Windows +machines, cores > 1 is not supported.
quiet

Should we suppress messages?

Should we suppress the message "Profiling the likelihood"
...