From 31fd7412f46c9715962763d435cb0060ea420752 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 4 Nov 2019 17:21:04 +0100 Subject: Include fixed parameters in model names in lrtest --- docs/reference/confint.mkinfit.html | 20 ++++++++++---------- 1 file changed, 10 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 13662adf..ea69c3bb 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/reference/confint.mkinfit.html @@ -40,10 +40,10 @@ the curvature of the likelihood function at the maximum likelihood parameter estimates. The alternative method 'profile' is based on the profile likelihood for each -parameter. The method uses two nested optimisations and can take a very long -time, even if parallelized by specifying 'cores' on unixoid platforms. The -speed of the method could likely be improved by using the method of Venzon -and Moolgavkar (1988)." /> +parameter. The 'profile' method uses two nested optimisations and can take a +very long time, even if parallelized by specifying 'cores' on unixoid +platforms. The speed of the method could likely be improved by using the +method of Venzon and Moolgavkar (1988)." /> @@ -144,10 +144,10 @@ and Moolgavkar (1988)." /> the curvature of the likelihood function at the maximum likelihood parameter estimates. The alternative method 'profile' is based on the profile likelihood for each -parameter. The method uses two nested optimisations and can take a very long -time, even if parallelized by specifying 'cores' on unixoid platforms. The -speed of the method could likely be improved by using the method of Venzon -and Moolgavkar (1988).

+parameter. The 'profile' method uses two nested optimisations and can take a +very long time, even if parallelized by specifying 'cores' on unixoid +platforms. The speed of the method could likely be improved by using the +method of Venzon and Moolgavkar (1988).

# S3 method for mkinfit
@@ -254,13 +254,13 @@ On Windows machines, cores > 1 is currently not supported.

   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, method = "profile", cores = 1, quiet = TRUE))
#>        User      System verstrichen 
-#>      51.341       0.000      51.370 
# Using more cores does not save much time here, as parent_0 takes up most of the time
+#>      51.058       0.000      51.088 
# Using more cores does not save much time here, as parent_0 takes up most of the time
 # If we additionally exclude parent_0 (the confidence of which is often of
 # minor interest), we get a nice performance improvement from about 50
 # seconds to about 12 seconds if we use at least four cores
 system.time(ci_profile_no_parent_0 <- confint(f_d_1, method = "profile",
   c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = n_cores))
#> Profiling the likelihood
#>        User      System verstrichen 
-#>       0.001       0.007      11.432 
ci_profile
#>                       2.5%        97.5%
+#>       0.005       0.004      11.349 
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
-- 
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