Confidence intervals for parameters of mkinfit objects

Usage
# S3 method for mkinfit
+    # S3 method for class 'mkinfit'
 confint(
   object,
   parm,
@@ -119,30 +106,32 @@ method of Venzon and Moolgavkar (1988).
 
     
     Arguments
-    object
+
+
+object
 An mkinfit object
 
 
-parm
+parm
 A vector of names of the parameters which are to be given
 confidence intervals. If missing, all parameters are considered.
 
 
-level
+level
 The confidence level required
 
 
-alpha
+alpha
 The allowed error probability, overrides 'level' if specified.
 
 
-cutoff
+cutoff
 Possibility to specify an alternative cutoff for the difference
 in the log-likelihoods at the confidence boundary. Specifying an explicit
 cutoff value overrides arguments 'level' and 'alpha'
 
 
-method
+method
 The 'quadratic' method approximates the likelihood function at
 the optimised parameters using the second term of the Taylor expansion,
 using a second derivative (hessian) contained in the object.
@@ -150,41 +139,39 @@ The 'profile' method searches the parameter space for the
 cutoff of the confidence intervals by means of a likelihood ratio test.
 
 
-transformed
+transformed
 If the quadratic approximation is used, should it be
 applied to the likelihood based on the transformed parameters?
 
 
-backtransform
+backtransform
 If we approximate the likelihood in terms of the
 transformed parameters, should we backtransform the parameters with
 their confidence intervals?
 
 
-cores
+cores
 The number of cores to be used for multicore processing.
 On Windows machines, cores > 1 is currently not supported.
 
 
-rel_tol
+rel_tol
 If the method is 'profile', what should be the accuracy
 of the lower and upper bounds, relative to the estimate obtained from
 the quadratic method?
 
 
-quiet
+quiet
 Should we suppress the message "Profiling the likelihood"
 
 
-...
+...
 Not used
 
 
     
     Value
-    
-
-A matrix with columns giving lower and upper confidence limits for
+    
A matrix with columns giving lower and upper confidence limits for
 each parameter.
     
     
@@ -230,7 +217,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 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 elapsed 
-#>   1.203   0.000   1.203 
+#>   1.206   0.000   1.206 
 # 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 if we use at least 4 cores
@@ -238,7 +225,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
   c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = n_cores))
 #> Profiling the likelihood
 #>    user  system elapsed 
-#>   0.438   0.097   0.301 
+#>   0.406   0.172   0.317 
 ci_profile
 #>                       2.5%        97.5%
 #> parent_0      96.456003640 1.027703e+02
@@ -373,23 +360,23 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 # }