You may want to do other things as well, such as converting error estimates to standard deviation or CV%. If you want to modify the table, save it first.ġ001 %>% partab( format = F, ci = F, relative = F, digits = NULL ) # parameter estimate se lo hi symbol
# 12 interindividual volume-Ka covariance # 11 interindividual clearance-Ka covariance # 10 interindividual variability on central volume # 9 interindividual clearance-volume covariance # 8 interindividual variability on clearance
Partab( 1001) # parameter estimate prse ci symbol If, rather, we want to automate the generation of parameter tables, we’re going to have to be more systematic about where and how the data sources are stored. But manual table generation can be tedious, time-consuming, and error-prone.
When you are making parameter tables by hand, no problem: your memory supplies all the integration, as well as missing details. Some of the most important information may not be captured anywhere! You may know that THETA1 is “apparent oral clearance” in your model, and that the units are liters. Various ancillary outputs may be available, such as *.ext, *.cov, *.xml, etc, giving more regular and/or more specific versions of model components.īootstrap estimates of parameter uncertainty are probably in some third-party format, since bootstrapping is usually performed independently of model estimation. The list file has the final parameter estimates in a readable form, as well as many diagnostic values, such as standard errors (when available) and shrinkage estimates.
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