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Nested loop plots

Alessandro Gasparini

2024-03-03

As of version0.6.0,rsimsum supports thefully automated creation of nested loop plots (Rücker and Schwarzer,2014).

library(rsimsum)

A dataset that can be purposefully used to illustrate nested loopplots is bundled and shipped withrsimsum:

data("nlp",package ="rsimsum")

This data set contains the results of a simulation study on survivalmodelling with 150 distinct data-generating mechanisms:

head(nlp)#>   dgm  i model           b        se baseline  ss beta esigma pars#> 1   1  1     1  0.17119413 0.2064344        E 100    0    0.1  0.5#> 2   1  1     2  0.19822898 0.2048353        E 100    0    0.1  0.5#> 3   1 50     2 -0.03404229 0.2071766        E 100    0    0.1  0.5#> 4   1 82     1 -0.09263968 0.2040281        E 100    0    0.1  0.5#> 5   1 82     2 -0.05095914 0.2026813        E 100    0    0.1  0.5#> 6   1 33     1 -0.17013365 0.2038076        E 100    0    0.1  0.5

Further information on the data could be find in the help file(?nlp).

We can analyse this simulation study usingrsimsum asusual:

s<- rsimsum::simsum(data = nlp,estvarname ="b",true =0,se ="se",methodvar ="model",by =c("baseline","ss","esigma"))#> 'ref' method was not specified, 1 set as the references#> Summary of a simulation study with a single estimand.#> True value of the estimand: 0#>#> Method variable: model#>  Unique methods: 1, 2#>  Reference method: 1#>#> By factors: baseline, ss, esigma#>#> Monte Carlo standard errors were computed.

Finally, a nested loop plot can be automatically produced via theautoplot method, e.g. for bias:

library(ggplot2)autoplot(s,type ="nlp",stats ="bias")

However:

  1. Nested loop plots are suited for several DGMs but not for severalmethods;
  2. The decision on how tonest the results is subjective - thetop-level of nesting receives most emphasis;
  3. It gives anoverall impression, without focusing too muchon details;
  4. It is cumbersome to incorporate Monte Carlo errors in the plot.

References


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