rsimsum and the tidyverse

rsimsum plays nice with the tidyverse.

library(rsimsum)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

library(ggplot2)
library(knitr)
#> 
#> Attaching package: 'knitr'
#> The following object is masked from 'package:rsimsum':
#> 
#>     kable

For instance, it is possible to chain functions using the piping operator %>% to obtain tables and plots with a single call:

data("MIsim", package = "rsimsum")
MIsim %>%
  simsum(estvarname = "b", se = "se", methodvar = "method", true = 0.5) %>%
  summary() %>%
  tidy() %>%
  kable()
#> 'ref' method was not specified, CC set as the reference

stat	est	mcse	method	lower	upper
nsim	1000.0000000	NA	CC	NA	NA
thetamean	0.5167662	NA	CC	NA	NA
thetamedian	0.5069935	NA	CC	NA	NA
se2mean	0.0216373	NA	CC	NA	NA
se2median	0.0211425	NA	CC	NA	NA
bias	0.0167662	0.0047787	CC	0.0074001	0.0261322
rbias	0.0335323	0.0095574	CC	0.0148003	0.0522644
empse	0.1511150	0.0033807	CC	0.1444889	0.1577411
mse	0.0230940	0.0011338	CC	0.0208717	0.0253163
relprec	0.0000000	0.0000000	CC	0.0000000	0.0000000
modelse	0.1470963	0.0005274	CC	0.1460626	0.1481300
relerror	-2.6593842	2.2054817	CC	-6.9820490	1.6632806
cover	0.9430000	0.0073315	CC	0.9286305	0.9573695
becover	0.9400000	0.0075100	CC	0.9252807	0.9547193
power	0.9460000	0.0071473	CC	0.9319915	0.9600085
nsim	1000.0000000	NA	MI_LOGT	NA	NA
thetamean	0.5009231	NA	MI_LOGT	NA	NA
thetamedian	0.4969223	NA	MI_LOGT	NA	NA
se2mean	0.0182091	NA	MI_LOGT	NA	NA
se2median	0.0172157	NA	MI_LOGT	NA	NA
bias	0.0009231	0.0041744	MI_LOGT	-0.0072586	0.0091048
rbias	0.0018462	0.0083488	MI_LOGT	-0.0145172	0.0182096
empse	0.1320064	0.0029532	MI_LOGT	0.1262182	0.1377947
mse	0.0174091	0.0008813	MI_LOGT	0.0156818	0.0191364
relprec	31.0463410	3.9374726	MI_LOGT	23.3290364	38.7636456
modelse	0.1349413	0.0006046	MI_LOGT	0.1337563	0.1361263
relerror	2.2232593	2.3323382	MI_LOGT	-2.3480396	6.7945582
cover	0.9490000	0.0069569	MI_LOGT	0.9353647	0.9626353
becover	0.9490000	0.0069569	MI_LOGT	0.9353647	0.9626353
power	0.9690000	0.0054808	MI_LOGT	0.9582579	0.9797421
nsim	1000.0000000	NA	MI_T	NA	NA
thetamean	0.4988092	NA	MI_T	NA	NA
thetamedian	0.4939111	NA	MI_T	NA	NA
se2mean	0.0179117	NA	MI_T	NA	NA
se2median	0.0169319	NA	MI_T	NA	NA
bias	-0.0011908	0.0042510	MI_T	-0.0095226	0.0071409
rbias	-0.0023817	0.0085020	MI_T	-0.0190452	0.0142819
empse	0.1344277	0.0030074	MI_T	0.1285333	0.1403221
mse	0.0180542	0.0009112	MI_T	0.0162682	0.0198401
relprec	26.3681613	3.8423791	MI_T	18.8372366	33.8990859
modelse	0.1338346	0.0005856	MI_T	0.1326867	0.1349824
relerror	-0.4412233	2.2695216	MI_T	-4.8894038	4.0069573
cover	0.9430000	0.0073315	MI_T	0.9286305	0.9573695
becover	0.9430000	0.0073315	MI_T	0.9286305	0.9573695
power	0.9630000	0.0059692	MI_T	0.9513006	0.9746994

MIsim %>%
  simsum(estvarname = "b", se = "se", methodvar = "method", true = 0.5) %>%
  summary() %>%
  tidy(stats = "bias") %>%
  ggplot(aes(x = method, y = est, ymin = lower, ymax = upper)) +
  geom_hline(yintercept = 0, color = "red", lty = "dashed") +
  geom_point() +
  geom_errorbar(width = 1 / 3) +
  theme_bw() +
  labs(x = "Method", y = "Bias")
#> 'ref' method was not specified, CC set as the reference

Alessandro Gasparini

2024-05-16