Last updated on 2025-09-11 15:49:25 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 25.81 | 115.50 | 141.31 | OK | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 17.85 | 77.56 | 95.41 | OK | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 192.69 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 227.17 | OK | |||
| r-devel-windows-x86_64 | 1.0.0 | 30.00 | 140.00 | 170.00 | OK | |
| r-patched-linux-x86_64 | 1.0.0 | 26.60 | 105.99 | 132.59 | OK | |
| r-release-linux-x86_64 | 1.0.0 | 25.07 | 105.68 | 130.75 | OK | |
| r-release-macos-arm64 | 1.0.0 | 63.00 | OK | |||
| r-release-macos-x86_64 | 1.0.0 | 122.00 | OK | |||
| r-release-windows-x86_64 | 1.0.0 | 31.00 | 135.00 | 166.00 | OK | |
| r-oldrel-macos-arm64 | 1.0.0 | 68.00 | OK | |||
| r-oldrel-macos-x86_64 | 1.0.0 | 123.00 | OK | |||
| r-oldrel-windows-x86_64 | 1.0.0 | 37.00 | 177.00 | 214.00 | OK |
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘dynConfiR-Ex.R’ failed
The error most likely occurred in:
> ### Name: RaceModels
> ### Title: Independent and partially anti-correlated Race Model for
> ### Decision Confidence
> ### Aliases: RaceModels dIRM dPCRM rRM dRM rIRM rPCRM racemodels dIRM2
> ### dIRM3
>
> ### ** Examples
>
> # Plot rt distribution ignoring confidence
> curve(dPCRM(x, 1, mu1=0.5, mu2=-0.5, a=1, b=1, th1=-Inf, th2=Inf, t0=0.1), xlim=c(0,2.5))
> curve(dPCRM(x, 2, mu1=0.5, mu2=-0.5, a=1, b=1, th1=-Inf, th2=Inf, t0=0.1), col="red", add=TRUE)
> curve(dIRM(x, 1, mu1=0.5, mu2=-0.5, a=1, b=1, th1=-Inf, th2=Inf, t0=0.1), lty=2,add=TRUE)
> curve(dIRM(x, 2, mu1=0.5, mu2=-0.5, a=1, b=1, th1=-Inf, th2=Inf, t0=0.1),
+ col="red", lty=2, add=TRUE)
> # t0 indicates minimal response time possible
> abline(v=0.1)
> ## Following example may be equivalently used for the IRM model functions.
> # Generate a random sample
> df1 <- rPCRM(5000, mu1=0.2, mu2=-0.2, a=1, b=1, t0=0.1,
+ wx = 1) # Balance of Evidence
> # Same RT and response distribution but different confidence distribution
> df2 <- rPCRM(5000, mu1=0.2, mu2=-0.2, a=1, b=1, t0=0.1,
+ wint = 0.2, wrt=0.8)
> head(df1)
rt response xl conf
1 0.65 1 -0.1666653 1.1666653
2 11.59 1 -6.8019691 7.8019691
3 1.89 2 -0.3117717 1.3117717
4 0.85 2 0.5670769 0.4329231
5 0.77 2 0.2080419 0.7919581
6 0.32 1 -0.1913931 1.1913931
>
> # Compute density with rt and response as separate arguments
> dPCRM(seq(0, 2, by =0.4), response= 2, mu1=0.2, mu2=-0.2, a=1, b=1, th1=0.5,
+ th2=2, wx = 0.3, wint=0.4, wrt=0.1, t0=0.1)
[1] 0.00000000 0.26919023 0.20371811 0.10962250 0.06320931 0.03928042
> # Compute density with rt and response in data.frame argument
> df1 <- subset(df1, response !=0) # drop trials where no accumulation hit its boundary
> dPCRM(df1[1:5,], mu1=0.2, mu2=-0.2, a=1, b=1, th1=0, th2=Inf, t0=0.1)
[1] 0.449022313 0.001737302 0.051678757 0.215121579 0.244686625
> # s1 and s2 scale other decision relevant parameters
> s <- 2 # common (equal) standard deviation
> dPCRM(df1[1:5,], mu1=0.2*s, mu2=-0.2*s, a=1*s, b=1*s, th1=0, th2=Inf, t0=0.1, s1=s, s2=s)
[1] 0.449022313 0.001737302 0.051678757 0.215121579 0.244686625
> s1 <- 2 # different standard deviations
> s2 <- 1.5
> dPCRM(df1[1:5,], mu1=0.2*s1, mu2=-0.2*s2, a=1*s1, b=1*s2, th1=0, th2=Inf, t0=0.1, s1=s1, s2=s2)
[1] 0.449022313 0.001737302 0.051678757 0.215121579 0.244686625
>
>
> # s1 and s2 scale also confidence parameters
> df1[1:5,]$response <- 2 # set response to 2
> # for confidence it is important to scale confidence parameters with
> # the right variation parameter (the one of the loosing accumulator)
> dPCRM(df1[1:5,], mu1=0.2, mu2=-0.2, a=1, b=1,
+ th1=0.5, th2=2, wx = 0.3, wint=0.4, wrt=0.1, t0=0.1)
[1] 0.253627090 0.000290214 0.044476406 0.188375881 0.213369269
> dPCRM(df1[1:5,], mu1=0.2*s1, mu2=-0.2*s2, a=1*s1, b=1*s2,
+ th1=0.5, th2=2, wx = 0.3/s1, wint = 0.4/s1, wrt = 0.1, t0=0.1, s1=s1, s2=s2)
[1] 0.253627090 0.000290214 0.044476406 0.188375881 0.213369269
> dPCRM(df1[1:5,], mu1=0.2*s1, mu2=-0.2*s2, a=1*s1, b=1*s2,
+ th1=0.5*s1, th2=2*s1, wx = 0.3, wint = 0.4, wrt = 0.1*s1, t0=0.1, s1=s1, s2=s2)
[1] 0.253627090 0.000290214 0.044476406 0.188375881 0.213369269
>
> two_samples <- rbind(cbind(df1, ws="BoE"),
+ cbind(df2, ws="RT"))
> # drop not finished decision processes
> two_samples <- two_samples[two_samples$response!=0,]
> # no difference in RT distributions
> boxplot(rt~ws+response, data=two_samples)
> # but different confidence distributions
> boxplot(conf~ws+response, data=two_samples)
> if (requireNamespace("ggplot2", quietly = TRUE)) {
+ require(ggplot2)
+ ggplot(two_samples, aes(x=rt, y=conf))+
+ stat_density_2d(aes(fill = after_stat(density)),
+ geom = "raster", contour = FALSE, h=c(0.3, 0.7)) +
+ xlim(c(0.2, 1.3))+ ylim(c(0, 2.5))+
+ facet_grid(cols=vars(ws), rows=vars(response), labeller = "label_both")
+ }
Loading required package: ggplot2
Error in `stat_density_2d()`:
! Problem while computing stat.
ℹ Error occurred in the 1st layer.
Caused by error in `compute_layer()`:
! The package "MASS" is required for calculating 2D density.
Backtrace:
▆
1. ├─base (local) `<fn>`(x)
2. ├─ggplot2 (local) `print.ggplot2::ggplot`(x)
3. │ ├─ggplot2::ggplot_build(x)
4. │ └─ggplot2 (local) `ggplot_build.ggplot2::ggplot`(x)
5. │ └─ggplot2:::by_layer(...)
6. │ ├─rlang::try_fetch(...)
7. │ │ ├─base::tryCatch(...)
8. │ │ │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
9. │ │ │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
10. │ │ │ └─base (local) doTryCatch(return(expr), name, parentenv, handler)
11. │ │ └─base::withCallingHandlers(...)
12. │ └─ggplot2 (local) f(l = layers[[i]], d = data[[i]])
13. │ └─l$compute_statistic(d, layout)
14. │ └─ggplot2 (local) compute_statistic(..., self = self)
15. │ └─self$stat$compute_layer(data, self$computed_stat_params, layout)
16. │ └─ggplot2 (local) compute_layer(..., self = self)
17. │ └─rlang::check_installed("MASS", reason = "for calculating 2D density.")
18. │ └─base::stop(cnd)
19. └─rlang (local) `<fn>`(`<rlb_r___>`)
20. └─handlers[[1L]](cnd)
21. └─cli::cli_abort(...)
22. └─rlang::abort(...)
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang