Last updated on 2025-09-16 21:50:29 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 1.0.0 | 26.42 | 98.77 | 125.19 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 1.0.0 | 18.69 | 67.45 | 86.14 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 1.0.0 | 190.38 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 1.0.0 | 241.60 | 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 | 25.54 | 93.06 | 118.60 | ERROR | |
| 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 | 30.00 | 118.00 | 148.00 | ERROR | |
| 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 | 38.00 | 147.00 | 185.00 | ERROR |
Version: 1.0.0
Check: examples
Result: ERROR
Running examples in ‘dynConfiR-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### 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
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-release-linux-x86_64
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
Flavors: r-devel-linux-x86_64-fedora-clang, r-release-windows-x86_64, r-oldrel-windows-x86_64
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.