Installation
If you encounter any issues with installation, try the following
script to see if it resolves your issue, as this will also install the
other packages that are necessary for concurve to
function.
install.packages("concurve", dep = TRUE)If that doesn’t work, please try installing, and resinstalling
R, and then installing the package again.
You can also try installing the developer version with
library(devtools)
install_github("zadrafi/concurve")Unable to Plot Function
If you encounter an error such as “Error: ‘data’ must be a data frame
from ‘concurve’.”, it is very likely that you are not providing
ggcurve() the correct argument. If you used a function like
curve_gen() to generate intervals and saved it to an object
called ‘object’, you need to provide ggcurve() a data
argument such as object[[1]] rather than
object or object[1]. This is because although
you saved your results to something called object, you
ended up with a list with multiple components used for different
purposes, usually with the first part of the list being the most
commonly used part.
We can actually see these components one by one. Let’s generate some example data.
library(concurve)
set.seed(1031)
GroupA <- rnorm(500)
GroupB <- rnorm(500)
RandomData <- data.frame(GroupA, GroupB)
object <- curve_mean(GroupA, GroupB,
data = RandomData, method = "default"
)As stated, the first part of the list object[[1]]
contains what we usually want (I’m restricting to the first 5 results
using the head() function so that we don’t print a giant
list with 1000 rows.)
head(object[[1]], 5)
#> lower.limit upper.limit intrvl.width intrvl.level cdf pvalue svalue
#> 1 -0.1125581 -0.1125581 0.000000e+00 0e+00 0.50000 1.0000 0.0000000000
#> 2 -0.1125658 -0.1125504 1.543412e-05 1e-04 0.50005 0.9999 0.0001442767
#> 3 -0.1125736 -0.1125427 3.086824e-05 2e-04 0.50010 0.9998 0.0002885679
#> 4 -0.1125813 -0.1125350 4.630236e-05 3e-04 0.50015 0.9997 0.0004328734
#> 5 -0.1125890 -0.1125273 6.173649e-05 4e-04 0.50020 0.9996 0.0005771935while the second and third parts of the list contain dataframes and
lists for other functions such as generating density functions or for
other functions such as curve_table().
head(object[[2]], 5)
#> x
#> 1 -0.1125581
#> 2 -0.1125658
#> 3 -0.1125736
#> 4 -0.1125813
#> 5 -0.1125890
head(object[[3]], 5)
#> Lower Limit Upper Limit Interval Width Interval Level (%) CDF P-value
#> 2501 -0.132 -0.093 0.039 25 0.625 0.75
#> 5001 -0.154 -0.071 0.083 50 0.750 0.50
#> 7501 -0.183 -0.042 0.142 75 0.875 0.25
#> 8001 -0.192 -0.034 0.158 80 0.900 0.20
#> 8501 -0.201 -0.024 0.177 85 0.925 0.15
#> S-value (bits)
#> 2501 0.415
#> 5001 1.000
#> 7501 2.000
#> 8001 2.322
#> 8501 2.737Distorted Plots
If you encounter issues when plotting the functions, it is because there are a large number of points being plotted, which could lead to the graph being slightly distorted or not loading at all. The simplest solution to this is to refresh the plot and try the function again.
This applies to the ggcurve(),
curve_compare(), and plot_compare()
functions.
I would also recommend saving plots using the
cowplot::save_plot() function with the actual
ggcurve() object. It has better default settings than the
ggsave() function.
Slow Performance
Because this package is computing thousands of interval estimates via
iterations and bootstrapping, it requires a lot of computational power.
Luckily, concurve supports parallelization, although it is
disabled by default because some users, such as those who use Windows,
are unable to use it.
However, if you are able to use parallelization, you can enable it with the following script
The script will detect the number of cores on your machine via the
parallel package and use them to speed up the computations,
especially for bootstrapping.
However, if you would like to speed up the computations and are
unable to use parallelization, then you can reduce the number of
steps in the each of the concurve functions,
which will drastically reduce the time it takes to complete the
operation. By default, most of the steps arguments are set
to 10000.
For example, here I changed the number of steps to 100, which is the
minimum needed to plot a function, and the process is now much quicker.
We can evaluate this using a microbenchmark. Here I use the
bench package and the mark() function. Because
we are using parallelization, we must also set the memory
argument to FALSE.
library(bench)
library(parallel)
options(mc.cores = 1)
getOption("mc.cores", 1L)
set.seed(1031)
func1 <- mark(df1 <- curve_rev(
point = 1.61, LL = 0.997, UL = 2.59,
measure = "ratio", steps = 100
), memory = FALSE)
func2 <- mark(df1 <- curve_rev(
point = 1.61, LL = 0.997, UL = 2.59,
measure = "ratio", steps = 500000
), memory = FALSE)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.I’ll now enable parallelization by setting the mc.cores
option to detect the max number of cores available using
detectCores(). The mc.cores option is the
argument that almost all the curve functions in the
concurve package use for parallel computing.
getOption("mc.cores", 1L)
set.seed(1031)
func3 <- mark(df1 <- curve_rev(
point = 1.61, LL = 0.997, UL = 2.59,
measure = "ratio", steps = 100
), memory = FALSE)
func4 <- mark(df1 <- curve_rev(
point = 1.61, LL = 0.997, UL = 2.59,
measure = "ratio", steps = 500000
), memory = FALSE)
#> Warning: Some expressions had a GC in every iteration; so filtering is
#> disabled.
func1$median
#> [1] 2.15ms
func2$median
#> [1] 1.68s
func3$median
#> [1] 2.17ms
func4$median
#> [1] 1.56sWhen setting the number of iterations to 100, utilizing parallelization doesn’t seem to help much, but when setting the number of iterations to 500000 and using multiple cores, there seems to be a computational advantage.
Bugs
If you encounter any other bugs, please report them at https://github.com/zadrafi/concurve/issues
Session info
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8
#> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8
#> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C
#> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] parallel stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] bench_1.1.4 concurve_3.0.0
#>
#> loaded via a namespace (and not attached):
#> [1] gtable_0.3.6 jsonlite_2.0.0 dplyr_1.2.1 compiler_4.6.1
#> [5] tidyselect_1.2.1 tidyr_1.3.2 jquerylib_0.1.4 systemfonts_1.3.2
#> [9] scales_1.4.0 textshaping_1.0.5 yaml_2.3.12 fastmap_1.2.0
#> [13] ggplot2_4.0.3 R6_2.6.1 generics_0.1.4 knitr_1.51
#> [17] htmlwidgets_1.6.4 tibble_3.3.1 desc_1.4.3 bslib_0.12.0
#> [21] pillar_1.11.1 RColorBrewer_1.1-3 rlang_1.3.0 cachem_1.1.0
#> [25] xfun_0.60 S7_0.2.2 fs_2.1.0 sass_0.4.10
#> [29] otel_0.2.0 cli_3.6.6 pkgdown_2.2.1 magrittr_2.0.5
#> [33] digest_0.6.39 grid_4.6.1 lifecycle_1.0.5 vctrs_0.7.3
#> [37] evaluate_1.0.5 glue_1.8.1 farver_2.1.2 ragg_1.5.2
#> [41] colorspace_2.1-3 purrr_1.2.2 rmarkdown_2.31 tools_4.6.1
#> [45] pkgconfig_2.0.3 htmltools_0.5.9