Comments (3)
This is because there are not enough observations to compute bootstrapped confidence intervals for the effect size (epsilon-squared).
You can see that the error actually traces back to rcompanion::epsilonSquared
, which is what ggstatsplot
uses in the backdrop to compute effect size + CIs.
# dataframe
r2 <- data.frame(
treatment = c("a", "a", "a", "a", "a", "e", "f"),
Age = 1:7,
Group = c("G2", "G2", "G2", "G2", "G2", "G2", "G2"),
stringsAsFactors = F
)
# effect size measure
rcompanion::epsilonSquared(x = r2$Age, g = r2$treatment, ci = FALSE)
#> epsilon.squared
#> 0.643
# effect size measure + CI
rcompanion::epsilonSquared(x = r2$Age, g = r2$treatment, ci = TRUE)
#> Error in kruskal.test.default(Input$x, Input$g): all observations are in the same group
Created on 2019-09-10 by the reprex package (v0.3.0)
Session info
devtools::session_info()
#> - Session info ----------------------------------------------------------
#> setting value
#> version R version 3.6.1 (2019-07-05)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz Europe/Berlin
#> date 2019-09-10
#>
#> - Packages --------------------------------------------------------------
#> package * version date lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
#> backports 1.1.4 2019-04-10 [1] CRAN (R 3.6.0)
#> boot 1.3-23 2019-07-05 [1] CRAN (R 3.6.1)
#> callr 3.3.1 2019-07-18 [1] CRAN (R 3.6.1)
#> cli 1.1.0 2019-03-19 [1] CRAN (R 3.6.0)
#> codetools 0.2-16 2018-12-24 [1] CRAN (R 3.5.2)
#> coin 1.3-1 2019-08-28 [1] CRAN (R 3.6.1)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 3.5.1)
#> desc 1.2.0 2019-04-03 [1] Github (r-lib/desc@c860e7b)
#> DescTools 0.99.28 2019-03-17 [1] CRAN (R 3.6.0)
#> devtools 2.2.0 2019-09-07 [1] CRAN (R 3.6.1)
#> digest 0.6.20 2019-07-04 [1] CRAN (R 3.6.0)
#> DT 0.8 2019-08-07 [1] CRAN (R 3.6.1)
#> ellipsis 0.2.0.1 2019-07-02 [1] CRAN (R 3.6.0)
#> EMT 1.1 2013-01-29 [1] CRAN (R 3.5.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
#> expm 0.999-4 2019-03-21 [1] CRAN (R 3.6.0)
#> foreign 0.8-71 2018-07-20 [2] CRAN (R 3.6.1)
#> fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.0)
#> glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.0)
#> highr 0.8 2019-03-20 [1] CRAN (R 3.6.0)
#> htmltools 0.3.6 2017-04-28 [1] CRAN (R 3.5.1)
#> htmlwidgets 1.3 2018-09-30 [1] CRAN (R 3.5.1)
#> knitr 1.24 2019-08-08 [1] CRAN (R 3.6.1)
#> lattice 0.20-38 2018-11-04 [2] CRAN (R 3.6.1)
#> libcoin 1.0-5 2019-08-27 [1] CRAN (R 3.6.1)
#> lmtest 0.9-37 2019-04-30 [1] CRAN (R 3.6.0)
#> magrittr 1.5 2014-11-22 [1] CRAN (R 3.5.1)
#> manipulate 1.0.1 2014-12-24 [1] CRAN (R 3.5.0)
#> MASS 7.3-51.4 2019-03-31 [1] CRAN (R 3.6.0)
#> Matrix 1.2-17 2019-03-22 [1] CRAN (R 3.6.0)
#> matrixStats 0.55.0 2019-09-07 [1] CRAN (R 3.6.1)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.0)
#> modeltools 0.2-22 2018-07-16 [1] CRAN (R 3.5.1)
#> multcomp 1.4-10 2019-03-05 [1] CRAN (R 3.5.2)
#> multcompView 0.1-7 2015-07-31 [1] CRAN (R 3.5.1)
#> mvtnorm 1.0-11 2019-06-19 [1] CRAN (R 3.6.0)
#> nortest 1.0-4 2015-07-30 [1] CRAN (R 3.5.2)
#> pkgbuild 1.0.5 2019-08-26 [1] CRAN (R 3.6.1)
#> pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.0)
#> plyr 1.8.4 2016-06-08 [1] CRAN (R 3.5.1)
#> prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.5.1)
#> processx 3.4.1 2019-07-18 [1] CRAN (R 3.6.1)
#> ps 1.3.0 2018-12-21 [1] CRAN (R 3.6.0)
#> R6 2.4.0 2019-02-14 [1] CRAN (R 3.6.0)
#> rcompanion 2.3.0 2019-08-27 [1] local
#> Rcpp 1.0.2 2019-07-25 [1] CRAN (R 3.6.1)
#> remotes 2.1.0 2019-06-24 [1] CRAN (R 3.6.0)
#> rlang 0.4.0 2019-06-25 [1] CRAN (R 3.6.0)
#> rmarkdown 1.15 2019-08-21 [1] CRAN (R 3.6.1)
#> rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.5.1)
#> sandwich 2.5-1 2019-04-06 [1] CRAN (R 3.6.0)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
#> stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
#> survival 2.44-1.1 2019-04-01 [2] CRAN (R 3.6.1)
#> testthat 2.2.1 2019-07-25 [1] CRAN (R 3.6.1)
#> TH.data 1.0-10 2019-01-21 [1] CRAN (R 3.5.2)
#> usethis 1.5.1.9000 2019-09-01 [1] Github (r-lib/usethis@4377307)
#> withr 2.1.2 2018-03-15 [1] CRAN (R 3.5.1)
#> xfun 0.9 2019-08-21 [1] CRAN (R 3.6.1)
#> yaml 2.2.0 2018-07-25 [1] CRAN (R 3.5.1)
#> zoo 1.8-6 2019-05-28 [1] CRAN (R 3.6.0)
#>
#> [1] C:/Users/inp099/Documents/R/win-library/3.6
#> [2] C:/Program Files/R/R-3.6.1/library
from statsexpressions.
I think this should ideally return NA
s for CIs rather than just failing to work completely.
from statsexpressions.
# dataframe
r2 <- data.frame(
treatment = c("a", "a", "a", "a", "a", "e", "f"),
Age = 1:7,
Group = c("G2", "G2", "G2", "G2", "G2", "G2", "G2"),
stringsAsFactors = F
)
# effect size measure
rcompanion::epsilonSquared(x = r2$Age, g = r2$treatment, ci = TRUE)
#> epsilon.squared lower.ci upper.ci
#> 1 0.643 NA NA
Created on 2019-12-27 by the reprex package (v0.3.0)
Session info
devtools::session_info()
#> - Session info ---------------------------------------------------------------
#> setting value
#> version R version 3.6.1 (2019-07-05)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz Europe/Berlin
#> date 2019-12-27
#>
#> - Packages -------------------------------------------------------------------
#> package * version date lib source
#> assertthat 0.2.1 2019-03-21 [1] CRAN (R 3.6.0)
#> backports 1.1.5 2019-10-02 [1] CRAN (R 3.6.1)
#> boot 1.3-24 2019-12-20 [1] CRAN (R 3.6.1)
#> callr 3.4.0 2019-12-09 [1] CRAN (R 3.6.1)
#> cli 2.0.0.9000 2019-12-23 [1] Github (r-lib/cli@0293ae7)
#> codetools 0.2-16 2018-12-24 [1] CRAN (R 3.5.2)
#> coin 1.3-1 2019-08-28 [1] CRAN (R 3.6.1)
#> crayon 1.3.4 2017-09-16 [1] CRAN (R 3.5.1)
#> desc 1.2.0 2019-11-11 [1] Github (r-lib/desc@61205f6)
#> DescTools 0.99.31 2019-12-02 [1] CRAN (R 3.6.1)
#> devtools 2.2.1 2019-09-24 [1] CRAN (R 3.6.1)
#> digest 0.6.23 2019-11-23 [1] CRAN (R 3.6.1)
#> ellipsis 0.3.0 2019-09-20 [1] CRAN (R 3.6.1)
#> EMT 1.1 2013-01-29 [1] CRAN (R 3.5.2)
#> evaluate 0.14 2019-05-28 [1] CRAN (R 3.6.0)
#> expm 0.999-4 2019-03-21 [1] CRAN (R 3.6.0)
#> fansi 0.4.0 2018-11-05 [1] Github (brodieG/fansi@ab11e9c)
#> fs 1.3.1 2019-05-06 [1] CRAN (R 3.6.0)
#> glue 1.3.1 2019-03-12 [1] CRAN (R 3.6.0)
#> highr 0.8 2019-03-20 [1] CRAN (R 3.6.0)
#> htmltools 0.4.0 2019-10-04 [1] CRAN (R 3.6.1)
#> knitr 1.26 2019-11-12 [1] CRAN (R 3.6.1)
#> lattice 0.20-38 2018-11-04 [2] CRAN (R 3.6.1)
#> libcoin 1.0-5 2019-08-27 [1] CRAN (R 3.6.1)
#> lmtest 0.9-37 2019-04-30 [1] CRAN (R 3.6.0)
#> magrittr 1.5 2014-11-22 [1] CRAN (R 3.5.1)
#> MASS 7.3-51.5 2019-12-20 [1] CRAN (R 3.6.1)
#> Matrix 1.2-18 2019-11-27 [1] CRAN (R 3.6.1)
#> matrixStats 0.55.0 2019-09-07 [1] CRAN (R 3.6.1)
#> memoise 1.1.0 2017-04-21 [1] CRAN (R 3.6.0)
#> modeltools 0.2-22 2018-07-16 [1] CRAN (R 3.5.1)
#> multcomp 1.4-11 2019-12-10 [1] CRAN (R 3.6.1)
#> multcompView 0.1-8 2019-12-19 [1] CRAN (R 3.6.1)
#> mvtnorm 1.0-11 2019-06-19 [1] CRAN (R 3.6.0)
#> nortest 1.0-4 2015-07-30 [1] CRAN (R 3.5.2)
#> pkgbuild 1.0.6 2019-10-09 [1] CRAN (R 3.6.1)
#> pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.0)
#> plyr 1.8.5 2019-12-10 [1] CRAN (R 3.6.1)
#> prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.5.1)
#> processx 3.4.1 2019-07-18 [1] CRAN (R 3.6.1)
#> ps 1.3.0 2018-12-21 [1] CRAN (R 3.6.0)
#> R6 2.4.1 2019-11-12 [1] CRAN (R 3.6.1)
#> rcompanion 2.3.9 2019-12-27 [1] local
#> Rcpp 1.0.3 2019-11-08 [1] CRAN (R 3.6.1)
#> remotes 2.1.0 2019-06-24 [1] CRAN (R 3.6.0)
#> rlang 0.4.2.9000 2019-12-24 [1] Github (r-lib/rlang@ce4f717)
#> rmarkdown 2.0 2019-12-12 [1] CRAN (R 3.6.1)
#> rprojroot 1.3-2 2018-01-03 [1] CRAN (R 3.5.1)
#> sandwich 2.5-1 2019-04-06 [1] CRAN (R 3.6.0)
#> sessioninfo 1.1.1 2018-11-05 [1] CRAN (R 3.6.0)
#> stringi 1.4.3 2019-03-12 [1] CRAN (R 3.6.0)
#> stringr 1.4.0 2019-02-10 [1] CRAN (R 3.6.0)
#> survival 3.1-8 2019-12-03 [1] CRAN (R 3.6.1)
#> testthat 2.3.1 2019-12-01 [1] CRAN (R 3.6.1)
#> TH.data 1.0-10 2019-01-21 [1] CRAN (R 3.5.2)
#> usethis 1.5.1.9000 2019-12-12 [1] Github (r-lib/usethis@23dd62c)
#> withr 2.1.2 2018-03-15 [1] CRAN (R 3.5.1)
#> xfun 0.11 2019-11-12 [1] CRAN (R 3.6.1)
#> yaml 2.2.0 2018-07-25 [1] CRAN (R 3.5.1)
#> zoo 1.8-6 2019-05-28 [1] CRAN (R 3.6.0)
#>
#> [1] C:/Users/inp099/Documents/R/win-library/3.6
#> [2] C:/Program Files/R/R-3.6.1/library
from statsexpressions.
Related Issues (20)
- Add CI method attribute from easystats to output data frames
- Remove `n` parameter from `add_expression_col()`?
- Use `{patrick}` for parametrized testing?
- Guidance for using statsExpressions to customize plots?
- In case of `NA`, show it in the label instead of a blank space
- revdep issues with insight HOT 1
- Use `safe_model_parameters()` in `tidy_model_parameters()` instead?
- Avoid duplicating examples
- Don't include CIs in `centrality_description()` by default
- Exact p-value calculation using Wilcoxon rank sum test HOT 1
- DRY test dataset
- Stop converting to factors
- Revep check failure for {insight} HOT 4
- i wonder if `pairwise_comparisons` can do Tukey's HSD? HOT 1
- Use `{roxyglobals}` HOT 1
- Rename `stats_type_switch()` to `extract_stats_type()`
- wilcoxon test default parameters modification HOT 1
- Fix failures on R-devel
- Necessity of patch-level version bumps in dependencies HOT 1
- Introduce parameters to pass extra arguments
Recommend Projects
-
React
A declarative, efficient, and flexible JavaScript library for building user interfaces.
-
Vue.js
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
-
Typescript
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
-
TensorFlow
An Open Source Machine Learning Framework for Everyone
-
Django
The Web framework for perfectionists with deadlines.
-
Laravel
A PHP framework for web artisans
-
D3
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
-
Recommend Topics
-
javascript
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
-
web
Some thing interesting about web. New door for the world.
-
server
A server is a program made to process requests and deliver data to clients.
-
Machine learning
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
-
Visualization
Some thing interesting about visualization, use data art
-
Game
Some thing interesting about game, make everyone happy.
Recommend Org
-
Facebook
We are working to build community through open source technology. NB: members must have two-factor auth.
-
Microsoft
Open source projects and samples from Microsoft.
-
Google
Google ❤️ Open Source for everyone.
-
Alibaba
Alibaba Open Source for everyone
-
D3
Data-Driven Documents codes.
-
Tencent
China tencent open source team.
from statsexpressions.