Closed IndrajeetPatil closed 3 years ago
Doesn't seem to be a ggsignif
issue. The 80-90
combo is present here.
Minimal reprex:
# setup
set.seed(123)
library(tidyverse)
options(tibble.print_max = Inf)
# dataframe
df <- structure(list(
x = c(
30, 40, 50, 60, 70, 80, 90, 30, 40, 50,
60, 70, 80, 90, 30, 40, 50, 60, 70, 80, 90, 30, 40, 50, 60, 70,
80, 90, 30, 40, 50, 60, 70, 80, 90
),
Participant = c(
"FH2", "FH2",
"FH2", "FH2", "FH2", "FH2", "FH2", "ZW", "ZW", "ZW", "ZW", "ZW",
"ZW", "ZW", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "CL", "CL",
"CL", "CL", "CL", "CL", "CL", "AG", "AG", "AG", "AG", "AG", "AG",
"AG"
),
Method = c(
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
),
y = c(
2571.25, 2688.003333, 2779.363333, 2832.046667,
3050.72, 3255.553333, 3327.173667, 1766.296667, 2107.890333,
2391.7, 2569.24, 2680.22, 2807.59, 2807.953333, 2078.734,
2414.366667, 2583.27, 2923.253333, 3085.96, 3094.003333,
3121.49, 2824.990667, 2716.429667, 2844.323333, 3124.713333,
3252.863333, 3424.24, 3674.463333, 2401.996667, 2719.046667,
2712.99, 2951.965667, 3046.526667, 3100.902667, 3195.331333
)
),
class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"),
row.names = c(NA, -35L),
spec = structure(list(
cols = list(
x = structure(list(), class = c("collector_double", "collector")),
Participant = structure(list(), class = c(
"collector_character",
"collector"
)),
Method = structure(list(), class = c(
"collector_double",
"collector"
)),
y = structure(list(), class = c(
"collector_double",
"collector"
))
),
default = structure(list(), class = c(
"collector_guess",
"collector"
)), skip = 1
),
class = "col_spec"
)
)
library(ggplot2)
library(ggsignif)
df$x <- as.character(df$x)
combos <- utils::combn(unique(df$x), 2, simplify = FALSE)
ggplot(df, aes(x, y)) +
geom_boxplot() +
geom_signif(comparisons = combos, step_increase = 0.2)
Created on 2020-02-19 by the reprex package (v0.3.0)
Clearly this has something to do with the message:
Warning in log(det(U)): NaNs produced
# setup
set.seed(123)
library(tidyverse)
options(tibble.print_max = Inf)
# dataframe
df <- structure(list(
x = c(
30, 40, 50, 60, 70, 80, 90, 30, 40, 50,
60, 70, 80, 90, 30, 40, 50, 60, 70, 80, 90, 30, 40, 50, 60, 70,
80, 90, 30, 40, 50, 60, 70, 80, 90
),
Participant = c(
"FH2", "FH2",
"FH2", "FH2", "FH2", "FH2", "FH2", "ZW", "ZW", "ZW", "ZW", "ZW",
"ZW", "ZW", "KS", "KS", "KS", "KS", "KS", "KS", "KS", "CL", "CL",
"CL", "CL", "CL", "CL", "CL", "AG", "AG", "AG", "AG", "AG", "AG",
"AG"
),
Method = c(
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2
),
y = c(
2571.25, 2688.003333, 2779.363333, 2832.046667,
3050.72, 3255.553333, 3327.173667, 1766.296667, 2107.890333,
2391.7, 2569.24, 2680.22, 2807.59, 2807.953333, 2078.734,
2414.366667, 2583.27, 2923.253333, 3085.96, 3094.003333,
3121.49, 2824.990667, 2716.429667, 2844.323333, 3124.713333,
3252.863333, 3424.24, 3674.463333, 2401.996667, 2719.046667,
2712.99, 2951.965667, 3046.526667, 3100.902667, 3195.331333
)
),
class = c("spec_tbl_df", "tbl_df", "tbl", "data.frame"),
row.names = c(NA, -35L),
spec = structure(list(
cols = list(
x = structure(list(), class = c("collector_double", "collector")),
Participant = structure(list(), class = c(
"collector_character",
"collector"
)),
Method = structure(list(), class = c(
"collector_double",
"collector"
)),
y = structure(list(), class = c(
"collector_double",
"collector"
))
),
default = structure(list(), class = c(
"collector_guess",
"collector"
)), skip = 1
),
class = "col_spec"
)
)
# 80-90 comparison present
ggstatsplot::ggwithinstats(
data = dplyr::filter(df, x > 40),
x = x,
y = y,
pairwise.display = "everything",
)
#> Registered S3 method overwritten by 'broom.mixed':
#> method from
#> tidy.gamlss broom
#> Registered S3 methods overwritten by 'lme4':
#> method from
#> cooks.distance.influence.merMod car
#> influence.merMod car
#> dfbeta.influence.merMod car
#> dfbetas.influence.merMod car
# 80-90 comparison present
ggstatsplot::ggwithinstats(
data = dplyr::filter(df, x > 30),
x = x,
y = y,
pairwise.display = "everything",
)
# 80-90 comparison absent
ggstatsplot::ggwithinstats(
data = dplyr::filter(df, x > 20),
x = x,
y = y,
pairwise.display = "everything",
)
#> Warning in log(det(U)): NaNs produced
Created on 2020-11-18 by the reprex package (v0.3.0)
Hmm, don't see how can this affect pairwise comparison display
options(warn = 2)
ggstatsplot::ggwithinstats(
data = dplyr::filter(df, x > 20),
x = x,
y = y,
pairwise.display = "everything",
)
Error:
Error in log(det(U)) : (converted from warning) NaNs produced
Error in ezANOVA_main(data = data, dv = dv, wid = wid, within = within, :
The car::Anova() function used to compute results and assumption tests seems to have failed. Most commonly this is because you have too few subjects relative to the number of cells in the within-Ss design. It is possible that trying the ANOVA again with "type=1" may yield results (but definitely no assumption tests).
Since the following still works:
ggstatsplot::ggwithinstats(
data = dplyr::filter(df, x > 20),
x = x,
y = y,
pairwise.display = "everything",
)
I am closing this for now. Will reopen it only if this behavior is observed with any other dataset. I am getting the feeling that this problem is specific to the current dataframe and not a general bug affecting all datasets.
The comparison between
80
and90
is missing.You can see it in the
pairwiseComparisons
output, and yet it is not present in thegeom_ggsignif
data.So is this
ggstatsplot
orggsignif
issue?Created on 2020-02-19 by the reprex package (v0.3.0)
Session info
``` r devtools::session_info() #> ─ Session info ─────────────────────────────────────────────────────────────── #> setting value #> version R version 3.6.2 (2019-12-12) #> os macOS Mojave 10.14.6 #> system x86_64, darwin15.6.0 #> ui X11 #> language (EN) #> collate en_US.UTF-8 #> ctype en_US.UTF-8 #> tz Europe/Berlin #> date 2020-02-19 #> #> ─ Packages ─────────────────────────────────────────────────────────────────── #> package * version date lib #> abind 1.4-5 2016-07-21 [1] #> assertthat 0.2.1 2019-03-21 [1] #> backports 1.1.5 2019-10-02 [1] #> base64enc 0.1-3 2015-07-28 [1] #> BayesFactor 0.9.12-4.2 2018-05-19 [1] #> bayestestR 0.5.2 2020-02-13 [1] #> bbmle 1.0.23.1 2020-02-03 [1] #> bdsmatrix 1.3-4 2020-01-13 [1] #> boot 1.3-24 2019-12-20 [1] #> bridgesampling 0.8-1 2020-01-16 [1] #> Brobdingnag 1.2-6 2018-08-13 [1] #> broom 0.5.4 2020-01-27 [1] #> broom.mixed 0.2.4 2019-02-21 [1] #> broomExtra 1.0.1 2020-01-07 [1] #> callr 3.4.2 2020-02-12 [1] #> car 3.0-6 2019-12-23 [1] #> carData 3.0-3 2019-11-16 [1] #> cellranger 1.1.0 2016-07-27 [1] #> cli 2.0.1 2020-01-08 [1] #> cluster 2.1.0 2019-06-19 [2] #> coda 0.19-3 2019-07-05 [1] #> codetools 0.2-16 2018-12-24 [2] #> coin 1.3-1 2019-08-28 [1] #> colorspace 1.4-1 2019-03-18 [1] #> cowplot 1.0.0 2019-07-11 [1] #> crayon 1.3.4 2017-09-16 [1] #> curl 4.3 2019-12-02 [1] #> data.table 1.12.8 2019-12-09 [1] #> DBI 1.1.0 2019-12-15 [1] #> dbplyr 1.4.2 2019-06-17 [1] #> desc 1.2.0 2018-05-01 [1] #> DescTools 0.99.32 2020-01-17 [1] #> devtools 2.2.2 2020-02-17 [1] #> dichromat 2.0-0 2013-01-24 [1] #> digest 0.6.24 2020-02-12 [1] #> dplyr * 0.8.4 2020-01-31 [1] #> effectsize 0.1.2 2020-02-13 [1] #> ellipsis 0.3.0 2019-09-20 [1] #> emmeans 1.4.4 2020-01-28 [1] #> EMT 1.1 2013-01-29 [1] #> estimability 1.3 2018-02-11 [1] #> evaluate 0.14 2019-05-28 [1] #> expm 0.999-4 2019-03-21 [1] #> ez 4.4-0 2016-11-02 [1] #> fansi 0.4.1 2020-01-08 [1] #> farver 2.0.3 2020-01-16 [1] #> fastGHQuad 1.0 2018-09-30 [1] #> fastmap 1.0.1 2019-10-08 [1] #> forcats * 0.4.0 2019-02-17 [1] #> foreign 0.8-72 2019-08-02 [2] #> fs 1.3.1 2019-05-06 [1] #> generics 0.0.2 2018-11-29 [1] #> ggcorrplot 0.1.3 2019-05-19 [1] #> ggExtra 0.9 2019-08-27 [1] #> ggplot2 * 3.3.0.9000 2020-02-18 [1] #> ggrepel 0.8.1 2019-05-07 [1] #> ggsignif 0.6.0 2019-08-08 [1] #> ggstatsplot 0.3.0.9000 2020-02-18 [1] #> glue 1.3.1 2019-03-12 [1] #> gridExtra 2.3 2017-09-09 [1] #> groupedstats 0.1.1 2020-01-14 [1] #> gtable 0.3.0 2019-03-25 [1] #> gtools 3.8.1 2018-06-26 [1] #> haven 2.2.0 2019-11-08 [1] #> highr 0.8 2019-03-20 [1] #> hms 0.5.3 2020-01-08 [1] #> htmltools 0.4.0 2019-10-04 [1] #> httpuv 1.5.2 2019-09-11 [1] #> httr 1.4.1 2019-08-05 [1] #> inline 0.3.15 2018-05-18 [1] #> insight 0.8.1.1 2020-02-13 [1] #> ipmisc 1.1.0 2020-02-09 [1] #> jcolors 0.0.4 2019-05-22 [1] #> jmv 1.2.5 2020-02-17 [1] #> jmvcore 1.2.5 2020-02-05 [1] #> jsonlite 1.6.1 2020-02-02 [1] #> knitr 1.28 2020-02-06 [1] #> labeling 0.3 2014-08-23 [1] #> LaplacesDemon 16.1.4 2020-02-06 [1] #> later 1.0.0 2019-10-04 [1] #> lattice 0.20-38 2018-11-04 [2] #> libcoin 1.0-5 2019-08-27 [1] #> lifecycle 0.1.0 2019-08-01 [1] #> lme4 1.1-21 2019-03-05 [1] #> lmtest 0.9-37 2019-04-30 [1] #> logspline 2.1.15 2019-11-08 [1] #> loo 2.2.0 2019-12-19 [1] #> lubridate 1.7.4 2018-04-11 [1] #> magrittr 1.5 2014-11-22 [1] #> mapproj 1.2.7 2020-02-03 [1] #> maps 3.3.0 2018-04-03 [1] #> MASS 7.3-51.4 2019-03-31 [2] #> Matrix 1.2-18 2019-11-27 [2] #> MatrixModels 0.4-1 2015-08-22 [1] #> matrixStats 0.55.0 2019-09-07 [1] #> mc2d 0.1-18 2017-03-06 [1] #> memoise 1.1.0 2017-04-21 [1] #> metaBMA 0.6.2 2019-09-16 [1] #> metafor 2.1-0 2019-05-14 [1] #> metaplus 0.7-11 2018-04-01 [1] #> mgcv 1.8-31 2019-11-09 [2] #> mime 0.9 2020-02-04 [1] #> miniUI 0.1.1.1 2018-05-18 [1] #> minqa 1.2.4 2014-10-09 [1] #> mnormt 1.5-6 2020-02-03 [1] #> modelr 0.1.5 2019-08-08 [1] #> modeltools 0.2-22 2018-07-16 [1] #> multcomp 1.4-12 2020-01-10 [1] #> multcompView 0.1-8 2019-12-19 [1] #> munsell 0.5.0 2018-06-12 [1] #> mvtnorm 1.0-12 2020-01-09 [1] #> nlme 3.1-142 2019-11-07 [2] #> nloptr 1.2.1 2018-10-03 [1] #> nortest 1.0-4 2015-07-30 [1] #> numDeriv 2016.8-1.1 2019-06-06 [1] #> oompaBase 3.2.9 2019-08-24 [1] #> openxlsx 4.1.4 2019-12-06 [1] #> pairwiseComparisons 0.2.5 2020-02-11 [1] #> paletteer 1.0.0 2019-12-18 [1] #> palr 0.2.0 2020-01-30 [1] #> pals 1.6 2019-12-04 [1] #> parameters 0.5.0.1 2020-02-13 [1] #> pbapply 1.4-2 2019-08-31 [1] #> performance 0.4.4.1 2020-02-13 [1] #> pillar 1.4.3 2019-12-20 [1] #> pkgbuild 1.0.6 2019-10-09 [1] #> pkgconfig 2.0.3 2019-09-22 [1] #> pkgload 1.0.2 2018-10-29 [1] #> plyr 1.8.5 2019-12-10 [1] #> prettyunits 1.1.1 2020-01-24 [1] #> prismatic 0.2.0 2019-12-01 [1] #> processx 3.4.2 2020-02-09 [1] #> promises 1.1.0 2019-10-04 [1] #> ps 1.3.2 2020-02-13 [1] #> psych 1.9.12.31 2020-01-08 [1] #> purrr * 0.3.3 2019-10-18 [1] #> R6 2.4.1 2019-11-12 [1] #> rcompanion 2.3.25 2020-02-09 [1] #> Rcpp 1.0.3 2019-11-08 [1] #> readr * 1.3.1 2018-12-21 [1] #> readxl 1.3.1 2019-03-13 [1] #> rematch2 2.1.0 2019-07-11 [1] #> remotes 2.1.1 2020-02-15 [1] #> repr 1.1.0 2020-01-28 [1] #> reprex 0.3.0 2019-05-16 [1] #> reshape 0.8.8 2018-10-23 [1] #> reshape2 1.4.3 2017-12-11 [1] #> rio 0.5.16 2018-11-26 [1] #> rjson 0.2.20 2018-06-08 [1] #> rlang 0.4.4 2020-01-28 [1] #> rmarkdown 2.1 2020-01-20 [1] #> rprojroot 1.3-2 2018-01-03 [1] #> rstan 2.19.3 2020-02-11 [1] #> rstantools 2.0.0 2019-09-15 [1] #> rstudioapi 0.11 2020-02-07 [1] #> rvest 0.3.5 2019-11-08 [1] #> sandwich 2.5-1 2019-04-06 [1] #> scales 1.1.0 2019-11-18 [1] #> scico 1.1.0 2018-11-20 [1] #> sessioninfo 1.1.1 2018-11-05 [1] #> shiny 1.4.0 2019-10-10 [1] #> sjlabelled 1.1.3 2020-01-28 [1] #> sjmisc 2.8.3 2020-01-10 [1] #> sjstats 0.17.9 2020-02-06 [1] #> skimr 2.1 2020-02-01 [1] #> StanHeaders 2.21.0-1 2020-01-19 [1] #> statsExpressions 0.3.1 2020-02-14 [1] #> stringi 1.4.6 2020-02-17 [1] #> stringr * 1.4.0 2019-02-10 [1] #> survival 3.1-8 2019-12-03 [2] #> testthat 2.3.1 2019-12-01 [1] #> TH.data 1.0-10 2019-01-21 [1] #> tibble * 2.1.3 2019-06-06 [1] #> tidyr * 1.0.2 2020-01-24 [1] #> tidyselect 1.0.0 2020-01-27 [1] #> tidyverse * 1.3.0 2019-11-21 [1] #> TMB 1.7.16 2020-01-15 [1] #> usethis 1.5.1.9000 2020-02-18 [1] #> utf8 1.1.4 2018-05-24 [1] #> vctrs 0.2.2 2020-01-24 [1] #> withr 2.1.2 2018-03-15 [1] #> WRS2 1.0-0 2019-06-06 [1] #> xfun 0.12 2020-01-13 [1] #> xml2 1.2.2 2019-08-09 [1] #> xtable 1.8-4 2019-04-21 [1] #> yaml 2.2.1 2020-02-01 [1] #> zeallot 0.1.0 2018-01-28 [1] #> zip 2.0.4 2019-09-01 [1] #> zoo 1.8-7 2020-01-10 [1] #> source #> CRAN (R 3.6.0) #> CRAN (R 3.6.0) #> CRAN (R 3.6.0) #> CRAN (R 3.6.0) #> CRAN (R 3.6.0) #> Github (easystats/bayestestR@4350b4f) #> CRAN (R 3.6.2) #> CRAN (R 3.6.2) #> CRAN (R 3.6.0) #> CRAN (R 3.6.0) #> CRAN (R 3.6.0) #> CRAN (R 3.6.2) #> CRAN (R 3.6.0) #> CRAN (R 3.6.2) #> CRAN (R 3.6.2) #> CRAN (R 3.6.0) #> CRAN (R 3.6.0) #> CRAN (R 3.6.0) #> CRAN (R 3.6.2) #> CRAN (R 3.6.2) #> CRAN (R 3.6.0) #> CRAN (R 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