indrajeetpatil / statsexpressions Goto Github PK
View Code? Open in Web Editor NEWTidy data frames and expressions with statistical summaries 📜
Home Page: https://indrajeetpatil.github.io/statsExpressions/
License: Other
statsexpressions's Issues
get rid of the warning from `easystats`
error in `paired` version of ANOVA when dataframe not sorted by subject ID
# data
df <-
structure(list(
score = c(
70, 82.5, 97.5, 100, 52.5, 62.5,
92.5, 70, 90, 92.5, 90, 75, 60, 90, 85, 67.5, 90, 72.5, 45, 60,
72.5, 80, 100, 100, 97.5, 95, 65, 87.5, 90, 62.5, 100, 100, 97.5,
100, 97.5, 95, 82.5, 82.5, 40, 92.5, 85, 72.5, 35, 27.5, 82.5
), condition = structure(c(
5L, 1L, 2L, 3L, 4L, 4L, 5L, 1L,
2L, 3L, 2L, 3L, 3L, 4L, 2L, 1L, 5L, 5L, 4L, 1L, 1L, 4L, 3L, 5L,
2L, 5L, 1L, 2L, 3L, 4L, 4L, 5L, 1L, 2L, 3L, 2L, 3L, 4L, 1L, 5L,
3L, 2L, 5L, 4L, 1L
), .Label = c("1", "2", "3", "4", "5"), class = "factor"),
id = structure(c(
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L,
2L, 3L, 3L, 4L, 3L, 4L, 3L, 4L, 3L, 4L, 4L, 5L, 5L, 5L, 5L,
5L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L,
8L, 9L, 9L, 9L, 9L, 9L
), .Label = c(
"1", "2", "3", "4", "5",
"6", "7", "8", "9"
), class = "factor")
), row.names = c(
NA,
45L
), class = "data.frame")
# expected output
friedman.test(formula = score ~ condition | id, data = df)
#>
#> Friedman rank sum test
#>
#> data: score and condition and id
#> Friedman chi-squared = 12.729, df = 4, p-value = 0.01268
# ggstatsplot output
# incorrect
set.seed(123)
statsExpressions::expr_anova_nonparametric(
data = df,
x = condition,
y = score,
paired = TRUE,
output = "dataframe"
)
#> # A tibble: 1 x 7
#> statistic p.value parameter method estimate conf.low conf.high
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 13.9 0.00773 4 Friedman rank sum test 0.543 0.482 0.855
# correct
set.seed(123)
statsExpressions::expr_anova_nonparametric(
data = dplyr::arrange(df, id),
x = condition,
y = score,
paired = TRUE,
output = "dataframe"
)
#> # A tibble: 1 x 7
#> statistic p.value parameter method estimate conf.low conf.high
#> <dbl> <dbl> <dbl> <chr> <dbl> <dbl> <dbl>
#> 1 12.7 0.0127 4 Friedman rank sum test 0.527 0.458 0.849Created on 2020-10-17 by the reprex package (v0.3.0.9001)
adding support for robust meta-analysis
library(robumeta)
# Load data
data(hierdat)
# Small-Sample Corrections - Hierarchical Dependence Model
HierModSm <- robu(formula = effectsize ~ binge + followup + sreport
+ age, data = hierdat, studynum = studyid,
var.eff.size = var, modelweights = "HIER", small = TRUE)
class(HierModSm)
#> [1] "robu"
tibble::as_tibble(HierModSm$reg_table)
#> # A tibble: 5 x 9
#> labels b.r SE t dfs prob CI.L CI.U sig
#> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 X.Intercept. 0.397 0.658 0.603 3.06 0.588 -1.68 2.47 ""
#> 2 binge 0.452 0.102 4.44 3.59 0.0144 0.156 0.747 "**"
#> 3 followup 0.00133 0.000723 1.84 2.03 0.205 -0.00173 0.00439 ""
#> 4 sreport 0.539 0.143 3.76 4.36 0.0170 0.153 0.924 "**"
#> 5 age -0.0437 0.0379 -1.15 2.69 0.340 -0.172 0.0849 ""Created on 2020-01-09 by the reprex package (v0.3.0)
Session info
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 2020-01-09
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib source
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#> pkgbuild 1.0.6 2019-10-09 [1] CRAN (R 3.6.0)
#> pkgconfig 2.0.3 2019-09-22 [1] CRAN (R 3.6.0)
#> pkgload 1.0.2 2018-10-29 [1] CRAN (R 3.6.0)
#> prettyunits 1.0.2 2015-07-13 [1] CRAN (R 3.6.0)
#> processx 3.4.1 2019-07-18 [1] CRAN (R 3.6.0)
#> ps 1.3.0 2018-12-21 [1] CRAN (R 3.6.0)
#> R6 2.4.1 2019-11-12 [1] CRAN (R 3.6.0)
#> 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 2020-01-07 [1] Github (r-lib/rlang@e48b07d)
#> rmarkdown 2.0 2019-12-12 [1] CRAN (R 3.6.0)
#> robumeta * 2.0 2017-05-29 [1] CRAN (R 3.6.0)
#> rprojroot 1.3-2 2018-01-03 [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)
#> testthat 2.3.1 2019-12-01 [1] CRAN (R 3.6.0)
#> tibble 2.1.3 2019-06-06 [1] CRAN (R 3.6.0)
#> usethis 1.5.1 2019-07-04 [1] CRAN (R 3.6.0)
#> utf8 1.1.4 2018-05-24 [1] CRAN (R 3.6.0)
#> vctrs 0.2.1 2019-12-17 [1] CRAN (R 3.6.2)
#> withr 2.1.2 2018-03-15 [1] CRAN (R 3.6.0)
#> xfun 0.11 2019-11-12 [1] CRAN (R 3.6.0)
#> yaml 2.2.0 2018-07-25 [1] CRAN (R 3.6.0)
#> zeallot 0.1.0 2018-01-28 [1] CRAN (R 3.6.0)
#>
#> [1] /Users/patil/Library/R/3.6/library
#> [2] /Library/Frameworks/R.framework/Versions/3.6/Resources/librarybrain dump
- retire
sphericity.correctionargument - remove
insightfromDESCRIPTION - more explicit description of robust correlation test than just
pb - add
"ordinal"to epsilon-squared effect size - remove alias
bf_meta - retire all
_bayesfunction here and just reexport them fromtidyBF(this means the default output fromtidyBFwill have to be"expression", just to be consistent. A breaking change but can't see any other way to avoid all the code duplication happening here) - using
correlationfor Bayesian analysis thanBayesFactor - add
lifecyclebadges to functions - use
tidy_model_parametersinbf_extractor - retire
log_e_bf10column - make the switch functions less fragile (https://github.com/IndrajeetPatil/statsExpressions/blob/master/R/helpers_expr_templates.R)
- use AKP as effect size consistently throughout robust statistics?
- make sure all instances of
effectsizefunctions useverbose = FALSE - change
HedgetoHedgesin expression (https://en.wikipedia.org/wiki/Effect_size#Hedges'_g) - is it necessary to
quotethe expression elements inswitchfunctions? - add example to
READMEwithkable - with
ipmisc 6.0, you don't need to do this; just uselength(unique(data$rowid))
statsExpressions/R/t_twosample.R
Line 276 in 9d6752e
Feature Request one-tailed
This package is absolutely wonderful and makes some amazing plots! It would be awesome if it could also report one-tailed t-tests, rather than two-tailed.
Thank you so much for this package!
different CIs for V depending on x versus y
library(rcompanion)
data(Anderson)
Species = c(rep("Species1", 16), rep("Species2", 16))
Color = c(rep(c("blue", "blue", "blue", "green"),4),
rep(c("green", "green", "green", "blue"),4))
set.seed(123)
cramerV(Species, Color, ci = TRUE, digits = 5)
#> Cramer.V lower.ci upper.ci
#> 1 0.5 0.18787 0.80501
set.seed(123)
cramerV(Color, Species, ci = TRUE, digits = 5)
#> Cramer.V lower.ci upper.ci
#> 1 0.5 0.18787 0.80501
library(statsExpressions)
set.seed(123)
expr_contingency_tab(mtcars, am, cyl)
#> paste(NULL, chi["Pearson"]^2, "(", "2", ") = ", "8.74", ", ",
#> italic("p"), " = ", "0.013", ", ", widehat(italic("V"))["Cramer"],
#> " = ", "0.46", ", CI"["95%"], " [", "0.08", ", ", "0.75",
#> "]", ", ", italic("n")["obs"], " = ", 32L)
set.seed(123)
expr_contingency_tab(mtcars, cyl, am)
#> paste(NULL, chi["Pearson"]^2, "(", "2", ") = ", "8.74", ", ",
#> italic("p"), " = ", "0.013", ", ", widehat(italic("V"))["Cramer"],
#> " = ", "0.46", ", CI"["95%"], " [", "0.11", ", ", "0.73",
#> "]", ", ", italic("n")["obs"], " = ", 32L)Created on 2020-10-09 by the reprex package (v0.3.0)
Add arguments to control Bayes factor output
I appreciate the response to issue IndrajeetPatil/ggstatsplot#153---the defaults make sense. But would it be possible to have an argument that controls whether BF10 or BF01 and log or not log is displayed? Personally, I like displaying the frequentist on top and Bayesian on bottom, but I would prefer to see the regular BF10. Again, I appreciate the defaults, but can you make it customizable by the user?
Effect Size Has Opposite Sign to Test Statistic
Intuitively, I expect the test statistic and effect size to have the same sign. Why is it opposite?
library(ggstatsplot)
data <- data.frame(scores = c(rpois(10, 10), rpois(10, 20)),
groups = factor(rep(c("No", "Yes"), each = 10)))
ggbetweenstats(data, x = groups, y = scores, type = "nonparametric")
Is the convention supposed to be factor's level 2 - factor's level 1 (baseline) and the effect size should have opposite sign?
`WRS2` feature requests: Part 2
Expecting the future releases of WRS2 to offer the following functionality so that the internal helper functions can be removed.
All of these may or may not be supported.
New functionality
- Effect size measure + its confidence intervals for
WRS2::rmanova(wAKP.avg):
(https://dornsife.usc.edu/assets/sites/239/docs/Rallfun-v38.txt) - Supporting bootstrap-t method for one-sample test (new function? See
WRS2::trimcibtin the comment below)
Bugs
- Fix bug (#13 (comment))
Enhancements to existing functions
- Add
...to all functions to make writing wrappers around them easier
Consistency-related issues across functions
- Leave out
p.critcolumn that is sporadically found only in certainWRS2objects -
yuenoutput containsse, whileyuenddoesn't - better to be consistent across these two function outputs - contain the
trvalue in returned objects - Just like
t1way, contain the CIs for explanatory effect sizes in the output ofyuenitself, so that the user doesn't have to run an additional testyuen.effect.ci
> yuen(Anxiety ~ Group, data = spider)
Call:
yuen(formula = Anxiety ~ Group, data = spider)
Test statistic: 1.2958 (df = 13.91), p-value = 0.21614
Trimmed mean difference: -6.75
95 percent confidence interval:
-17.9294 4.4294
Explanatory measure of effect size: 0.38
> yuen.effect.ci(Anxiety ~ Group, data = spider)
$effsize
[1] 0.3784167
$CI
[1] 0.0000000 0.8152107General
- where are tests? can't find them here: https://github.com/cran/WRS2
feature request: reporting results in `rmarkdown`
I would like to know if there's a way to use the APA-formatted text of the statistical results prepared with the subtitle helper functions in a plain RMarkdown file (probably with inline R code?)
For example-
The correlation analysis showed that `x` and `y` were correlated:
`r corr_test(data, x, y)`.Also see:
https://easystats.github.io/report/
correct way to enter argument for `cohenG` for `rcompanion`
# setup
library(rcompanion)
library(statsExpressions)
set.seed(123)
# data
df <-
data.frame(cat1 = rep(c("A", "B"), 10),
cat2 = c(rep("C", 10), rep("D", 10)))
# method-1
as.matrix(table(df))
#> cat2
#> cat1 C D
#> A 5 5
#> B 5 5
rcompanion::cohenG(as.matrix(table(df)), ci = TRUE, type = "norm", R = 10)
#> $Global.statistics
#> Dimensions Statistic Value lower.ci upper.ci
#> 1 2 x 2 OR 1.0 -0.0311 2.170
#> 2 2 x 2 P 0.5 0.2420 0.896
#> 3 2 x 2 g 0.0 -0.2580 0.396
# method-2
factor.levels <- dplyr::union(levels(df$cat1), levels(df$cat2))
df %<>%
dplyr::mutate_at(
.tbl = .,
.vars = dplyr::vars(cat1, cat2),
.funs = factor,
levels = factor.levels
)
as.matrix(table(df))
#> cat2
#> cat1 A B C D
#> A 0 0 5 5
#> B 0 0 5 5
#> C 0 0 0 0
#> D 0 0 0 0
rcompanion::cohenG(as.matrix(table(df)), ci = TRUE, type = "norm", R = 10)
#> Error in if (const(t, min(1e-08, mean(t, na.rm = TRUE)/1e+06))) {: missing value where TRUE/FALSE neededCreated on 2019-10-09 by the reprex package (v0.3.0)
Session info
devtools::session_info()
#> - Session info ----------------------------------------------------------
#> setting value
#> version R version 3.6.1 (2019-07-05)
#> os Windows 10 x64
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate English_United States.1252
#> ctype English_United States.1252
#> tz Europe/Berlin
#> date 2019-10-09
#>
#> - Packages --------------------------------------------------------------
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#> sjstats 0.17.6 2019-09-08 [1]
#> skimr 1.0.7 2019-06-20 [1]
#> SparseM 1.77 2017-04-23 [1]
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#> xfun 0.10 2019-10-01 [1]
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#> source
#> CRAN (R 3.5.0)
#> CRAN (R 3.6.0)
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#> [1] C:/Users/inp099/Documents/R/win-library/3.6
#> [2] C:/Program Files/R/R-3.6.1/libraryremove reliance on `groupedstats`
Directly use effectsize package
p-value When Two Groups Are Completely Separated
Should be p less than some value, shouldn't it?
Select specific stats
Hello,
Is there a way to do a specific test, e.g. just the chi-squared test without the Crammer V?
Many thanks.
`expr_contingency_tab` needs to protect against wrong test label
Introduced in b536416
library(statsExpressions)
df <- dplyr::filter(mtcars, am == "0")
stats::chisq.test(table(df$am, df$cyl))
#>
#> Chi-squared test for given probabilities
#>
#> data: table(df$am, df$cyl)
#> X-squared = 7.6842, df = 2, p-value = 0.02145
expr_contingency_tab(mtcars, am, cyl)
#> Warning in stats::chisq.test(x = x_arg, correct = FALSE): Chi-squared
#> approximation may be incorrect
#> paste(chi["Pearson"]^2, "(", "2", ") = ", "8.74", ", ", italic("p"),
#> " = ", "0.013", ", ", widehat(italic("V"))["Cramer"], " = ",
#> "0.46", ", CI"["95%"], " [", "0.00", ", ", "0.78", "]", ", ",
#> italic("n")["obs"], " = ", 32L)Created on 2020-10-14 by the reprex package (v0.3.0.9001)
checking results from metaBMA
- MacOS
library(metaBMA)
#> Loading required package: Rcpp
data(towels)
set.seed(123)
meta_random(logOR, SE, study, data = towels)
#> Warning: There were 2 divergent transitions after warmup. Increasing adapt_delta above 0.95 may help. See
#> http://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> Warning: Examine the pairs() plot to diagnose sampling problems
#> ### Bayesian Random-Effects Meta-Analysis ###
#> Prior on d: 'norm' (mean=0, sd=0.3) with support on the interval [-Inf,Inf].
#> Prior on tau: 'invgamma' (shape=1, scale=0.15) with support on the interval [0,Inf].
#>
#> # Bayes factors:
#> (denominator)
#> (numerator) random_H0 random_H1
#> random_H0 1 0.501
#> random_H1 2 1.000
#> # Posterior summary statistics of random-effects model:
#> mean sd 2.5% 50% 97.5% hpd95_lower hpd95_upper n_eff Rhat
#> d 0.183 0.102 -0.041 0.189 0.368 -0.027 0.376 5352.8 1.001
#> tau 0.135 0.097 0.033 0.109 0.391 0.019 0.321 3909.1 1.000Created on 2020-01-26 by the reprex package (v0.3.0)
Session info
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 3.6.2 (2019-12-12)
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#> ui X11
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#> tz Europe/Berlin
#> date 2020-01-26
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#>
#> [1] /Users/patil/Library/R/3.6/library
#> [2] /Library/Frameworks/R.framework/Versions/3.6/Resources/librarybrain dump
Maybe use https://easystats.github.io/effectsize/reference/phi.html to compute Cramer's V instead of rcompanion?
brain dump
- Drop
crayonre-exports. These are not being used anywhere exceptggstatsplot - Remove
outlier_df- it's not used anywhere butggstatsplot - Have a tidy alias for
specify_decimal_p(something likeformat_num?) - Remove
set_cwd(). I think this is a bad idea.here::hereshould be encouraged. - retire
specify_decimal_pin favor offormat_num - remove reference to
groupedstats - don't use
parametersfor testing
ungroup before running correlation analysis
df <- dplyr::group_by(mtcars, am)
statsExpressions::expr_corr_test(df, wt, mpg)
#> Adding missing grouping variables: `am`
#> # A tibble: 2 x 14
#> group parameter1 parameter2 estimate conf.level conf.low conf.high statistic
#> <chr> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 0 wt mpg -0.768 0.95 -0.906 -0.481 -4.94
#> 2 1 wt mpg -0.909 0.95 -0.973 -0.717 -7.23
#> # … with 6 more variables: df.error <int>, p.value <dbl>, method <chr>,
#> # n.obs <int>, effectsize <chr>, expression <list>Created on 2021-02-26 by the reprex package (v1.0.0)
extra space between subtitle and title for Bayes Factor results
write `tidy_correlation` to be exported and used in `ggstatsplot`?
- to be consistent across functions, change robust correlation to Winsorized correlation with
trargument
`yuen.effect.ci` and `statsExpressions` outputs aren't identical
library(statsExpressions)
#> 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
library(WRS2)
set.seed(123)
yuen.effect.ci(wt ~ am,
data = mtcars,
tr = 0.2,
nboot = 100)
#> $effsize
#> [1] 0.9149793
#>
#> $CI
#> [1] 0.7388653 0.9785818
set.seed(123)
expr_t_robust(mtcars,
am,
wt,
type = "r",
tr = 0.2,
nboot = 100,
k = 4,
messages = FALSE)
#> paste(NULL, italic("t"), "(", "13.5838", ") = ", "5.8400", ", ",
#> italic("p"), " = ", "< 0.001", ", ", widehat(italic(xi)),
#> " = ", "0.9177", ", CI"["95%"], " [", "0.6794", ", ", "0.9942",
#> "]", ", ", italic("n")["obs"], " = ", 32L)Created on 2019-12-29 by the reprex package (v0.3.0)
Session info
devtools::session_info()
#> - Session info ---------------------------------------------------------------
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#> rstudioapi 0.10 2019-03-19 [1]
#> sandwich 2.5-1 2019-04-06 [1]
#> scales 1.1.0 2019-11-18 [1]
#> sessioninfo 1.1.1 2018-11-05 [1]
#> sjlabelled 1.1.1 2019-09-13 [1]
#> sjmisc 2.8.2 2019-09-24 [1]
#> sjstats 0.17.7 2019-11-14 [1]
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#> zeallot 0.1.0 2018-01-28 [1]
#> zip 2.0.4 2019-09-01 [1]
#> zoo 1.8-6 2019-05-28 [1]
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#> [1] C:/Users/inp099/Documents/R/win-library/3.6
#> [2] C:/Program Files/R/R-3.6.2/libraryrefactor `effsize_t_parametric` so that it doesn't need `tobject`
Although this function is not exported, to be consistent with other functions, think of if there is a way to refactor this so that it doesn't need tobject argument.
McNemar's test: is this supposed to work?
# setup
set.seed(123)
library(ggplot2)
library(ggalluvial)
data(vaccinations)
df <- vaccinations %>%
dplyr::mutate(.data = ., survey = droplevels(survey)) %>%
tidyr::uncount(., freq)
table(df$survey, df$response)
#>
#> Missing Never Sometimes Always
#> ms153_NSA 92 132 584 145
#> ms432_NSA 498 93 215 147
#> ms460_NSA 7 163 401 382
stats::mcnemar.test(table(df$survey, df$response))
#> Error in stats::mcnemar.test(table(df$survey, df$response)): 'x' must be square with at least two rows and columns
rcompanion::cohenG(table(df$survey, df$response))
#> Error in rcompanion::cohenG(table(df$survey, df$response)): Matrix must be square with at least two rows and columnsCreated on 2020-03-27 by the reprex package (v0.3.0.9001)
move from `broomExtra` to `parameters`
The first thing to resolve for this to happen is to see if model_parameter works properly inside the expr_meta_random function environment:
easystats/parameters#176
The other replacements are straightforward.
use `conf.level` in the dataframe itself, rather than externally specifying it
collapse all separate `_meta` functions into single function
breaking tests after parameter update
These are the issues for the current parameters I'm submitting:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: expr_anova_bayes
> ### Title: Expression containing Bayesian one-way ANOVA results
> ### Aliases: expr_anova_bayes
>
> ### ** Examples
>
> # setup
> set.seed(123)
> library(statsExpressions)
>
> expr_anova_bayes(
+ data = ggplot2::msleep,
+ x = vore,
+ y = sleep_rem
+ )
Error: Can't recycle `..1` (size 7) to match `..2` (size 0).
Backtrace:
█
1. ├─statsExpressions::expr_anova_bayes(...)
2. │ └─tidyBF::bf_oneway_anova(...)
3. │ └─tidyBF::bf_extractor(bf_object, ...)
4. │ └─dplyr::bind_cols(...)
5. │ └─vctrs::vec_cbind(!!!dots, .name_repair = .name_repair)
6. └─vctrs::stop_incompatible_size(...)
7. └─vctrs:::stop_incompatible(...)
8. └─vctrs:::stop_vctrs(...)
Execution haltedHave you resolved this issue, i.e. does statsExpressions work with the latest parameters?
return dataframe containing combined stats and effect size dataframes
Might be a feature worth exploring since currently the only thing the functions can return are expressions
conditionally use all of the meta-analysis packages
Customisation of Subtitle Formatting
Could it be easier in future to customise the subtitle formatting style? For example,
I would like to have the test statistic on linear scale rather than log scale (most texts use linear scale) and p-value shown in format
Also, isn't it commonly known as the U test? I wonder why W is used for the test statistic.
Using `alternative` argument
I'm using one_sample_test in one-tailed comparison, and the alternative argument is missing.
Either this is something that you may not consider as from previous issue (#10), or is this something related with issue from another repository IndrajeetPatil/pairwiseComparisons#28
Any plan to add this in near future?
Thanks
tweaks to Bayesian expression template
-
Instead of the median, replace with:
-
ANOVA: Bayesian
-
Correlation: Pearson
-
t-test: difference
-
contingency: cramer
-
Add sample sizes
roadmap for `0.7.0`
- use
parametersto extract a dataframe fromonesampb - remove summary table here https://indrajeetpatil.github.io/statsExpressions/articles/stats_details.html#two-sample-tests-1
- use
effectsizeinstead ofrcompanionto extract effect sizes for non-parametric tests - see if
effectsizewould add a columneffsize.methodto the output dataframe with more detailed description of the effect size - if
model_parametersreturns effect sizes for non-parametric tests as well, collapse individualexpr_t_*andexpr_anova_*type functions to one function - move
bf_functions tostatsExpressionsand cover them undertypeargument
pending `WRS2` feature requests
Expecting the future releases of WRS2 to offer the following functionality so that the internal helper functions can be removed. All of these may or may not be supported.
- Effect size measure + its confidence intervals for
WRS2::rmanova - Confidence intervals for effect size measure for
WRS2::t1way - Confidence intervals for percentage bend correlation, both
WRS2::pbcorandWRS2::pball - Confidence intervals for effect size measure for
WRS2::yuend - Supporting bootstrap-t method for one-sample test (new function?
WRS2::trimcibt) - Fix bug (#13 (comment))
- IndrajeetPatil/pairwiseComparisons#12
failing rev-dep checks for insight
insight was just submitted, and this is the result of the rev-dep-checks:
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(statsExpressions)
>
> test_check("statsExpressions")
Starting 2 test processes
══ Skipped tests ═══════════════════════════════════════════════════════════════
● On CRAN (19)
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure (test-t-dataframes.R:23:5): dataframes for parametric t-tests ──────
`df_1` (`actual`) not equal to structure(...) (`expected`).
names(actual) | names(expected)
[4] "p.value" | "p.value" [4]
[5] "method" | "method" [5]
[6] "estimate" | "estimate" [6]
[7] "conf.level" - "ci.width" [7]
[8] "conf.low" | "conf.low" [8]
[9] "conf.high" | "conf.high" [9]
[10] "effectsize" | "effectsize" [10]
`actual$conf.level` is a double vector (0.89, 0.99, 0.9, 0.5)
`expected$conf.level` is absent
`actual$ci.width` is absent
`expected$ci.width` is a double vector (0.89, 0.99, 0.9, 0.5)
── Failure (test-t-dataframes.R:98:5): dataframes for parametric t-tests ──────
`df_2_between` (`actual`) not equal to structure(...) (`expected`).
names(actual) | names(expected)
[7] "p.value" | "p.value" [7]
[8] "method" | "method" [8]
[9] "estimate" | "estimate" [9]
[10] "conf.level" - "ci.width" [10]
[11] "conf.low" | "conf.low" [11]
[12] "conf.high" | "conf.high" [12]
[13] "effectsize" | "effectsize" [13]
`actual$conf.level` is a double vector (0.89, 0.99, 0.9, 0.5)
`expected$conf.level` is absent
`actual$ci.width` is absent
`expected$ci.width` is a double vector (0.89, 0.99, 0.9, 0.5)
── Failure (test-t-dataframes.R:191:5): dataframes for parametric t-tests ─────
`df_2_within` (`actual`) not equal to structure(...) (`expected`).
names(actual) | names(expected)
[5] "p.value" | "p.value" [5]
[6] "method" | "method" [6]
[7] "estimate" | "estimate" [7]
[8] "conf.level" - "ci.width" [8]
[9] "conf.low" | "conf.low" [9]
[10] "conf.high" | "conf.high" [10]
[11] "effectsize" | "effectsize" [11]
`actual$conf.level` is a double vector (0.89, 0.99, 0.9, 0.5)
`expected$conf.level` is absent
`actual$ci.width` is absent
`expected$ci.width` is a double vector (0.89, 0.99, 0.9, 0.5)
[ FAIL 3 | WARN 0 | SKIP 19 | PASS 69 ]
Error: Test failures
Execution halted
I think you have already adressed this? But just wanted to be sure so your update is ready once insight is on CRAN. :-)
Use a weights vector/column for raw data
I've tried using the ggstatsplot package with raw survey response data, and I find it really straightforward to do. However, it would be great if I could specify the weight column in the same formula, or some other way.
The following functions have a weight argument:
count(x, wt = weight, ...) # weighted count
xtabs(data[, "weight"] ~ data[, x] + data[, y]) # weighted crosstab
weighted.mean(x, w = weight) # weighted mean
radiant.data::weighted.sd(x, wt = weight) # weighted standard deviation...It would be great to be able to pass the weight argument from the same dataset, or from an external vector, into the ggstatsplot functions like so:
ggstatsplot::ggwithinstats(dataset, x = var1, y = var2, wt = weight)ggbarstats - paired - subtitle/caption not shown
Seems like stats are not shown when paired option is enabled (using ggstatsplot "v0.1.2")
Cheers
dfEx<-data.frame(cat1= rep(c("A","B"),10), cat2=c(rep("C",10),rep("D",10)))
ggstatsplot::ggbarstats(
data=dfEx,
x=cat1,
y=cat2,
paired=T,
nboot=10
)
#> Registered S3 methods overwritten by 'broom.mixed':
#> method from
#> augment.lme broom
#> augment.merMod broom
#> glance.lme broom
#> glance.merMod broom
#> glance.stanreg broom
#> tidy.brmsfit broom
#> tidy.gamlss broom
#> tidy.lme broom
#> tidy.merMod broom
#> tidy.rjags broom
#> tidy.stanfit broom
#> tidy.stanreg broom
#> Registered S3 methods overwritten by 'car':
#> method from
#> influence.merMod lme4
#> cooks.distance.influence.merMod lme4
#> dfbeta.influence.merMod lme4
#> dfbetas.influence.merMod lme4
#> # A tibble: 2 x 11
#> cat2 counts perc N A B statistic p.value parameter method
#> <fct> <int> <dbl> <chr> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 D 10 50 (n =~ 50.0~ 50.0~ 0 1 1 Chi-s~
#> 2 C 10 50 (n =~ 50.0~ 50.0~ 0 1 1 Chi-s~
#> # ... with 1 more variable: significance <chr>
Created on 2019-10-08 by the reprex package (v0.3.0)
use `afex` instead of `ez` for `expr_anova_parametric`
within
library(afex)
library(statsExpressions)
set.seed(123)
fit_all <- aov_ez("id", "value", iris_long, within = "condition")
as.data.frame(parameters::model_parameters(fit_all, omega_squared = "partial"))
#> Parameter Sum_Squares Sum_Squares_Error df df_error Mean_Square F
#> 1 condition 1656.263 317.8893 1.14911 171.2174 1.856641 776.3182
#> p Omega2_partial
#> 1 1.324548e-69 0.7068709
set.seed(123)
expr_anova_parametric(iris_long, condition, value, id, paired = TRUE, output = "dataframe")
#> # A tibble: 1 x 10
#> statistic parameter1 parameter2 p.value group term estimate ci.width
#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> <dbl> <dbl>
#> 1 776. 1.15 171. 1.32e-69 rowi~ cond~ 0.707 0.95
#> # ... with 2 more variables: conf.low <dbl>, conf.high <dbl>Created on 2020-12-21 by the reprex package (v0.3.0.9001)
`expr_t_` and `expr_anova_`: add more details
- Student's t or Welch's t or Yuen's t
- Fisher's or Welch's ANOVA
- Cohen's d or Hedge's g
use `parameters` to tidy up meta-analysis results
library(metafor)
#> Loading required package: Matrix
#> Loading 'metafor' package (version 2.1-0). For an overview
#> and introduction to the package please type: help(metafor).
library(ggstatsplot)
#> Registered S3 method overwritten by 'broom.mixed':
#> method from
#> tidy.gamlss broom
#> Registered S3 methods overwritten by 'car':
#> method from
#> influence.merMod lme4
#> cooks.distance.influence.merMod lme4
#> dfbeta.influence.merMod lme4
#> dfbetas.influence.merMod lme4
### calculate log risk ratios and corresponding sampling variances
dat <- escalc(measure="RR", ai=tpos, bi=tneg, ci=cpos, di=cneg, data=dat.bcg)
### random-effects model, using log risk ratios and variances as input
### note: method="REML" is the default, so one could leave this out
x <- rma(yi, vi, data=dat, method="REML")
# setup
ggstatsplot:::parameters_tidy(x) %>%
dplyr::filter(., !grepl(x = term, pattern = "Study", ignore.case = TRUE))
#> # A tibble: 1 x 8
#> term estimate std.error conf.low conf.high statistic p.value weight
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -0.715 0.180 -1.07 -0.362 -3.97 0.0000705 NAnew function to replace `ggcoefstats_label_maker`?
https://github.com/IndrajeetPatil/ggstatsplot/blob/master/R/helpers_ggcoefstats_graphics.R
Need to work on easystats/insight#135 before working on this
long-term restructuring
- add sample size info to bf-related expressions
- check if a 0-parameter template can be repurposed for bf-expressions
- only support symbols, and not strings as argument inputs
- check if things work as expected with
forloops - instead of showing
centralityin Bayesian template, show what the estimate actually is - use
{glue}for creating expressions - Use
rlang::parse_expr()to parse expressions
retire the whole `top.text` and `caption` support
Will first have to first remove the meta_random's caption component for parametric tests
Issue with "kruskal.test.default"
Hi, we've been trying to run these two simple use cases described below using "ggbetweenstats", but we are only able to run the first one. The second gives an error related to kruskal.test. However, we've been able to run kruskal test on the entire group or pairwise. We don't quite understand what's going on. It might seem obvious to someone else but we are a bit ofuscated with this. We've noticed that setting 'results.subtitle=FALSE' allows to make the plot, but stats are not reported, as expected. Any clue on this issue will be appreciated, we would expect to get stats and plot without issue.
Many thanks for your time
library(ggstatsplot)
r1<-data.frame(treatment=c("a", "b", "b", "b", "c", "d", "d"),
Age=1:7,
Group=c("G1", "G1", "G1", "G1", "G1", "G1", "G1"),
stringsAsFactors=F)
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)
str(r1)
#> 'data.frame': 7 obs. of 3 variables:
#> $ treatment: chr "a" "b" "b" "b" ...
#> $ Age : int 1 2 3 4 5 6 7
#> $ Group : chr "G1" "G1" "G1" "G1" ...
str(r2)
#> 'data.frame': 7 obs. of 3 variables:
#> $ treatment: chr "a" "a" "a" "a" ...
#> $ Age : int 1 2 3 4 5 6 7
#> $ Group : chr "G2" "G2" "G2" "G2" ...
r1
#> treatment Age Group
#> 1 a 1 G1
#> 2 b 2 G1
#> 3 b 3 G1
#> 4 b 4 G1
#> 5 c 5 G1
#> 6 d 6 G1
#> 7 d 7 G1
r2
#> treatment Age Group
#> 1 a 1 G2
#> 2 a 2 G2
#> 3 a 3 G2
#> 4 a 4 G2
#> 5 a 5 G2
#> 6 e 6 G2
#> 7 f 7 G2
table(r1)
#> , , Group = G1
#>
#> Age
#> treatment 1 2 3 4 5 6 7
#> a 1 0 0 0 0 0 0
#> b 0 1 1 1 0 0 0
#> c 0 0 0 0 1 0 0
#> d 0 0 0 0 0 1 1
table(r2)
#> , , Group = G2
#>
#> Age
#> treatment 1 2 3 4 5 6 7
#> a 1 1 1 1 1 0 0
#> e 0 0 0 0 0 1 0
#> f 0 0 0 0 0 0 1
ggstatsplot::ggbetweenstats(
data = r1,
x = "treatment",
y = "Age",
type="np",
messages=F
)
#> Warning in ~as.numeric(as.character(.)): NAs introduced by coercion
#> Warning in stats::qt(p = 1 - (0.05/2), df = n - 1, lower.tail = TRUE): NaNs
#> produced
#> Warning in stats::qt(p = 1 - (0.05/2), df = n - 1, lower.tail = TRUE): NaNs
#> produced
ggstatsplot::ggbetweenstats(
data = r2,
x = "treatment",
y = "Age",
type="np",
messages=F
)
#> Error in kruskal.test.default(Input$x, Input$g): all observations are in the same groupCreated on 2019-09-10 by the reprex package (v0.3.0)
adding CI related info for effect sizes
mod <- effectsize::cohens_d(sleep$extra, sleep$group)
A <- c(48, 48, 77, 86, 85, 85)
B <- c(14, 34, 34, 77)
mod2 <- effectsize::rank_biserial(A, B)
get_ci_method <- purrr::compose(tibble::as_tibble, purrr::attr_getter("ci_method"))
dplyr::rename_with(get_ci_method(mod), ~ paste0("ci_", .x))
#> # A tibble: 1 x 2
#> ci_method ci_distribution
#> <chr> <chr>
#> 1 ncp t
dplyr::rename_with(get_ci_method(mod2), ~ paste0("ci_", .x))
#> # A tibble: 1 x 2
#> ci_method ci_iterations
#> <chr> <dbl>
#> 1 bootstrap 200Created on 2021-02-16 by the reprex package (v1.0.0)
return expressions as a list column in a dataframe
proof of concept
library(ggplot2)
library(statsExpressions)
df <- tibble(x = rnorm(100))
df_res <- expr_t_onesample(df, x, output = "dataframe")
attr(df_res, "subtitle") <- expr_t_onesample(df, x, type = "p")
attr(df_res, "caption") <- expr_t_onesample(df, x, type = "bf")
ggplot(df, aes(x)) +
geom_histogram(alpha = 0.5) +
labs(
subtitle = attributes(df_res)$subtitle,
caption = attributes(df_res)$caption
)
#> `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Created on 2021-01-19 by the reprex package (v0.3.0)
Session info
devtools::session_info()
#> ─ Session info ───────────────────────────────────────────────────────────────
#> setting value
#> version R version 4.0.3 (2020-10-10)
#> os macOS Mojave 10.14.6
#> system x86_64, darwin17.0
#> ui X11
#> language (EN)
#> collate en_US.UTF-8
#> ctype en_US.UTF-8
#> tz Europe/Berlin
#> date 2021-01-19
#>
#> ─ Packages ───────────────────────────────────────────────────────────────────
#> package * version date lib
#> abind 1.4-5 2016-07-21 [1]
#> afex 0.28-1 2021-01-12 [1]
#> assertthat 0.2.1 2019-03-21 [1]
#> BayesFactor 0.9.12-4.2 2018-05-19 [1]
#> bayestestR 0.8.0.1 2021-01-07 [1]
#> bbmle 1.0.23.1 2020-02-03 [1]
#> bdsmatrix 1.3-4 2020-01-13 [1]
#> boot 1.3-25 2020-04-26 [2]
#> bridgesampling 1.0-0 2020-02-26 [1]
#> Brobdingnag 1.2-6 2018-08-13 [1]
#> callr 3.5.1 2020-10-13 [1]
#> car 3.0-10 2020-09-29 [1]
#> carData 3.0-4 2020-05-22 [1]
#> cellranger 1.1.0 2016-07-27 [1]
#> cli 2.2.0 2020-11-20 [1]
#> coda 0.19-4 2020-09-30 [1]
#> codetools 0.2-16 2018-12-24 [2]
#> colorspace 2.0-0 2020-11-11 [1]
#> correlation 0.5.0 2021-01-01 [1]
#> crayon 1.3.4 2017-09-16 [1]
#> curl 4.3 2019-12-02 [1]
#> data.table 1.13.6 2020-12-30 [1]
#> DBI 1.1.1 2021-01-15 [1]
#> desc 1.2.0 2018-05-01 [1]
#> devtools 2.3.2 2020-09-18 [1]
#> digest 0.6.27 2020-10-24 [1]
#> dplyr 1.0.3 2021-01-15 [1]
#> effectsize 0.4.3 2021-01-18 [1]
#> ellipsis 0.3.1 2020-05-15 [1]
#> emmeans 1.5.3 2020-12-09 [1]
#> estimability 1.3 2018-02-11 [1]
#> evaluate 0.14 2019-05-28 [1]
#> fansi 0.4.2 2021-01-15 [1]
#> farver 2.0.3 2020-01-16 [1]
#> fastGHQuad 1.0 2018-09-30 [1]
#> forcats 0.5.0 2020-03-01 [1]
#> foreign 0.8-80 2020-05-24 [2]
#> fs 1.5.0 2020-07-31 [1]
#> generics 0.1.0 2020-10-31 [1]
#> ggplot2 * 3.3.3 2020-12-30 [1]
#> glue 1.4.2 2020-08-27 [1]
#> gridExtra 2.3 2017-09-09 [1]
#> gtable 0.3.0 2019-03-25 [1]
#> gtools 3.8.2 2020-03-31 [1]
#> haven 2.3.1 2020-06-01 [1]
#> highr 0.8 2019-03-20 [1]
#> hms 1.0.0 2021-01-13 [1]
#> htmltools 0.5.1 2021-01-12 [1]
#> httr 1.4.2 2020-07-20 [1]
#> inline 0.3.17 2020-12-01 [1]
#> insight 0.12.0 2021-01-14 [1]
#> ipmisc 5.0.2 2021-01-09 [1]
#> jsonlite 1.7.2 2020-12-09 [1]
#> knitr 1.30 2020-09-22 [1]
#> labeling 0.4.2 2020-10-20 [1]
#> LaplacesDemon 16.1.4 2020-02-06 [1]
#> lattice 0.20-41 2020-04-02 [2]
#> lifecycle 0.2.0 2020-03-06 [1]
#> lme4 1.1-26 2020-12-01 [1]
#> lmerTest 3.1-3 2020-10-23 [1]
#> logspline 2.1.16 2020-05-08 [1]
#> loo 2.4.1 2020-12-09 [1]
#> magrittr 2.0.1 2020-11-17 [1]
#> MASS 7.3-53 2020-09-09 [2]
#> Matrix 1.2-18 2019-11-27 [2]
#> MatrixModels 0.4-1 2015-08-22 [1]
#> matrixStats 0.57.0 2020-09-25 [1]
#> mc2d 0.1-18 2017-03-06 [1]
#> memoise 1.1.0 2017-04-21 [1]
#> metaBMA 0.6.6 2021-01-08 [1]
#> metafor 2.4-0 2020-03-19 [1]
#> metaplus 0.7-11 2018-04-01 [1]
#> mime 0.9 2020-02-04 [1]
#> minqa 1.2.4 2014-10-09 [1]
#> multcomp 1.4-15 2020-11-14 [1]
#> munsell 0.5.0 2018-06-12 [1]
#> mvtnorm 1.1-1 2020-06-09 [1]
#> nlme 3.1-149 2020-08-23 [2]
#> nloptr 1.2.2.2 2020-07-02 [1]
#> numDeriv 2016.8-1.1 2019-06-06 [1]
#> openxlsx 4.2.3 2020-10-27 [1]
#> parameters 0.11.0 2021-01-15 [1]
#> pbapply 1.4-3 2020-08-18 [1]
#> performance 0.6.1.1 2021-01-01 [1]
#> pillar 1.4.7 2020-11-20 [1]
#> pkgbuild 1.2.0 2020-12-15 [1]
#> pkgconfig 2.0.3 2019-09-22 [1]
#> pkgload 1.1.0 2020-05-29 [1]
#> plyr 1.8.6 2020-03-03 [1]
#> prettyunits 1.1.1 2020-01-24 [1]
#> processx 3.4.5 2020-11-30 [1]
#> ps 1.5.0 2020-12-05 [1]
#> purrr 0.3.4 2020-04-17 [1]
#> R6 2.5.0 2020-10-28 [1]
#> Rcpp 1.0.6 2021-01-15 [1]
#> RcppParallel 5.0.2 2020-06-24 [1]
#> readxl 1.3.1 2019-03-13 [1]
#> remotes 2.2.0 2020-07-21 [1]
#> reshape 0.8.8 2018-10-23 [1]
#> reshape2 1.4.4 2020-04-09 [1]
#> rio 0.5.16 2018-11-26 [1]
#> rlang 0.4.10 2020-12-30 [1]
#> rmarkdown 2.6 2020-12-14 [1]
#> rprojroot 2.0.2 2020-11-15 [1]
#> rstan 2.21.2 2020-07-27 [1]
#> rstantools 2.1.1 2020-07-06 [1]
#> sandwich 3.0-0 2020-10-02 [1]
#> scales 1.1.1 2020-05-11 [1]
#> sessioninfo 1.1.1 2018-11-05 [1]
#> StanHeaders 2.21.0-7 2020-12-17 [1]
#> statmod 1.4.35 2020-10-19 [1]
#> statsExpressions * 0.7.0 2021-01-19 [1]
#> stringi 1.5.3 2020-09-09 [1]
#> stringr 1.4.0 2019-02-10 [1]
#> survival 3.2-7 2020-09-28 [2]
#> testthat 3.0.1 2020-12-17 [1]
#> TH.data 1.0-10 2019-01-21 [1]
#> tibble 3.0.5 2021-01-15 [1]
#> tidyr 1.1.2 2020-08-27 [1]
#> tidyselect 1.1.0 2020-05-11 [1]
#> usethis 2.0.0 2020-12-10 [1]
#> V8 3.4.0 2020-11-04 [1]
#> vctrs 0.3.6 2020-12-17 [1]
#> withr 2.4.0 2021-01-16 [1]
#> WRS2 1.1-0 2020-06-19 [1]
#> xfun 0.20 2021-01-06 [1]
#> xml2 1.3.2 2020-04-23 [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 2.1.1 2020-08-27 [1]
#> zoo 1.8-8 2020-05-02 [1]
#> source
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.2)
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#> Github (easystats/bayestestR@f19a7e5)
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#> CRAN (R 4.0.2)
#> Github (easystats/correlation@6f68768)
#> CRAN (R 4.0.2)
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#> CRAN (R 4.0.2)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.3)
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#> CRAN (R 4.0.2)
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.2)
#> Github (easystats/performance@429b7ff)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.3)
#> CRAN (R 4.0.2)
#> CRAN (R 4.0.2)
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#>
#> [1] /Users/patil/Library/R/4.0/library
#> [2] /Library/Frameworks/R.framework/Versions/4.0/Resources/librarysupporting for loop for all functions
- expr_anova_bayes
- expr_anova_nonparametric
- expr_anova_parametric
- expr_anova_robust
- expr_contingency_tab
- expr_corr_test
- expr_meta_random
- expr_t_bayes
- expr_t_nonparametric
- expr_t_onesample
- expr_t_parametric
- expr_t_robust
use `insight` helpers
- use
insight::check_if_installed - use
insight::format_valueinstead ofipmisc::format_numwhen not p-values
ipmisc::format_num(1.2344, k = 3)
#> [1] "1.234"
insight::format_value(1.2344, digits = 3)
#> [1] "1.234"Created on 2021-03-31 by the reprex package (v1.0.0)
`expr_anova_parametric`: changing `conf.level` doesn't work
set.seed(123)
library(statsExpressions)
expr_anova_parametric(mtcars, cyl, wt, conf.level = 0.90)
#> paste(NULL, italic("F")["Welch"], "(", "2", ",", "18.97", ") = ",
#> "20.25", ", ", italic("p"), " = ", "< 0.001", ", ", widehat(omega["p"]^2),
#> " = ", "0.58", ", CI"["90%"], " [", "0.36", ", ", "0.71",
#> "]", ", ", italic("n")["obs"], " = ", 32L)
expr_anova_parametric(mtcars, cyl, wt, conf.level = 0.95)
#> paste(NULL, italic("F")["Welch"], "(", "2", ",", "18.97", ") = ",
#> "20.25", ", ", italic("p"), " = ", "< 0.001", ", ", widehat(omega["p"]^2),
#> " = ", "0.58", ", CI"["95%"], " [", "0.36", ", ", "0.71",
#> "]", ", ", italic("n")["obs"], " = ", 32L)Created on 2020-04-22 by the reprex package (v0.3.0.9001)
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