Problem using bootstraps from tidyverse with anova_test from rstatix (2)

I am trying to do a repeated-measures multivariate ANOVA on some data, however it is not normally distributed therefore, I am trying to use bootstrapping to be more confident about my results. The error I get is when I try to apply my model to the bootstrapped data.

Thank you very much

library(tidyverse)
library(ggpubr)
library(rstatix)
#> 
#> Attaching package: 'rstatix'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(rsample)
library(reprex)

#Load data
allA<-read.csv('alldata_A_R.csv')
allA<-as.data.frame(allA)
head(allA) #to show you what my raw data looks like 
#>   subject exp Oddball PvN         fitA
#> 1       1   1       E   N 7.998360e-02
#> 2       2   1       E   N 5.507097e-01
#> 3       3   1       E   N 7.541230e-11
#> 4       4   1       E   N 1.512817e-01
#> 5       5   1       E   N 1.162404e-01
#> 6       6   1       E   N 9.120629e-02

allA %>%
  group_by(exp,Oddball,PvN) %>%
  get_summary_stats(fitA,type='mean_sd')
#> # A tibble: 8 x 7
#>     exp Oddball PvN   variable     n  mean    sd
#>   <int> <chr>   <chr> <chr>    <dbl> <dbl> <dbl>
#> 1     1 E       N     fitA        70 0.117 0.069
#> 2     1 E       P     fitA        70 0.158 0.115
#> 3     1 P       N     fitA        70 0.111 0.045
#> 4     1 P       P     fitA        70 0.163 0.069
#> 5     2 E       N     fitA        30 0.099 0.047
#> 6     2 E       P     fitA        30 0.153 0.116
#> 7     2 P       N     fitA        30 0.113 0.042
#> 8     2 P       P     fitA        30 0.144 0.066

bxp<-ggboxplot(allA, x='Oddball',y='fitA',
               color='PvN', palette='jco',
               facet.by='exp', short.panel.labs=FALSE)
bxp

[image]

#check assumptions
allA %>%
  group_by(exp,Oddball,PvN) %>%
  identify_outliers(fitA) #test for outliers
#> # A tibble: 12 x 7
#>      exp Oddball PvN   subject     fitA is.outlier is.extreme
#>    <int> <chr>   <chr>   <int>    <dbl> <lgl>      <lgl>     
#>  1     1 E       N           2 5.51e- 1 TRUE       TRUE      
#>  2     1 E       N           3 7.54e-11 TRUE       FALSE     
#>  3     1 E       N          62 2.30e- 1 TRUE       FALSE     
#>  4     1 E       P           7 4.15e- 1 TRUE       TRUE      
#>  5     1 E       P          39 4.38e- 1 TRUE       TRUE      
#>  6     1 E       P          57 8.86e- 1 TRUE       TRUE      
#>  7     1 P       P          11 3.00e- 1 TRUE       FALSE     
#>  8     1 P       P          17 2.70e- 1 TRUE       FALSE     
#>  9     1 P       P          39 5.63e- 1 TRUE       TRUE      
#> 10     1 P       P          68 3.18e- 1 TRUE       FALSE     
#> 11     2 E       P          12 6.77e- 1 TRUE       TRUE      
#> 12     2 P       N          28 2.19e- 1 TRUE       FALSE

#test for normality
normality<-allA %>%
  group_by(exp,Oddball,PvN) %>%
  shapiro_test(fitA)
normality
#> # A tibble: 8 x 6
#>     exp Oddball PvN   variable statistic        p
#>   <int> <chr>   <chr> <chr>        <dbl>    <dbl>
#> 1     1 E       N     fitA         0.713 2.16e-10
#> 2     1 E       P     fitA         0.634 6.23e-12
#> 3     1 P       N     fitA         0.971 1.10e- 1
#> 4     1 P       P     fitA         0.753 1.60e- 9
#> 5     2 E       N     fitA         0.986 9.60e- 1
#> 6     2 E       P     fitA         0.674 6.72e- 7
#> 7     2 P       N     fitA         0.976 7.16e- 1
#> 8     2 P       P     fitA         0.945 1.26e- 1

#Create QQ plot for each cell of design
ggqqplot(allA,'fitA',ggtheme=theme_bw())+
  facet_grid(exp+Oddball~PvN, labeller='label_both')

[image]

res.aov<-anova_test(
  data=allA,dv=fitA,wid=subject,between=c(exp),
  within=c(Oddball,PvN)
)
#> Warning: The 'wid' column contains duplicate ids across between-subjects
#> variables. Automatic unique id will be created
get_anova_table(res.aov) #this give the EXACT same results as JASP.
#> ANOVA Table (type III tests)
#> 
#>            Effect DFn DFd      F        p p<.05      ges
#> 1             exp   1  98  1.105 2.96e-01       4.00e-03
#> 2         Oddball   1  98  0.021 8.84e-01       3.80e-05
#> 3             PvN   1  98 22.963 5.88e-06     * 6.60e-02
#> 4     exp:Oddball   1  98  0.049 8.25e-01       8.74e-05
#> 5         exp:PvN   1  98  0.041 8.40e-01       1.26e-04
#> 6     Oddball:PvN   1  98  0.167 6.84e-01       3.42e-04
#> 7 exp:Oddball:PvN   1  98  1.385 2.42e-01       3.00e-03

##test bootstrapping here
set.seed(123)
dataA.boot<-bootstraps(allA,times=1000,apparent=TRUE)
#dataA.boot<-as.data.frame(dataA.boot)

#fit a model to all of these bootstraps
dataA.boot %>%
mutate(model=map(splits,~anova_test(
 data=.,dv=fitA,wid=subject,between=c(exp),
 within=c(Oddball,PvN))),
 coef_info=map(model,tidy))
#> Error: Problem with `mutate()` input `model`.
#> x Can't subset columns that don't exist.
#> x Column `fitA` doesn't exist.
#> ℹ Input `model` is `map(...)`.


#post-hoc compairson on the main effect 
PvN.pwc<-allA %>%
  group_by(exp,Oddball)%>%
  pairwise_t_test(fitA~PvN, paired=TRUE, p.adjust.method='bonferroni') %>%
  select(-df,-statistic) #remove details 
PvN.pwc         
#> # A tibble: 4 x 10
#>     exp Oddball .y.   group1 group2    n1    n2         p     p.adj p.adj.signif
#>   <int> <chr>   <chr> <chr>  <chr>  <int> <int>     <dbl>     <dbl> <chr>       
#> 1     1 E       fitA  N      P         70    70   1.40e-2   1.40e-2 *           
#> 2     1 P       fitA  N      P         70    70   1.59e-6   1.59e-6 ****        
#> 3     2 E       fitA  N      P         30    30   2.70e-2   2.70e-2 *           
#> 4     2 P       fitA  N      P         30    30   3.80e-2   3.80e-2 *

Hello there @apy! Can you create a reproducible example so that other folks can help you out?

The main reason that folks here won't be able to help you with your problem is that we don't have access to your data. Check out Reprex do's and don'ts, especially noticing the first section:

Use the smallest, simplest, most built-in data possible.

I know it can seem like a lot of work to create a reprex that other folks can run on their computers, but it's a super important skill for debugging and problem solving with folks online.