roistats

The goal of this package is to apply t-tests and basic data description across several sub-groups, with the output being a nice arranged data.frame instead of detailed listed information. Multiple comparison and significance symbols are wrapped in as options.

This kind of analyses are commonly seen in ROI (Region-of-interest) analyses for brain imaging data and this is why the package is called roistats.

Installation

You can install the released version of roistats from CRAN with:

install.packages("roistats")

And the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("Irisfee/roistats")

Usage

See Get Started page for detailed usage

Get some basic description about the data by brain region

library(roistats)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
color_index %>% 
  group_by(roi_id) %>%   
  df_sem(color_index) 
#> # A tibble: 8 x 5
#>   roi_id  mean_color_index         sd     n          se
#>   <chr>              <dbl>      <dbl> <int>       <dbl>
#> 1 AnG          0.005370652 0.05071557    29 0.009417644
#> 2 dLatIPS      0.01588446  0.05096974    29 0.009464843
#> 3 LO           0.01806413  0.04284959    29 0.007956968
#> 4 pIPS         0.01019600  0.02971026    29 0.005517056
#> 5 V1           0.009550089 0.04211448    29 0.007820463
#> 6 vIPS         0.01623826  0.03271157    29 0.006074385
#> 7 vLatIPS      0.01617011  0.05141337    29 0.009547223
#> 8 VTC          0.004683526 0.02181639    29 0.004051201

One-sample t-tests for all sub-groups

color_index %>% 
  group_by(roi_id) %>% 
  t_test_one_sample(color_index)
#> # A tibble: 8 x 5
#> # Groups:   roi_id [8]
#>   roi_id     tvalue    df          p p_bonferroni
#>   <chr>       <dbl> <dbl>      <dbl>        <dbl>
#> 1 AnG     0.5702755    28 0.5730390    1         
#> 2 dLatIPS 1.678259     28 0.1044252    0.8354017 
#> 3 LO      2.270227     28 0.03108491   0.2486792 
#> 4 pIPS    1.848088     28 0.07517831   0.6014264 
#> 5 V1      1.221167     28 0.2322062    1         
#> 6 vIPS    2.673234     28 0.01238958   0.09911667
#> 7 vLatIPS 1.693697     28 0.1014206    0.8113652 
#> 8 VTC     1.156083     28 0.2574165    1

With significance symbol as output

color_index_one_sample_t_with_sig <- color_index %>% 
  group_by(roi_id) %>% 
  t_test_one_sample(color_index, p_adjust = c("bonferroni","fdr")) %>% 
  mutate(sig_origin_p = p_range(p))
  
knitr::kable(color_index_one_sample_t_with_sig, digits = 3)

roi_id	tvalue	df	p	p_bonferroni	p_fdr	sig_origin_p
AnG	0.570	28	0.573	1.000	0.573
dLatIPS	1.678	28	0.104	0.835	0.167
LO	2.270	28	0.031	0.249	0.124	*
pIPS	1.848	28	0.075	0.601	0.167
V1	1.221	28	0.232	1.000	0.294
vIPS	2.673	28	0.012	0.099	0.099	*
vLatIPS	1.694	28	0.101	0.811	0.167
VTC	1.156	28	0.257	1.000	0.294

Two-sample t-tests for all sub-groups

color_index_two_sample %>% 
  group_by(roi_id) %>% 
  t_test_two_sample(x = color_effect, y = group, paired = TRUE)
#> # A tibble: 8 x 5
#> # Groups:   roi_id [8]
#>   roi_id     tvalue    df          p p_bonferroni
#>   <chr>       <dbl> <dbl>      <dbl>        <dbl>
#> 1 AnG     0.5702755    28 0.5730390    1         
#> 2 dLatIPS 1.678259     28 0.1044252    0.8354017 
#> 3 LO      2.270227     28 0.03108491   0.2486792 
#> 4 pIPS    1.848088     28 0.07517831   0.6014264 
#> 5 V1      1.221167     28 0.2322062    1         
#> 6 vIPS    2.673234     28 0.01238958   0.09911667
#> 7 vLatIPS 1.693697     28 0.1014206    0.8113652 
#> 8 VTC     1.156083     28 0.2574165    1