From 82ce9dd8e946c39dcfe4746615688b0893c4b51a Mon Sep 17 00:00:00 2001 From: AngelosPsy Date: Mon, 18 Sep 2023 22:19:42 +0200 Subject: [PATCH] Removed travis and included github actions --- .travis.yml | 13 -- README.Rmd | 2 +- README.md | 164 +++++++++--------- .../figure-gfm/unnamed-chunk-14-1.png | Bin 41176 -> 40224 bytes .../figure-gfm/unnamed-chunk-17-1.png | Bin 41898 -> 41021 bytes 5 files changed, 82 insertions(+), 97 deletions(-) delete mode 100644 .travis.yml diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 5ad5883..0000000 --- a/.travis.yml +++ /dev/null @@ -1,13 +0,0 @@ -# R for travis: see documentation at https://docs.travis-ci.com/user/languages/r - -language: R - -cache: packages - -addons: - apt: - packages: - - libgit2-dev - -after_success: - - Rscript -e 'covr::codecov()' diff --git a/README.Rmd b/README.Rmd index 5b7f06b..24482d2 100644 --- a/README.Rmd +++ b/README.Rmd @@ -12,9 +12,9 @@ knitr::opts_chunk$set( ``` -[![Travis build status](https://travis-ci.org/AngelosPsy/multifear.svg?branch=master)](https://travis-ci.org/AngelosPsy/multifear) [![codecov](https://codecov.io/gh/AngelosPsy/multifear/branch/master/graph/badge.svg)](https://codecov.io/gh/AngelosPsy/multifear) [![R-CMD-check](https://github.com/AngelosPsy/multifear/workflows/R-CMD-check/badge.svg)](https://github.com/AngelosPsy/multifear/actions) +[![R-CMD-check](https://github.com/AngelosPsy/multifear/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/AngelosPsy/multifear/actions/workflows/R-CMD-check.yaml) # Multifear diff --git a/README.md b/README.md index d62c924..cf49ea7 100644 --- a/README.md +++ b/README.md @@ -2,10 +2,9 @@ -[![Travis build -status](https://travis-ci.org/AngelosPsy/multifear.svg?branch=master)](https://travis-ci.org/AngelosPsy/multifear) [![codecov](https://codecov.io/gh/AngelosPsy/multifear/branch/master/graph/badge.svg)](https://codecov.io/gh/AngelosPsy/multifear) [![R-CMD-check](https://github.com/AngelosPsy/multifear/workflows/R-CMD-check/badge.svg)](https://github.com/AngelosPsy/multifear/actions) +[![R-CMD-check](https://github.com/AngelosPsy/multifear/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/AngelosPsy/multifear/actions/workflows/R-CMD-check.yaml) # Multifear @@ -135,24 +134,24 @@ Now we need to analyse the data. For this we will use the *multifear::universe_cs* function. In order for this function to work, we need to provide the following arguments. -- CS1: This will be the column names that contain the conditioned - responses for the CS+ (i.e., CSP1 until CSP10). +- CS1: This will be the column names that contain the conditioned + responses for the CS+ (i.e., CSP1 until CSP10). -- CS2: This will be the column names that contain the conditioned - response for the CS- (i.e., CSM1 until CSM10). +- CS2: This will be the column names that contain the conditioned + response for the CS- (i.e., CSM1 until CSM10). -- data: This is our data frame that contain that data for the CS+, - CS-, as well as the column with the participant number. +- data: This is our data frame that contain that data for the CS+, CS-, + as well as the column with the participant number. -- group. In case of a group, then we need to specify the column with - the group name. The default option is that there are no groups. In - this first example we do not use any groups but we will do that in - our second example – see below. +- group. In case of a group, then we need to specify the column with the + group name. The default option is that there are no groups. In this + first example we do not use any groups but we will do that in our + second example – see below. -- phase. Here we define the conditioning phase that the data were - collected in (e.g., acquisition phase, extinction phase, etc). - Please note that in case the user has multiple phases, she/he needs - to run the function separately for each phases. +- phase. Here we define the conditioning phase that the data were + collected in (e.g., acquisition phase, extinction phase, etc). Please + note that in case the user has multiple phases, she/he needs to run + the function separately for each phases. There are some other options in the function, such as defining the type of conditioning response. However, these are not necessary for now. So, @@ -173,11 +172,11 @@ res #> # A tibble: 4 × 20 #> x y exclusion cut_off model controls method p.value effect.size #> -#> 1 cs scr full data full data t-test NA great… 0.00244 0.577 -#> 2 cs scr full data full data t-test NA two.s… 0.00488 0.577 -#> 3 cs:time scr full data full data rep ANO… NA rep A… 0.0152 0.0296 -#> 4 cs scr full data full data rep ANO… NA rep A… 0.00488 0.147 -#> # … with 11 more variables: effect.size.lci , effect.size.hci , +#> 1 cs scr full data full data t-test NA great… 3.33e-5 0.577 +#> 2 cs scr full data full data t-test NA two.s… 6.67e-5 0.577 +#> 3 cs:time scr full data full data rep ANO… NA rep A… 1.52e-2 0.0296 +#> 4 cs scr full data full data rep ANO… NA rep A… 4.88e-3 0.147 +#> # ℹ 11 more variables: effect.size.lci , effect.size.hci , #> # effect.size.ma , effect.size.ma.lci , effect.size.ma.hci , #> # estimate , statistic , conf.low , conf.high , #> # framework , data_used @@ -185,49 +184,46 @@ res Let’s go through each column separately -- x : is the effect that you are testing. For example, the cs means - that you are testing cs differences. cs:time the cs X time - interaction is tested. Be careful: when testing interactions, we - only report the highest order interaction. That means that if you - have a cs x time interaction, you do not get the results of the cs - or the time main effect. +- x : is the effect that you are testing. For example, the cs means that + you are testing cs differences. cs:time the cs X time interaction is + tested. Be careful: when testing interactions, we only report the + highest order interaction. That means that if you have a cs x time + interaction, you do not get the results of the cs or the time main + effect. -- y: the dependent variable. In the example this is the *scr* - responses. +- y: the dependent variable. In the example this is the *scr* responses. -- exclusion: This columns reports as to what data were included in the - data set. For example, here you see that we have only full data sets - – no exclusion. This is because the multifear::universe_cs() only - analyses full data sets. If we want to apply some exclusion - criteria, we need to run the multifear::multiverse_cs() function – - see later on. +- exclusion: This columns reports as to what data were included in the + data set. For example, here you see that we have only full data sets – + no exclusion. This is because the multifear::universe_cs() only + analyses full data sets. If we want to apply some exclusion criteria, + we need to run the multifear::multiverse_cs() function – see later on. -- model: What model was used. For example, here we see t-tests, and - rep ANOVA (which means repeated measures ANOVA). +- model: What model was used. For example, here we see t-tests, and rep + ANOVA (which means repeated measures ANOVA). -- controls: This column is left empty. I included it because the specs - R package had it so we may need to use it later on. +- controls: This column is left empty. I included it because the specs R + package had it so we may need to use it later on. -- method: The method is a combination of the *model* and *x* column. - Not really necessary if the other two columns exist. +- method: The method is a combination of the *model* and *x* column. Not + really necessary if the other two columns exist. -- p.value: The p-value of the test +- p.value: The p-value of the test -- estimate: The estimate that is returned from the test. Keep in mind - though that this applies only for the t-test at the moment. We need - to see what we can do for the ANOVA, +- estimate: The estimate that is returned from the test. Keep in mind + though that this applies only for the t-test at the moment. We need to + see what we can do for the ANOVA, -- statistic. The statistic of the test +- statistic. The statistic of the test -- conf.low and conf.high In case you have an estimate, this returns - the low and high levels of it +- conf.low and conf.high In case you have an estimate, this returns the + low and high levels of it -- framework were the data analysed within a NHST or Bayesian - framework? +- framework were the data analysed within a NHST or Bayesian framework? -- data_used Here you have a data frame with the data used for the - performed analyses. This is because someone maybe wants to recreate - the results and also as a check that nothing went wrong. +- data_used Here you have a data frame with the data used for the + performed analyses. This is because someone maybe wants to recreate + the results and also as a check that nothing went wrong. Now, we want to perform the same analyses but for different data reduction procedures (see below). We can do it simply by: @@ -242,8 +238,8 @@ res_multi #> # A tibble: 116 × 21 #> x y exclusion cut_off model controls method p.value effect.size #> -#> 1 cs scr full_data full d… t-te… NA great… 2.44e- 3 0.577 -#> 2 cs scr full_data full d… t-te… NA two.s… 4.88e- 3 0.577 +#> 1 cs scr full_data full d… t-te… NA great… 3.33e- 5 0.577 +#> 2 cs scr full_data full d… t-te… NA two.s… 6.67e- 5 0.577 #> 3 cs scr full_data full d… Baye… NA Bayes… NA NA #> 4 cs scr full_data full d… Baye… NA Bayes… NA NA #> 5 cs:time scr full_data full d… rep … NA rep A… 1.52e- 2 0.0296 @@ -252,10 +248,11 @@ res_multi #> 8 cscs2:ti… scr full_data mixe… NA mixed… 5.92e- 7 NA #> 9 cscs2 scr full_data mixe… NA mixed… 1.82e- 5 NA #> 10 cscs2:ti… scr full_data mixe… NA mixed… 2.16e- 2 NA -#> # … with 106 more rows, and 12 more variables: effect.size.lci , -#> # effect.size.hci , effect.size.ma , effect.size.ma.lci , -#> # effect.size.ma.hci , estimate , statistic , conf.low , -#> # conf.high , framework , data_used , efffect.size.ma +#> # ℹ 106 more rows +#> # ℹ 12 more variables: effect.size.lci , effect.size.hci , +#> # effect.size.ma , effect.size.ma.lci , effect.size.ma.hci , +#> # estimate , statistic , conf.low , conf.high , +#> # framework , data_used , efffect.size.ma ``` In terms of calling the function, we see that we need exactly the same @@ -276,50 +273,50 @@ res_multi$exclusion %>% unique() The explanation of each level is the following: -1. fl2trials: first and last two trials. +1) fl2trials: first and last two trials. -2. fltrials: first and last trial +2) fltrials: first and last trial -3. full_data: full data set +3) full_data: full data set -4. halves: use the first and last half of the trial. So, if you have 10 +4) halves: use the first and last half of the trial. So, if you have 10 trials, you will have the first 5 and last 5 trials -5. min_first: take all trials apart from the first one +5) min_first: take all trials apart from the first one -6. separate trials per 2 +6) separate trials per 2 -7. separate trials per 10% +7) separate trials per 10% -8. separate trials per 33% +8) separate trials per 33% -9. separate trials per 20% +9) separate trials per 20% ## Inferences This is the most challenging part. For now you can use the following function and you will get: -1. A histogram will all the p value and a red line showing the +1) A histogram will all the p value and a red line showing the significance limit – by default alpha = 0.05 -2. A histogram will all the Bayes factors and a red line showing the +2) A histogram will all the Bayes factors and a red line showing the limit of inconclusive evidence – by default this is 0 -3. Mean and median p values +3) Mean and median p values -4. the number of p values below the significance level +4) the number of p values below the significance level -5. Mean and median of Bayes factors +5) Mean and median of Bayes factors -6. the proportion of Bayes factors above 1 +6) the proportion of Bayes factors above 1 ``` r multifear::inference_cs(res_multi, na.rm = TRUE) #> mean_p_value median_p_value sd_p_value prop_p_value mean_bf_value -#> 1 0.1074323 0.00638261 0.2341194 82.35294 1868.888 +#> 1 0.04462268 0.0002425968 0.1662026 94.11765 1888.148 #> median_bf_value sd_bf_value prop_bf_value -#> 1 4.341644 10044.94 73.52941 +#> 1 4.319848 10161.98 73.52941 ``` And here we have a barplot of the results: @@ -363,20 +360,21 @@ res_multi_group #> # A tibble: 116 × 21 #> x y exclusion cut_off model controls method p.value effect.size #> -#> 1 cs scr full_data full d… t-te… NA great… 3.70e- 2 0.577 -#> 2 cs scr full_data full d… t-te… NA two.s… 7.41e- 2 0.577 +#> 1 cs scr full_data full d… t-te… NA great… 3.33e- 5 0.577 +#> 2 cs scr full_data full d… t-te… NA two.s… 6.67e- 5 0.577 #> 3 cs scr full_data full d… Baye… NA Bayes… NA NA #> 4 cs scr full_data full d… Baye… NA Bayes… NA NA #> 5 group:cs… scr full_data full d… rep … NA rep A… 3.43e- 1 0.00288 -#> 6 group:cs scr full_data full d… rep … NA rep A… 4.60e- 1 -0.00475 +#> 6 group:cs scr full_data full d… rep … NA rep A… 4.60e- 1 0 #> 7 cscs2 scr full_data mixe… NA mixed… 1.80e-13 NA #> 8 cscs2:ti… scr full_data mixe… NA mixed… 5.92e- 7 NA #> 9 cscs2 scr full_data mixe… NA mixed… 1.82e- 5 NA #> 10 cscs2:ti… scr full_data mixe… NA mixed… 2.16e- 2 NA -#> # … with 106 more rows, and 12 more variables: effect.size.lci , -#> # effect.size.hci , effect.size.ma , effect.size.ma.lci , -#> # effect.size.ma.hci , estimate , statistic , conf.low , -#> # conf.high , framework , data_used , efffect.size.ma +#> # ℹ 106 more rows +#> # ℹ 12 more variables: effect.size.lci , effect.size.hci , +#> # effect.size.ma , effect.size.ma.lci , effect.size.ma.hci , +#> # estimate , statistic , conf.low , conf.high , +#> # framework , data_used , efffect.size.ma ``` Accordingly, the inference plots 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