angelospsy / multifear Goto Github PK
View Code? Open in Web Editor NEWA suite of functions for running multiverse analyses for fear conditioning data.
A suite of functions for running multiverse analyses for fear conditioning data.
New method is described here: https://psyarxiv.com/fb8zn/
Excellent work, I greatly appreciate all your effort you put into this project!
I have a few ideas on how to expand the relevant functionalities:
What about including the option of having more than two CS, e.g. a three CS design or a 2x2 design, e.g. using CS+/CS- as one within-subjects factor and female/male face as another within-subjects factor? This would certainly add even more possible statistical comparisons, but the whole package would become even more flexible and stronger. What do you think?
Best wishes!
Christian
Hi!
Thank you for putting together the package, I'm really excited about it. Today I tried to use it for the first time, but run into an error message. Can you help me solve it?
Error in dplyr::mutate(., method = ttl$alternative, x = "cs", y = dv, : object 'ttl' not found
6.
dplyr::mutate(., method = ttl$alternative, x = "cs", y = dv, exclusion = exclusion, cut_off = cut_off, model = "t-test", controls = NA, framework = "NHST")
5.
dplyr::select(., x, y, exclusion, cut_off, model, controls, method, p.value, effect.size, effect.size.ma, effect.size.ma.lci, effect.size.ma.hci, estimate, statistic, conf.low, conf.high, framework)
4.
dplyr::mutate(., data_used = list(data))
3.
ttl %>% dplyr::mutate(method = ttl$alternative, x = "cs", y = dv, exclusion = exclusion, cut_off = cut_off, model = "t-test", controls = NA, framework = "NHST") %>% dplyr::select(x, y, exclusion, cut_off, model, controls, method, p.value, effect.size, ...
2.
multifear::t_test_mf(cs1 = cs1, cs2 = cs2, data = data, subj = subj, paired = paired, phase = phase, exclusion = exclusion, cut_off = cut_off, meta.effect = meta.effect)
1.
multifear::universe_cs(cs1 = cs1, cs2 = cs2, data = Mega_wideNA, subj = "ID_Maya", group = NULL, phase = "recall", include_bayes = FALSE)
Solved with 2549a50
Hi, we're about to update bayestestR on CRAN, which will break one example and two tests in your package due to the following change: the Bayes factors functions now return the column log_BF
instead of BF
(and the log has to be exponentiated to get back to "normal" BFs).
Apologies for the breaking change, and let us know if you need any help to fix it :)
Package: multifear
Check: examples
New result: ERROR
Running examples in ‘multifear-Ex.R’ failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: rm_banova_mf
> ### Title: rm_banova_mf
> ### Aliases: rm_banova_mf
>
> ### ** Examples
>
> # Briefly define argument values that will be plugged in later on in the functions.
> # We only use two trials as the function takes a long time to run.
>
> cs1 <- paste0("CSP", 1:2)
> cs2 <- paste0("CSM", 1:2)
> subj <- "id"
>
> # Bayesian Repeated measures ANOVA without groups
> rm_banova_mf(cs1 = cs1, cs2 = cs2, subj = subj,
+ data = example_data, time = TRUE)
Error: Can't subset columns that don't exist.
✖ Column `BF` doesn't exist.
Backtrace:
â–ˆ
1. ├─multifear::rm_banova_mf(...)
2. │ └─`%>%`(...)
3. ├─dplyr::select(., BF)
4. └─dplyr:::select.data.frame(., BF)
5. └─tidyselect::eval_select(expr(c(...)), .data)
6. └─tidyselect:::eval_select_impl(...)
7. ├─tidyselect:::with_subscript_errors(...)
8. │ ├─base::tryCatch(...)
9. │ │ └─base:::tryCatchList(expr, classes, parentenv, handlers)
10. │ │ └─base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
11. │ │ └─base:::doTryCatch(return(expr), name, parentenv, handler)
12. │ └─tidyselect:::instrument_base_errors(expr)
13. │ └─base::withCallingHandlers(...)
14. └─tidyselect:::vars_select_eval(...)
15. └─tidyselect:::walk_data_tree(expr, data_mask, context_mask)
16. └─tidyselect:::eval_c(expr, data_mask, context_mask)
17. └─tidyselect:::reduce_sels(node, data_mask, context_mask, init = init)
18. └─tidyselect:::walk_data_tree(new, data_mask, context_mask)
19. └─tidyselect:::as_indices_sel_impl(...)
20. └─tidyselect:::as_indices_impl(x, vars, strict = strict)
21. └─tidyselect:::chr_as_locations(x, vars)
22. └─vctrs::vec_as_location(x, n = length(vars), names = vars)
23. └─(function () ...
24. └─vctrs:::stop_subscript_oob(...)
25. └─vctrs:::stop_subscript(...)
Execution halted
Package: multifear
Check: tests
New result: ERROR
Running ‘testthat.R’ [37s/37s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(multifear)
>
> test_check("multifear")
�� Failed tests ����������������������������������������������������������������
── Error (test.output.R:47:3): rm_banova_mf works ──────────────────────────────
Error: Can't subset columns that don't exist.
✖ Column `BF` doesn't exist.
Backtrace:
â–ˆ
1. ├─testthat::expect_known_output(...) test.output.R:47:2
2. │ ├─base::append(act, eval_with_output(object, print = print, width = width))
3. │ └─testthat:::eval_with_output(object, print = print, width = width)
4. │ ├─withr::with_output_sink(temp, withVisible(code))
5. │ │ └─base::force(code)
6. │ └─base::withVisible(code)
7. ├─multifear::rm_banova_mf(cs1, cs2, subj = subj, data = example_data)
8. │ └─`%>%`(...)
9. ├─dplyr::select(., BF)
10. └─dplyr:::select.data.frame(., BF)
11. └─tidyselect::eval_select(expr(c(...)), .data)
12. └─tidyselect:::eval_select_impl(...)
13. ├─tidyselect:::with_subscript_errors(...)
14. │ ├─base::tryCatch(...)
15. │ │ └─base:::tryCatchList(expr, classes, parentenv, handlers)
16. │ │ └─base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
17. │ │ └─base:::doTryCatch(return(expr), name, parentenv, handler)
18. │ └─tidyselect:::instrument_base_errors(expr)
19. │ └─base::withCallingHandlers(...)
20. └─tidyselect:::vars_select_eval(...)
21. └─tidyselect:::walk_data_tree(expr, data_mask, context_mask)
22. └─tidyselect:::eval_c(expr, data_mask, context_mask)
23. └─tidyselect:::reduce_sels(node, data_mask, context_mask, init = init)
24. └─tidyselect:::walk_data_tree(new, data_mask, context_mask)
25. └─tidyselect:::as_indices_sel_impl(...)
26. └─tidyselect:::as_indices_impl(x, vars, strict = strict)
27. └─tidyselect:::chr_as_locations(x, vars)
28. └─vctrs::vec_as_location(x, n = length(vars), names = vars)
29. └─(function () ...
30. └─vctrs:::stop_subscript_oob(...)
31. └─vctrs:::stop_subscript(...)
── Error (test.output.R:51:3): rm_banova_mf for groups works ───────────────────
Error: Can't subset columns that don't exist.
✖ Column `BF` doesn't exist.
Backtrace:
â–ˆ
1. ├─testthat::expect_known_output(...) test.output.R:51:2
2. │ ├─base::append(act, eval_with_output(object, print = print, width = width))
3. │ └─testthat:::eval_with_output(object, print = print, width = width)
4. │ ├─withr::with_output_sink(temp, withVisible(code))
5. │ │ └─base::force(code)
6. │ └─base::withVisible(code)
7. ├─multifear::rm_banova_mf(...)
8. │ └─`%>%`(...)
9. ├─dplyr::select(., BF)
10. └─dplyr:::select.data.frame(., BF)
11. └─tidyselect::eval_select(expr(c(...)), .data)
12. └─tidyselect:::eval_select_impl(...)
13. ├─tidyselect:::with_subscript_errors(...)
14. │ ├─base::tryCatch(...)
15. │ │ └─base:::tryCatchList(expr, classes, parentenv, handlers)
16. │ │ └─base:::tryCatchOne(expr, names, parentenv, handlers[[1L]])
17. │ │ └─base:::doTryCatch(return(expr), name, parentenv, handler)
18. │ └─tidyselect:::instrument_base_errors(expr)
19. │ └─base::withCallingHandlers(...)
20. └─tidyselect:::vars_select_eval(...)
21. └─tidyselect:::walk_data_tree(expr, data_mask, context_mask)
22. └─tidyselect:::eval_c(expr, data_mask, context_mask)
23. └─tidyselect:::reduce_sels(node, data_mask, context_mask, init = init)
24. └─tidyselect:::walk_data_tree(new, data_mask, context_mask)
25. └─tidyselect:::as_indices_sel_impl(...)
26. └─tidyselect:::as_indices_impl(x, vars, strict = strict)
27. └─tidyselect:::chr_as_locations(x, vars)
28. └─vctrs::vec_as_location(x, n = length(vars), names = vars)
29. └─(function () ...
30. └─vctrs:::stop_subscript_oob(...)
31. └─vctrs:::stop_subscript(...)
[ FAIL 2 | WARN 1 | SKIP 0 | PASS 15 ]
Error: Test failures
Execution halted
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.