Comments (18)
danheck
commented on June 3, 2024
1
Thanks for noticing this issue! This is clearly a bug, the argument ci is not passed to the internal summary functions.
dfa24c6 provides a solution, but I have not checked this thoroughly yet. I will update the CRAN version as soon as possible.
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danheck
commented on June 3, 2024
1
Okay, I will try to fix this asap and check whether renaming is a bigger issue or not.
EDIT: I will let you know until tomorrow about the naming scheme.
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strengejacke
commented on June 3, 2024
1
This is how it looks (and works) with current dev of metaBMA.
library(metaBMA)
#> Loading required package: Rcpp
library(parameters)
data(towels)
set.seed(123)
m1 <- suppressWarnings(meta_bma(logOR, SE, study, data = towels, ci = 0.95))
model_parameters(m1)
#> # Studies
#>
#> Parameter | Coefficient | SE | 95% CI | Weight
#> -------------------------------------------------------------------------------------------------
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 1 | 0.38 | 0.20 | [-0.01, 0.77] | 25.59
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 2 | 0.30 | 0.14 | [ 0.04, 0.57] | 53.97
#> Schultz, Khazian, & Zaleski (2008), Exp. 2 | 0.21 | 0.19 | [-0.17, 0.58] | 27.24
#> Schultz, Khazian, & Zaleski (2008), Exp. 3 | 0.25 | 0.17 | [-0.08, 0.58] | 34.57
#> Mair & Bergin-Seers (2010), Exp. 1 | 0.29 | 0.82 | [-1.33, 1.90] | 1.47
#> Bohner & Schluter (2014), Exp. 1 | -0.12 | 0.25 | [-0.61, 0.36] | 16.25
#> Bohner & Schluter (2014), Exp. 2 | -1.46 | 0.76 | [-2.95, 0.03] | 1.73
#>
#> # Meta-Parameters
#>
#> Parameter | Coefficient | SE | 95% CI | BF | Rhat | ESS
#> ------------------------------------------------------------------------
#> averaged | 0.20 | 0.09 | [ 0.03, 0.37] | | |
#> fixed | 0.21 | 0.07 | [ 0.07, 0.36] | 11.97 | 1.000 | 3712.10
#> random | 0.18 | 0.11 | [-0.03, 0.37] | 5.37 | 1.002 | 3670.80
#>
#> Using highest density intervals as credible intervals.
set.seed(123)
m2 <- suppressWarnings(meta_bma(logOR, SE, study, data = towels, ci = 0.99))
model_parameters(m2)
#> # Studies
#>
#> Parameter | Coefficient | SE | 99% CI | Weight
#> -------------------------------------------------------------------------------------------------
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 1 | 0.38 | 0.20 | [-0.01, 0.77] | 25.59
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 2 | 0.30 | 0.14 | [ 0.04, 0.57] | 53.97
#> Schultz, Khazian, & Zaleski (2008), Exp. 2 | 0.21 | 0.19 | [-0.17, 0.58] | 27.24
#> Schultz, Khazian, & Zaleski (2008), Exp. 3 | 0.25 | 0.17 | [-0.08, 0.58] | 34.57
#> Mair & Bergin-Seers (2010), Exp. 1 | 0.29 | 0.82 | [-1.33, 1.90] | 1.47
#> Bohner & Schluter (2014), Exp. 1 | -0.12 | 0.25 | [-0.61, 0.36] | 16.25
#> Bohner & Schluter (2014), Exp. 2 | -1.46 | 0.76 | [-2.95, 0.03] | 1.73
#>
#> # Meta-Parameters
#>
#> Parameter | Coefficient | SE | 99% CI | BF | Rhat | ESS
#> ------------------------------------------------------------------------
#> averaged | 0.20 | 0.09 | [-0.06, 0.43] | | |
#> fixed | 0.21 | 0.07 | [ 0.02, 0.39] | 11.97 | 1.000 | 3712.10
#> random | 0.18 | 0.11 | [-0.16, 0.44] | 5.37 | 1.002 | 3670.80
#>
#> Using highest density intervals as credible intervals.
set.seed(123)
m3 <- suppressWarnings(meta_random(logOR, SE, study, data = towels, ci = 0.95))
model_parameters(m3, ci_method = "eti")
#> # Studies
#>
#> Parameter | Coefficient | SE | 95% CI | Weight
#> -------------------------------------------------------------------------------------------------
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 1 | 0.38 | 0.20 | [-0.01, 0.77] | 25.59
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 2 | 0.30 | 0.14 | [ 0.04, 0.57] | 53.97
#> Schultz, Khazian, & Zaleski (2008), Exp. 2 | 0.21 | 0.19 | [-0.17, 0.58] | 27.24
#> Schultz, Khazian, & Zaleski (2008), Exp. 3 | 0.25 | 0.17 | [-0.08, 0.58] | 34.57
#> Mair & Bergin-Seers (2010), Exp. 1 | 0.29 | 0.82 | [-1.33, 1.90] | 1.47
#> Bohner & Schluter (2014), Exp. 1 | -0.12 | 0.25 | [-0.61, 0.36] | 16.25
#> Bohner & Schluter (2014), Exp. 2 | -1.46 | 0.76 | [-2.95, 0.03] | 1.73
#>
#> # Meta-Parameters
#>
#> Parameter | Coefficient | SE | 95% CI | BF | Rhat | ESS
#> -----------------------------------------------------------------------
#> Overall | 0.18 | 0.10 | [-0.04, 0.37] | 2.00 | 1.000 | 3974.80
#> tau | 0.14 | 0.10 | [ 0.03, 0.40] | | 1.000 | 3145.40
#>
#> Using equal-tailed intervals as credible intervals.
set.seed(123)
m4 <- suppressWarnings(meta_random(logOR, SE, study, data = towels, ci = 0.99))
model_parameters(m4, ci_method = "eti")
#> # Studies
#>
#> Parameter | Coefficient | SE | 99% CI | Weight
#> -------------------------------------------------------------------------------------------------
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 1 | 0.38 | 0.20 | [-0.01, 0.77] | 25.59
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 2 | 0.30 | 0.14 | [ 0.04, 0.57] | 53.97
#> Schultz, Khazian, & Zaleski (2008), Exp. 2 | 0.21 | 0.19 | [-0.17, 0.58] | 27.24
#> Schultz, Khazian, & Zaleski (2008), Exp. 3 | 0.25 | 0.17 | [-0.08, 0.58] | 34.57
#> Mair & Bergin-Seers (2010), Exp. 1 | 0.29 | 0.82 | [-1.33, 1.90] | 1.47
#> Bohner & Schluter (2014), Exp. 1 | -0.12 | 0.25 | [-0.61, 0.36] | 16.25
#> Bohner & Schluter (2014), Exp. 2 | -1.46 | 0.76 | [-2.95, 0.03] | 1.73
#>
#> # Meta-Parameters
#>
#> Parameter | Coefficient | SE | 99% CI | BF | Rhat | ESS
#> -----------------------------------------------------------------------
#> Overall | 0.18 | 0.10 | [-0.16, 0.43] | 2.00 | 1.000 | 3974.80
#> tau | 0.14 | 0.10 | [ 0.02, 0.60] | | 1.000 | 3145.40
#>
#> Using equal-tailed intervals as credible intervals.
set.seed(123)
m5 <- suppressWarnings(meta_fixed(logOR, SE, study, data = towels, ci = 0.95))
model_parameters(m5)
#> # Studies
#>
#> Parameter | Coefficient | SE | 95% CI | Weight
#> -------------------------------------------------------------------------------------------------
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 1 | 0.38 | 0.20 | [-0.01, 0.77] | 25.59
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 2 | 0.30 | 0.14 | [ 0.04, 0.57] | 53.97
#> Schultz, Khazian, & Zaleski (2008), Exp. 2 | 0.21 | 0.19 | [-0.17, 0.58] | 27.24
#> Schultz, Khazian, & Zaleski (2008), Exp. 3 | 0.25 | 0.17 | [-0.08, 0.58] | 34.57
#> Mair & Bergin-Seers (2010), Exp. 1 | 0.29 | 0.82 | [-1.33, 1.90] | 1.47
#> Bohner & Schluter (2014), Exp. 1 | -0.12 | 0.25 | [-0.61, 0.36] | 16.25
#> Bohner & Schluter (2014), Exp. 2 | -1.46 | 0.76 | [-2.95, 0.03] | 1.73
#>
#> # Meta-Parameters
#>
#> Parameter | Coefficient | SE | 95% CI | BF
#> -----------------------------------------------------
#> Overall | 0.21 | 0.08 | [0.06, 0.36] | 11.97
#>
#> Using highest density intervals as credible intervals.
set.seed(123)
m6 <- suppressWarnings(meta_fixed(logOR, SE, study, data = towels, ci = 0.99))
model_parameters(m6)
#> # Studies
#>
#> Parameter | Coefficient | SE | 99% CI | Weight
#> -------------------------------------------------------------------------------------------------
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 1 | 0.38 | 0.20 | [-0.01, 0.77] | 25.59
#> Goldstein, Cialdini, & Griskevicius (2008), Exp. 2 | 0.30 | 0.14 | [ 0.04, 0.57] | 53.97
#> Schultz, Khazian, & Zaleski (2008), Exp. 2 | 0.21 | 0.19 | [-0.17, 0.58] | 27.24
#> Schultz, Khazian, & Zaleski (2008), Exp. 3 | 0.25 | 0.17 | [-0.08, 0.58] | 34.57
#> Mair & Bergin-Seers (2010), Exp. 1 | 0.29 | 0.82 | [-1.33, 1.90] | 1.47
#> Bohner & Schluter (2014), Exp. 1 | -0.12 | 0.25 | [-0.61, 0.36] | 16.25
#> Bohner & Schluter (2014), Exp. 2 | -1.46 | 0.76 | [-2.95, 0.03] | 1.73
#>
#> # Meta-Parameters
#>
#> Parameter | Coefficient | SE | 99% CI | BF
#> -----------------------------------------------------
#> Overall | 0.21 | 0.08 | [0.01, 0.41] | 11.97
#>
#> Using highest density intervals as credible intervals.Created on 2020-10-26 by the reprex package (v0.3.0)
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IndrajeetPatil
commented on June 3, 2024
Would the column names for the intervals still contain 95? Or will it change according to the specified confidence level?
I know this would be a breaking change, but i feel like the names for these columns should be more generic, like conf.low, ci.lower, etc.
What do you think?
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danheck
commented on June 3, 2024
Yes, the names will change depending on the value of ci.
On the one hand, generic names would be beneficial for accessing the estimates (e.g., in your package or within JASP). On the other hand, having the names in the columns will tell the user what level of ci was used. And changing this will require more time and checks.
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IndrajeetPatil
commented on June 3, 2024
Another option to have a generic column names and then a separate column called ci.width or conf.level that contains this information, if the user is curious.
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IndrajeetPatil
commented on June 3, 2024
By the way, do you plan to submit the patched version to CRAN any time soon?
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danheck
commented on June 3, 2024
Good idea, thanks for the suggestion.
Since the semester is starting again, I will require 1-2 weeks for this. Is this fast enough?
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IndrajeetPatil
commented on June 3, 2024
Unfortunately, I have a deadline of 5th of November to submit revised version of my package because the tests are currently failing.
That said, if you can already tell us (cc @strengejacke) what naming schema you will adopt, we can put in contingencies in our packages to make it work with both the CRAN and GitHub versions.
Sounds reasonable?
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IndrajeetPatil
commented on June 3, 2024
Thanks, Daniel! :)
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strengejacke
commented on June 3, 2024
I revised model_parameters(), so it should now work with flexible column names that include the ci-level (e.g. hdp95_upper or hdp89_upper...).
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danheck
commented on June 3, 2024
Great, this was fast. Just for your information, the column names when using ci=.99 are currently:
mean sd 0.5% 50% 99.5% hpd99_lower hpd99_upper n_eff Rhat
Since there are both quantiles and the HPD interval, a consistent renaming of the table column names would require not only renaming but also reordering the columns, for instance, as follows:
mean sd median ci bci_lower bci_upper hpd_lower hpd_upper n_eff Rhat
Alternatively, I could merely remove the information about the ci in the HPD labels. The ci level would then be implicit in the reported quantiles. This would be a compromise:
mean sd 0.5% 50% 99.5% hpd_lower hpd_upper n_eff Rhat
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IndrajeetPatil
commented on June 3, 2024
Thanks @strengejacke!
I like this one-
mean sd median ci bci_lower bci_upper hpd_lower hpd_upper n_eff Rhatfrom metabma.
strengejacke
commented on June 3, 2024
Great, this was fast. Just for your information, the column names when using
ci=.99are currently:
mean sd 0.5% 50% 99.5% hpd99_lower hpd99_upper n_eff RhatSince there are both quantiles and the HPD interval, a consistent renaming of the table column names would require not only renaming but also reordering the columns, for instance, as follows:
mean sd median ci bci_lower bci_upper hpd_lower hpd_upper n_eff RhatAlternatively, I could merely remove the information about the
ciin the HPD labels. Thecilevel would then be implicit in the reported quantiles. This would be a compromise:
mean sd 0.5% 50% 99.5% hpd_lower hpd_upper n_eff Rhat
Yes, the pattern to detect the appropriate CI columns in parameters::model_parameters() should be OK:
ci_method <- "hdi"
ci <- .95
switch(
toupper(ci_method),
"HDI" = sprintf(c("hpd%i_lower", "hpd%i_upper"), 100 * ci),
c(sprintf("%g%%", (100 * (1 - ci)) / 2), sprintf("%g%%", 100 - (100 * (1 - ci)) / 2))
)
#> [1] "hpd95_lower" "hpd95_upper"
ci_method <- "hdi"
ci <- .99
switch(
toupper(ci_method),
"HDI" = sprintf(c("hpd%i_lower", "hpd%i_upper"), 100 * ci),
c(sprintf("%g%%", (100 * (1 - ci)) / 2), sprintf("%g%%", 100 - (100 * (1 - ci)) / 2))
)
#> [1] "hpd99_lower" "hpd99_upper"
ci_method <- "eti"
ci <- .95
switch(
toupper(ci_method),
"HDI" = sprintf(c("hpd%i_lower", "hpd%i_upper"), 100 * ci),
c(sprintf("%g%%", (100 * (1 - ci)) / 2), sprintf("%g%%", 100 - (100 * (1 - ci)) / 2))
)
#> [1] "2.5%" "97.5%"
ci_method <- "eti"
ci <- .99
switch(
toupper(ci_method),
"HDI" = sprintf(c("hpd%i_lower", "hpd%i_upper"), 100 * ci),
c(sprintf("%g%%", (100 * (1 - ci)) / 2), sprintf("%g%%", 100 - (100 * (1 - ci)) / 2))
)
#> [1] "0.5%" "99.5%"Created on 2020-10-26 by the reprex package (v0.3.0)
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strengejacke
commented on June 3, 2024
I'm ok with both approaches. It would be good to know your decision the next days, so I can already take care of this in my package update, which should be submitted the next days.
If your current implementation is mean sd 0.5% 50% 99.5% hpd99_lower hpd99_upper n_eff Rhat, it will already work.
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danheck
commented on June 3, 2024
Okay, I think I will just leave the names as they are. I am a bit hesitant due to other dependencies (e.g., JASP).
Keeping the current naming scheme also has the benefit that it is not necessary that metaBMA is submitted before your package. You could disable unit tests with ci values other than 95% with:
if(packageVersion("metaBMA") < "0.6.4") ...
Anyways, I will of course still check everything and submit the revision asap. Thanks again for pointing out the issue.
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strengejacke
commented on June 3, 2024
This is how it looks (and works) with current dev of metaBMA.
library(metaBMA)
library(parameters)
data(towels)
set.seed(123)
m1 <- meta_bma(logOR, SE, study, data = towels, ci = 0.95)
model_parameters(m1)
#> Parameter | Coefficient | SE | 95% CI | BF | Rhat | ESS
#> ------------------------------------------------------------------------
#> averaged | 0.20 | 0.09 | [ 0.03, 0.37] | | |
#> fixed | 0.21 | 0.07 | [ 0.07, 0.36] | 11.97 | 1.000 | 3712.10
#> random | 0.18 | 0.11 | [-0.03, 0.37] | 5.37 | 1.002 | 3670.80
#>
#> Using highest density intervals as credible intervals.
set.seed(123)
m2 <- meta_bma(logOR, SE, study, data = towels, ci = 0.99)
model_parameters(m2)
#> Parameter | Coefficient | SE | 99% CI | BF | Rhat | ESS
#> ------------------------------------------------------------------------
#> averaged | 0.20 | 0.09 | [-0.06, 0.43] | | |
#> fixed | 0.21 | 0.07 | [ 0.02, 0.39] | 11.97 | 1.000 | 3712.10
#> random | 0.18 | 0.11 | [-0.16, 0.44] | 5.37 | 1.002 | 3670.80
#>
#> Using highest density intervals as credible intervals.
set.seed(123)
m3 <- meta_random(logOR, SE, study, data = towels, ci = 0.95)
model_parameters(m3, ci_method = "eti")
#> Parameter | Coefficient | SE | 95% CI | BF | Rhat | ESS
#> -----------------------------------------------------------------------
#> Overall | 0.18 | 0.10 | [-0.04, 0.37] | 2.00 | 1.000 | 3974.80
#> tau | 0.14 | 0.10 | [ 0.03, 0.40] | | 1.000 | 3145.40
#>
#> Using equal-tailed intervals as credible intervals.
set.seed(123)
m4 <- meta_random(logOR, SE, study, data = towels, ci = 0.99)
model_parameters(m4, ci_method = "eti")
#> Parameter | Coefficient | SE | 99% CI | BF | Rhat | ESS
#> -----------------------------------------------------------------------
#> Overall | 0.18 | 0.10 | [-0.16, 0.43] | 2.00 | 1.000 | 3974.80
#> tau | 0.14 | 0.10 | [ 0.02, 0.60] | | 1.000 | 3145.40
#>
#> Using equal-tailed intervals as credible intervals.
set.seed(123)
m5 <- meta_fixed(logOR, SE, study, data = towels, ci = 0.95)
model_parameters(m5)
#> Parameter | Coefficient | SE | 95% CI | BF
#> -----------------------------------------------------
#> Overall | 0.21 | 0.08 | [0.06, 0.36] | 11.97
#>
#> Using highest density intervals as credible intervals.
set.seed(123)
m6 <- meta_fixed(logOR, SE, study, data = towels, ci = 0.99)
model_parameters(m6)
#> Parameter | Coefficient | SE | 99% CI | BF
#> -----------------------------------------------------
#> Overall | 0.21 | 0.08 | [0.01, 0.41] | 11.97
#>
#> Using highest density intervals as credible intervals.Created on 2020-10-26 by the reprex package (v0.3.0)
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IndrajeetPatil
commented on June 3, 2024
closed in dfa24c6
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Related Issues (10)
- Update dev or README HOT 1
- feature request: supporting methods to compute centrality and credible intervals for estimates HOT 1
- plot_forest: cex.axis not altering size of the effect size axis labels HOT 1
- plot_forest: 'main' doesn't let you change the plot title text HOT 2
- error loading library in MacOS Catalina HOT 1
- Compilation error HOT 7
- error in load action .__A__.1 for package metaBMA: is(module, "character"): object 'm' not found HOT 1
- Robustness check HOT 2
- changing the prior default to `cauchy(0, 0.707)` HOT 8
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