inconsistency between the prior scale information included in output dataframe #222
Labels
3 investigators ❔❓
Need to look further into this issue
Bug 🐛
Something isn't working
Low priority 😴
This issue can be easily workaround or happens only in edge cases
Comments
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
|
It's hard to match the different parameters to the describe_prior(result)
#> Parameter Prior_Distribution Prior_Location Prior_Scale
#> 1 fixed cauchy 0 0.5000000
#> 2 random cauchy 0 1.0000000
#> 3 continuous cauchy 0 0.3535534(And does not return the dist/loc/scale on a parameter basis...) It is unfortunate that the docs and internals from I suggest opening this as a feature request on |
IndrajeetPatil
changed the title
|
Doesn't describe_priors rely itself on I'm not sure I understand what is the issue? |
|
The issue is that For example, one-sample t-test library(BayesFactor)
library(parameters)
model <- ttestBF(x = rnorm(100, 1, 1))
as.data.frame(model_parameters(model))
#> Parameter Median CI_low CI_high pd ROPE_Percentage Prior_Distribution
#> 1 Difference 0.8943024 0.7003134 1.105907 1 0 cauchy
#> Prior_Location Prior_Scale Effects Component BF
#> 1 0 0.7071068 fixed conditional 7.536535e+15 |
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
|
Ah, thanks for the details. I guess then the issue is if the info about prior scale present in |
|
(note that I fixed the weird BF column given here - these should all be the
same value for a given model)
I thought the issue was that the location/scale were not given for each
parameter (what Indrajeet wanted?), but is given for the different
parameter **types** (fixed, random...).
…--
Mattan S. Ben-Shachar, PhD student
Department of Psychology & Zlotowski Center for Neuroscience
Ben-Gurion University of the Negev
The Developmental ERP Lab
On Sat, Sep 12, 2020, 09:47 Indrajeet Patil ***@***.***> wrote:
Ah, thanks for the details. I guess then the issue is if the info about
prior scale present in bayestestR::describe_posterior can also be
included in parameters::model_parameters.
—
You are receiving this because you were mentioned.
Reply to this email directly, view it on GitHub
<https://github.com/easystats/bayestestR/issues/336#issuecomment-691427675>,
or unsubscribe
<https://github.com/notifications/unsubscribe-auth/AINRP6EQCANYPZI6J5N4J7TSFMKRXANCNFSM4RH5SNBA>
.
|
|
If I understand, in order to fix it and to have prior information displayed alongside each parameter, we would in theory need to retrieve which parameters are "continuous" "fixed" and "random" and combine accordingly with the parameters table is that correct? |
|
Yes, that's how I understood this as well. |
|
I feel like it would make sense to retrieve this kind of info at insight's level no? |
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
|
Now we need someone who knows whether a parameter is fixed, random or continuous. |
strengejacke
added
3 investigators ❔❓
|
This won't work for the cases detailed in #223 ... get_type <- function(trm) {
if (grepl(":", trm, fixed = TRUE)) {
trm <- unlist(strsplit(trm, ":", fixed = TRUE))
}
if (any(trm %in% dataTypes[["random"]])) {
"random"
} else if (any(trm %in% dataTypes[["continuous"]])) {
"continuous"
} else if (any(trm %in% dataTypes[["fixed"]])) {
"fixed"
} else {
""
}
}
library(BayesFactor)
#> Loading required package: coda
#> Loading required package: Matrix
#> ************
#> Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
#>
#> Type BFManual() to open the manual.
#> ************
iris$ID <- factor(rep(1:30, each = 5))
res <- lmBF(Sepal.Length ~ Species * Sepal.Width + Petal.Width + ID + Sepal.Width:ID, data = iris,
whichRandom = c("ID","Sepal.Width:ID"),
progress = FALSE)
#> Warning in doNwaySampling(method, y, X, rscale, iterations, gMap, incCont, :
#> Some NAs were removed from sampling results: 10000 in total.
pars <- insight::get_parameters(res)
if (insight::model_info(res)$is_linear) {
dataTypes <- res@numerator[[1]]@dataTypes
dataTypes <- tapply(names(dataTypes), dataTypes, "[", simplify = FALSE)
par_names_clean <- sub("\\-(.*)", "", colnames(pars))
# par_names_clean <- par_names_clean[-(which(par_names_clean=="sig2"):length(par_names_clean))]
par_types <- sapply(par_names_clean, get_type)
}
par_types <- setNames(names(par_types),par_types)
priors <- insight::get_priors(res)
idx <- sapply(names(par_types), function(x) {
if (!any(i <- x==priors$Parameter)) return(NA)
which(i)
})
priors <- priors[idx,]
priors$Parameter <- colnames(pars)
priors
#> Parameter Distribution Location
#> NA mu <NA> NA
#> 1 Species-setosa cauchy 0
#> 1.1 Species-versicolor cauchy 0
#> 1.2 Species-virginica cauchy 0
#> 3 Sepal.Width-Sepal.Width cauchy 0
#> 3.1 Petal.Width-Petal.Width cauchy 0
#> 2 ID-1 cauchy 0
#> 2.1 ID-2 cauchy 0
#> 2.2 ID-3 cauchy 0
#> 2.3 ID-4 cauchy 0
#> 2.4 ID-5 cauchy 0
#> 2.5 ID-6 cauchy 0
#> 2.6 ID-7 cauchy 0
#> 2.7 ID-8 cauchy 0
#> 2.8 ID-9 cauchy 0
#> 2.9 ID-10 cauchy 0
#> 2.10 ID-11 cauchy 0
#> 2.11 ID-12 cauchy 0
#> 2.12 ID-13 cauchy 0
#> 2.13 ID-14 cauchy 0
#> 2.14 ID-15 cauchy 0
#> 2.15 ID-16 cauchy 0
#> 2.16 ID-17 cauchy 0
#> 2.17 ID-18 cauchy 0
#> 2.18 ID-19 cauchy 0
#> 2.19 ID-20 cauchy 0
#> 2.20 ID-21 cauchy 0
#> 2.21 ID-22 cauchy 0
#> 2.22 ID-23 cauchy 0
#> 2.23 ID-24 cauchy 0
#> 2.24 ID-25 cauchy 0
#> 2.25 ID-26 cauchy 0
#> 2.26 ID-27 cauchy 0
#> 2.27 ID-28 cauchy 0
#> 2.28 ID-29 cauchy 0
#> 2.29 ID-30 cauchy 0
#> 3.2 Species:Sepal.Width-setosa.&.Sepal.Width cauchy 0
#> 3.3 Species:Sepal.Width-versicolor.&.Sepal.Width cauchy 0
#> 3.4 Species:Sepal.Width-virginica.&.Sepal.Width cauchy 0
#> 2.30 Sepal.Width:ID-1 cauchy 0
#> 2.31 Sepal.Width:ID-2 cauchy 0
#> 2.32 Sepal.Width:ID-3 cauchy 0
#> 2.33 Sepal.Width:ID-4 cauchy 0
#> 2.34 Sepal.Width:ID-5 cauchy 0
#> 2.35 Sepal.Width:ID-6 cauchy 0
#> 2.36 Sepal.Width:ID-7 cauchy 0
#> 2.37 Sepal.Width:ID-8 cauchy 0
#> 2.38 Sepal.Width:ID-9 cauchy 0
#> 2.39 Sepal.Width:ID-10 cauchy 0
#> 2.40 Sepal.Width:ID-11 cauchy 0
#> 2.41 Sepal.Width:ID-12 cauchy 0
#> 2.42 Sepal.Width:ID-13 cauchy 0
#> 2.43 Sepal.Width:ID-14 cauchy 0
#> 2.44 Sepal.Width:ID-15 cauchy 0
#> 2.45 Sepal.Width:ID-16 cauchy 0
#> 2.46 Sepal.Width:ID-17 cauchy 0
#> 2.47 Sepal.Width:ID-18 cauchy 0
#> 2.48 Sepal.Width:ID-19 cauchy 0
#> 2.49 Sepal.Width:ID-20 cauchy 0
#> 2.50 Sepal.Width:ID-21 cauchy 0
#> 2.51 Sepal.Width:ID-22 cauchy 0
#> 2.52 Sepal.Width:ID-23 cauchy 0
#> 2.53 Sepal.Width:ID-24 cauchy 0
#> 2.54 Sepal.Width:ID-25 cauchy 0
#> 2.55 Sepal.Width:ID-26 cauchy 0
#> 2.56 Sepal.Width:ID-27 cauchy 0
#> 2.57 Sepal.Width:ID-28 cauchy 0
#> 2.58 Sepal.Width:ID-29 cauchy 0
#> 2.59 Sepal.Width:ID-30 cauchy 0
#> NA.1 sig2 <NA> NA
#> NA.2 g_Species <NA> NA
#> NA.3 g_ID <NA> NA
#> NA.4 g_continuous <NA> NA
#> Scale
#> NA NA
#> 1 0.5000000
#> 1.1 0.5000000
#> 1.2 0.5000000
#> 3 0.3535534
#> 3.1 0.3535534
#> 2 1.0000000
#> 2.1 1.0000000
#> 2.2 1.0000000
#> 2.3 1.0000000
#> 2.4 1.0000000
#> 2.5 1.0000000
#> 2.6 1.0000000
#> 2.7 1.0000000
#> 2.8 1.0000000
#> 2.9 1.0000000
#> 2.10 1.0000000
#> 2.11 1.0000000
#> 2.12 1.0000000
#> 2.13 1.0000000
#> 2.14 1.0000000
#> 2.15 1.0000000
#> 2.16 1.0000000
#> 2.17 1.0000000
#> 2.18 1.0000000
#> 2.19 1.0000000
#> 2.20 1.0000000
#> 2.21 1.0000000
#> 2.22 1.0000000
#> 2.23 1.0000000
#> 2.24 1.0000000
#> 2.25 1.0000000
#> 2.26 1.0000000
#> 2.27 1.0000000
#> 2.28 1.0000000
#> 2.29 1.0000000
#> 3.2 0.3535534
#> 3.3 0.3535534
#> 3.4 0.3535534
#> 2.30 1.0000000
#> 2.31 1.0000000
#> 2.32 1.0000000
#> 2.33 1.0000000
#> 2.34 1.0000000
#> 2.35 1.0000000
#> 2.36 1.0000000
#> 2.37 1.0000000
#> 2.38 1.0000000
#> 2.39 1.0000000
#> 2.40 1.0000000
#> 2.41 1.0000000
#> 2.42 1.0000000
#> 2.43 1.0000000
#> 2.44 1.0000000
#> 2.45 1.0000000
#> 2.46 1.0000000
#> 2.47 1.0000000
#> 2.48 1.0000000
#> 2.49 1.0000000
#> 2.50 1.0000000
#> 2.51 1.0000000
#> 2.52 1.0000000
#> 2.53 1.0000000
#> 2.54 1.0000000
#> 2.55 1.0000000
#> 2.56 1.0000000
#> 2.57 1.0000000
#> 2.58 1.0000000
#> 2.59 1.0000000
#> NA.1 NA
#> NA.2 NA
#> NA.3 NA
#> NA.4 NACreated on 2020-09-15 by the reprex package (v0.3.0) |
|
We should first fix library(insight)
library(BayesFactor)
#> Loading required package: coda
#> Loading required package: Matrix
#> ************
#> Welcome to BayesFactor 0.9.12-4.2. If you have questions, please contact Richard Morey (richarddmorey@gmail.com).
#>
#> Type BFManual() to open the manual.
#> ************
iris$ID <- factor(rep(1:30, each = 5))
m1 <- lmBF(Sepal.Length ~ Species * Sepal.Width + Petal.Width + ID + Sepal.Width:ID, data = iris,
rscaleCont = c(Sepal.Width = 0.123, Petal.Width = 0.321),
whichRandom = c("ID","Sepal.Width:ID"))
#> Warning in rscale[rscaleTypes == "continuous"] <- rscaleCont: number of items to
#> replace is not a multiple of replacement length
#> Warning in doNwaySampling(method, y, X, rscale, iterations, gMap, incCont, :
#> Some NAs were removed from sampling results: 10000 in total.
clean_parameters(m1)
#> Warning in rscale[rscaleTypes == "continuous"] <- rscaleCont: number of items to
#> replace is not a multiple of replacement length
#> Parameter Effects Component
#> 1 Species-setosa fixed conditional
#> 2 Species-versicolor fixed conditional
#> 3 Species-virginica fixed conditional
#> 4 Sepal.Width fixed conditional
#> 5 Petal.Width fixed conditional
#> 6 ID-1 fixed conditional
#> 7 ID-2 fixed conditional
#> 8 ID-3 fixed conditional
#> 9 ID-4 fixed conditional
#> 10 ID-5 fixed conditional
#> 11 ID-6 fixed conditional
#> 12 ID-7 fixed conditional
#> 13 ID-8 fixed conditional
#> 14 ID-9 fixed conditional
#> 15 ID-10 fixed conditional
#> 16 ID-11 fixed conditional
#> 17 ID-12 fixed conditional
#> 18 ID-13 fixed conditional
#> 19 ID-14 fixed conditional
#> 20 ID-15 fixed conditional
#> 21 ID-16 fixed conditional
#> 22 ID-17 fixed conditional
#> 23 ID-18 fixed conditional
#> 24 ID-19 fixed conditional
#> 25 ID-20 fixed conditional
#> 26 ID-21 fixed conditional
#> 27 ID-22 fixed conditional
#> 28 ID-23 fixed conditional
#> 29 ID-24 fixed conditional
#> 30 ID-25 fixed conditional
#> 31 ID-26 fixed conditional
#> 32 ID-27 fixed conditional
#> 33 ID-28 fixed conditional
#> 34 ID-29 fixed conditional
#> 35 ID-30 fixed conditional
#> 36 Species:Sepal.Width-setosa.&.Sepal.Width fixed conditional
#> 37 Species:Sepal.Width-versicolor.&.Sepal.Width fixed conditional
#> 38 Species:Sepal.Width-virginica.&.Sepal.Width fixed conditional
#> 39 Sepal.Width:ID-1 fixed conditional
#> 40 Sepal.Width:ID-2 fixed conditional
#> 41 Sepal.Width:ID-3 fixed conditional
#> 42 Sepal.Width:ID-4 fixed conditional
#> 43 Sepal.Width:ID-5 fixed conditional
#> 44 Sepal.Width:ID-6 fixed conditional
#> 45 Sepal.Width:ID-7 fixed conditional
#> 46 Sepal.Width:ID-8 fixed conditional
#> 47 Sepal.Width:ID-9 fixed conditional
#> 48 Sepal.Width:ID-10 fixed conditional
#> 49 Sepal.Width:ID-11 fixed conditional
#> 50 Sepal.Width:ID-12 fixed conditional
#> 51 Sepal.Width:ID-13 fixed conditional
#> 52 Sepal.Width:ID-14 fixed conditional
#> 53 Sepal.Width:ID-15 fixed conditional
#> 54 Sepal.Width:ID-16 fixed conditional
#> 55 Sepal.Width:ID-17 fixed conditional
#> 56 Sepal.Width:ID-18 fixed conditional
#> 57 Sepal.Width:ID-19 fixed conditional
#> 58 Sepal.Width:ID-20 fixed conditional
#> 59 Sepal.Width:ID-21 fixed conditional
#> 60 Sepal.Width:ID-22 fixed conditional
#> 61 Sepal.Width:ID-23 fixed conditional
#> 62 Sepal.Width:ID-24 fixed conditional
#> 63 Sepal.Width:ID-25 fixed conditional
#> 64 Sepal.Width:ID-26 fixed conditional
#> 65 Sepal.Width:ID-27 fixed conditional
#> 66 Sepal.Width:ID-28 fixed conditional
#> 67 Sepal.Width:ID-29 fixed conditional
#> 68 Sepal.Width:ID-30 fixed conditional
#> 69 ID-1 random conditional
#> 70 ID-2 random conditional
#> 71 ID-3 random conditional
#> 72 ID-4 random conditional
#> 73 ID-5 random conditional
#> 74 ID-6 random conditional
#> 75 ID-7 random conditional
#> 76 ID-8 random conditional
#> 77 ID-9 random conditional
#> 78 ID-10 random conditional
#> 79 ID-11 random conditional
#> 80 ID-12 random conditional
#> 81 ID-13 random conditional
#> 82 ID-14 random conditional
#> 83 ID-15 random conditional
#> 84 ID-16 random conditional
#> 85 ID-17 random conditional
#> 86 ID-18 random conditional
#> 87 ID-19 random conditional
#> 88 ID-20 random conditional
#> 89 ID-21 random conditional
#> 90 ID-22 random conditional
#> 91 ID-23 random conditional
#> 92 ID-24 random conditional
#> 93 ID-25 random conditional
#> 94 ID-26 random conditional
#> 95 ID-27 random conditional
#> 96 ID-28 random conditional
#> 97 ID-29 random conditional
#> 98 ID-30 random conditional
#> 99 mu fixed extra
#> 100 Sepal.Width-Sepal.Width fixed extra
#> 101 Petal.Width-Petal.Width fixed extra
#> 102 sig2 fixed extra
#> 103 g_Species fixed extra
#> 104 g_ID fixed extra
#> 105 g_continuous fixed extra
#> Cleaned_Parameter
#> 1 Species-setosa
#> 2 Species-versicolor
#> 3 Species-virginica
#> 4 Sepal.Width
#> 5 Petal.Width
#> 6 ID-1
#> 7 ID-2
#> 8 ID-3
#> 9 ID-4
#> 10 ID-5
#> 11 ID-6
#> 12 ID-7
#> 13 ID-8
#> 14 ID-9
#> 15 ID-10
#> 16 ID-11
#> 17 ID-12
#> 18 ID-13
#> 19 ID-14
#> 20 ID-15
#> 21 ID-16
#> 22 ID-17
#> 23 ID-18
#> 24 ID-19
#> 25 ID-20
#> 26 ID-21
#> 27 ID-22
#> 28 ID-23
#> 29 ID-24
#> 30 ID-25
#> 31 ID-26
#> 32 ID-27
#> 33 ID-28
#> 34 ID-29
#> 35 ID-30
#> 36 Species:Sepal.Width-setosa.&.Sepal.Width
#> 37 Species:Sepal.Width-versicolor.&.Sepal.Width
#> 38 Species:Sepal.Width-virginica.&.Sepal.Width
#> 39 Sepal.Width:ID-1
#> 40 Sepal.Width:ID-2
#> 41 Sepal.Width:ID-3
#> 42 Sepal.Width:ID-4
#> 43 Sepal.Width:ID-5
#> 44 Sepal.Width:ID-6
#> 45 Sepal.Width:ID-7
#> 46 Sepal.Width:ID-8
#> 47 Sepal.Width:ID-9
#> 48 Sepal.Width:ID-10
#> 49 Sepal.Width:ID-11
#> 50 Sepal.Width:ID-12
#> 51 Sepal.Width:ID-13
#> 52 Sepal.Width:ID-14
#> 53 Sepal.Width:ID-15
#> 54 Sepal.Width:ID-16
#> 55 Sepal.Width:ID-17
#> 56 Sepal.Width:ID-18
#> 57 Sepal.Width:ID-19
#> 58 Sepal.Width:ID-20
#> 59 Sepal.Width:ID-21
#> 60 Sepal.Width:ID-22
#> 61 Sepal.Width:ID-23
#> 62 Sepal.Width:ID-24
#> 63 Sepal.Width:ID-25
#> 64 Sepal.Width:ID-26
#> 65 Sepal.Width:ID-27
#> 66 Sepal.Width:ID-28
#> 67 Sepal.Width:ID-29
#> 68 Sepal.Width:ID-30
#> 69 ID-1
#> 70 ID-2
#> 71 ID-3
#> 72 ID-4
#> 73 ID-5
#> 74 ID-6
#> 75 ID-7
#> 76 ID-8
#> 77 ID-9
#> 78 ID-10
#> 79 ID-11
#> 80 ID-12
#> 81 ID-13
#> 82 ID-14
#> 83 ID-15
#> 84 ID-16
#> 85 ID-17
#> 86 ID-18
#> 87 ID-19
#> 88 ID-20
#> 89 ID-21
#> 90 ID-22
#> 91 ID-23
#> 92 ID-24
#> 93 ID-25
#> 94 ID-26
#> 95 ID-27
#> 96 ID-28
#> 97 ID-29
#> 98 ID-30
#> 99 mu
#> 100 Sepal.Width-Sepal.Width
#> 101 Petal.Width-Petal.Width
#> 102 sig2
#> 103 g_Species
#> 104 g_ID
#> 105 g_continuousCreated on 2020-09-17 by the reprex package (v0.3.0) |
|
The issue in the above examples is:
|
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
strengejacke
added
the
Low priority 😴
This comment has been minimized.
This comment has been minimized.
15 tasks
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
This comment has been minimized.
8 tasks
|
Any possibility we can get this fixed before the next release cycle? |
|
I can't even remember the exact issue we had here... ^^ |
|
The issue was to match a prior to each parameter 🧶 |
library(parameters)
library(BayesFactor)
iris$ID <- factor(rep(1:30, each = 5))
m1 <- lmBF(Sepal.Length ~ Species * Sepal.Width + Petal.Width + ID + Sepal.Width:ID, data = iris,
rscaleCont = c(Sepal.Width = 0.123, Petal.Width = 0.321),
whichRandom = c("ID","Sepal.Width:ID"))
model_parameters(m1)
#> # Extra Parameters
#>
#> Parameter | pd | Prior
#> -----------------------------------------------------
#> mu | 98.95% |
#> Sepal.Width-Sepal.Width | 99.12% | Cauchy (0 +- 0.12)
#> Petal.Width-Petal.Width | 99.08% | Cauchy (0 +- 0.32)
#> sig2 | 100% |
#> g_Species | 100% |
#> g_ID | 100% |
#> g_continuous | 100% |
#>
#> # Fixed Effects
#>
#> Parameter | pd | Prior
#> --------------------------------------------------------------------------
#> Species-setosa | 98.90% | Cauchy (0 +- 0.50)
#> Species-versicolor | 98.98% | Cauchy (0 +- 0.50)
#> Species-virginica | 98.98% | Cauchy (0 +- 0.50)
#> ID-1 | 99.00% | Cauchy (0 +- 1.00)
#> ID-1 | 99.00% | Cauchy (0 +- 1.00)
#> ID-2 | 98.90% | Cauchy (0 +- 1.00)
#> ID-2 | 98.90% | Cauchy (0 +- 1.00)
#> ID-3 | 98.98% | Cauchy (0 +- 1.00)
#> ID-3 | 98.98% | Cauchy (0 +- 1.00)
#> ID-4 | 98.98% | Cauchy (0 +- 1.00)
#> ID-4 | 98.98% | Cauchy (0 +- 1.00)
#> ID-5 | 98.92% | Cauchy (0 +- 1.00)
#> ID-5 | 98.92% | Cauchy (0 +- 1.00)
#> ID-6 | 99.08% | Cauchy (0 +- 1.00)
#> ID-6 | 99.08% | Cauchy (0 +- 1.00)
#> ID-7 | 98.98% | Cauchy (0 +- 1.00)
#> ID-7 | 98.98% | Cauchy (0 +- 1.00)
#> ID-8 | 98.98% | Cauchy (0 +- 1.00)
#> ID-8 | 98.98% | Cauchy (0 +- 1.00)
#> ID-9 | 98.95% | Cauchy (0 +- 1.00)
#> ID-9 | 98.95% | Cauchy (0 +- 1.00)
#> ID-10 | 98.98% | Cauchy (0 +- 1.00)
#> ID-10 | 98.98% | Cauchy (0 +- 1.00)
#> ID-11 | 98.98% | Cauchy (0 +- 1.00)
#> ID-11 | 98.98% | Cauchy (0 +- 1.00)
#> ID-12 | 98.90% | Cauchy (0 +- 1.00)
#> ID-12 | 98.90% | Cauchy (0 +- 1.00)
#> ID-13 | 99.08% | Cauchy (0 +- 1.00)
#> ID-13 | 99.08% | Cauchy (0 +- 1.00)
#> ID-14 | 99.10% | Cauchy (0 +- 1.00)
#> ID-14 | 99.10% | Cauchy (0 +- 1.00)
#> ID-15 | 99.02% | Cauchy (0 +- 1.00)
#> ID-15 | 99.02% | Cauchy (0 +- 1.00)
#> ID-16 | 99.02% | Cauchy (0 +- 1.00)
#> ID-16 | 99.02% | Cauchy (0 +- 1.00)
#> ID-17 | 99.00% | Cauchy (0 +- 1.00)
#> ID-17 | 99.00% | Cauchy (0 +- 1.00)
#> ID-18 | 99.17% | Cauchy (0 +- 1.00)
#> ID-18 | 99.17% | Cauchy (0 +- 1.00)
#> ID-19 | 99.02% | Cauchy (0 +- 1.00)
#> ID-19 | 99.02% | Cauchy (0 +- 1.00)
#> ID-20 | 98.90% | Cauchy (0 +- 1.00)
#> ID-20 | 98.90% | Cauchy (0 +- 1.00)
#> ID-21 | 98.90% | Cauchy (0 +- 1.00)
#> ID-21 | 98.90% | Cauchy (0 +- 1.00)
#> ID-22 | 99.02% | Cauchy (0 +- 1.00)
#> ID-22 | 99.02% | Cauchy (0 +- 1.00)
#> ID-23 | 99.02% | Cauchy (0 +- 1.00)
#> ID-23 | 99.02% | Cauchy (0 +- 1.00)
#> ID-24 | 98.95% | Cauchy (0 +- 1.00)
#> ID-24 | 98.95% | Cauchy (0 +- 1.00)
#> ID-25 | 99.00% | Cauchy (0 +- 1.00)
#> ID-25 | 99.00% | Cauchy (0 +- 1.00)
#> ID-26 | 99.12% | Cauchy (0 +- 1.00)
#> ID-26 | 99.12% | Cauchy (0 +- 1.00)
#> ID-27 | 98.95% | Cauchy (0 +- 1.00)
#> ID-27 | 98.95% | Cauchy (0 +- 1.00)
#> ID-28 | 98.92% | Cauchy (0 +- 1.00)
#> ID-28 | 98.92% | Cauchy (0 +- 1.00)
#> ID-29 | 98.95% | Cauchy (0 +- 1.00)
#> ID-29 | 98.95% | Cauchy (0 +- 1.00)
#> ID-30 | 99.02% | Cauchy (0 +- 1.00)
#> ID-30 | 99.02% | Cauchy (0 +- 1.00)
#> Species:Sepal.Width-setosa.&.Sepal.Width | 98.95% | Cauchy (0 +- 0.12)
#> Species:Sepal.Width-versicolor.&.Sepal.Width | 98.92% | Cauchy (0 +- 0.12)
#> Species:Sepal.Width-virginica.&.Sepal.Width | 99.05% | Cauchy (0 +- 0.12)
#> Sepal.Width:ID-1 | 99.08% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-2 | 98.92% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-3 | 98.92% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-4 | 99.10% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-5 | 98.90% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-6 | 99.10% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-7 | 98.98% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-8 | 99.05% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-9 | 99.08% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-10 | 98.92% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-11 | 98.92% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-12 | 99.05% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-13 | 98.98% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-14 | 98.92% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-15 | 99.17% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-16 | 98.98% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-17 | 99.10% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-18 | 99.17% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-19 | 98.98% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-20 | 99.00% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-21 | 99.00% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-22 | 98.98% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-23 | 98.98% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-24 | 99.20% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-25 | 99.17% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-26 | 98.95% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-27 | 98.95% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-28 | 98.92% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-29 | 98.98% | Cauchy (0 +- 1.00)
#> Sepal.Width:ID-30 | 99.10% | Cauchy (0 +- 1.00)
#> Petal.Width | | Cauchy (0 +- 0.32)
#> Sepal.Width | | Cauchy (0 +- 0.12)
#>
#> # Random Effects
#>
#> Parameter | pd | Prior
#> ------------------------------------
#> ID-1 | 99.00% | Cauchy (0 +- 1)
#> ID-1 | 99.00% | Cauchy (0 +- 1)
#> ID-2 | 98.90% | Cauchy (0 +- 1)
#> ID-2 | 98.90% | Cauchy (0 +- 1)
#> ID-3 | 98.98% | Cauchy (0 +- 1)
#> ID-3 | 98.98% | Cauchy (0 +- 1)
#> ID-4 | 98.98% | Cauchy (0 +- 1)
#> ID-4 | 98.98% | Cauchy (0 +- 1)
#> ID-5 | 98.92% | Cauchy (0 +- 1)
#> ID-5 | 98.92% | Cauchy (0 +- 1)
#> ID-6 | 99.08% | Cauchy (0 +- 1)
#> ID-6 | 99.08% | Cauchy (0 +- 1)
#> ID-7 | 98.98% | Cauchy (0 +- 1)
#> ID-7 | 98.98% | Cauchy (0 +- 1)
#> ID-8 | 98.98% | Cauchy (0 +- 1)
#> ID-8 | 98.98% | Cauchy (0 +- 1)
#> ID-9 | 98.95% | Cauchy (0 +- 1)
#> ID-9 | 98.95% | Cauchy (0 +- 1)
#> ID-10 | 98.98% | Cauchy (0 +- 1)
#> ID-10 | 98.98% | Cauchy (0 +- 1)
#> ID-11 | 98.98% | Cauchy (0 +- 1)
#> ID-11 | 98.98% | Cauchy (0 +- 1)
#> ID-12 | 98.90% | Cauchy (0 +- 1)
#> ID-12 | 98.90% | Cauchy (0 +- 1)
#> ID-13 | 99.08% | Cauchy (0 +- 1)
#> ID-13 | 99.08% | Cauchy (0 +- 1)
#> ID-14 | 99.10% | Cauchy (0 +- 1)
#> ID-14 | 99.10% | Cauchy (0 +- 1)
#> ID-15 | 99.02% | Cauchy (0 +- 1)
#> ID-15 | 99.02% | Cauchy (0 +- 1)
#> ID-16 | 99.02% | Cauchy (0 +- 1)
#> ID-16 | 99.02% | Cauchy (0 +- 1)
#> ID-17 | 99.00% | Cauchy (0 +- 1)
#> ID-17 | 99.00% | Cauchy (0 +- 1)
#> ID-18 | 99.17% | Cauchy (0 +- 1)
#> ID-18 | 99.17% | Cauchy (0 +- 1)
#> ID-19 | 99.02% | Cauchy (0 +- 1)
#> ID-19 | 99.02% | Cauchy (0 +- 1)
#> ID-20 | 98.90% | Cauchy (0 +- 1)
#> ID-20 | 98.90% | Cauchy (0 +- 1)
#> ID-21 | 98.90% | Cauchy (0 +- 1)
#> ID-21 | 98.90% | Cauchy (0 +- 1)
#> ID-22 | 99.02% | Cauchy (0 +- 1)
#> ID-22 | 99.02% | Cauchy (0 +- 1)
#> ID-23 | 99.02% | Cauchy (0 +- 1)
#> ID-23 | 99.02% | Cauchy (0 +- 1)
#> ID-24 | 98.95% | Cauchy (0 +- 1)
#> ID-24 | 98.95% | Cauchy (0 +- 1)
#> ID-25 | 99.00% | Cauchy (0 +- 1)
#> ID-25 | 99.00% | Cauchy (0 +- 1)
#> ID-26 | 99.12% | Cauchy (0 +- 1)
#> ID-26 | 99.12% | Cauchy (0 +- 1)
#> ID-27 | 98.95% | Cauchy (0 +- 1)
#> ID-27 | 98.95% | Cauchy (0 +- 1)
#> ID-28 | 98.92% | Cauchy (0 +- 1)
#> ID-28 | 98.92% | Cauchy (0 +- 1)
#> ID-29 | 98.95% | Cauchy (0 +- 1)
#> ID-29 | 98.95% | Cauchy (0 +- 1)
#> ID-30 | 99.02% | Cauchy (0 +- 1)
#> ID-30 | 99.02% | Cauchy (0 +- 1)
data(puzzles)
result = anovaBF(RT ~ shape*color + ID, data = puzzles, whichRandom = "ID",
whichModels = 'top', progress=FALSE)
model_parameters(result)
#> Multiple `BFBayesFactor` models detected - posteriors are extracted from the first numerator model.
#> See help("get_parameters", package = "insight").
#> Multiple `BFBayesFactor` models detected - posteriors are extracted from the first numerator model.
#> See help("get_parameters", package = "insight").
#> # Extra Parameters
#>
#> Parameter | Median | 89% CI | pd | % in ROPE | BF
#> --------------------------------------------------------------
#> mu | 45.00 | [43.91, 46.10] | 100% | 0% | 2.75
#> sig2 | 1.76 | [ 1.12, 2.45] | 100% | 0% | 0.265
#> g_shape | 0.35 | [ 0.01, 2.23] | 100% | 44.73% | 2.75
#> g_color | 0.36 | [ 0.02, 2.32] | 100% | 43.67% | 0.253
#> g_ID | 2.64 | [ 0.76, 5.23] | 100% | 0% | 0.265
#>
#> # Fixed Effects
#>
#> Parameter | Median | 89% CI | pd | % in ROPE | Prior | BF
#> -------------------------------------------------------------------------------------------------
#> shape [round] | 0.43 | [ 0.14, 0.74] | 99.00% | 12.69% | Cauchy (0 +- 0.50) | 0.253
#> shape [square] | -0.43 | [-0.74, -0.14] | 99.00% | 12.69% | Cauchy (0 +- 0.50) | 0.265
#> color | -0.43 | [-0.73, -0.14] | 98.92% | 12.66% | Cauchy (0 +- 0.50) | 2.75
#> color [monochromatic] | 0.43 | [ 0.14, 0.73] | 98.92% | 12.66% | Cauchy (0 +- 0.50) | 0.253
#>
#> # Random Effects
#>
#> Parameter | Median | 89% CI | pd | % in ROPE | Prior | BF
#> ----------------------------------------------------------------------------------
#> ID [1] | 2.48 | [ 1.11, 3.96] | 99.62% | 0% | Cauchy (0 +- 1) | 0.265
#> ID [2] | 0.48 | [-0.87, 1.99] | 70.33% | 21.68% | Cauchy (0 +- 1) | 2.75
#> ID [3] | 0.92 | [-0.42, 2.43] | 85.50% | 15.45% | Cauchy (0 +- 1) | 0.253
#> ID [4] | 0.46 | [-0.94, 1.95] | 70.03% | 22.04% | Cauchy (0 +- 1) | 0.265
#> ID [5] | 3.19 | [ 1.75, 4.64] | 99.98% | 0% | Cauchy (0 +- 1) | 2.75
#> ID [6] | 0.47 | [-0.93, 1.86] | 69.88% | 22.52% | Cauchy (0 +- 1) | 0.253
#> ID [7] | -3.14 | [-4.60, -1.70] | 99.98% | 0% | Cauchy (0 +- 1) | 0.265
#> ID [8] | -0.18 | [-1.70, 1.19] | 58.27% | 25.67% | Cauchy (0 +- 1) | 2.75
#> ID [9] | -2.47 | [-3.98, -1.07] | 99.67% | 0% | Cauchy (0 +- 1) | 0.253
#> ID [10] | 0.69 | [-0.82, 2.03] | 77.03% | 19.77% | Cauchy (0 +- 1) | 0.265
#> ID [11] | 0.67 | [-0.73, 2.13] | 77.62% | 18.81% | Cauchy (0 +- 1) | 2.75
#> ID [12] | -3.35 | [-4.95, -2.03] | 99.98% | 0% | Cauchy (0 +- 1) | 0.253Created on 2021-01-03 by the reprex package (v0.3.0) |
|
In the first example, #> mu | 98.95% |
#> Sepal.Width-Sepal.Width | 99.12% | Cauchy (0 +- 0.12)
#> Petal.Width-Petal.Width | 99.08% | Cauchy (0 +- 0.32)Are fixed effects, and all the ID and Sepal.Width:ID parameters are random effects (some of which are doubled an also appear in the random effects part. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
@mattansb Thinking of this further, another thing I notice is that
Prior_Scalevalues areNAforanovaBFoutputs.Should we be including
rscaleFixedandrscaleRandomargument values here?The text was updated successfully, but these errors were encountered: