vadis_line1 | R Documentation |
Calculate the first line of evidence for the VADIS method
vadis_line1(
mod_list,
path = NULL,
alpha = 0.05,
method = c("freq", "pd", "rope", "map"),
overwrite = c("no", "yes", "reload"),
verbose = FALSE
)
mod_list |
A list of regression model objects. |
path |
Path in which to save the output as an R data file ( |
alpha |
The significance threshold. Default is .05 |
method |
The method for calculating significance values. See details. |
overwrite |
Should the function overwrite data to location in |
verbose |
Should messages be printed? Default is |
The function loops through a list of model objects, extracts the coefficient estimates, and compiles them in a single dataframe. There are four possible values for the method
argument: "freq"
, "pd"
, "rope"
, "map"
. For frequentist (non-Bayesian) models, only the standard outputs of glm
and glmer
are used, i.e. method = "freq"
.
For Bayesian models, all four methods are available. For method = "freq"
, significance is determined based on the Highest Posterior Density Interval (HDI), which is determined as 1 - alpha
. Significance is defined as whether the HDI contains 0. For method = "pd"
("probability of direction"), the p-value is defined as the proportion of the posterior distribution that is of the median’s sign. In other words, the p-value represents the proportion of the posterior distribution that is above/below 0, whichever is larger). For method = "rope"
, the p-value is defined as the proportion of the entire posterior distribution that lies within the Region of Practical Equivalence (ROPE), which is defined here as c(-0.1, 0.1)
(see Kruschke \& Liddell 2018; Makowski et al. 2019). For method = "map"
, the p-value is defined as the density value at 0 divided by the density at the Maximum A Posteriori (MAP). See p_direction
, p_rope
, and p_map
for further details.
A list
of length 3.
signif.table
A dataframe of P predictors by M models, containing a binary value indicating statistical significance (1 = significant) for each predictor in each model.
distance.matrix
An M by M distance matrix of class dist
, derived from signif.table
. Values are squared Euclidean distances normalized by the number of predictors P.
similarity.scores
A dataframe of similarity scores derive from distance.matrix
. See Szmrecsanyi et al. (2019) for details.
Jason Grafmiller
Kruschke, John K. & Torrin M. Liddell. 2018. The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective. Psychonomic Bulletin & Review 25(1). 178–206. doi: 10.3758/s13423-016-1221-4.
Makowski, Dominique, Mattan S. Ben-Shachar, S. H. Annabel Chen & Daniel Lüdecke. 2019. Indices of effect existence and significance in the Bayesian framework. Frontiers in Psychology. 10. doi: 10.3389/fpsyg.2019.02767.
Szmrecsanyi, Benedikt, Jason Grafmiller & Laura Rosseel. 2019. Variation-Based Distance and Similarity Modeling: A Case Study in World Englishes. Frontiers in Artificial Intelligence 2. https://doi.org/10.3389/frai.2019.00023.
## Not run:
data_list <- split(particle_verbs_short, particle_verbs_short$Variety, drop = TRUE)
fmla <- Response ~ DirObjWordLength + DirObjDefiniteness + DirObjGivenness + DirObjConcreteness + DirObjThematicity + DirectionalPP + PrimeType + Semantics + Surprisal.P + Surprisal.V + Register
glm_func <- function(x) glm(fmla, data = x, family = binomial)
glm_list <- lapply(data_list, glm_func)
names(glm_list) <- names(data_list)
line1 <- vadis_line1(glm_list, path = FALSE)
## End(Not run)
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