Package 'EloSteepness' reference manual

Title:	Bayesian Dominance Hierarchy Steepness via Elo Rating and David's Scores
Description:	Obtain Bayesian posterior distributions of dominance hierarchy steepness (Neumann and Fischer (2023) <doi:10.1111/2041-210X.14021>). Steepness estimation is based on Bayesian implementations of either Elo-rating or David's scores.
Authors:	Christof Neumann [aut, cre]
Maintainer:	Christof Neumann <[email protected]>
License:	GPL (>= 2)
Version:	0.5.0
Built:	2025-01-24 05:01:19 UTC
Source:	https://github.com/gobbios/elosteepness

The 'EloSteepness' package.

Description

Dominance Hierarchy Steepness Via Elo Rating

catch warnings alongside results without returning warning

Description

helper function

Usage

catch_warnings(expr)
catch_warnings(expr)

Arguments

expr

an R expression to evaluate

Value

a list where the first entry is the result of expr and the second provides information about warnings

Source

demo(error.catching)

Examples

log(3)
catch_warnings(log(3))

# produces warning
# log(-3)
# catch it
catch_warnings(log(-3))

# produces error
# log("x")
# catch it
catch_warnings(log("x"))
log(3)
catch_warnings(log(3))

# produces warning
# log(-3)
# catch it
catch_warnings(log(-3))

# produces error
# log("x")
# catch it
catch_warnings(log("x"))

David's scores and steepness with Bayesian flavor

Description

David's scores and steepness with Bayesian flavor

Usage

davids_steepness(mat, silent = FALSE, ...)
davids_steepness(mat, silent = FALSE, ...)

Arguments

`mat`	square interaction matrix
`silent`	logical, suppress warnings (default is `FALSE`)
`...`	additional arguments for `sampling()`

Value

a list with results of the modelling fitting, containing the following list items:

steepness: a one-column matrix with the posterior samples for steepness. Each row is one iteration.
norm_ds: an matrix with posterior normalized David's scores for each individual. Each column is one individual. Each row is one iteration.
ids: a character vector with individual ID codes as supplied in mat
diagnostics: a list with information regarding sampling problems
stanfit: the actual stanfit object
mat: the input matrix

Examples

data(dommats, package = "EloRating")
res <- davids_steepness(dommats$elephants, refresh = 0)
plot_steepness(res)
data(dommats, package = "EloRating")
res <- davids_steepness(dommats$elephants, refresh = 0)
plot_steepness(res)

steepness based on Bayesian Elo-rating

Description

for interaction data with unknown sequence of observations

Usage

elo_steepness_from_matrix(
  mat,
  algo = c("fixed_sd", "original", "fixed_k"),
  n_rand = NULL,
  silent = FALSE,
  k = NULL,
  ...
)
elo_steepness_from_matrix(
  mat,
  algo = c("fixed_sd", "original", "fixed_k"),
  n_rand = NULL,
  silent = FALSE,
  k = NULL,
  ...
)

Arguments

`mat`	square interaction matrix
`algo`	character, either `"fixed_sd"`, `"original"`, or `"fixed_k"`.This determines which algorithm to estimate Elo-ratings is used. Default is `"fixed_sd"`, which is a slight modification from Goffe et al's original code. `"fixed_k"` fixes the k parameter ('shift coefficient' in Goffe et al) to the set value rather than estimating it from the data.
`n_rand`	numeric, number of randomized sequences. Default is `NULL`, which uses a rule of thumb to determine the number (see below for more details).
`silent`	logical, suppress warnings (default is `FALSE`)
`k`	numeric, provides a fixed k parameter. This only has effects if `algo = "fixed_k"`. At its default `NULL` a value of 0.4 is used.
`...`	additional arguments for `sampling()`

Details

The number of randomizations is set in the following way, unless a specific number is provided. If there are more than 500 observed interactions, n_rand = 5. If there are less than 100 interactions, n_rand = 50. In the remaining cases, n_rand = 20.

If the function call produces warnings about divergent transitions, large Rhat values or low effective sample sizes, increase the number of iterations (via iter=) and/or adjust the sampling controls (e.g. via control = list(adapt_delta = 0.9)).

If the argument seed = is supplied, its value will be passed to sampling() to ensure reproducibility of the MCMC sampling, but the same seed will then also apply to the randomization of the interaction sequence order(s).

Value

a list with results of the modelling fitting, containing the following list items:

steepness: a matrix with the posterior samples for steepness. Each column corresponds to one randomization (as set via n_rand). Each row is one iteration.
cumwinprobs: an array with posterior cumulative winning probabilities for each individual.
k: an array with posterior k values.
ids: a character vector with individual ID codes as supplied in mat
diagnostics: a list with information regarding sampling problems
stanfit: the actual stanfit object
mat: the input matrix
algo: character, describing whether the original fitting algorithm was used ("original") or the one with fixed SD of start ratings ("fixed_sd")
sequence_supplied: logical, were data supplied as matrix (FALSE) or as sequence via winner/loser vector (TRUE)

Examples

data(dommats, package = "EloRating")
# using small numbers for iterations etc to speed up running time
res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1, cores = 2,
                                 iter = 800, warmup = 300, 
                                 refresh = 0, chains = 2, seed = 1)
plot_steepness(res)


# use the original underlying algorithm by Goffe et al 2018
# will warn about divergent iterations and low effective sample sizes
# but warnings can be caught/suppressed by setting silent = TRUE

res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1,
                                 algo = "original", silent = TRUE,
                                 iter = 1000, warmup = 500, refresh = 0)
res$diagnostics

# or the sampling can be tweaked to achieve better convergence:
# (this still might produce some divergent transitions on occasion)
# (and the number of iterations should be set higher)
res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1, chains = 2,
                                 algo = "original", silent = TRUE, seed = 1,
                                 iter = 1000, warmup = 500, refresh = 0,
                                 control = list(adapt_delta = 0.99))
res$diagnostics

data(dommats, package = "EloRating")
# using small numbers for iterations etc to speed up running time
res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1, cores = 2,
                                 iter = 800, warmup = 300, 
                                 refresh = 0, chains = 2, seed = 1)
plot_steepness(res)


# use the original underlying algorithm by Goffe et al 2018
# will warn about divergent iterations and low effective sample sizes
# but warnings can be caught/suppressed by setting silent = TRUE

res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1,
                                 algo = "original", silent = TRUE,
                                 iter = 1000, warmup = 500, refresh = 0)
res$diagnostics

# or the sampling can be tweaked to achieve better convergence:
# (this still might produce some divergent transitions on occasion)
# (and the number of iterations should be set higher)
res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1, chains = 2,
                                 algo = "original", silent = TRUE, seed = 1,
                                 iter = 1000, warmup = 500, refresh = 0,
                                 control = list(adapt_delta = 0.99))
res$diagnostics

steepness based on Bayesian Elo-rating

Description

for interaction data with known sequence of observations

Usage

elo_steepness_from_sequence(
  winner,
  loser,
  algo = c("fixed_sd", "original", "fixed_k"),
  silent = FALSE,
  k = NULL,
  ...
)
elo_steepness_from_sequence(
  winner,
  loser,
  algo = c("fixed_sd", "original", "fixed_k"),
  silent = FALSE,
  k = NULL,
  ...
)

Arguments

`winner`	character (or factor) of winning individuals
`loser`	character (or factor) of losing individuals
`algo`	character, either `"fixed_sd"`, `"original"`, or `"fixed_k"`.This determines which algorithm to estimate Elo-ratings is used. Default is `"fixed_sd"`, which is a slight modification from Goffe et al's original code. `"fixed_k"` fixes the k parameter ('shift coefficient' in Goffe et al) to the set value rather than estimating it from the data.
`silent`	logical, suppress warnings (default is `FALSE`)
`k`	numeric, provides a fixed k parameter. This only has effects if `algo = "fixed_k"`. At its default `NULL` a value of 0.4 is used.
`...`	additional arguments for `sampling()`

Value

a list with results of the model fitting (see elo_steepness_from_matrix) for details

Examples

data(adv, package = "EloRating")
res <- elo_steepness_from_sequence(winner = adv$winner, loser = adv$loser,
                                   cores = 1, chains = 2, iter = 1000, 
                                   warmup = 500, seed = 1, refresh = 0)
plot_steepness(res)

data(adv, package = "EloRating")
res <- elo_steepness_from_sequence(winner = adv$winner, loser = adv$loser,
                                   cores = 1, chains = 2, iter = 1000, 
                                   warmup = 500, seed = 1, refresh = 0)
plot_steepness(res)

generate dyadic interaction probabilities for a group with fixed individual and dyadic biases

Description

generate dyadic interaction probabilities for a group with fixed individual and dyadic biases

Usage

generate_interaction_probs(n_ind, id_bias = 0, rank_bias = 0)
generate_interaction_probs(n_ind, id_bias = 0, rank_bias = 0)

Arguments

`n_ind`	numeric, number of individuals
`id_bias`	numeric, between 0 and 1. If 0 all individual are equally likely to interact. If 1, some individuals have higher propensities to interact
`rank_bias`	numeric, between 0 and 1. If 0 there is no relationship between rank distance and interaction propensity. If 1 there is a strong relationship: dyads closer in rank interact more often.

Value

a matrix

Examples



x <- generate_interaction_probs(n_ind = 10, id_bias = 0.2, rank_bias = 1)
rankdiff <- x[, 2] - x[, 1]
interactprob <- x[, "final"]
# closer in rank (smaller rank diff) = interaction more likely
plot(rankdiff, interactprob)

x <- generate_interaction_probs(n_ind = 10, id_bias = 0.2, rank_bias = 0)
rankdiff <- x[, 2] - x[, 1]
interactprob <- x[, "final"]
# approx. equal probs for all dyads regardless of rank diff
plot(rankdiff, interactprob)


x <- generate_interaction_probs(n_ind = 10, id_bias = 0, rank_bias = 0)
interactprob <- x[, "final"]
y <- sample(1:nrow(x), 1000, replace = TRUE, prob = interactprob)
y <- as.numeric(x[y, 1:2])
# approx. equal numbers of interactions per ID
sort(table(y))

# skewed interaction numbers
x <- generate_interaction_probs(n_ind = 10, id_bias = 1, rank_bias = 0)
interactprob <- x[, "final"]
y <- sample(1:nrow(x), 1000, replace = TRUE, prob = interactprob)
y <- as.numeric(x[y, 1:2])
sort(table(y))
x <- generate_interaction_probs(n_ind = 10, id_bias = 0.2, rank_bias = 1)
rankdiff <- x[, 2] - x[, 1]
interactprob <- x[, "final"]
# closer in rank (smaller rank diff) = interaction more likely
plot(rankdiff, interactprob)

x <- generate_interaction_probs(n_ind = 10, id_bias = 0.2, rank_bias = 0)
rankdiff <- x[, 2] - x[, 1]
interactprob <- x[, "final"]
# approx. equal probs for all dyads regardless of rank diff
plot(rankdiff, interactprob)


x <- generate_interaction_probs(n_ind = 10, id_bias = 0, rank_bias = 0)
interactprob <- x[, "final"]
y <- sample(1:nrow(x), 1000, replace = TRUE, prob = interactprob)
y <- as.numeric(x[y, 1:2])
# approx. equal numbers of interactions per ID
sort(table(y))

# skewed interaction numbers
x <- generate_interaction_probs(n_ind = 10, id_bias = 1, rank_bias = 0)
interactprob <- x[, "final"]
y <- sample(1:nrow(x), 1000, replace = TRUE, prob = interactprob)
y <- as.numeric(x[y, 1:2])
sort(table(y))

plot (rather than print) a matrix

Description

a helper function

Usage

plot_matrix(mat, greyout = NULL, prunkcol = NULL, label_col = "black")
plot_matrix(mat, greyout = NULL, prunkcol = NULL, label_col = "black")

Arguments

`mat`	square matrix
`greyout`	numeric, the values to be grayed out
`prunkcol`	color value, which if set to some color will highlight unknown relationships with rectangles of that color.
`label_col`	color values for column and row labels

Value

a plot and an invisible list with coordinates and content of the matrix to be plotted

plot posteriors of individual scores

Description

either summed winning probabilities or David's scores

Usage

plot_scores(
  x,
  adjustpar = 4,
  color = TRUE,
  subset_ids = NULL,
  include_others = TRUE
)
plot_scores(
  x,
  adjustpar = 4,
  color = TRUE,
  subset_ids = NULL,
  include_others = TRUE
)

Arguments

`x`	result from `elo_steepness_from_matrix`, `elo_steepness_from_sequence` or `davids_steepness`
`adjustpar`	numeric, parameter for smoothing posterior of individual scores
`color`	logical, default is `TRUE` where individuals get color-coded. If `FALSE`: a gray scale is used. It is also possible to hand over a vector with colors, which then must correspond in length to the number of individuals.
`subset_ids`	character, plot only those individual codes. Default is `NULL`, i.e. all individuals are included in the plot.
`include_others`	logical, should other IDs (those not in `subset_ids`) be included as contours. Default is `TRUE`. This only has an effect if `subset_ids` is different from `NULL`,

Value

a plot

Examples

data(dommats, package = "EloRating")

res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1,
                                 silent = TRUE, refresh = 0,
                                 iter = 1000, warmup = 500)
plot_scores(res)

res <- davids_steepness(dommats$elephants, refresh = 0)
plot_scores(res)
plot_scores(res, color = FALSE)
plot_scores(res, adjustpar = 0.3)
data(dommats, package = "EloRating")

res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1,
                                 silent = TRUE, refresh = 0,
                                 iter = 1000, warmup = 500)
plot_scores(res)

res <- davids_steepness(dommats$elephants, refresh = 0)
plot_scores(res)
plot_scores(res, color = FALSE)
plot_scores(res, adjustpar = 0.3)

plot steepness density

Description

plot steepness density

Usage

plot_steepness(x, adjustpar = 1.5, print_numbers = TRUE)
plot_steepness(x, adjustpar = 1.5, print_numbers = TRUE)

Arguments

`x`	result from `elo_steepness_from_matrix`, `elo_steepness_from_sequence` or `davids_steepness`
`adjustpar`	numeric, parameter for smoothing posterior of individual scores
`print_numbers`	logical, if `TRUE` (default) print numeric summaries into into the plot and omit them if `FALSE`

Value

a plot

Examples


data("dommats", package = "EloRating")
m <- dommats$elephants
res <- elo_steepness_from_matrix(m, n_rand = 3, refresh = 0, cores = 2, 
                                 iter = 1000, warmup = 500)
plot_steepness(res)

data("dommats", package = "EloRating")
m <- dommats$elephants
res <- elo_steepness_from_matrix(m, n_rand = 3, refresh = 0, cores = 2, 
                                 iter = 1000, warmup = 500)
plot_steepness(res)

plot steepness regression

Description

visually combine individual scores with group-level steepness

Usage

plot_steepness_regression(
  x,
  adjust = 3,
  color = TRUE,
  width_fac = 0.1,
  axis_extend = 0.1
)
plot_steepness_regression(
  x,
  adjust = 3,
  color = TRUE,
  width_fac = 0.1,
  axis_extend = 0.1
)

Arguments

`x`	result from `elo_steepness_from_matrix`, `elo_steepness_from_sequence` or `davids_steepness`
`adjust`	numeric, parameter for smoothing posterior of individual scores
`color`	logical, default is `TRUE` where individuals get color- coded. If `FALSE`: a gray scale is used. It is also possible to hand over a vector with colors, which then must be correspond in length to the number of individuals.
`width_fac`	numeric, relative width of posterior distributions. This is actually affects the 'height' but since the posteriors are rotated it visually represents width.
`axis_extend`	numeric, an extension factor to extend the horizontal axis to leave space for the posteriors. When set to 0 the axis stops at n (the number of individuals, which represents the lowest rank).

Value

a plot

Examples

data("bonobos", package = "EloRating")
res <- davids_steepness(bonobos, refresh = 0, iter = 1000)
plot_steepness_regression(res, width_fac = 0.5)
data("bonobos", package = "EloRating")
res <- davids_steepness(bonobos, refresh = 0, iter = 1000)
plot_steepness_regression(res, width_fac = 0.5)

prepare data for stan call

Description

prepare data for stan call

Usage

prep_data_for_rstan(mat, n_rand = 1, silent = FALSE, for_elo_model = TRUE)
prep_data_for_rstan(mat, n_rand = 1, silent = FALSE, for_elo_model = TRUE)

Arguments

`mat`	square interaction matrix
`n_rand`	numeric, number of randomizations
`silent`	logical, omit printing messages regarding non-fatal data issues. Default is `FALSE`, i.e. do print messages.
`for_elo_model`	logical, output ready for Elo steepness (default, `TRUE`). If `FALSE`, prep for David's score steepness.

Value

a list that is formatted so that it can be handed over to the respective Stan models

remove interactions from matrix to increase sparseness

Description

remove interactions from matrix to increase sparseness

Usage

remove_dyads(
  m,
  removal_mode = c("mix", "by_interaction", "by_dyad"),
  stop_at = 0.5,
  max_out = NULL
)
remove_dyads(
  m,
  removal_mode = c("mix", "by_interaction", "by_dyad"),
  stop_at = 0.5,
  max_out = NULL
)

Arguments

`m`	input matrix
`removal_mode`	character, should interactions be removed interaction by interaction (`"by_interaction"`), or by removing one dyad entirely at a time (`"by_dyad"`). Default is `"mix"`, i.e. a random mix between the two strategies.
`stop_at`	numeric, fraction of unknown relationships to be reached
`max_out`	numeric, the number of matrices to be returned maximally. This is useful if the input matrix is fairly large. If set, this will return the input matrix plus `max_out` randomly selected matrices from the remaining produced matrices. So in fact, the output comprises `max_out + 1` matrices (subject to the `stop_at` specification).

Value

a list with two items. $summary is a data frame with an overview. matrices contains the actual interaction matrices with increasing proportion of unknown relationships.

Examples

data(bonobos)
res <- remove_dyads(bonobos)
res$summary
length(res$matrices)
lapply(res$matrices, prunk)

res <- remove_dyads(bonobos, max_out = 2)
# first plus two randomly selected = 3 matrices
length(res$matrices)
res$summary
data(bonobos)
res <- remove_dyads(bonobos)
res$summary
length(res$matrices)
lapply(res$matrices, prunk)

res <- remove_dyads(bonobos, max_out = 2)
# first plus two randomly selected = 3 matrices
length(res$matrices)
res$summary

steepness via repeatability (cf aniDom package)

Description

steepness via repeatability (cf aniDom package)

Usage

repeatability_steepness(mat, n_rand = 1000)
repeatability_steepness(mat, n_rand = 1000)

Arguments

`mat`	square interaction matrix
`n_rand`	numeric, number of randomized sequences (default is `1000`)

Value

a steepness value

References

Sanchez-Tojar et al 2018

Examples

data(bonobos, package = "EloRating")
repeatability_steepness(bonobos, n_rand = 20)
data(bonobos, package = "EloRating")
repeatability_steepness(bonobos, n_rand = 20)

catch Stan sampling issues without throwing a warning

Description

catch Stan sampling issues without throwing a warning

Usage

sampler_diagnostics(object)
sampler_diagnostics(object)

Arguments

object

stanfit object

Value

a list regarding any sampling issues encountered during fitting

numeric summaries of individual scores

Description

either based on summed winning probabilities or David's scores

Usage

scores(x, quantiles = c(0.045, 0.955), elo_scores = FALSE)
scores(x, quantiles = c(0.045, 0.955), elo_scores = FALSE)

Arguments

`x`	result from `elo_steepness_from_matrix`, `elo_steepness_from_sequence` or `davids_steepness`
`quantiles`	numeric, the quantiles to be returned
`elo_scores`	logical, with default `FALSE`. If `TRUE` Elo-ratings are returned, rather than the default summed winning probabilities. This argument has no consequences if `x` is the result of `davids_steepness`.

Value

a data.frame with one line per individual, providing summaries of posteriors for individual scores

Examples


data("bonobos", package = "EloRating")
res <- davids_steepness(bonobos, refresh = 0, cores = 2)
scores(res)

data("dommats", package = "EloRating")
m <- dommats$elephants
res <- elo_steepness_from_matrix(m, n_rand = 1, refresh = 0,
                                 iter = 1000, warmup = 500)
scores(res)

data("bonobos", package = "EloRating")
res <- davids_steepness(bonobos, refresh = 0, cores = 2)
scores(res)

data("dommats", package = "EloRating")
m <- dommats$elephants
res <- elo_steepness_from_matrix(m, n_rand = 1, refresh = 0,
                                 iter = 1000, warmup = 500)
scores(res)

generate dominance interactions with specified steepness

Description

generate dominance interactions with specified steepness

Usage

simple_steep_gen(
  n_ind,
  n_int,
  steep,
  id_bias = 0,
  rank_bias = 0,
  sequential = TRUE
)
simple_steep_gen(
  n_ind,
  n_int,
  steep,
  id_bias = 0,
  rank_bias = 0,
  sequential = TRUE
)

Arguments

`n_ind`	integer, the number of individuals
`n_int`	integer, the number of interactions
`steep`	numeric (between 0 and 1), the desired steepness value
`id_bias`	numeric, between 0 and 1. If 0 all individual are equally likely to interact. If 1, some individuals have higher propensities to interact.
`rank_bias`	numeric, between 0 and 1. If 0 there is no relationship between rank distance and interaction propensity. If 1 there is a strong relationship: dyads closer in rank interact more often.
`sequential`	logical, default is `TRUE`. See details.

Details

Initially (and this is still the default), the function generated interactions and their outcomes sequentially: first a dyad was chosen that interacted and then its winner was determined. This was repeated for as many interactions as set by n_int=.

The same results can be achieved much more efficiently by first setting the number of interactions per dyad and then looping through all dyads and then generate the interactions and their outcomes per dyad. This can be achieved by setting sequential = FALSE. In this latter case the 'sequence' of interactions reported in the results is just a randomized version of all interactions, whereas in the former case there is a 'natural sequence' (although it is meaningless because the sequence is irrelevant with respect to outcomes of individual interactions (the system is stable)).

Value

a list with the first item being the interactions in sequence form ($sequence). The second item ($matrix) is the square interaction matrix and the third item ($settings) is a list with input settings (including probabilities to interact for each dyad).

Examples

res <- simple_steep_gen(n_ind = 5, n_int = 30, steep = 0.99)
res$sequence
res$matrix


library(EloRating)
steeps <- runif(20, 0, 1)
nids <- sample(6:10, length(steeps), TRUE)
mats <- sapply(1:length(steeps), function(x) {
  simple_steep_gen(nids[x], nids[x] ^ 2.5, steeps[x], 0)[[2]]
 })
obs_steeps <- unlist(lapply(mats, function(x)steepness(x)[1]))
plot(steeps, obs_steeps, xlim = c(0, 1), ylim = c(0, 1))
abline(0, 1)
res <- simple_steep_gen(n_ind = 5, n_int = 30, steep = 0.99)
res$sequence
res$matrix


library(EloRating)
steeps <- runif(20, 0, 1)
nids <- sample(6:10, length(steeps), TRUE)
mats <- sapply(1:length(steeps), function(x) {
  simple_steep_gen(nids[x], nids[x] ^ 2.5, steeps[x], 0)[[2]]
 })
obs_steeps <- unlist(lapply(mats, function(x)steepness(x)[1]))
plot(steeps, obs_steeps, xlim = c(0, 1), ylim = c(0, 1))
abline(0, 1)

numeric summary of steepness

Description

numeric summary of steepness

Usage

steepness_precis(x, quantiles = c(0.055, 0.25, 0.75, 0.945))
steepness_precis(x, quantiles = c(0.055, 0.25, 0.75, 0.945))

Arguments

`x`	result from `elo_steepness_from_matrix`, `elo_steepness_from_sequence` or `davids_steepness`
`quantiles`	numeric, the quantiles to be returned

Value

a data.frame with one row providing a summary of the steepness posterior

Examples

data(dommats, package = "EloRating")

res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1, iter = 1000,
                                 silent = TRUE, refresh = 0)
steepness_precis(res)
data(dommats, package = "EloRating")

res <- elo_steepness_from_matrix(dommats$elephants, n_rand = 1, iter = 1000,
                                 silent = TRUE, refresh = 0)
steepness_precis(res)

summary

Description

summary

Usage

## S3 method for class 'elo_steepness'
summary(object, ...)

## S3 method for class 'david_steepness'
summary(object, ...)
## S3 method for class 'elo_steepness'
summary(object, ...)

## S3 method for class 'david_steepness'
summary(object, ...)

Arguments

`object`	result from `elo_steepness_from_matrix`, `elo_steepness_from_sequence` or `davids_steepness`
`...`	further arguments passed to or from other methods (ignored)

Value

Nothing returned. Called for side effects of textual output to console.

proportion of interactions against the rank order

Description

proportion of interactions against the rank order

Usage

upward_steepness(mat)
upward_steepness(mat)

Arguments

mat

square interaction matrix

Value

numeric value of upward steepness

Examples

data(bonobos, package ="EloRating")
upward_steepness(bonobos)
data(bonobos, package ="EloRating")
upward_steepness(bonobos)

Package 'EloSteepness'

Help Index

The 'EloSteepness' package.

Description

catch warnings alongside results without returning warning

Description

Usage

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Value

Source

Examples

David's scores and steepness with Bayesian flavor

Description

Usage

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Value

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steepness based on Bayesian Elo-rating

Description

Usage

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Details

Value

Examples

steepness based on Bayesian Elo-rating

Description

Usage

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Value

Examples

generate dyadic interaction probabilities for a group with fixed individual and dyadic biases

Description

Usage

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Value

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plot (rather than print) a matrix

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Value

plot posteriors of individual scores

Description

Usage

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Value

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plot steepness density

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Value

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plot steepness regression

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Value

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prepare data for stan call

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remove interactions from matrix to increase sparseness

Description

Usage

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Value

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steepness via repeatability (cf aniDom package)

Description

Usage

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Value

References

Examples

catch Stan sampling issues without throwing a warning

Description

Usage