| Title: | Simulating the Development of h-Index Values |
| Version: | 0.2.0 |
| Description: | H-index and h-alpha are a bibliometric indicators. This package provides functions to simulate how these indicators may develop over time for a given set of researchers and to visualize the simulation data. The implementation is based on the 'STATA' ado h-index and is described in more detail in Bornmann et al. (2019) <doi:10.48550/arXiv.1905.11052>. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| LazyData: | true |
| Suggests: | testthat |
| Imports: | foreach, stats, ggplot2, purrr |
| RoxygenNote: | 7.0.2 |
| NeedsCompilation: | no |
| Packaged: | 2020-02-20 14:59:41 UTC; alex |
| Author: | Alexander Tekles [aut, cre], Lutz Bornmann [ctb], Christian Ganser [ctb] |
| Maintainer: | Alexander Tekles <alexander.tekles@soziologie.uni-muenchen.de> |
| Repository: | CRAN |
| Date/Publication: | 2020-02-22 22:20:02 UTC |
Plot the result of simulate_hindex
Description
Plot the result of a simulation computed by simulate_hindex.
Usage
plot_hsim(
simdata,
plot_hindex = FALSE,
plot_halpha = FALSE,
plot_toppapers = FALSE,
plot_mindex = FALSE,
subgroups = FALSE,
group_boundaries = NULL,
exclude_group_boundaries = FALSE,
plot_group_diffs = FALSE
)
Arguments
simdata |
The result of a simulation returned
by |
plot_hindex |
If this parameter is set to TRUE, the h-index values are plotted. |
plot_halpha |
If this parameter is set to TRUE, the h-alpha values are plotted. |
plot_toppapers |
If this parameter is set to TRUE, the numbers of top-10% papers are plotted. |
plot_mindex |
If this parameter is set to TRUE, the mindex values are plotted. |
subgroups |
If this parameter is set to TRUE, the subgroups in simdata are considered for grouping plotting the index values separately for each of these groups. |
group_boundaries |
Alternative to subgroups for specifying groups of scientists for plotting the index values separately for these groups. Here, the groups are specified based on the initial h-index of the agents. group_boundaries must be a list of vectors or a vector of integers specifying the groups. If a list is specified, each element must be a vector of length 2 representing the lower and the upper bound for the initial h-index (if the boundaries are included in the corresponding intervals is specified by the exclude_group_boundaries parameter). If a vector of integers is specified, each element in group_boundaries separates two groups such that all agents with an initial h-index below this boundary (and equal to or above any lower boundary; if exclude_group_boundaries is set to TRUE, the initial h-index has to be above any lower boundary) are in the first group, and all agents with an initial h-index equal to or above this boundary (and below any higher boundary) are in the second group. |
exclude_group_boundaries |
If this parameter is set to TRUE, the scientists are grouped such that those scientists whose initial h-index is equal to a boundary are not included. |
plot_group_diffs |
If this parameter is specified, the difference between the groups that are specified by group_boundaries is plotted. |
Value
A ggplot object (ggplot).
Examples
set.seed(123)
simdata <- simulate_hindex(runs = 2, n = 20, periods = 3)
plot_hsim(simdata, plot_hindex = TRUE, plot_halpha = TRUE)
Simulate h-index and h-alpha values
Description
Simulate the effect of publishing, being cited, and (strategic) collaborating on the development of h-index and h-alpha values for a specified set of agents.
Usage
simulate_hindex(
runs = 1,
n = 100,
periods = 20,
subgroups_distr = 1,
subgroup_advantage = 1,
subgroup_exchange = 0,
init_type = "fixage",
distr_initial_papers = "poisson",
max_age_scientists = 5,
dpapers_pois_lambda = 2,
dpapers_nbinom_dispersion = 1.1,
dpapers_nbinom_mean = 2,
productivity = 80,
distr_citations = "poisson",
dcitations_speed = 2,
dcitations_peak = 3,
dcitations_mean = 2,
dcitations_dispersion = 1.1,
coauthors = 5,
strategic_teams = FALSE,
diligence_share = 1,
diligence_corr = 0,
selfcitations = FALSE,
update_alpha_authors = FALSE,
boost = FALSE,
boost_size = 0.1,
alpha_share = 0.33
)
Arguments
runs |
Number of times the simulation is repeated. |
n |
Number of agents acting in each simulation. |
periods |
Number of periods the agents collaborate across in each period. |
subgroups_distr |
Share of scientists in the first subgroup among all scientists |
subgroup_advantage |
Factor by which citations of papers published by agents of subgroup 2 exceed those of papers published by subgroup 1. This option is intended to reflect subdisciplines with different citation levels. |
subgroup_exchange |
Share of agents publishing (alone or in collaboration) with the other subgroup in each period. For example, when specifying subgroup_exchange = .1, 10% of each subgroup join the other subgroup each period. |
init_type |
Type of the initial setup. May be 'fixage' or 'varage'. For init_type = 'fixage', all initial papers have the same age (specified by max_age_scientists). For init_type = 'varage', papers get a random age which is less than or equal to max_age_scientists. |
distr_initial_papers |
Distribution of the papers the scientists have already published at the start of the simulation. Currently, the poisson distribution ("poisson") and the negative binomial distribution ("nbinomial") are supported. |
max_age_scientists |
Maximum age of scientists at the start of the simulation. For init_type = varage, a random age less than or equal to max_age_scientists is assigned to the initial papers. For init_type = fixage, all papers are max_age_scientists old. |
dpapers_pois_lambda |
The distribution parameter for a poisson distribution of initial papers. |
dpapers_nbinom_dispersion |
Dispersion parameter of a negative binomial distribution of initial papers. |
dpapers_nbinom_mean |
Expected value of a negative binomial distribution of initial papers. |
productivity |
The share of papers published by the 20% most productive agents in percentage. This parameter is only used for init_type = 'varage'. For init_type = 'fixage', diligence_share and diligence_corr can be used to control the productivity of scientists. |
distr_citations |
Distribution of citations the papers get. The expected value of this distribution follows a log-logistic function of time. Currently, the poisson distribution ("poisson") and the negative binomial distribution ("nbinomial") are supported. |
dcitations_speed |
The steepness (shape parameter) of the log-logistic time function of the expected citation values. |
dcitations_peak |
The period after publishing when the expected value of the citation distribution reaches its maximum. |
dcitations_mean |
The maximum expected value of the citation distribution (at period dcitations_peak after publishing, the citation distribution has dcitations_mean). |
dcitations_dispersion |
For a negative binomial citation distribution, dcitations_dispersion is a factor by which the variance exceeds the expected value. |
coauthors |
Average number of coauthors publishing papers. |
strategic_teams |
If this parameter is set to TRUE, agents with high h-index avoid co-authorships with agents who have equal or higher h-index values (they strategically select co-authors to improve their h-alpha index). This is implemented by assigning the agents with the highest h-index values to separate teams and randomly assigning the other agents to the teams. Otherwise, the collaborating agents are assigned to co-authorships at random. |
diligence_share |
The share of agents publishing in each period. Only used for init_type = 'fixage'. |
diligence_corr |
The correlation between the initial h-index value and the probability to publish in a given period. This parameter only has an effect if diligence_share < 1. Only used for init_type = 'fixage'. |
selfcitations |
If this parameter is set to TRUE, a paper gets one additional citation if at least one of its authors has a h-index value that exceeds the number of previous citations of the paper by one or two. This reflects agents strategically citing their own papers with citations just below their h-index to accelerate the growth of their h-index. |
update_alpha_authors |
If this parameter is set to TRUE, the alpha author of newly written papers is determined every period based on the current h-index values of its authors. Without this option, the alpha author is determined when the paper is written and held constant from then on. |
boost |
If this parameter is set to TRUE, papers of agents with a higher h-index are cited more frequently than papers of agents with lower h-index. For each team, this effect is based on the team's co-author with the highest h-index within this team. |
boost_size |
Magnitude of the boost effect. For every additional h point of a paper's co-author who has the highest h-index among all of the paper's co-authors, citations of the paper are increased by boost_size, rounded to the next integer. |
alpha_share |
The share of previously published papers where the corresponding agent is alpha author. |
Value
For each run, the h-index values and the h-alpha values for each period are stored in a list of lists.
Examples
set.seed(123)
simdata <- simulate_hindex(runs = 2, n = 20, periods = 3)
plot_hsim(simdata, plot_hindex = TRUE)