EquationCitations {AER} | R Documentation

## Number of Equations and Citations for Evolutionary Biology Publications

### Description

Analysis of citations of evolutionary biology papers published in 1998 in the top three journals (as judged by their 5-year impact factors in the Thomson Reuters Journal Citation Reports 2010).

### Usage

    data("EquationCitations")

### Format

A data frame containing 649 observations on 13 variables.

journal

  Factor. Journal in which the paper was published (The American Naturalist, Evolution, Proceedings of the Royal Society of London B: Biological Sciences).

authors

  Character. Names of authors.

volume

  Volume in which the paper was published.

startpage

  Starting page of publication.

pages

  Number of pages.

equations

  Number of equations in total.

mainequations

  Number of equations in main text.

appequations

  Number of equations in appendix.

cites

  Number of citations in total.

selfcites

  Number of citations by the authors themselves.

othercites

  Number of citations by other authors.

theocites

  Number of citations by theoretical papers.

nontheocites

  Number of citations by nontheoretical papers.

### Details

Fawcett and Higginson (2012) investigate the relationship between the number of citations evolutionary biology papers receive, depending on the number of equations per page in the cited paper. Overall it can be shown that papers with many mathematical equations significantly lower the number of citations they receive, in particular from nontheoretical papers.

### Source

Online supplements to Fawcett and Higginson (2012).

http://www.pnas.org/lookup/suppl/doi:10.1073/pnas.1205259109/-/DCSupplemental

### References

Fawcett, T.W. and Higginson, A.D. (2012). Heavy Use of Equations Impedes Communication among Biologists. PNAS – Proceedings of the National Academy of Sciences of the United States of America, 109, 11735–11739. http://dx.doi.org/10.1073/pnas.1205259109

### See Also

`PhDPublications`

### Examples

    ## load data and MASS package
    data("EquationCitations", package = "AER")
    library("MASS")

    ## convenience function for summarizing NB models
    nbtable <- function(obj, digits = 3) round(cbind(
      "OR" = exp(coef(obj)),
      "CI" = exp(confint.default(obj)),
      "Wald z" = coeftest(obj)[,3],
      "p" = coeftest(obj)[, 4]), digits = digits)

    #################
    ## Replication ##
    #################

    ## Table 1
    m1a <- glm.nb(othercites ~ I(equations/pages) * pages + journal,
      data = EquationCitations)
    m1b <- update(m1a, nontheocites ~ .)
    m1c <- update(m1a, theocites ~ .)
    nbtable(m1a)
    nbtable(m1b)
    nbtable(m1c)

    ## Table 2
    m2a <- glm.nb(
      othercites ~ (I(mainequations/pages) + I(appequations/pages)) * pages + journal,
      data = EquationCitations)
    m2b <- update(m2a, nontheocites ~ .)
    m2c <- update(m2a, theocites ~ .)
    nbtable(m2a)
    nbtable(m2b)
    nbtable(m2c)

    ###############
    ## Extension ##
    ###############

    ## nonlinear page effect: use log(pages) instead of pages+interaction
    m3a <- glm.nb(othercites ~ I(equations/pages) + log(pages) + journal,
      data = EquationCitations)
    m3b <- update(m3a, nontheocites ~ .)
    m3c <- update(m3a, theocites ~ .)

    ## nested models: allow different equation effects over journals
    m4a <- glm.nb(othercites ~ journal / I(equations/pages) + log(pages),
      data = EquationCitations)
    m4b <- update(m4a, nontheocites ~ .)
    m4c <- update(m4a, theocites ~ .)

    ## nested model best (wrt AIC) for all responses
    AIC(m1a, m2a, m3a, m4a)
    nbtable(m4a)
    AIC(m1b, m2b, m3b, m4b)
    nbtable(m4b)
    AIC(m1c, m2c, m3c, m4c)
    nbtable(m4c)
    ## equation effect by journal/response
    ##           comb nontheo theo
    ## AmNat     =/-  -       +
    ## Evolution =/+  =       +
    ## ProcB     -    -       =/+

[Package AER version 1.2-5 Index]