<b>simplexreg</b>: An<i>R</i>Package for Regression Analysis of Proportional Data Using the Simplex Distribution

Zhang, Peng; Qiu, Zhenguo; Shi, Chengchun

doi:10.18637/jss.v071.i11

Cited by 36 publications

(21 citation statements)

References 20 publications

(38 reference statements)

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“…The R package gamlss (Rigby and Stasinopoulos 2005;Stasinopoulos and Rigby 2008) also provides beta distribution models, including 0-and 1-inflated models. A recent addition to these resources is the simplexreg package (Zhang, Qiu, and Shi 2016), which also estimates two-parameter models, using the simplex distribution.…”

Section: The R Package Cdfquantregmentioning

confidence: 99%

cdfquantreg: An R Package for CDF-Quantile Regression

Shou¹,

Smithson²

2019

J. Stat. Soft.

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The CDF-quantile family of two-parameter distributions with support (0, 1) described in Smithson and Merkle (2014) and recently elaborated by Smithson and Shou (2017), considerably expands the variety of distributions available for modeling random variables on the unit interval. This family is especially useful for modeling quantiles, and also sometimes out-performs the other distributions. The distributions are very tractable, with a location and dispersion parameter, explicit probability distribution functions, cumulative distribution functions, and quantiles. They enable a wide variety of quantile regression models with predictors for the location and dispersion parameters, and simple interpretations of those parameters. The R package cdfquantreg (Shou and Smithson 2019) (at least R 3.2.0) presented in this paper includes 36 distributions from the CDF-quantile family. Separate submodels may be specified for the location and for the dispersion parameters, with different or overlapping sets of predictors in each. The package offers maximum likelihood, Bayesian MCMC, and bootstrap estimation methods. Model diagnostics, including the gradient, three types of residuals, and the dfbeta influence measures, are available for evaluating models. The package also provides pseudo-random generators for all of its distributions. Many of its functions and their usage have forms familiar to R users, and the documentation is extensive. We also present a SAS macro for general linear models using the CDF-quantile family that includes many of the same capabilities as the cdfquantreg package. The paper provides examples of applications to real data-sets.

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Section: The R Package Cdfquantregmentioning

confidence: 99%

cdfquantreg: An R Package for CDF-Quantile Regression

Shou¹,

Smithson²

2019

J. Stat. Soft.

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“…Beta regression was performed with betareg package [28] whereas 0/1 inflated beta regression was carried out with the gamlss package [29]. Simplex regression was fitted with simplexreg package [30] while inflated simplex regression was fitted with the ZOIP R package.…”

Section: Simplex Nonementioning

confidence: 99%

“…Data distribution For each CpG, the best distribution of its beta-values across samples was assessed in terms of the Akaike's information criterion (AIC) as well as by performing a Kolmogorov-Smirnov (KS) test. This was done with R packages fitdistrplus[35] and simplexreg[30]. Besides, for each CpG; the simplex, beta and normal distribution parameters were estimated using maximum likelihood estimation (MLE) using fitdistrplus[35], ZOIP[27] and VGAM [36] R packages.…”

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confidence: 99%

Are methylation beta-values simplex distributed?

Nonell

González

2019

Preprint

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DNA methylation plays an important role in the development and progression of disease. Beta-values are the standard methylation measures. Different statistical methods have been proposed to assess differences in methylation between conditions. However, most of them do not completely account for the distribution of beta-values. The simplex distribution can accommodate beta-values data. We hypothesize that simplex is a quite flexible distribution which is able to model methylation data.To test our hypothesis, we conducted several analyses using four real data sets obtained from microarrays and sequencing technologies. Standard data distributions were studied and modelled in comparison to the simplex. Besides, some simulations were conducted in different scenarios encompassing several distribution assumptions, regression models and sample sizes. Finally, we compared DNA methylation between females and males in order to benchmark the assessed methodologies under different scenarios.According to the results obtained by the simulations and real data analyses, DNA methylation data are concordant with the simplex distribution in many situations. Simplex regression models work well in small sample size data sets. However, when sample size increases, other models such as the beta regression or even the linear regression can be employed to assess group comparisons and obtain unbiased results. Based on these results, we can provide some practical recommendations when analyzing methylation data: 1) use data sets of at least 10 samples per studied condition for microarray data sets or 30 in NGS data sets, 2) apply a simplex or beta regression model for microarray data, 3) apply a linear model in any other case.

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“…For the same reason, from the published distributions the simplex also would be a poor choice for these data. Indeed, according to the simplexreg R package (Zhang, Qiu & Shi, ), its model log‐likelihood is −1,755.…”

Section: Example: Lay Interpretations Of Ipcc Report Probability Phrasesmentioning

confidence: 99%

CDF‐quantile distributions for modelling random variables on the unit interval

Smithson

Shou

2017

Brit J Math & Statis

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This paper introduces a two-parameter family of distributions for modelling random variables on the (0,1) interval by applying the cumulative distribution function of one 'parent' distribution to the quantile function of another. Family members have explicit probability density functions, cumulative distribution functions and quantiles in a location parameter and a dispersion parameter. They capture a wide variety of shapes that the beta and Kumaraswamy distributions cannot. They are amenable to likelihood inference, and enable a wide variety of quantile regression models, with predictors for both the location and dispersion parameters. We demonstrate their applicability to psychological research problems and their utility in modelling real data.

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simplexreg: AnRPackage for Regression Analysis of Proportional Data Using the Simplex Distribution

Cited by 36 publications

References 20 publications

cdfquantreg: An R Package for CDF-Quantile Regression

cdfquantreg: An R Package for CDF-Quantile Regression

Are methylation beta-values simplex distributed?

CDF‐quantile distributions for modelling random variables on the unit interval

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