Package semisup. March 10, Version Title Semi-Supervised Mixture Model
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1 Version Title Semi-Supervised Mixture Model Package semisup March 10, 2019 Description Useful for detecting SNPs with interactive effects on a quantitative trait. This R packages moves away from testing interaction terms, and moves towards testing whether an individual SNP is involved in any interaction. This reduces the multiple testing burden to one test per SNP, and allows for interactions with unobserved factors. Analysing one SNP at a time, it splits the individuals into two groups, based on the number of minor alleles. If the quantitative trait differs in mean between the two groups, the SNP has a main effect. If the quantitative trait differs in distribution between some individuals in one group and all other individuals, it possibly has an interactive effect. Implicitly, the membership probabilities may suggest potential interacting variables. biocviews SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Depends R (>= 3.0.0) Imports VGAM Suggests knitr, testthat, SummarizedExperiment VignetteBuilder knitr License GPL-3 LazyData true RoxygenNote URL BugReports git_url git_branch master git_last_commit 7c0d2e1 git_last_commit_date Date/Publication Author Armin Rauschenberger [aut, cre] Maintainer Armin Rauschenberger <a.rauschenberger@vumc.nl> 1
2 2 mixtura R topics documented: semisup-package mixtura scrutor Index 6 semisup-package Semi-supervised mixture model Description This R package implements the semi-supervised mixture model. Use mixtura for model fitting, and scrutor for hypothesis testing. Getting started Please type the following commands: utils::vignette("semisup")?semisup::mixtura?semisup::scrutor More information A Rauschenberger, RX Menezes, MA van de Wiel, NM van Schoor, and MA Jonker (2017). "Detecting SNPs with interactive effects on a quantitative trait", Manuscript in preparation. <a.rauschenberger@vumc.nl> mixtura Model fitting Description This function fits a semi-supervised mixture model. It simultaneously estimates two mixture components, and assigns the unlabelled observations to these. Usage mixtura(y, z, dist = "norm", phi = NULL, pi = NULL, gamma = NULL, test = NULL, iter = 100, kind = 0.05, debug = TRUE,...)
3 mixtura 3 Arguments y z dist phi pi gamma test iter kind debug observations: numeric vector of length n class labels: integer vector of length n, with entries 0, 1 and NA distributional assumption: character "norm" (Gaussian), "nbinom" (negative bionomial), or "zinb" (zero-inflated negative binomial) dispersion parameter: positive numeric, or NULL zero-inflation parameter: numeric between 0 and 1, or NULL offset: numeric vector of length n, or NULL resampling procedure: character "perm" (permutation) or "boot" (parametric bootstrap), or NULL (maximum) number of resampling iterations : positive integer, or NULL resampling accuracy: numeric between 0 and 1, or NULL; all p-values above kind are approximate verification of arguments: TRUE or FALSE... settings EM algorithm: starts, it.em and epsilon (see arguments) Details By default, phi and pi are estimated by the maximum likelihood method, and gamma is replaced by a vector of ones. Value This function fits and compares a one-component (H0) and a two-component (H1) mixture model. posterior converge estim0 estim1 loglik0 loglik1 lrts p.value probability of belonging to class 1: numeric vector of length n path of the log-likelihood: numeric vector with maximum length it.em parameter estimates under H0: data frame parameter estimates under H1: data frame log-likelihood under H0: numeric log-likelihood under H1: numeric likelihood-ratio test statistic: positive numeric H0 versus H1: numeric between 0 and 1, or NULL Reference A Rauschenberger, RX Menezes, MA van de Wiel, NM van Schoor, and MA Jonker (2017). "Detecting SNPs with interactive effects on a quantitative trait", Manuscript in preparation. See Also Use scrutor for hypothesis testing. All other functions are internal.
4 4 scrutor Examples # data simulation n <- 100 z <- rep(0:1,each=n/2) y <- rnorm(n=n,mean=2,sd=1) z[(n/4):n] <- NA # model fitting mixtura(y,z,dist="norm",test="perm") scrutor Hypothesis testing Description This function tests whether the unlabelled observations come from a mixture of two distributions. Usage scrutor(y, Z, dist = "norm", phi = NULL, pi = NULL, gamma = NULL, test = "perm", iter = NULL, kind = NULL, debug = TRUE,...) Arguments Y Z dist phi pi gamma test iter kind Details debug observations: numeric vector of length n, or numeric matrix with n rows (samples) and q columns (variables) class labels: numeric vector of length n, or numeric matrix with n rows (samples) and p columns (variables), with entries 0 and NA distributional assumption: character "norm" (Gaussian), "nbinom" (negative bionomial), or "zinb" (zero-inflated negative binomial) dispersion parameter(s): numeric vector of length q, or NULL (norm: none, nbinom: MLE) zero-inflation parameter(s): numeric vector of length q, or NULL (norm: none,nbinom: MLE) offset: numeric vector of length n, or NULL resampling procedure: character "perm" (permutation) or "boot" (parametric bootstrap), or NULL (maximum) number of resampling iterations : positive integer, or NULL resampling accuracy: numeric between 0 and 1, or NULL; all p-values above kind are approximate verification of arguments: TRUE or FALSE... settings EM algorithm: starts, it.em and epsilon (see arguments) By default, phi and pi are estimated by the maximum likelihood method, and gamma is replaced by a vector of ones.
5 scrutor 5 Value This function tests a one-component (H0) against a two-component mixture model (H1). y z lrts p.value index observations index class labels test statistic p-value Reference A Rauschenberger, RX Menezes, MA van de Wiel, NM van Schoor, and MA Jonker (2017). "Detecting SNPs with interactive effects on a quantitative trait", Manuscript in preparation. See Also Use mixtura for model fitting. All other functions are internal. Examples # data simulation n <- 100 z <- rep(0:1,each=n/2) y <- rnorm(n=n,mean=2*z,sd=1) z[(n/4):n] <- NA # hypothesis testing scrutor(y,z,dist="norm")
6 Index Topic documentation semisup-package, 2 Topic methods mixtura, 2 scrutor, 4 arguments, 3, 4 internal, 3, 5 mixtura, 2, 2, 5 scrutor, 2, 3, 4 semisup (semisup-package), 2 semisup-package, 2 6
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