Efficient Jonckheere-Terpstra Test Statistics Description. This Rcpp-based package implements highly efficient functions for the calculation of the Jonkheere-Terpstra statistic.It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes.the JT test statistic. mean: the mean of the JT test statistic. variance: the variance of the JT test statistic. Z: the standardized test statistic. p.value: the p-value of the test. alpha: the level of significance. method: the character string "JT test". data: a data frame containing the variables in which NA values (if exist) are removed .R/jonckheere.test.R defines the following functions: jonckheereTest.formula jonckheereTest.default jonckheereTestPerform Jonckheere-Terpstra test Description jonckheere.terpstra() performs the Jonckheere-Terpstra test and is used in chapters 7, 8 and 12 of "Applied Nonparametric Statistical Methods" (5th edition)
Both SAS and R, amongst others, contain the functionality to perform the Jonckheere-Terpstra test. The ‘clinfun’ package in R can perform a Jonckheere-Terpstra test, with the exact test as the default if the sample size is less than 100 and there are no ties, otherwise the test defaults to the permutation test.Jonckheere-Terpstra k-Sample Test for Increasing Alternatives Description. The Jonckheere-Terpstra k-sample test statistic JT is defined as JT = \sum_{i # Example 1 Jonckheere-Terpstra test data: JT = 2, p-value = 7.29e-09 alternative hypothesis: decreasing > # Example 2 . The test can be performed using the perm package in R. In this example, I set the number of permutations P rep= 50000.
Details. The null hypothesis, H_0: \theta_1 = \theta_2 = \ldots = \theta_k is tested against a simple order hypothesis, H_\mathrm{A}: \theta_1 \le \theta_2 \le \ldots \le \theta_k,~\theta_1 < \theta_k.. The p-values are estimated from the standard normal distribution. Value. A list with class "htest" containing the following components: . method. a character . I'm using jonckheere.test from the clinfun package to evaluate whether there is a trend across groups in my data. More specifically, I have ~60 samples that can be grouped into one of 5 categories. I see that one parameter for the .This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core .
The Jonckheere-Terpstra is a non-parametric test for trend. This function is a wrapper for jonckheere.test from the clinfun package. rdrr.io Find an R package R language docs Run R in your browser. StatCharrms Statistical Analysis of Chemistry, Histopathology, and Reproduction Endpoints Including Repeated Measures and Multi-Generation Studies .Compares k samples using the Anderson-Darling test, Kruskal-Wallis type tests with different rank score criteria, Steel's multiple comparison test, and the Jonckheere-Terpstra (JT) test. It computes asymptotic, simulated or (limited) exact P-values, all valid under randomization, with or without ties, or conditionally under random sampling from populations, given the observed tie .The Jonckheere-Terpstra is a non-parametric test for trend. This function is a wrapper for jonckheere.test from the clinfun package.The test is testing for a monotone trend in terms of the class parameter. The number of times that an individual of a higher class has a higher gene expression forms a basis for the inference. Rdocumentation. powered by. Learn R Programming. SAGx (version 1.46.0) .
The asymptotic normality and consistency of Kendall's test against trend, when ties are present in one ranking. Indagationes Mathematicae 14:327-333. Examples set.seed(1234) g <- rep(1:5, rep(10,5)) x <- rnorm(50) jonckheere.test(x+0.3*g, g) x[1:2] <- mean(x[1:2]) # tied data jonckheere.test(x+0.3*g, g) jonckheere.test(x+0.3*g, g, nperm=5000)We would like to show you a description here but the site won’t allow us.•H 0: θ 1 =θ 2 =θ 3 =.=θ k • H A: θ 1≤θ 2≤θ 3≤.≤θ k (with at least one strict inequality) • k>2 • Θ i is the population median for ith . the JT test statistic. mean: the mean of the JT test statistic. variance: the variance of the JT test statistic. Z: the standardized test statistic. p.value: the p-value of the test. alpha: the level of significance. method: the character string "JT test". data: a data frame containing the variables in which NA values (if exist) are removed .
R/jonkheere.R defines the following functions: jtperm.p jonckheere.test jtpdf djonckheere. rdrr.io Find an R package R language docs Run R in your browser. clinfun Clinical Trial Design and Data Analysis Functions . R Package Documentation. rdrr.io home R language documentation Run R code online. Browse R Packages.This test or a test with a similar function is sometimes called “ordinal chi-square” test. In Agresti, the method used is called the linear-by-linear association model. In R, the test can be performed by permutation test with the coin package. Association in ordinal tables can also be tested with Kendall’s tau correlation.
This 'Rcpp'-based package implements highly efficient functions for the calculation of the Jonckheere-Terpstra statistic. It can be used for a variety of applications, including feature selection in machine learning problems, or to conduct genome-wide association studies (GWAS) with multiple quantitative phenotypes. The code leverages 'OpenMP' directives for multi-core . View source: R/mainFunction.R. Description. rain detects rhythms in time-series using non parametric methods. It uses an extension of the rank test for Umbrella Alternatives (Mack & Wolfe, 1981), based on on the Jonckheere-Terpstra test, which tests whether sets of groups have a trend or not.
alpha size of the test (default 5%) power power of the test (default 80%) r treatments are randomized in 1:r ratio (default r=1) npm the sample size program searches for sample sizes in a range (+/- npm) to get the exact power mmax the maximum group size for which exact power is calculated n total number of subjectsA method to compute the Jonckheere-Terpstra test statistics for large numbers of dependent and independent variables, with optional multi-threaded execution. The calculation of the standardized test statistics employs the null variance equation as defined by Hollander and Wolfe (1999, eq. 6.19) to account for ties in the data.Tools for Descriptive Statistics and Exploratory Data Analysis. DescTools is an extensive collection of miscellaneous basic statistics functions and comfort wrappers not available in the R basic system for efficient description of data. The author’s intention was to create a toolbox, which facilitates the (notoriously time consuming) first descriptive tasks in data analysis, consisting of . Using coin . answer provided by Prof. Hothorn. control <- c(40, 35, 38, 43, 44, 41) rough <- c(38, 40, 47, 44, 40, 42) accurate <- c(48, 40, 45, 43, 46, 44) pieces .
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