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Family Wise Error Rate R

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may be challenged and removed. (June 2016) (Learn how and when to remove this template message) In statistics, family-wise family wise error rate post hoc error rate (FWER) is the probability of making one or familywise error rate more false discoveries, or type I errors, among all the hypotheses when performing multiple hypotheses tests.

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Contents 1 History 2 Background 2.1 Classification of multiple hypothesis tests 3 Definition 4 Controlling procedures 4.1 The Bonferroni procedure 4.2 The Šidák procedure 4.3

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Tukey's procedure 4.4 Holm's step-down procedure (1979) 4.5 Hochberg's step-up procedure 4.6 Dunnett's correction 4.7 Scheffé's method 4.8 Resampling procedures 5 Alternative approaches 6 References History[edit] Tukey coined the terms experimentwise error rate and "error rate per-experiment" to indicate error rates that the researcher could use as a control level per comparison error rate in a multiple hypothesis experiment.[citation needed] Background[edit] Within the statistical framework, there are several definitions for the term "family": Hochberg & Tamhane defined "family" in 1987 as "any collection of inferences for which it is meaningful to take into account some combined measure of error".[1][pageneeded] According to Cox in 1982, a set of inferences should be regarded a family:[citation needed] To take into account the selection effect due to data dredging To ensure simultaneous correctness of a set of inferences as to guarantee a correct overall decision To summarize, a family could best be defined by the potential selective inference that is being faced: A family is the smallest set of items of inference in an analysis, interchangeable about their meaning for the goal of research, from which selection of results for action, presentation or highlighting could be made (Yoav Benjamini).[citation needed] Classification of multiple hypothesis tests[edit] Main article: Classi

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How To Calculate Family Wise Error Rate

If you are an R blogger yourself you are invited to add family wise error calculator your own R content feed to this site (Non-English R bloggers should add themselves- here) Jobs for R-usersData experiment wise error rate Science Consultant @ Notre Dame, Indiana, United StatesFinance Manager @ Seattle, U.S.Data Scientist – AnalyticsTransportation Market Research Analyst @ Arlington, U.S.Data Analyst Popular Searches web scraping heatmap twitter maps https://en.wikipedia.org/wiki/Family-wise_error_rate time series boxplot animation shiny how to import image file to R hadoop ggplot2 trading latex finance eclipse excel quantmod sql googlevis PCA RStudio knitr ggplot market research rattle Regression coplot map rcmdr Tutorial Recent Posts Reactive shiny filters through collapsible d3js trees Linpe: make sending and receiving data analysis faster and easier Dates and Times – Simple and Easy https://www.r-bloggers.com/example-9-32-multiple-testing-simulation/ with lubridate Exercises (part 3) Building Scalable Data Pipelines with Microsoft R Server and Azure Data Factory Visualizing the Daily Variability of Bitcoin with Quandl and Highcharts Notes from 4th Bayesian Mixer Meetup Program of the european R users meeting [only 7 days to go] Journal of Open Source Software Homer, not Bart, is the star of the Simpsons A Bayesian Information Criterion for Singular Models ggplot2 2.2.0 coming soon! ODSC West 2016 - 20% off discount code for training with leading R experts Analyzing the first Presidential Debate Rocker - explanation and motivation for Docker containers usage in applications development Proofing statistics in papers Other sites Jobs for R-users SAS blogs Example 9.32: Multiple testing simulation May 21, 2012By Ken Kleinman (This article was first published on SAS and R, and kindly contributed to R-bloggers) In examples 9.30 and 9.31 we explored corrections for multiple testing and then extracting p-values adjusted by the Benjamini and Hochberg (or FDR) procedure. In this post we'll develop a simulation to explore the impact of "strong" and "weak" contr

Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow http://stats.stackexchange.com/questions/61875/how-can-i-perform-a-familywise-error-rate-correction-for-johansen-cointegration the company Business Learn more about hiring developers or posting ads with us Cross Validated Questions Tags Users Badges Unanswered Ask Question _ Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and wise error rise to the top How can I perform a familywise error rate correction for Johansen cointegration tests? up vote 2 down vote favorite I want to test multiple possible cointegration relationships with the Johansen cointegration test. I'm currently using the urca package in R with the ca.jo test. I was going to use the Bonferroni correction to correct the familywise error rate. However, ca.jo doesn't report p-values. It only reports test statistics and wise error rate critical values: ###################### # Johansen-Procedure # ###################### Test type: maximal eigenvalue statistic (lambda max) , with linear trend Eigenvalues (lambda): [1] 0.335639191 0.001256000 Values of teststatistic and critical values of test: test 10pct 5pct 1pct r <= 1 | 1.26 6.50 8.18 11.65 r = 0 | 408.52 12.91 14.90 19.19 [More ca.jo output snipped] How can I correct for rejecting the null incorrectly when I perform multiple cointegration tests? r hypothesis-testing cointegration bonferroni share|improve this question asked Jun 16 '13 at 16:56 Thomas Johnson 25019 add a comment| 1 Answer 1 active oldest votes up vote 2 down vote accepted You could use alternatively the function rank.test() from package tsDyn, which provides the p-values for the Johansen test, based on the gamma approximation of Doornik (1998, 1999) Compare: > library(tsDyn) > library(urca) > > data(denmark) > sjd <- denmark[, c("LRM", "LRY", "IBO", "IDE")] > summary(ca.jo(sjd, type="eigen", K=2)) ###################### # Johansen-Procedure # ###################### Test type: maximal eigenvalue statistic (lambda max) , with linear trend Values of teststatistic and critical values of test: test 10pct 5pct 1pct r <= 3 | 0.56 6.50 8.18 11.65 r <= 2 | 6.59 12.91 14.90 19.19 r <= 1 | 10.15 18.90 21.07 25.75 r = 0 | 31.51 24.78 27.14 32.14 > ve <- VECM(sjd, lag=1, estim="ML") > summary(rank.test(ve))[,c(1,5,6)] r e

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