R package bootstrap tests

Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It only takes a minute to sign up. I have two groups of individuals 22 in each groupwhich I compared using a t-test. Because the p-value was quite low, my supervisor suggested I bootstrap the t-test, to get a sense of the distribution of t-values and p-values we are concerned we are making a type-II error, given the small sample sizes.

So I want to: a randomly extract with replacement 22 cases for each of my two groups, b perform a t-test on these new samples, c repeat e. So far I have been using the boot package. For a single vector Data with 22 components, I got the boot package to bootstrap the mean value using:.

However, my problem is to expand this logic to a dataframe, and using a t-test instead of a mean function. In my mind, I have to use the "strata" argument to ensure I am bootstrapping the two samples separately, and to nest the t-test within a function. However, all attempts to do so have been unsuccessful. Applying the same procedure from the simple example code above; I'm thinking that I need to do something in the lines of:.

But as the question marks imply, I am not sure how to complete the code and is my basic setup of the code even correct? I've never used the boot package. Bootstrapping is so trivial you can just code it from scratch. Below, I just use t. I set the seed, so your results would be identical, if you don't do anything different. For the qq-plot for the t-distribution, I used the df that corresponds to equal variances, which won't quite match the bootstrap where each iteration will have a different effective df.

Under the null, p-values should be uniformly distributed, but yours clearly aren't. I'm not sure I'd draw any conclusions from that, though. Sign up to join this community. The best answers are voted up and rise to the top. Bootstrapping a t-test in R Ask Question. Asked 5 years, 7 months ago. Active 5 years, 7 months ago. Viewed 7k times.

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1. Why Bootstrap?

Fanitullen Fanitullen 21 1 1 silver badge 2 2 bronze badges.S3 method for default boot. You can specify just the initial letter. If TRUE then the pooled variance is used to estimate the variance otherwise the Welch or Satterthwaite approximation to the degrees of freedom is used. If TRUE then the symmetric bootstrap p value otherwise the equal-tail boostrap p value is computed.

By default the variables are taken from environment formula. Defaults to getOption "na. The implemented test corresponds to the proposal of Chapter 16 of Efron and Tibshirani The function returns bootstrapped p values and confidence intervals as well as the results ot the t-test without bootstrap. If paired is TRUE then both x and y must be specified and they must be the same length. Missing values are silently removed in pairs if paired is TRUE.

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If var. By default, if var. If the input data are effectively constant compared to the larger of the two means an error is generated. A list with class "boot. Code and documentation are for large parts identical to function t. Efron, R. An Introduction to the Bootstrap. Created by DataCamp. Bootstrap t-Test Performs one and two sample bootstrap t-tests on vectors of data. Community examples Looks like there are no examples yet.

Post a new example: Submit your example. API documentation. Put your R skills to the test Start Now.Unit root tests form an essential part of any time series analysis. We provide practitioners with a single, unified framework for comprehensive and reliable unit root testing in the R package bootUR.

The package's backbone is the popular augmented Dickey-Fuller ADF test paired with a union of rejections principle, which can be performed directly on single time series or multiple including panel time series. Accurate inference is ensured through the use of bootstrap methods. The package addresses the needs of both novice users, by providing user-friendly and easy-to-implement functions with sensible default options, as well as expert users, by giving full user-control to adjust the tests to one's desired settings.

Stephan Smeekes. Ines Wilms. To perform statistical inference for time series, one should be able to In this article, we introduce the R package portes with extensive illust Leadership is a process that leaders influence followers to achieve coll In this paper, a unified approach is proposed to derive the exact local A generalization of the definition of records to functional data is prop We propose a new unit-root test based on Lagrange Multipliers, where we This paper introduces the R package ForecastTB that can be used to compa Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.

In this paper, we introduce the bootUR package Smeekes and Wilms, for R R Core Team,which implements several bootstrap tests for unit roots.

Unit root testing is an essential part of any statistical analysis of time series. Although unit root tests play some role in assessing particular economic hypothesis such as in the classical study of Nelson and Plosser,the by far most important use of unit root tests is as a pre-test, to determine whether a time series is non -stationary, possesses a unit root.

How can I generate bootstrap statistics in R? | R FAQ

Globally speaking, the bootUR package offers three major contributions to existing R packages. First, it offers a comprehensive, easy-to-use and reliable set of unit root tests not found as generally in other packages. Second, it offers accurate p -values based on bootstrap methods.Join Stack Overflow to learn, share knowledge, and build your career.

r package bootstrap tests

Stack Overflow for Teams is a private, secure spot for you and your coworkers to find and share information. I would like to get CI for a paired t-test using bootstrap in R. Unfortunately, I dont have privileges to install the MKinfer.

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How would I do this for paired data with CI's and p-values not relying on MKinfer relying on boot would be fine? We can calculate the confidence interval of the estimated change in the mean using boot. Learn more. Asked 6 months ago. Active 6 months ago. Viewed times. Improve this question. Cyrus Mohammadian. Cyrus Mohammadian Cyrus Mohammadian 4, 4 4 gold badges 24 24 silver badges 48 48 bronze badges.

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Question feed. Stack Overflow works best with JavaScript enabled.The R package boot allows a user to easily generate bootstrap samples of virtually any statistic that they can calculate in R.

From these samples, you can generate estimates of bias, bootstrap confidence intervals, or plots of your bootstrap replicates. We will demonstrate a few of these techniques in this page and you can read more details at its CRAN package page.

Before using commands in the boot package, you must first download the package and load it in your workspace. We will be using the hsb2 dataset for all of the examples on this page.

The boot command executes the resampling of your dataset and calculation of your statistic s of interest on these samples. Before calling bootyou need to define a function that will return the statistic s that you would like to bootstrap.

The first argument passed to the function should be your dataset. The second argument can be an index vector of the observations in your dataset to use or a frequency or weight vector that informs the sampling probabilities. The example below uses the default index vector and assumes we wish to use all of our observations. The statistic of interest here is the correlation coefficient of write and math.

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With the function fc defined, we can use the boot command, providing our dataset name, our function, and the number of bootstrap samples to be drawn.

While the printed output for bootcorr is brief, R saves additional information that can be listed:. Knowing the seed value would allow us to replicate this analysis, if needed, and from the t vector and t0we could calculate the bias and standard error:. The histogram includes a dotted vertical line indicating the location of the original statistic. Using the boot. A fifth type, the studentized intervals, requires variances from each bootstrap sample.

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r package bootstrap tests

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r package bootstrap tests

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bootUR: An R Package for Bootstrap Unit Root Tests

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