Correlation Estimate Error
Contents |
Tour Start here for a quick overview of the site Help Center Detailed answers to any questions you
Regression Estimate
might have Meta Discuss the workings and policies of this site covariance estimate About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting standard deviation estimate 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
Variance Estimate
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 rise to the top Standard error from correlation coefficient up vote 2 down vote favorite 1 Many studies only
Linear Regression Estimate
report the relationship between two variables (e.g. linear or logistic equation), $n$, and $r^2$. I want to use these reported statistics to reproduce this relationship with its variation. Most statistical software will generate a parameter distribution from a mean and standard error. Assuming a normal distribution, can the standard error of the parameter estimates be calculated with just these three statistics? Essentially, can I get a standard error from $r^2$? Or will I need to do some kind of bootstrapping procedure to generate a distribution that has the same $r^2$ and then calculate the standard error? if so are there better ones for linear vs. nonlinear equations? distributions correlation normal-distribution variance share|improve this question edited Oct 23 '13 at 23:50 Nick Cox 28.2k35684 asked Oct 23 '13 at 18:53 janice 1112 Sorry for the typo, it should be correlation coefficient, not correction coefficient. –janice Oct 23 '13 at 19:05 Welcome to our site, Janice! We encourage you to continue improvin
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 standard error of correlation coefficient formula Stack Overflow the company Business Learn more about hiring developers or posting ads
Standard Error Of Estimate Formula
with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow standard error of regression is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up How to compute P-value and standard error from correlation analysis of R's cor() http://stats.stackexchange.com/questions/73621/standard-error-from-correlation-coefficient up vote 12 down vote favorite 2 I have data that contain 54 samples for each condition (x and y). I have computed the correlation the following way: > dat <- read.table("http://dpaste.com/1064360/plain/",header=TRUE) > cor(dat$x,dat$y) [1] 0.2870823 Is there a native way to produce SE of correlation in R's cor() functions above and p-value from T-test? As explained in this web (page 14.6) r correlation share|improve this question asked Apr 19 http://stackoverflow.com/questions/16097453/how-to-compute-p-value-and-standard-error-from-correlation-analysis-of-rs-cor '13 at 4:55 neversaint 10.4k50150248 4 Perhaps you're looking for ?cor.test instead. –A Handcart And Mohair Apr 19 '13 at 4:59 add a comment| 2 Answers 2 active oldest votes up vote 20 down vote accepted I think that what you're looking for is simply the cor.test() function, which will return everything you're looking for except for the standard error of correlation. However, as you can see, the formula for that is very straightforward, and if you use cor.test, you have all the inputs required to calculate it. Using the data from the example (so you can compare it yourself with the results on page 14.6): > cor.test(mydf$X, mydf$Y) Pearson's product-moment correlation data: mydf$X and mydf$Y t = -5.0867, df = 10, p-value = 0.0004731 alternative hypothesis: true correlation is not equal to 0 95 percent confidence interval: -0.9568189 -0.5371871 sample estimates: cor -0.8492663 If you wanted to, you could also create a function like the following to include the standard error of the correlation coefficient. For convenience, here's the equation: r = the correlation estimate and n - 2 = degrees of freedom, both of which are readily available in the output above. Thus, a simple function could be: cor.test.plus <- function(x) { list(x, Standard.
article Open Access Open Peer Review This article has Open Peer Review reports available. How does Open Peer Review work? Estimation of the correlation coefficient using the Bayesian http://bmcmedresmethodol.biomedcentral.com/articles/10.1186/1471-2288-3-5 Approach and its applications for epidemiologic researchEnriqueFSchisterman1Email author, KirstenBMoysich2, LucindaJEngland1 and MallaRao1BMC https://en.wikipedia.org/wiki/Propagation_of_uncertainty Medical Research Methodology20033:5DOI: 10.1186/1471-2288-3-5© Schisterman et al; licensee BioMed Central Ltd.2003Received: 6September2002Accepted: 25March2003Published: 25March2003 Open Peer Review reports Abstract Background The Bayesian approach is one alternative for estimating correlation coefficients in which knowledge from previous studies is incorporated to improve estimation. The purpose of this paper is to standard error illustrate the utility of the Bayesian approach for estimating correlations using prior knowledge. Methods The use of the hyperbolic tangent transformation (ρ = tanh ξ and r = tanh z) enables the investigator to take advantage of the conjugate properties of the normal distribution, which are expressed by combining correlation coefficients from different studies. Conclusions One of the strengths of the standard error of proposed method is that the calculations are simple but the accuracy is maintained. Like meta-analysis, it can be seen as a method to combine different correlations from different studies. Keywords Bayesian Analysis Correlation Coefficients Low Birthweight Meta-Analysis and Transformations BackgroundThe correlation coefficient is a standard measure of association between two random variables and is widely used in epidemiology. As such, considerable attention has been given to its interpretation [1–3] as well as to the methods for correcting attenuation due to random measurement error [4, 5]. Strategies for correcting measurement error require knowledge about the reliability of the measurements [2] for the use of an alloyed gold standard [6] to estimate reliability coefficients. In many epidemiological studies, the reliability of the measurements is unknown making it impossible to correct for attenuation.Classical methods are based solely on collected data, and ignore any prior knowledge of the association under investigation. The Bayesian approach is one alternative for estimating correlation coefficients in which knowledge from previous studies is incorporated to improve estimation. The purpose of this paper is to illustrate the utility of the Bay
propagation of error) is the effect of variables' uncertainties (or errors, more specifically random errors) on the uncertainty of a function based on them. When the variables are the values of experimental measurements they have uncertainties due to measurement limitations (e.g., instrument precision) which propagate to the combination of variables in the function. The uncertainty u can be expressed in a number of ways. It may be defined by the absolute error Δx. Uncertainties can also be defined by the relative error (Δx)/x, which is usually written as a percentage. Most commonly, the uncertainty on a quantity is quantified in terms of the standard deviation, σ, the positive square root of variance, σ2. The value of a quantity and its error are then expressed as an interval x ± u. If the statistical probability distribution of the variable is known or can be assumed, it is possible to derive confidence limits to describe the region within which the true value of the variable may be found. For example, the 68% confidence limits for a one-dimensional variable belonging to a normal distribution are ± one standard deviation from the value, that is, there is approximately a 68% probability that the true value lies in the region x ± σ. If the uncertainties are correlated then covariance must be taken into account. Correlation can arise from two different sources. First, the measurement errors may be correlated. Second, when the underlying values are correlated across a population, the uncertainties in the group averages will be correlated.[1] Contents 1 Linear combinations 2 Non-linear combinations 2.1 Simplification 2.2 Example 2.3 Caveats and warnings 2.3.1 Reciprocal 2.3.2 Shifted reciprocal 3 Example formulas 4 Example calculations 4.1 Inverse tangent function 4.2 Resistance measurement 5 See also 6 References 7 Further reading 8 External links Linear combinations[edit] Let { f k ( x 1 , x 2 , … , x n ) } {\displaystyle \ ρ 4(x_ ρ 3,x_ ρ 2,\dots ,x_ ρ 1)\}} be a set of m functions which are linear combinations of n {\displaystyle n} variables x 1 , x 2 , … , x n {\displaystyle x_ σ 6,x_ σ 5,\dots ,x_ σ 4} with combination coefficients A k 1 , A k 2 , … , A k n , ( k = 1 … m ) {\displaystyle A_ σ 0,A_ ρ 9,\dots ,A_ ρ 8,(k=1\dots m)} . f k = ∑ i n A k i x i or f = A x {\displaystyle f_ ρ 4=\sum _ ρ 3^ ρ 2A_ ρ 1x_ ρ 0{\text{ or }}\mathrm