Calculation Of Standard Error In R
Contents |
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 the company Business Learn more about hiring developers or posting ads with us calculation of standard error of the mean Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community
Calculation Of Standard Error From Standard Deviation
Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up In
Calculation Of Standard Error Of The Mean In Excel
R, how to find the standard error of the mean? up vote 53 down vote favorite 14 Is there any command to find the standard error of the mean in R? r statistics share|improve this question edited Feb 2 at 13:38 jogo
Standard Error Of Measurement Calculation
3,53661127 asked Apr 20 '10 at 15:49 alex 348136 add a comment| 7 Answers 7 active oldest votes up vote 22 down vote accepted There's the plotrix package with has a built-in function for this: std.error share|improve this answer answered Apr 20 '10 at 15:56 Matt Ball 227k54450510 add a comment| Did you find this question interesting? Try our newsletter Sign up for our newsletter and get our top new questions delivered to your inbox (see an example). Subscribed! Success! Please click the link in calculation percent error the confirmation email to activate your subscription. up vote 93 down vote The standard error is just the standard deviation divided by the square root of the sample size. So you can easily make your own function: > std <- function(x) sd(x)/sqrt(length(x)) > std(c(1,2,3,4)) [1] 0.6454972 share|improve this answer answered Apr 20 '10 at 16:18 Ian Fellows 11.5k73149 add a comment| up vote 64 down vote It is probably more efficient to use var... since you actually sqrt twice in your code, once to get the sd (code for sd is in r and revealed by just typing "sd")... se <- function(x) sqrt(var(x)/length(x)) share|improve this answer edited Jan 13 '14 at 14:02 answered Apr 20 '10 at 19:03 John 15.2k32657 2 Interestingly, your function and Ian's are nearly identically fast. I tested them both 1000 times against 10^6 million rnorm draws (not enough power to push them harder than that). Conversely, plotrix's function was always slower than even the slowest runs of those two functions - but it also has a lot more going on under the hood. –Matt Parker Apr 20 '10 at 22:52 3 Note that stderr is a function name in base. –Tom Jan 13 '14 at 14:01 2 That's a very good point. I typically use se. I have changed this answer to reflect that. –John Jan 13 '14 at 14:02 2 Tom, NO stderr does NOT calculate standard error it displays display aspects. of connection –forecaster Jan 21 '15 at 0:01 3 @
standard error equals sd/√n: > x r square calculation se se [1] 4.236195 Calculate the SD of data frame (matrix): >BOD #R Biochemical http://stackoverflow.com/questions/2676554/in-r-how-to-find-the-standard-error-of-the-mean Oxygen Demand database Time demand 1 1 8.3 2 2 10.3 3 3 19.0 4 4 16.0 5 5 15.6 6 7 19.8 > apply(BOD,2,sd) Time demand 2.160247 4.630623 http://www.endmemo.com/program/R/sd.php R Tutorials R Data Types Loop, Condition Statements Plotting and Graphics String Manipulations Math Functions Matrix Manipulations Read & Write Data Statistical Analysis All Functions List :: Popular :: » R PCH Symbols » R Color Names » R Regular Expression » R tapply Function » R String Functions » R Plot Function » R Builtin Datasets List Python Tutorials HTML Tutorials JavaScript Tutorials Statistics News, Events Worldwide Unit Conversions Top Visited Websites Directory endmemo.com © 2016 Terms of Use
R Avoiding Pitfalls in R Help with R R Tutorials Formal Statistics Books Tests for http://rcompanion.org/rcompanion/c_03.html Nominal Variables Exact Test of Goodness-of-Fit Power Analysis Chi-square Test of http://stats.stackexchange.com/questions/44838/how-are-the-standard-errors-of-coefficients-calculated-in-a-regression Goodness-of-Fit G–test of goodness-of-fit Chi-square Test of Independence G–test of Independence Fisher’s Exact Test of Independence Small Numbers in Chi-square and G–tests Repeated G–tests of Goodness-of-Fit Cochran–Mantel–Haenszel Test for Repeated Tests of Independence Descriptive Statistics Statistics of Central Tendency Statistics of Dispersion Standard Error standard error of the Mean Confidence Limits Tests for One Measurement Variable Student’s t–test for One Sample Student’s t–test for Two Samples Mann–Whitney and Two-sample Permutation Test Chapters Not Covered in This Book Type I, II, and III Sums of Squares One-way Anova Kruskal–Wallis Test One-way Analysis with Permutation Test Nested Anova Two-way Anova Two-way Anova with calculation of standard Robust Estimation Paired t–test Wilcoxon Signed-rank Test Regressions Correlation and Linear Regression Spearman Rank Correlation Curvilinear Regression Analysis of Covariance Multiple Regression Simple Logistic Regression Multiple Logistic Regression Multiple Tests Multiple Comparisons Miscellany Chapters Not Covered in This Book Other Analyses Contrasts in Linear Models Cate–Nelson Analysis Additional Helpful Tips Reading SAS Datalines in R Other Books Summary and Analysis of Extension Program Evaluation in R Standard Error of the Mean The standard error of the mean can be calculated with standard functions in the native stats package. The describe function in the psych package includes the standard error of the mean along with other descriptive statistics. This function is useful to summarize multiple variables in a data frame. Introduction Similar statistics See the Handbook for information on these topics. Example Standard error example ### -------------------------------------------------------------- ### Standard error example, p. 115 ### -------------------------------------------------------------- Input =(" Stream Fish Mill_Creek_1 76 Mill_Creek_2 102 North_Branch_Rock_Creek_1 12 North_Branch_Rock_Creek_2 39 Rock_Creek_1 55 Rock_Creek_2
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 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 rise to the top How are the standard errors of coefficients calculated in a regression? up vote 53 down vote favorite 43 For my own understanding, I am interested in manually replicating the calculation of the standard errors of estimated coefficients as, for example, come with the output of the lm() function in R, but haven't been able to pin it down. What is the formula / implementation used? r regression standard-error lm share|improve this question edited Aug 2 '13 at 15:20 gung 73.6k19160307 asked Dec 1 '12 at 10:16 ako 368146 good question, many people know the regression from linear algebra point of view, where you solve the linear equation $X'X\beta=X'y$ and get the answer for beta. Not clear why we have standard error and assumption behind it. –hxd1011 Jul 19 at 13:42 add a comment| 3 Answers 3 active oldest votes up vote 68 down vote accepted The linear model is written as $$ \left| \begin{array}{l} \mathbf{y} = \mathbf{X} \mathbf{\beta} + \mathbf{\epsilon} \\ \mathbf{\epsilon} \sim N(0, \sigma^2 \mathbf{I}), \end{array} \right.$$ where $\mathbf{y}$ denotes the vector of responses, $\mathbf{\beta}$ is the vector of fixed effects parameters, $\mathbf{X}$ is the corresponding design matrix whose columns are the values of the explanatory variables, and $\mathbf{\epsilon}$ is the vector of random errors. It is well known that an estimate of $\mathbf{\beta}$ is given by (refer, e.g., to the wikipedia article) $$\hat{\mathbf{\beta}} = (\mathbf{X}^{\prime} \mathbf{X})^{-1} \mathbf{X}^{\prime} \mathbf{y}.$$ Hence $$ \textrm{Var}(\hat{\mathbf{\beta}}) = (\mathbf{X}^{\prime} \mathbf{X})^{-1} \mathbf{X}^{\prime} \;\sigma^2 \mathbf{I} \; \mathbf{X} (\