Asymmetric Error Bar Origin
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How to asymmetric error bars matlab specify/plot asymmetrical y-error bars? New Topic Reply to Topic asymmetric error bars matplotlib Printer Friendly Author Topic drreaf Germany 45 Posts Posted-05/04/2006: 08:34:33 AM asymmetric error bars sigmaplot Origin Version: OriginPro 7.5 SR6Operating System: XP ProfessionalI have a table with four columns, A,B,C,D.Column A is a label, gnuplot asymmetric error bars column B is the lower 95% confidence limit of column C, column D is the upper 95% confidence limit of column C which is the y value. The confidence intervals are asymmetric! How do I specify the type of columns C and D
Python Asymmetric Error Bars
e.g. in the WorksheetColumnFormat/PlotDesignation dropdown-list and how do I plot these 4 columns as a bar-plot as e.g. shown in http://www.originlab.com/index.aspx?lm=+155&s=9&pid=770,however with asymmetrical error bars.Thank you in advance, Rainer Facius easwar USA 1861 Posts Posted-05/04/2006: 09:19:55 AM Hi Rainer,Please see:http://originlab.com/forum/topic.asp?TOPIC_ID=2523EaswarOriginLab Topic New Topic Reply to Topic Printer Friendly Jump To: Select Forum Origin Forum Origin Viewer and Orglab Forum Origin Forum Origin Forum for Programming LabTalk Forum Forum for Origin C Forum for Automation Server/COM and LabVIEW Origin中文论坛 Origin 中文论坛 (Chinese Origin Forum) Japanese Origin Forum Origin日本語フォーラム (Japanese Origin Forum) Origin on Linux The Origin on Linux Forum Private Forums Distributor Forum -------------------- Home Active Topics Frequently Asked Questions Member Information Search Page The Origin Forum © 2008 Originlab Corporation Snitz Forums 2000
Origin • Power Laws • Dimensional Analysis Plotting and Analyzing Data with Origin 7.5 Data Calculations Functions Plotting Fitting Printing Exporting for
How To Plot Error Bar In Origin
a Paper Troubleshooting Plot Styles Error Bars how to calculate error bars in origin Functions Axes Fitting Residuals To learn how to make a graph such as the how to calculate error in origin one shown above, follow the discussion below the graph. Click on a feature of the graph, or the text links beneath it, to jump to the instructions for http://www.originlab.com/forum/topic.asp?TOPIC_ID=4798 that feature. Introduction uparrow(); ?> Origin is a convenient data analysis and graphics program that runs in Windows on PCs. You can use Origin to plot data, transform raw data to more meaningful quantities through column-based calculations, compare data to a theoretical model using linear and nonlinear least-squares fitting, and determine the quantitative agreement between the http://www.physics.hmc.edu/analysis/origin.php data and model. Entering Data uparrow(); ?> You may type data directly into a data sheet or import data from the clipboard, from a text file, from an Excel data sheet, or from a large variety of other file formats. The action starts in the File|Import menu item, and you can learn about various file formats in the online help. Instructions for importing common kinds of data follow here. Bear in mind this important point: the basic unit of data is the column. In Origin, a column may be designated to represent X values, Y values, Z values, X error bars, Y error bars, or labels. By default, the first column is called A(X), the second is B(Y), and additional columns may be added using Column|Add New Columns... You can set the function of a column using the column box obtained by double-clicking on the column head, or with the pop-up menu obtained by right-clicking on the column head. Keyboard Entry uparrow(); ?> Type your data in co
Error bars on log-transformed plots Tweet Welcome to Talk Stats! Join the discussion today by registering your FREE account. Membership benefits: Get your questions answered by http://www.talkstats.com/showthread.php/4304-Error-bars-on-log-transformed-plots community gurus and expert researchers. Exchange your learning and research experience among http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4175406/ peers and get advice and insight. Join Today! + Reply to Thread Results 1 to 7 of 7 Thread: Error bars on log-transformed plots Thread Tools Show Printable Version Email this Page… Subscribe to this Thread… Display Linear Mode Switch to Hybrid Mode Switch to Threaded Mode 04-18-200802:07 PM #1 error bar gstuart View Profile View Forum Posts Give Away Points Posts 4 Thanks 0 Thanked 0 Times in 0 Posts Error bars on log-transformed plots Hello - I am a genetics researcher. I have a series of data points with errors (standard errors), that I wish to plot as a graph plot: GENE, AVG FOLD CHANGE, SE Gene1, 2193.10, 1200.74 Gene2, 96.28, 9.08 Gene3, asymmetric error bar 39.02, 22.51 Gene4, 5.88, 0.82 Gene5, -0.68, 0.33 Gene6, 1.14, 0.02 Gene7, -1.46, 0.16 Gene8, -1.56, 0.50 Gene9, -1.58, 0.10 Gene10, -1.88, 0.45 Gene11, -2.04, 0.45 Gene12, -6828.82, 975.41 Positive values are up-regulated genes; negative values are down-regulated genes (re: gene expression levels). I wish to plot this as a column plot on a log scale (y-axis) with negative values below the zero baseline, positive values above, and with the errors indicated. Something like: 1000 100 10 * 1 * 0------------------- -1 * -10 * -100 -1000 but with bars instead of the asterisks - you get the idea. I can do this easily enough using MS Excel, by taking the log of the absolute value, multiplying the result by +1 or -1 (to restore the original "directionality" - i.e. up- or down-regulated). A couple of questions: (Q1) Is it "better" to use log (base 10) or ln (natural) log transformations? (Q2) How would I present the error bars - would I log (or ln) -transform the standard errors, for example, and plot these [or the absolute values of these, since the log of numbers <1 are
Health Search databasePMCAll DatabasesAssemblyBioProjectBioSampleBioSystemsBooksClinVarCloneConserved DomainsdbGaPdbVarESTGeneGenomeGEO DataSetsGEO ProfilesGSSGTRHomoloGeneMedGenMeSHNCBI Web SiteNLM CatalogNucleotideOMIMPMCPopSetProbeProteinProtein ClustersPubChem BioAssayPubChem CompoundPubChem SubstancePubMedPubMed HealthSNPSRAStructureTaxonomyToolKitToolKitAllToolKitBookToolKitBookghUniGeneSearch termSearch Advanced Journal list Help Journal ListSpringer Open ChoicePMC4175406 Journal of Computer-Aided Molecular Design J Comput Aided Mol Des. 2014; 28(9): 887–918. Published online 2014 Jun 5. doi: 10.1007/s10822-014-9753-zPMCID: PMC4175406Confidence limits, error bars and method comparison in molecular modeling. Part 1: The calculation of confidence intervalsA. NichollsOpenEye Scientific Software, Inc., 9 Bisbee Court, Suite D, Santa Fe, NM 87508 USA A. Nicholls, Email: moc.neposeye@ynohtna.Corresponding author.Author information ► Article notes ► Copyright and License information ►Received 2014 Jan 13; Accepted 2014 May 14.Copyright © The Author(s) 2014 Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.This article has been cited by other articles in PMC.AbstractComputational chemistry is a largely empirical field that makes predictions with substantial uncertainty. And yet the use of standard statistical methods to quantify this uncertainty is often absent from published reports. This article covers the basics of confidence interval estimation for molecular modeling using classical statistics. Alternate approaches such as non-parametric statistics and bootstrapping are discussed.Keywords: Statistics, AUC, Virtual screening enrichment, Correlation coefficients, Linear regression, Error bars, Confidence intervalsIntroduction: Error barsWhen we report a number what do we mean by it? Clearly our intention is to convey information: after an experiment we think a property has a certain value; after this calculation our prediction of quantity X is Y. In reality, we know whatever number we report is only an estimate. For instance, we repeat an experiment to measure a partition coefficient between water and octanol five times and get an average, or we apply a computer model to a set of ten test systems and calculate a mean performance. In the former case, will a sixth measurement produce a similar number? In the latter case, do we know if the program will perform as well over a new test set? In other words, how do we know if the