R Plot Error Envelopes
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Plotting Confidence Bands In R
| Holtz Basics August 21, 2015June 28, 2016 | Holtz 3D August 21, 2015June 28, geom_ribbon 2016 | Holtz Boxplot August 21, 2015August 1, 2016 | Holtz R Colors March 21, 2015August 1, 2016 | Holtz Histograms February 4, 2015June 28, geom_ribbon color 2016 | Holtz Animation September 11, 2013July 19, 2016 | Holtz Maps September 5, 2013July 24, 2016 | Holtz Interactive February 11, 2011August 1, 2016 | Holtz #104 Plot lines with error envelopes ggplot2 Share the Gallery ! This graph has been made by Alastair Sanderson. You can
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have a look to his gallery here ! It shows mean temperature profiles and their error envelopes, using the ggplot2 package. library(ggplot2) # Get the data from the web ! CC <- read.table("http://www.sr.bham.ac.uk/~ajrs/papers/sanderson06/mean_Tprofile-CC.txt" , header=TRUE) nCC <- read.table("http://www.sr.bham.ac.uk/~ajrs/papers/sanderson06/mean_Tprofile-nCC.txt" , header=TRUE) CC$type <- "Cool core" nCC$type <- "Non-cool core" A <- rbind(CC, nCC) # Make the plot ggplot(data=A, aes(x=r.r500, y=sckT, ymin=sckT.lo, ymax=sckT.up, fill=type, linetype=type)) + geom_line() + geom_ribbon(alpha=0.5) + scale_x_log10() + scale_y_log10() + xlab(as.expression(expression( paste("Radius (", R[500], ")") ))) + ylab("Scaled Temperature") 1234567891011121314151617181920 library(ggplot2)# Get the data from the web !CC <- read.table("http://www.sr.bham.ac.uk/~ajrs/papers/sanderson06/mean_Tprofile-CC.txt" , header=TRUE)nCC <- read.table("http://www.sr.bham.ac.uk/~ajrs/papers/sanderson06/mean_Tprofile-nCC.txt" , header=TRUE)CC$type <- "Cool core"nCC$type <- "Non-cool core"A <- rbind(CC, nCC)# Make the plotggplot(data=A, aes(x=r.r500, y=sckT, ymin=sckT.lo, ymax=sckT.up, fill=type, linetype=type)) + geom_line() + geom_ribbon(alpha=0.5) + scale_x_log10() + scale_y_log10() + xlab(as.expression(expression( paste("Radius (", R[500], ")") ))) + ylab("Scaled Temperature") Not what you are looking for ? Make a ne
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Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Render envelope instead of error bars? up vote 0 http://www.r-graph-gallery.com/104-plot-lines-with-error-envelopes-ggplot2/ down vote favorite The R snippet below plots my data perfectly with a logarithmic fit and error bars for the standard deviation from my data source. I would like to change the error bars to use an envelope like I get with geom_smooth. However, I can't get geom_smooth to accept ymin/ymax from my data. It always auto-calculates the envelope using other algorithms, which is not what I want. http://stackoverflow.com/questions/22229587/render-envelope-instead-of-error-bars I've tried a dozen options with geom_smooth and stat_smooth and I can't figure out how to render an envelope with specific min/max values from my data set. library(ggplot2) temperature <- c(19.89, 19.46, 19.35, 19.16, 19.13, 19.14, 19.02, 19.07, 19.03, 19.03, 19.09, 19.00, 18.98, 19.02, 18.96, 18.81, 18.68, 18.60, 18.89, 18.84) temperature_stddev <- c(0.045, 0.595, 1.035, 1.370, 1.610, 1.775, 1.915, 2.030, 2.125, 2.200, 2.255, 2.330, 2.360, 2.445, 2.455, 2.520, 2.915, 2.810, 2.750, 2.680) data <- data.frame(time=(1:length(temperature)),temperature=temperature,temperature_stddev=temperature_stddev) ggplot(data, aes(x=time, y=temperature), fill="green") + geom_errorbar(aes(ymin=temperature-temperature_stddev, ymax=temperature+temperature_stddev), colour="black", width=.1) + geom_point() + geom_line() + stat_smooth(method="lm",formula=y~log(x),fill="red") r ggplot2 scatter-plot share|improve this question asked Mar 6 '14 at 15:59 user2684301 1,3021024 You're looking for geom_ribbon. –joran Mar 6 '14 at 16:06 wow, that worked. I spent hours looking through galleries and docs and samples. thanks! –user2684301 Mar 6 '14 at 16:16 add a comment| active oldest votes Know someone who can answer? Share a link to this question via email, Google+, Twitter, or Facebook. Your Answer draft saved draft discarded Sign up or log in Sign up using Google Sign up using Facebook Sign up using Email and Password Post as a guest Name Email Post as a guest Name Email discard By posti
by over 573 bloggers. There are many ways to follow us - By e-mail: https://www.r-bloggers.com/plotting-95-confidence-bands-in-r/ On Facebook: If you are an R blogger yourself you are invited to add your own R content feed to this site (Non-English R bloggers should add themselves- here) Jobs for R-usersStatistical Analyst @ Rostock, Mecklenburg-Vorpommern, GermanyData EngineerData Scientist – Post-Graduate Programme @ Nottingham, EnglandDirector, Real World Informatics & Analytics Data Science @ Northbrook, Illinois, U.S.Junior statistician/demographer confidence interval for UNICEF Popular Searches web scraping heatmap twitter maps time series animation boxplot shiny hadoop ggplot2 how to import image file to R trading finance latex eclipse rstudio excel SQL ggplot quantmod knitr googlevis PCA market research rattle regression map tutorial coplot rcmdr Recent Posts Election 2016: Tracking Emotions with R and Python Data science for executives and ggplot2 confidence interval managers The Worlds Economic Data, Shiny Apps and all you want to know about Propensity Score Matching! August Package Picks Slack all the things! Warsaw R-Ladies Notes from the Kölner R meeting, 14 October 2016 anytime 0.0.4: New features and fixes 2016-13 ‘DOM’ Version 0.3 Building a package automatically The new R Graph Gallery Network Analysis Part 3 Exercises Annotated Facets with ggplot2 Paper published: mlr - Machine Learning in R a grim knight [cont’d] Other sites SAS blogs Jobs for R-users Plotting 95% Confidence Bands in R July 26, 2012By Daniel Hocking (This article was first published on Quantitative Ecology, and kindly contributed to R-bloggers) I am comparing estimates from subject-specific GLMMs and population-average GEE models as part of a publication I am working on. As part of this, I want to visualize predictions of each type of model including 95% confidence bands. First I had to make a new data set for prediction. I could have compared fitted values with confidence intervals but I am specifically interested i