Plot Error Bars Cran
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Error Bar In R
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Error.bar Function R
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Scatter Plot With Error Bars In R
only takes a minute: Sign up Add error bars to show standard deviation on a plot in R up vote 23 down vote favorite 10 For each X-value I calculated the average Y-value and the standard deviation (sd) of each Y-value x = 1:5 y = c(1.1, 1.5, 2.9, 3.8, 5.2) sd = c(0.1, 0.3, 0.2, 0.2, 0.4) plot (x, errbar r y) How can I use the standard deviation to add error bars to each datapoint of my plot? r plot statistics standard-deviation share|improve this question edited Oct 16 '14 at 3:43 Craig Finch 11417 asked Feb 25 '13 at 8:59 John Garreth 4572413 also see plotrix::plotCI –Ben Bolker Feb 25 '13 at 15:13 add a comment| 5 Answers 5 active oldest votes up vote 16 down vote accepted A Problem with csgillespie solution appears, when You have an logarithmic X axis. The you will have a different length of the small bars on the right an the left side (the epsilon follows the x-values). You should better use the errbar function from the Hmisc package: d = data.frame( x = c(1:5) , y = c(1.1, 1.5, 2.9, 3.8, 5.2) , sd = c(0.2, 0.3, 0.2, 0.0, 0.4) ) ##install.packages("Hmisc", dependencies=T) library("Hmisc") # add error bars (without adjusting yrange) plot(d$x, d$y, type="n") with ( data = d , expr = errbar(x, y, y+sd, y-sd, add=T, pch=1, cap=.1) ) # new plot (adjusts Yrange automatically) with ( data
|| is.character(x)) "" else as.character(substitute(y)), add=FALSE, lty=1, type='p', ylim=NULL, lwd=1, pch=16, Type=rep(1, length(y)), ...) Arguments x vector of numeric x-axis values (for vertical error bars) or a factor or character variable (for horizontal error bars, x representing the group labels) y vector of r ggplot error bars y-axis values. yplus vector of y-axis values: the tops of the error bars. yminus vector of r plot standard deviation y-axis values: the bottoms of the error bars. cap the width of the little lines at the tops and bottoms of the error r arrows bars in units of the width of the plot. Defaults to 0.015. main a main title for the plot, see also title. sub a sub title for the plot. xlab optional x-axis labels if add=FALSE. ylab optional y-axis http://stackoverflow.com/questions/15063287/add-error-bars-to-show-standard-deviation-on-a-plot-in-r labels if add=FALSE. Defaults to blank for horizontal charts. add set to TRUE to add bars to an existing plot (available only for vertical error bars) lty type of line for error bars type type of point. Use type="b" to connect dots. ylim y-axis limits. Default is to use range of y, yminus, and yplus. For horizonal charts, ylim is really the x-axis range, excluding differences. lwd line width for line segments (not main line) pch character http://svitsrv25.epfl.ch/R-doc/library/Hmisc/html/errbar.html to use as the point. Type used for horizontal bars only. Is an integer vector with values 1 if corresponding values represent simple estimates, 2 if they represent differences. ... other parameters passed to all graphics functions. Details errbar adds vertical error bars to an existing plot or makes a new plot with error bars. It can also make a horizontal error bar plot that shows error bars for group differences as well as bars for groups. For the latter type of plot, the lower x-axis scale corresponds to group estimates and the upper scale corresponds to differences. The spacings of the two scales are identical but the scale for differences has its origin shifted so that zero may be included. If at least one of the confidence intervals includes zero, a vertical dotted reference line at zero is drawn. Author(s) Charles Geyer, University of Chicago. Modified by Frank Harrell, Vanderbilt University, to handle missing data, to add the parameters add and lty, and to implement horizontal charts with differences. Examples set.seed(1) x <- 1:10 y <- x + rnorm(10) delta <- runif(10) errbar( x, y, y + delta, y - delta ) # Show bootstrap nonparametric CLs for 3 group means and for # pairwise differences on same graph group <- sample(c('a','b','d'), 200, TRUE) y <- runif(200) + .25*(group=='b') + .5*(group=='d') cla <- smean.cl.boot(y[group=='a'],B=100,reps=TRUE) # usually B=1000 a <- attr(cla,'re
error bars Two within-subjects variables Note about normed means Helper functions Problem You want to plot means and error bars for a dataset. Solution To http://cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/ make graphs with ggplot2, the data must be in a data https://cran.r-project.org/doc/contrib/Lemon-kickstart/kr_erbar.html frame, and in “long” (as opposed to wide) format. If your data needs to be restructured, see this page for more information. Sample data The examples below will the ToothGrowth dataset. Note that dose is a numeric column here; in some situations it may be useful error bar to convert it to a factor. tg <- ToothGrowth head(tg) #> len supp dose #> 1 4.2 VC 0.5 #> 2 11.5 VC 0.5 #> 3 7.3 VC 0.5 #> 4 5.8 VC 0.5 #> 5 6.4 VC 0.5 #> 6 10.0 VC 0.5 plot error bars library(ggplot2) First, it is necessary to summarize the data. This can be done in a number of ways, as described on this page. In this case, we’ll use the summarySE() function defined on that page, and also at the bottom of this page. (The code for the summarySE function must be entered before it is called here). # summarySE provides the standard deviation, standard error of the mean, and a (default 95%) confidence interval tgc <- summarySE(tg, measurevar="len", groupvars=c("supp","dose")) tgc #> supp dose N len sd se ci #> 1 OJ 0.5 10 13.23 4.459709 1.4102837 3.190283 #> 2 OJ 1.0 10 22.70 3.910953 1.2367520 2.797727 #> 3 OJ 2.0 10 26.06 2.655058 0.8396031 1.899314 #> 4 VC 0.5 10 7.98 2.746634 0.8685620 1.964824 #> 5 VC 1.0 10 16.77 2.515309 0.7954104 1.799343 #> 6 VC 2.0 10
way to illustrate this quantity is with error bars. R has a function named arrows that can simplify this task. arrows requires at least four arguments, the x/y start and end points of each arrow (if each argument is a vector, an arrow will be drawn for each value in the vector). Note that the points will be specified in user units, that is, the units that are actually illustrated on the graph. Start with the start points. These are usually separated from the points marking the values by a small amount. I use the current height of a lower case "m". In order to get bars going up and down, there will have to be two sets of starting points. Similarly, two sets of end points will be needed, calculated by adding and subtracting the value of the standard errors for each of the data points - see plot.dstat(). First, the function checks that its argument is there, and is an object of class "dstat". Now have a look at the arguments to arrows. In addition to the first four arguments specifying the start and end points of the arrows, the length argument specifies the length of the arms in (blush) inches and the angle argument specifies the angle of the arms from the stem. R has inherited a lot of things from S. Some of them aren't the greatest, like the default units of inches and points. Well, nobody's perfect. Notice that the function will do its best to work out missing arguments from the data. If error bars are requested, get.dstat.ylim() is called to work out the maximum range of the entire dstat object. Notice the offset= option in plot.dstat. This allows you to ask for additional points and error bars produced by add.pointline() to be moved side to side so that they don't overlap. Your idea of a great point/line plot may be somewhat different. By now you should have an idea of the tools that can be used to get that plot. For more information, see An Introduction to R: High-level plotting commands. Back to Table of Contents