Error Bars In R Scatter Plot
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deparse(substitute(x)), ylab= deparse(substitute(y)), ...) scatter plot with error bars matlab Arguments x vector of x values. y vector of scatter plot with error bars python y values. yplus vector of y values: the tops of the error bars. yminus vector
Scatter Plot With Error Bars Excel
of y values: the bottoms of the error bars. cap the width of the little lines at the tops and bottoms of the error bars in units of the width
Error Bar In R
of the plot. Default is 0.015. ylim (numeric of length 2): the y-axis extents with a sensible default. xlab, ylab axis labels for the plot, as in plot.default. ... Graphical parameters (see par) may also be supplied as arguments to this function. Author(s) Originally Charles Geyer, U.Chicago, early 1991; then Martin Mächler. See Also errbar in package Hmisc is similar. Examples y <- rnorm(10); d <- 1 + .1*rnorm(10) errbar(1:10, y, y + d, y - d, main="Error Bars example") [Package sfsmisc version 1.0-20 Index]
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 error bars in r barplot To make graphs with ggplot2, the data must be in a data
Error.bar Function R
frame, and in “long” (as opposed to wide) format. If your data needs to be restructured, see errbar r 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 http://artax.karlin.mff.cuni.cz/r-help/library/sfsmisc/html/errbar.html 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 http://cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/ class="w"> 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
|| 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 http://svitsrv25.epfl.ch/R-doc/library/Hmisc/html/errbar.html variable (for horizontal error bars, x representing the group labels) y vector of http://davetang.org/muse/2014/06/25/plotting-error-bars-with-r/ y-axis values. yplus vector of y-axis values: the tops of the error bars. yminus vector of y-axis values: the bottoms of the error bars. cap the width of the little lines at the tops and bottoms of the error bars in units of the width of the plot. Defaults to 0.015. error bar 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 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. scatter plot with 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 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 H
25, 2014 by Davo Error bars may show confidence intervals, standard errors, and standard deviations. Each feature conveys a different message and this paper on error bars in experimental biology explains it very nicely. For this post I will demonstrate how to plot error bars that show the standard error (SE) or standard error of the mean (SEM). I found two nice resources that demonstrate the plotting of error bars with R and in this post I illustrate them with simple examples. The first method is from the website of James Holland Jones, where he wrote an R function that plots arrows to a bar plot. #generate some random numbers set.seed(31) a <- runif(10, 0, 10) b <- runif(10, 0, 10) c <- runif(10, 0, 10) #store them as a data.frame df <- data.frame(a=a, b=b, c=c) #function for error bars error.bar <- function(x, y, upper, lower=upper, length=0.1,...){ if(length(x) != length(y) | length(y) !=length(lower) | length(lower) != length(upper)) stop("vectors must be same length") arrows(x,y+upper, x, y-lower, angle=90, code=3, length=length, ...) } #function for standard error of the mean sem <- function(x){ sd(x)/sqrt(length(x)) } #calculate means my_mean <- apply(df, 2, mean) #calculate sem my_sem <- apply(df, 2, sem) #barplot barx <- barplot(my_mean, names.arg=names(df), ylim=c(0,ceiling(max(df))), xlab='Class', ylab='Unit of measure') error.bar(barx, my_mean, my_sem) The second resource I found on plotting error bars with R was from the Cookbook for R, which showed many examples using the R package ggplot2. Here is a simple example I adapted from their cookbook, using the same set of random numbers I generated above: #install if necessary install.packages('ggplot2') #load library library(ggplot2) set.seed(31) a <- runif(10, 0, 10) b <- runif(10, 0, 10) c <- runif(10, 0, 10) df <- data.frame(a=a, b=b, c=c) sem <- function(x){ sd(x)/sqrt(length(x)) } my_mean <- apply(df, 2, mean) my_sem <- apply(df, 2, sem) #new data frame for storing the mean and sem mean_sem <- data.frame(mean=my_mean, sem=my_sem, group=names(df)) #larger font theme_set(theme_gray(base_size = 20)) #plot using ggplot ggplot(mean_sem, aes(x=group, y=mean)) + geom_bar(stat='identity') + geom_errorbar(aes(ymin=mean-sem, ymax=mean+sem), width=.2) + xlab('Class') + ylab('Unit of measure') I had been using the first approach for plotting error bars with R but I think the ggplot2 plot looks much better (with less effort). Lastly, it has been over a month since my last post, though I have been updating old posts. I've been spending time writing my thesis and papers but I've also been preparing for a bigger-than-u