Mean And Standard Error Plot
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error bars Two within-subjects variables Note about normed means Helper functions Problem You want to plot means and plot mean and standard deviation in r error bars for a dataset. Solution To make graphs with
Plot Error Bars In R
ggplot2, the data must be in a data frame, and in “long” (as opposed to ggplot2 error bars 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 barplot with error bars r dose is a numeric column here; in some situations it may be useful 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
R Calculate Standard Error
7.3 VC 0.5 #> 4 5.8 VC 0.5 #> 5 6.4 VC 0.5 #> 6 10.0 VC 0.5 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 #
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Error.bar Function R
Topic Search Samples Search Usage Notes Search Installation Notes Search Problem Notes Focus Areas Sample 42515: Plot means with standard error bars from calculated data with PROC GPLOT This sample illustrates how to http://cookbook-r.com/Graphs/Plotting_means_and_error_bars_(ggplot2)/ plot means with standard error bars from calculated data with the GPLOT procedure. You can use a procedure such as PROC MEANS to calculate the means and standard errors. You can then use these values to calculate the upper and lower error bar limits in a DATA step. Use the HILOCTJ interpolation with PROC GPLOT to generate the graph. These sample files and code examples http://support.sas.com/kb/42515 are provided by SAS Institute Inc. "as is" without warranty of any kind, either express or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Recipients acknowledge and agree that SAS Institute shall not be liable for any damages whatsoever arising out of their use of this material. In addition, SAS Institute will provide no support for the materials contained herein. This sample illustrates how to plot means with standard error bars from calculated data with the GPLOT procedure. The graphics output on the Results tab was produced using SAS 9.2. Submitting the sample code with releases of SAS prior to SAS 9.2 might produce different results. /* Set the graphics environment */ goptions reset=all cback=white border htext=10pt htitle=12pt; /* Create sample data */ data test(drop=i); do i=1 to 10; do xvar=1 to 5; yvar=ranuni(0)*100; output; end; end; run; proc sort data=test; by xvar; run; /* Calculate the mean and standard error for each X */ proc means data=test noprint; by xvar; var yvar; output out=meansout mean=mean stderr=stderr; run; /* Reshape the data to contain three Y values for */ /* each X for u
varies between different groups of the data. The grouping is determined by the analyst. In most cases, the data provide a specific grouping variable. For example, the groups may be the levels of a factor variable. In the sample plot below, the http://www.itl.nist.gov/div898/handbook/eda/section3/sdplot.htm months of the year provide the grouping. Standard deviation plots can be used with ungrouped data to determine if the standard deviation is changing over time. In this case, the data are broken into an arbitrary number of equal-sized groups. For example, a data series with 400 points can be divided into 10 groups of 40 points each. A standard deviation plot can then be generated with these groups to see if the standard error bars deviation is increasing or decreasing over time. Although the standard deviation is the most commonly used measure of scale, the same concept applies to other measures of scale. For example, instead of plotting the standard deviation of each group, the median absolute deviation or the average absolute deviation might be plotted instead. This might be done if there were significant outliers in the data and a more robust measure of scale than the standard deviation mean and standard was desired. Standard deviation plots are typically used in conjunction with mean plots. The mean plot would be used to check for shifts in location while the standard deviation plot would be used to check for shifts in scale. Sample Plot This sample standard deviation plot shows there is a shift in variation; greatest variation is during the summer months. Definition: Group Standard Deviations Versus Group ID Standard deviation plots are formed by: Vertical axis: Group standard deviations Horizontal axis: Group identifier A reference line is plotted at the overall standard deviation. Questions The standard deviation plot can be used to answer the following questions. Are there any shifts in variation? What is the magnitude of the shifts in variation? Is there a distinct pattern in the shifts in variation? Importance: Checking Assumptions A common assumption in 1-factor analyses is that of equal variances. That is, the variance is the same for different levels of the factor variable. The standard deviation plot provides a graphical check for that assumption. A common assumption for univariate data is that the variance is constant. By grouping the data into equi-sized intervals, the standard deviation plot can provide a graphical test of this assumption. Related Techniques Mean Plot DOE Standard Deviation Plot Software Most general purpose statistical software programs do not support a standard deviati