Flexible box plot creation (allows unequal sample sizes and displays sample size and outlier index)
I like the fact that the function shows (and returns) the indizes of the outliers.
Suggestions for improvements:
- adjustable whisker size (now hardcoded to the boxplot standard of 1.5 - at least this fact should be documented)
- better display of outlier indizes - in the current version, numbers overlap and or thus often unreadable
03 Jun 2013
Fitting with single exponential curve in the form of:
yFit = yInf + (y0-yInf) * exp(-k*(x-x0))
Author: Jing Chen
Nicely does its job. Would be perfect for my use if it allowed an additional weight vector.
wvmread.m works just as it is described in the example included in the submission (wvf_logicsignals_example.m). I thought it would be self-explanatory.
What is your exact problem?
It is always very helpful to know which waveform you want to read in and how multiple waveforms are organized in the used data format, otherwise you will be lost with any import tool for wvf files. To use wvfread.m, you need a basic understanding of how the data is stored in the wvf files, i.e. you should understand what YOKOGAWA refers to as "groups" "traces" and "blocks".
For this purpose, read the wvf data format description in your oscilloscopes' manual or search it on the internet if your manual doesn't comprise this information. I will not include documents with the data format description in this submission due to YOKOGAWA's copyright.
It might help you to look into the m-files of the submission.
wvfread.m is just a wrapper function which calls wvfreadb.m with rearranged parameters. I have done this cumbersome construction in order to keep wvfread.m compatible with its older versions.
The actual stuff is done in wvfreadb.m, which enables direct access to the "blocks" in addition to the "traces". wvfread.m instead always reads the first block only, so if you want to access another block, you must use wvfreadb.m instead.
Yes, a box-plot shows the median and quartiles, etc, so can be asymmetric and what not. If that's what you want, then use the MATLAB boxplot function. This version is, as the name says, /not/, a box plot. It uses the mean and statistics relating to the mean. These produce symmetrical error bars. There is rationale to this and, TBH, this function is aimed more at replacing bar charts than at replacing box plots.
The rationale is that t-tests and ANOVA are often performed on data which are typically plotted as bar charts and sometimes box-plots. However, tests are based upon the mean, yet box-plots show the median. Bar charts are often found supplemented with errors bars displaying 1 standard error of the mean (1 SEM), which does not reflect the p=0.05 significance criterion often used in biology and the social sciences. The 95% confidence interval used here provides a visual indicator of significance. In most bar charts the raw data are not overlaid, which greatly reduces the utility of the plot as it hides the underlying data. Yet with carefully chosen plot options, which is facilitated by this function, it's often possible to plot all the raw data even for large numbers of groups. I believe that overlaid raw data are usually more informative than quartiles and whiskers of a box plot. Of course that's a personal preference.