I am using that site: http://167.71.255.125/#/dashboard/football/game/5359832 and i can't figure how it calculates the 'deviation' column whenever handicap home and away columns change. It probably uses standard deviation. Any help will be appreciated
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I am currently attempting to complete an RMarkdown document that requires the use of Tukey's Method, where sample means are compared by underlining them. Is there any efficient way to underline the sample means to get something similar to
but will allow for multiple underlines across different sample means. The issue I am running into is when something has been underlined already, but needs to be underlined a second our third time while still neatly showing that it corresponds to another sample mean that has also been underlined and joined to one of the other sample means. I have tried nesting \underline, but am unable to get a neat result that clearly represents the variance in the sample means. The goal I am trying to achieve is to attach the 1st two samples, then separately attach the 2nd two samples with their own line, then the 3rd and 4th with their own line, and then the last two samples with their own line as well. Any help is greatly appreciated.
I'm using DEOptim package in R and it works as intended for my purpose but I'd like for one variable to always be lower than the other (I'm optimising a moving average strategy). I can't seem to figure out how to do this, does anyone have any idea?
Thanks!
I have posted this question in the R tag but I am open to solutions in other languages.
Lets say you have some waveforms. The first is just a bar. It is completely horizontal so it has no deviation. The other waveforms look like these:
Now I am able to get these waveforms separated into a uniform box so that they are all the same pixel size and resolution. My first idea was to quantify the amount of whitespace within one of these uniform boxes that the waveform used up using the code found here:
Measuring whitespace in a jpeg
Now however I want to measure the deviation between waveforms. That is, how could I quantify how "jumpy" a waveform is? Looking at the picture above, the second waveform seems the most homogeneous, and the third waveform seems to display the most variation, but I am unsure about how to quantify this. Any suggestions would be greatly appreciated.
I would recommend starting by getting familiarized with the packages tuneR and seewave, you can import and extract a lot of parameters from these two packages. In particular you could use the function acustat from seewave, this is a worked example with data from the package
data(tico)
note <- cutw(tico, from=0.5, to=0.9, output="Wave")
a<-acoustat(note)
a will give you 10 acoustic parameters from the sound, you could also use other packages like soundecology, that also extract some other variables, in particular, the function acoustic_diversity measures sound complexity
I am trying to create a Control charts with rolling averages in R that closely matches something like this. I am relatively new to R, and my grasp of all the available pages is not great yet.
controlchart with moving average i'd like to recreate
Can someone help me figure this out? Maybe point me to the right packages I should install and study? Any help is appreciated.
I was looking for the answer to creating this type of chart myself.
One solution is the qicharts package. Instead of calling the changes rolling averages or stages (the term Minitab uses), they call the changes breaks.
Is there any way to analyze the data within a GEOSoft file, from NCBI, in R? I've tried converting it to an expression set, fitting it with lmFit(), then using eBayes(), but when I use topTable() to look at the results, the adjusted P.Values are always constant throughout the column.
Any help, or even pointing in the right direction would be greatly appreciated.
Thank you so much in advance