Thank you both for the replies. I didn't realize those functions existed in R, and they could make life a lot easier. They even work on a relatively complex function like glm(). Now that I feel comfortable with making R packages, I'm learning how to call R from C. Putting all of this into a nice R package should be doable in a few days, assuming I get things working.
With regards to implementation on Calc's end. The instructions provided were good and after modifying a few .hxx files I got my own functions working. I've written a rough draft for the wiki, and am now compiling my wiki tutorial to make sure the code is correct. I'll put it up tonight. Finally, I don't mind making a Calc add-in instead. I did notice the "scaddins" folder and actually toyed with the idea of using that instead. I just have a few questions about this: 1. How do we expect the user to install this stuff, in general? If I make a link between C and R, we can't ship it with OO due to licensing issues, so should we build some sort of built-in installer, or just have a link somewhere on the OO website? 2. If I do make an add-in, is there anywhere I can put it in the source code, or will it be an external package, as discussed in #1? If it is an external package, then can I just code a link to R directly in the add-in? Thank you, Wojciech On 5/1/07, Leonard Mada <[EMAIL PROTECTED]> wrote:
I have some additional comments to my previous post. If we perform some calculation inside R, e.g. > x <- fisher.test(matrix(c(60,40,70,30),2)) We assigned the results to the object 'x'. Now we can access various elements of x as follows: 1. How many elemts are stored inside x? > length(x) [1] 7 2. What are the names of those variables? > y <- names(x) > y [1] "p.value" "conf.int" "estimate" "null.value" "alternative" [6] "method" "data.name" This command has created an array (= y) with the names of the variables! We can get an individual name with y[[1]] (y[1] works as well). 3. How do we access the stored output by variable? - either iterate through x[[1]] -> to x[[length(x)]] > x[[i]] - OR iterate i = 1 to length(x), with > x[[y[[i]]]] - for subarrays, like x[[2]], we can apply the same thing one more time: > length(x[[2]]) [1] 2 (x[[2]] is the 95% confidence interval for the OddsRatio, so it has an upper and a lower limit) > x[[2]][1] // for first value = lower limit [1] 0.8317144 > x[[2]][2] // for second value = upper limit [1] 2.918995 CONCLUSIONS ============ A.) For statistical functions/techniques that return primarily a p-value, we can easily detect this, as one of the names will be "p.value" (it is usually the first element in the array, aka x[[1]]). B.) We can import the data from R back into Calc, and create a list with the names of the variables from the output (aka names(x) ) AND let the more advanced user choose which variable from this list to enter into a Calc cell. Well, hope this helps to overcome some of the obstacles. Sincerely, Leonard Leonard Mada wrote: > Hi Wojciech, > > I just read (http://www.utsc.utoronto.ca/~04grycwo/overview.pdf) and > began thinking on your ideas. > > Basically you are right, we need both methods: for advanced users and > for beginners. That's my point, too. > > Regarding your concerns with importing the R-output back into Calc, I > do admit that there are some problems and issues to discuss. I will > try to think of the best solution. > > Until then, I can give you some useful tips: > > 1. lets say we perform some calculations in R and store the output in > a new variable, e.g. > x <- fisher.test(matrix(c(40,60,30,70),2)) > then we can get the output by typing at the prompt:> x > OR > we can get the length of the return object: > :> length(x) > <output> 7 > and get every element individually from this output: (iterate > through x[[1]] -> x[[7]] ) > :> x[[1]] > <output>> [1] 0.1819324 (this is the p-value) > > 2. I imagine statistical functions as belonging to 2 large groups > (this is NOT necessarily accurate BUT useful here): > a.) those that report a p-value as the main result > - this is usually the first value (aka x[[1]]) > b.) those that perform more complex actions, like a multivariate > model, or a resampling, or graphic > > These latter functions will be more difficult to deal with. But lets > stick now to the first group. > > Hope this is helpful. I will try to work up a solution for the rest. > > Sincerely, > > Leonard > > --------------------------------------------------------------------- > To unsubscribe, e-mail: [EMAIL PROTECTED] > For additional commands, e-mail: [EMAIL PROTECTED] > --------------------------------------------------------------------- To unsubscribe, e-mail: [EMAIL PROTECTED] For additional commands, e-mail: [EMAIL PROTECTED]
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