similar to: adjusted R^2 vs. ordinary R^2

How can ordinary polynomial coefficients be calculated from an orthogonal polynomial fit? I'm trying to do something like find a,b,c,d from lm(billions ~ a+b*decade+c*decade^2+d*decade^3) but that gives: "Error in eval(expr, envir, enclos) : Object "a" not found" > decade <- c(1950, 1960, 1970, 1980, 1990) > billions <- c(3.5, 5, 7.5, 13, 40) > #

We have 4 class C networks, 199.129.206.* 199.129.207.* 199.129.208.* 199.129.209.* I see IP addresses from all 4 classes thru tcpdump. Some of these computers broadcast as C-class computers; eg, to 199.129.206.255 Some broadcast to 255.255.255.255 Of course, several C class networks would induce me to use a B-class broadcast, 199.129.255.255 This works for the computers I

Dear R experts: I need to get this plot, but also with 95% confidence interval bands: hour <- c(1, 2, 3, 4, 5, 6) millivolts <- c(3.5, 5, 7.5, 13, 40, 58) plot(hour, millivolts, xlim=c(1,10), ylim=c(0,1000)) pm <- lm(millivolts ~ poly(hour, 3)) curve(predict(pm, data.frame(hour=x)), add=TRUE) How can the 95% confidence interval band curves be plotted too? Sincerely,

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

Hello, I recently started using the metafor package (version 1.6-0) in R (2.15.1, 64-bit Windows 7) and noticed that I was getting slightly different values when I manually calculated the standardized mean difference versus what escalc was giving me. Here''s a very simple example: escalc(measure="SMD", m1i=5,m2i=10,n1i=5,n2i=5,sd1i=1,sd2i=2,vtype="LS") The result

Per http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2010 -- and http://rwiki.sciviews.org/doku.php?id=developers:projects:gsoc2010:syrfr -- I am applying to mentor the "Symbolic Regression for R" (syrfr) package for the Google Summer of Code 2010. I propose the following test which an applicant would have to pass in order to qualify for the topic: 1. Describe each of the

Hi All, How do i calculate KMO for a dataset? *Dataset:---------------------* m1 m2 m3 m4 m5 m6 m7 m8 1 2 20 20 2 1 4 14 12 2 9 16 3 5 2 5 5 15 3 18 18 18 13 17 9 2 4 4 7 7 2 12 2 11 11 11 5 7 8 5 19 5 2 20 18 6 7 4 7 4 7 9 3 3 7 5 5 5 12 5 13 13 12 8 6 6 4 3 5 17 17 16 9 12 12 4 2 4 4 14 14 10 5 14

I'm trying to plot an extrapolated logistic sigmoid curve using glm(..., family=binomial) as follows, but neither the fitted() points or the predict()ed curve are plotting correctly: > year <- c(2003+(6/12), 2004+(2/12), 2004+(10/12), 2005+(4/12)) > percent <- c(0.31, 0.43, 0.47, 0.50) > plot(year, percent, xlim=c(2003, 2007), ylim=c(0, 1)) > lm <- lm(percent ~ year)

Dear colleagues, I've been searching for information on the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy (MSA). This statistic is generated in SPSS and is often used to determine if a dataset is "appropriate" for factor analysis -- it's true utility seems quite low, but it seems to come up in stats classes a lot. It did in mine, and a glance through the R-help

Hi All, I have conducted a meta analysis using the metabin function. I want to plot 5 subgroups on the same forest plot. I have managed to do this using the byvar argument but when i plot the forest plot in R graphics I am unable to view the very top and very bottom of the image. It is as though the plot is too long. Is there a way in which I can ask R to show the entire plot within the

Well, it is no problem to use R or S-Plus for a principle component analysis (PCA). The function "princomp" works very well with my polychoric correlation matrix, that I use for this approach on ordinal variables. But I want to inspect the Kaiser-Meyer-Olkin-crieterion with the "measure of sampling adequancy (MSA)" and the anti-image-correlation matrix. I can''t find

> Date: Tue, 30 Mar 2010 19:52:42 +0400 > From: Max Lapshin <max.lapshin at gmail.com> > Subject: Re: [Speex-dev] Speex in flash player: how to work with? > > _microphone = Microphone.getMicrophone(); > _microphone.setSilenceLevel(0); > _microphone.codec = SoundCodec.SPEEX; > _microphone.encodeQuality = 10; > _microphone.rate = 16; > _microphone.framesPerPacket

I am having trouble compiling ffmpeg to support speex, which didn't work with the ubuntu libspeex-dev package, but looks like it might with the Speex version 1.2rc1 tarball from http://speex.org/downloads/ How do I tell ffmpeg's configure and/or make to use the 1.2rc1 version of libspeex in /usr/local/include instead of the older debian/ubuntu libspeex-dev package in /usr/include/speex?

I'm trying to fit a Gompertz sigmoid as follows: x <- c(15, 16, 17, 18, 19) # arbitrary example data here; y <- c(0.1, 1.8, 2.2, 2.6, 2.9) # actual data is similar gm <- nls(y ~ a+b*exp(-exp(-c*(x-d))), start=c(a=?, b=?, c=?, d=?)) I have been unable to properly set the starting value '?'s. All of my guesses yield either a "singular gradient" error if they

I've read through many postings about principle component analysis in the R-help archives, but haven't been able to piece together the information I need. I'd like to recreate an SPSS-like experience of factor analysis using R. Here's what SPSS produces: 1. Scatterplots of all possible variable pairs, with regression lines. xyplot(my.dataframe) is perfect but for the lack of

Dear R-Community, I'm currently trying to find a way to conduct a meta-analysis in R. I would like to analyze data from mostly-cross-sectional survey-studies. The effect sizes would be correlations. The R packages "meta" and "rmeta" are, as far as I can see, set up for analysis with effect sizes for differences (i.e. comparison of the means/odds-ratios of experimental

What happened to the category k-means package for performing k-means clustering on categorical variables? I expected it to become more prominent after the Netflix Challenge recommendation engine contest concluded, but instead it seems to have dropped from view. Where was it and where is it now, and why did it become less visible?

I am having trouble converting a .flv file uploaded from Adobe Flex / Flash Player 10 to a Red5 server using the speex coder: http://livedocs.adobe.com/flex/3/langref/flash/media/Microphone.html http://jira.red5.org/confluence/display/codecs/Speex+Codec Questions: 1. How do I extract the audio track out of such a .flv file? 2. How do I convert it from Speex to .wav PCM? Thanks.

Dear Jean-Marc: Would you please send Thierry an ontology mapping table specification for speex according to the format he links to at the end of his message below? Thank you. On Mon, Oct 11, 2010 at 9:54 AM, Thierry MICHEL <tmichel at w3.org> wrote: > Dear James, > > In your latest email to the MAWG response to your comment, you seem to > disagree with the MAWG proposal. >

Greetings, I am running a buch of wilcox tests and need to be able to rapidly export the results into a csv file. I have attached example code as well as my attempts to get what I need. I have tried unlist,cbind,rbind etc but I am obvously missing something simple. FYI I am actually running about 50 WRS tests per dataset, this is just an example. Thanks 10^6 Mike AKCCR <-