search for: maxn

...n and Analysis of Industrial Experiments). The function is neither elegant nor fast, but anyone is free to make use of it ... there being no guarantees or support with it, of course. cheers Bob Code follows ... function(delta = 1, alpha = 0.05, beta = 0.05, minn = 1, maxn = 20, bot = -10, top = 10) { # Create tables and graphs for Barnard's SPRT ("sequential t-test") # (a) uses expressions given in Wetherill & Glazebrook 'Sequential Methods in Statistics', p 60 # (b) succesfully reproduces table L in OL Davies De...

...t;ok. this is the codes that I used for small data and without the parameter loop, but would have CPU problem if I use the real data. Please run it and let me know if it works for you. >>>> >>>>search<-function(c11,c12,c1,c2){ >>>>d<-do.call(rbind,lapply(2:(maxN-6),function(m1)? >>>>do.call(rbind,lapply(2:(maxN-m1-4),function(n1) >>>>do.call(rbind,lapply(0:m1,function(x1) >>>>do.call(rbind,lapply(0:n1,function(y1) >>>>expand.grid(m1,n1,x1,y1)))))))))? >>>>names(d)<-c("m1","n1&quo...

Thank you very much for your reply. Your code work well with this example. I modified a little to fit my real data, I got an error massage. Error in split.default(x = seq_len(nrow(x)), f = f, drop = drop, ...) : Group length is 0 but data length > 0 On Thu, Jan 31, 2013 at 12:21 PM, arun kirshna [via R] < ml-node+s789695n4657196h87@n4.nabble.com> wrote: > Hi, > Try this: >

...+ (b0 + b1*x0 + b2*I(x0^2))*(nrate > x0), data=data.1, start=list(b0=125, b1=0.5, b2=-0.001, x0=135), trace=T) qp.nls.fit summary(qp.nls.fit) c.qp.fit <- coefficients(qp.nls.fit) attach(as.list(c.qp.fit)) yld.x0 <- b0 + b1*x0 + b2*x0^2 maxn <- -b1/(2*b2) eonr <- (ratio - b1)/(2*b2) eonr2 <- round(eonr, digits = 0) yldmaxn <- b0 + b1*maxn + b2*maxn^2 yldeonr <- b0 + b1*eonr + b2*eonr^2 yldeonr2 <- round(yldeonr, digits = 0) plot(yield ~ nrate, pch = 16, main = "Quadratic plus Plateau Model",...

Hi R-Group, I am stuck with the following problem: I am constructing a portfolio of 2 variables x and y x <- rnorm(100, mean=100, sd=4) y <- rnorm(100, mean=120, sd=10) which I am combining as follows to a portfolio for sampling purposes: portfolio <- c(rep(x, 8), rep(y, 2)) In this case I have assigned the weights of 8 and 2 to calculate the bootstrapped mean: mean.boot <-

...ars+concentration+cycle+polarity+propmid+terrgainer+ demgainer+ fedgainer+ popdengainer+ urbpopgainer+ tradeopgainer+ gdppcgainer+ terrloser+ demloser+ fedloser+ popdenloser+ urbpoploser+ tradeoploser+ gdppcloser, n.impute=100, defaultLinear=TRUE, data=d) par(mfrow=c(2,3)) plot(f, diagnostics=TRUE, maxn=2) fmi <- fit.mult.impute(y ~ podb2+propdemocracy+avetrade1984dollars+concentration+cycle+polarity+propmid+terrgainer+ demgainer+ fedgainer+ popdengainer+ urbpopgainer+ tradeopgainer+ gdppcgainer+ terrloser+ demloser+ fedloser+ popdenloser+ urbpoploser+ tradeoploser+ gdppcloser, lm, f,...

...(perhaps being stubborn) I was a little surprised that you did not discuss the ".amb" format as this is the official file format for Ambisonic B-Format.(3) This has also been used in the wild for many years, particularly at Ambisonia.com.(4) It uses Furse-Malham component ordering and MaxN normalization. (It also only works up to third-order.) Note that Vorbis is lossy. Ambisonics is picky about low-frequency phase and, as far as I know, nobody has checked the extent to which this is preserved in Vorbis. This may or may not be a problem. (It is obviously not a problem with lossl...

Hi Opus list. I subscribed because your discussion on the IETF draft ("Ambisonics in an Ogg Opus Container") was mentioned on the sursound list. I tried Opus for ambisonics more than a year ago. It does works with uncoupled channels (I had to patch the encoder). I don't know what else could be done to optimize support for ambisonics, as I'm not a codec expert. So I think that

...'', pch=16, lines=FALSE, lty='solid') { x1 <- stripmiss(xx) if (grouping) x1 <- grouping.v(x1,ngroups=ngroups) xv <- as.numeric(names(table(x1))) # actual values minxv <-min(xv) maxxv <-max(xv) xn <- as.numeric(table(x1)) # number of occurences nx <- length(xv) maxn <- max(xn) plot(x=xplace[1]+(xv-minxv)*(xplace[2]-xplace[1])/ (maxxv-minxv),y=xv,xlab='',ylab='', axes=FALSE,col='white') box() axis(1,at=xplace[3],labels=xlab) axis(2) for (value.nr in 1:nx) { n.act <- xn[value.nr] if (n.act==1) xpositions.act <- xplace[3] if (n....

...format is widely used and could easily be supported as well. > https://en.wikipedia.org/wiki/Ambisonic_data_exchange_formats Although FuMa conventions are commonly used, the ambix conventions of ACN channel ordering and SN3D normalization are increasingly becoming the norm. FuMa generally uses maxN normalization, which becomes complicated for fourth order and above. I decided to require ACN and SN3D to limit fracturing and potential confusion. > Down-mixing to stereo is a sort of ambisonic decoding; it is simple and > could be included in the Opus decoder. But decoding to binaural or 5...

...d could easily be supported as > > well. https://en.wikipedia.org/wiki/Ambisonic_data_exchange_formats > > Although FuMa conventions are commonly used, the ambix conventions of > ACN channel ordering and SN3D normalization are increasingly becoming > the norm. FuMa generally uses maxN normalization, which becomes > complicated for fourth order and above. I decided to require ACN and > SN3D to limit fracturing and potential confusion. I understand your point, and content creators will simply convert their FuMa sources to the Ambix format. That's not a major problem, an...