search for: sxx

...* * xdata <- na.omit(data.frame(data[,c(1,2)])) #i1,C1* * print("printing xdata.......................................")* * print(xdata)* * Sxx <- cov(xdata,xdata,m=method)* * print("Sxx...................................................")* * print(Sxx )* * * * xzdata <- na.omit(data)* * xdata <- data.frame(xzdata[,c(1,2)])*...

...ating-the-ornstein-uhlenbeck-model/ this is the mean-reverting process http://r.789695.n4.nabble.com/file/n4637271/process.txt process.txt and this is the script that i am using....... ouFit.ML=function(spread) { n=length(spread) delta=n/n Sx=sum(spread[1:n-1]) Sy=sum(spread[2:n]) Sxx=(Sx)^2 Syy=(Sy)^2 Sxy=Sx*Sy mu = (Sy*Sxx - Sx*Sxy) / ( n*(Sxx - Sxy) - (Sx^2 - Sx*Sy) ) lambda = -log( (Sxy - mu*Sx - mu*Sy + n*mu^2) / (Sxx -2*mu*Sx + n*mu^2) )/delta a = exp(-lambda*delta) sigmah2 = (Syy - 2*a*Sxy + a^2*Sxx - 2*mu*(1-a)*(Sy - a*Sx) + n*mu^2*(1-a)^2)/n; sigma = sqrt(...

...x <- c(x) y <- c(y) z <- as.data.frame(z) if(dim(z)[2] == 0){ stop("There should be given data\n") } data <- data.frame(x,y,z) if(na.rm == T){ data = na.omit(data) } xdata <- na.omit(data.frame(data[,c(1,2)])) Sxx <- cov(xdata,xdata,m=method) xzdata <- na.omit(data) xdata <- data.frame(xzdata[,c(1,2)]) zdata <- data.frame(xzdata[,-c(1,2)]) Sxz <- cov(xdata,zdata,m=method) zdata <- na.omit(data.frame(data[,-c(1,2)])) Szz <- cov(zdata,zdata,m=method) # is Sz...

Hello, I'm doing some linear regression: >lm<-lm(osas~alp,data) >summary(lm) However, the Rsquared in the output of summary() is not the same as the "standard" Rsquared calculated by spreadsheets, and outlined in statistical guidebooks, being SSR/SSTO. The output says "multiple Rsquared", but it is no multiple regression... What's the difference? Thanks,

...ector(std.err) ) cat("\n") print(t(z)) cat( "\nR-Square:", object at r.square, "\n\n" ) } ) ols1 <- function( y, x ){ size <- length(x) # number of ovservations xbar <- mean(x) ybar <- mean(y) Sxx <- sum( (x-xbar)^2 ) b <- sum( (x-xbar)*(y-ybar) )/Sxx # coefficient a <- ybar - b*xbar # interception e <- y - a - b*x # residuals # SSE (error sum of squares) SSE <- sum( e^2 ) # SST (total sum of squares) SST <- sum( (y-ybar)^2 ) # SSR (regression sum of squar...

...lity-------------------------------") return(1) } if(dim(z)[2] == 0){ stop("There should be given data\n") } data <- data.frame(x,y,z) if(na.rm == T){ data = na.omit(data) } xdata <- na.omit(data.frame(data[,c(1,2)])) Sxx <- cov(xdata,xdata,m=method) xzdata <- na.omit(data) xdata <- data.frame(xzdata[,c(1,2)]) zdata <- data.frame(xzdata[,-c(1,2)]) Sxz <- cov(xdata,zdata,m=method) zdata <- na.omit(data.frame(data[,-c(1,2)])) Szz <- cov(zdata,zdata,m=method) # is Sz...

...========================================= --- libspeex/mdf.c (revision 10357) +++ libspeex/mdf.c (working copy) @@ -314,11 +314,10 @@ /*printf ("%f\n", leak_estimate);*/ } - float adapt_rate; + float adapt_rate = 0; if (!st->adapted) { float Sxx; - adapt_rate; Sxx = inner_prod(st->x+st->frame_size, st->x+st->frame_size, st->frame_size); /* We consider that the filter is adapted if the following is true*/

...UE) } xj <- .subset2(.subset(xx, j), 1L) return(if (length(dim(xj)) != 2L) xj[i] else xj[i, , drop = FALSE]) } if (any(is.na(cols))) stop("undefined columns selected") nxx <- structure(seq_along(xx), names = names(xx)) sxx <- match(nxx[j], seq_along(xx)) } else sxx <- seq_along(x) rows <- NULL if (is.character(i)) { rows <- attr(xx, "row.names") i <- pmatch(i, rows, duplicates.ok = TRUE) } for (j in seq_along(x)) { xj <- xx[[sxx[j]]] x[[j]] <- if (length(dim(...

Hello, I wanted to set my own time intervals for shutdown. {poweroff,return,paused.return}, so I had to change sources, where it has been set in a hard way. poweroff: Sxx\r return: Zxx\r paused.return: SxxRyyyy\r Solution A: Why not adding an info about these commands in a generic way and issuing shutdown.{poweroff.xx,return.xx,paused.return.xxyyyy} Solution B: Why not creating a special command, which takes UPS command as an argument? Really, for A (with 2.0.5...

...2.87) It seems that the the standard error of prediction of the linear regression, caclulated with predict.lm is incorrect. Consider the following example where the standard error is first calculated with predict.lm, then using delta method. and finally, using the formula rms*sqrt(1+1/n+(xp-x0)^2/Sxx). Marek Ancukiewicz > n <- 10 > x <- 1:n > y <- x > y[c(2,4,6)] <- y[c(2,4,6)] + 0.1 > y[c(3,5,7)] <- y[c(3,5,7)] - 0.1 > a <- lm(y~x) > rms <- sqrt(sum(a$residuals^2)/(n-2)) > s <- covmat(a)*rms^2 > xp <- 3 > xm <- mean(x) > summary...

...uch) so it may be necessary/advisable to variance stabilize. Is their a best way to go about doing this in R? I am tempted to do something like the following: Use the logistic transformation on each proportion. Next do weighted least squares. Finally, adjust the beta coef by multiplying by sqrt(Sxx/Syy). However, this doesn't take into account the variance in the independant variable. Maybe its better too work with counts of successes instead of proportions? Is their a better way and how do I do it best with R? Thanks in advance for any help. _______________________________________...

...ws <- attr(xx, "row.names") - i <- pmatch(i, rows, duplicates.ok = TRUE) + i <- if (pmatch.rows) { + pmatch(i, rows, duplicates.ok = TRUE) + } else { + match(i, rows) + } } for(j in seq_along(x)) { xj <- xx[[ sxx[j] ]] Index: src/library/base/man/Extract.data.frame.Rd =================================================================== --- src/library/base/man/Extract.data.frame.Rd (revision 85664) +++ src/library/base/man/Extract.data.frame.Rd (working copy) @@ -15,7 +15,7 @@ Extract or replace subsets o...

...const celt_norm_t *X, const celt_norm_t *P, celt_pgain_t *gains) { int i; + int gain_sum = 0; const celt_int16_t *pBands = m->pBands; const int C = CHANNELS(m); @@ -234,9 +235,11 @@ void compute_pitch_gain(const CELTMode *m, const celt_norm_t *X, const celt_norm Sxy = Sxx; /* We need to be a bit conservative (multiply gain by 0.9), otherwise the residual doesn't quantise well */ - Sxy = MULT16_32_Q15(QCONST16(.9f, 15), Sxy); + Sxy = MULT16_32_Q15(QCONST16(.99f, 15), Sxy); /* gain = Sxy/Sxx */ gains[i] = EXTRACT16(celt_div...

Hi, I'm having problems while working with large data sets with R 1.5.1 in windows 2000. Given a integer matrix size of 30 columns and 15000 rows my function should return a boolean matrix size of about 5000 rows and 15000 columns. First of all I tried to run this function on computer with 256 MB of RAM. I increased memory limit of R with memory.limit() up to 512 MB. I was inspecting

Hello Jean-Marc, When you count the adaptation step size in case where the filter has already had minimal adaptation, you use the following expression: r = (0.7*r + 0.3*15*RER*e)/e*(power[i] + 10) = (0.7*leak_estimate*Yf[i] + 0.3*15*RER*(Rf[i] + 1))/((Rf[i] + 1)*(power[i] + 10)). Why do we need this weighted sum and the component 0.3*15*RER*(Rf[i] + 1)? Why use the correlation-based RER if

Hello All. I have PowerCOM ( http://pcmups.com.tw/ ) UPS management protocol specification at http://www.sovico.org/pcm.txt Current version of NUT contains any drivers include driver for Powercom, but not fully funcionality. This specification describe protocol for next series UPS: BLACK KNIGHT: All UPS AP series WARRIOR: WAR-1000AP WOW: All UPS U series IMPERIAL: All UPS KING: All UPS SMART

...rver" script is run during startup is that the "S91winbind" script has not finished setting up winbind before the vncserver script tries to set up a session for the winbind user. Maybe somebody more familiar with SysV can suggest something that would make this work through the "Sxx" scripts so it can be done without resorting to rc.local. But for now, I've got the ability to VNC into the FC3 server without having to create a local account; I can use a winbind user account.

...- i <- pmatch(i, rows, duplicates.ok = TRUE) > + i <- if (pmatch.rows) { > + pmatch(i, rows, duplicates.ok = TRUE) > + } else { > + match(i, rows) > + } > } > for(j in seq_along(x)) { > xj <- xx[[ sxx[j] ]] > Index: src/library/base/man/Extract.data.frame.Rd > =================================================================== > --- src/library/base/man/Extract.data.frame.Rd (revision 85664) > +++ src/library/base/man/Extract.data.frame.Rd (working copy) > @@ -15,7 +15,7 @@ >...

...efore register allocation llvm machine code accesses values through virtual registers, these get assigned to physical registers later. Each virtual register has an assigned register class which is a set of physical registers. So for example on ARM you have a register class containing all the 'SXX' registers and another one containing all the 'DXX' registers, ... But sometimes you want to mix narrow and wide accesses to values. Like loading the 'D0' register but later reading the 'S0' and 'S1' components separately. This is modeled with subregister oper...

Dear Ivan, thanks a lot, that is helpful. Still, I feel that default partial matching cripples the functionality of data.frame for larger tables. Thanks again and best regards Hilmar On 12.12.23 13:55, Ivan Krylov wrote: > ? Mon, 11 Dec 2023 21:11:48 +0100 > Hilmar Berger via R-devel <r-devel at r-project.org> ?????: > >> What was unexpected is that in this case was that