search for: sd2

I have written a function to plot data which will be used for various different chemistries. A simplified version is: plot_data <- function(risk,levels,chem,sd2,measure){ plot(risk, levels,main=paste ("per", sd2, measure, "\n in usual", chem)) } The problem is with the title. This works fine if the variable "chem" is just text, but if it is an expression then obviously it won't work. I have experimented with various thin...

...servers in these domains, but up to now I have trouble adding Samba 4 member servers to Windows AD. My test environment is made of 2 Networks, which are connected through a VPN site2site tunnel. On one of this Networks I have the root domain (RD.LAN) with its DC rddc1.rd.and and a subdomain 2 (SD2.RD.LAN) with its DC sd2dc.sd2.rd.lan On the other network I have the subdomain 1 (SD1.RD.LAN) with its DC sd1dc1.sd1.rd.lan. I try the following steps on mserver1.sd1.rd.lan: first I install these required packages, as written in wiki.samba.org: build-essential libacl1-dev libattr1-dev libblki...

...questions, (i) how is the function printing when there is no code to print and (ii) how is n1 which equals 1 being reported as 2? I suspect there is something fundamental about R that I don't know. Thank you for the help. John library(epicalc) n.for.2means.js <- function (mu1, mu2, sd1, sd2, ratio = 1, alpha = 0.05, power = 0.8) { n1 <- (sd1^2 + sd2^2/ratio) * (qnorm(1 - alpha/2) - qnorm(1 - power))^2/(mu1 - mu2)^2 n1 <- round(n1) n2 <- ratio * n1 if (length(alpha) == 1) { alpha1 <- NULL } else { alpha1 <- alpha } if (length(power) == 1) {...

Dear all: Assume I have 3 distributions, x1, x2, and x3. x1 ~ normal(mu1, sd1) x2 ~ normal(mu2, sd2) x3 ~ normal(mu3, sd3) y1 = x1 + x2 y2 = x1 + x3 Now that the data I can observed is only y1 and y2. It is easy to estimate (mu1+m2), (mu1+mu3), (sd1^2+sd2^2) and (sd1^2+sd3^2) by EM algorithm since y1 ~ normal(mu1+mu2, sqrt(sd1^2+sd2^2)) and y2 ~ normal(mu1+mu3, sqrt(sd1^2+sd3^2)) However, I wan...

...            DEF = c(22,28,20,20,28,26,29,18,24,21),                                           GHI = c(32,27,32,36,37,37,34,23,25,32),                                           JKL = c(47,60,60,43,62,38,44,53,61,41)) sd_prices <- c(3.3483,3.9497,4.9721,9.3387)   # standard deviations say(sd1, sd2, sd3, sd4)                         weights <- c(0.10, 0.25, 0.20, 0.45)      # say (w1, w2, w3, w4) I need to calculate the standard deviation of the portfolio. The formula is stdev_portfolio = sqrt((w1*sd1)^2+(w2*sd2)^2+(w3*sd3)^2+(w4*sd4)^2 +                         2*w1*w2*sd1*sd2*correlat...

Hi, I''m running Opensolaris 2009.06, and I''m facing a serious performance loss with ZFS ! It''s a raidz1 pool, made of 4 x 1TB SATA disks : zfs_raid ONLINE 0 0 0 raidz1 ONLINE 0 0 0 c7t2d0 ONLINE 0 0 0 c7t3d0 ONLINE 0 0 0 c7t4d0 ONLINE 0 0

Does anyone know how to import a SAS .sd2 file into R? I can't see anything in library(foreign). Thank you. Mark

I am trying to read a SAS dataset into R that is in a .SD2 format, which is the Version 6 standard format from SAS. I see the routines that read the SAS XPORT format (foreign, Hmisc), but is there any way to read this one? Any help would be greatly appreciated. Corey Sparks Corey Sparks Assistant Professor Department of Demography and Organization Studies...

Hi, I already found a conversation on the integration of a normal distribution and two suggested solutions (https://stat.ethz.ch/pipermail/r-help/2007-January/124008.html): 1) integrate(dnorm, 0,1, mean = 0, sd = 1.2) and 2) pnorm(1, mean = 0, sd = 1.2) - pnorm(0, mean = 0, sd = 1.2) where the pnorm-approach is supposed to be faster and with higher precision. I want to integrate a mixed

...tterplot3d(y[,1], y[,2], p , highlight.3d=TRUE, col.axis="blue", col.grid="lightblue",main="val iniz mistura normali multivariata", angle=120, pch=20) #verosimiglianza iniziale l0<-sum(log(p)) l1<-l0 alpha<-alpha0 mu1<-mu01 mu2<-mu02 sd1<-sd01 sd2<-sd02 for (iter in 1:itermax) { #passo E for (i in 1:n) { tau[i,1]<-(alpha*f1[i])/p[i] tau[i,2]<-((1-alpha)*f2[i])/p[i] } #passo M alpha= mean(tau[,1]) mu1=colSums(tau[,1]*y)/sum(tau[,1]) mu2=colSums(tau[,2]*y)/sum(tau[,2]) ycen1<-(y-mu1) ycen2<-(y-mu2) cov1<-matrix(0,2,2...

...tterplot3d(y[,1], y[,2], p , highlight.3d=TRUE, col.axis="blue", col.grid="lightblue",main="val iniz mistura normali multivariata", angle=120, pch=20) #verosimiglianza iniziale l0<-sum(log(p)) l1<-l0 alpha<-alpha0 mu1<-mu01 mu2<-mu02 sd1<-sd01 sd2<-sd02 for (iter in 1:itermax) { #passo E for (i in 1:n) { tau[i,1]<-(alpha*f1[i])/p[i] tau[i,2]<-((1-alpha)*f2[i])/p[i] } #passo M alpha= mean(tau[,1]) mu1=colSums(tau[,1]*y)/sum(tau[,1]) mu2=colSums(tau[,2]*y)/sum(tau[,2]) ycen1<-(y-mu1) ycen2<-(y-mu2) cov1<-matrix(0,2,2...

...series of datasets that have a bimodal distribution. I have used the following functions, in particular the ms() function, to estimate the parameters: two means, two standard deviations, and one proportion. Here is the code I've been using in S: btmp.bi <- function(vec, p, m1, m2, sd1, sd2) { (exp(p)/(1+exp(p)))*dnorm(vec,mean=m1,sd=abs(sd1))+ (1-(exp(p)/(1+exp(p))))*dnorm(vec,mean=m2,sd=abs(sd2)) } btmp11 <- ms( ~ - sum(log((btmp.bi(btmp1$Temp, p, m1, m2, s1, s2)))), start = list(p = 0.4, m1 = 38, m2 = 40, s1 = 1, s2 = 1)...

...oefficient[2]) int2 <- lapply(glsrun2, function(x) x$coefficient[1]) slo2 <- lapply(glsrun2, function(x) x$coefficient[2]) # Step 6 Compute SD of intercepts and slopes int.sd1 <- sapply(glsrun1, function(x) x$coefficient[1]) slo.sd1 <- sapply(glsrun1, function(x) x$coefficient[2]) int.sd2 <- sapply(glsrun2, function(x) x$coefficient[1]) slo.sd2 <- sapply(glsrun2, function(x) x$coefficient[2]) cat("Original Standard Errors","\n", "Intercept","\t", sd(int.sd1),"\n","Slope","\t","\t", sd(slo.sd1),&qu...

...)*S2*m2 a31[i]<-sm[i]*S1*(1-m1)*S2*(1-m2)*S3 sum[i]<-a11[i]+a21[i]+a31[i] } #priors #priors are dbeta(0.5,0.5), uniform is dbeta(1,1) S1~dbeta(1,1)#I(0,0.2) # S2~dbeta(1,1)#I(0.15,0.5) # S3~dbeta(1,1)#I(0.3,1) m1~dbeta(1,1)#I(0.01,1) m2~dbeta(1,1)#I(0,1) sd ~ dunif(0,100) sd1 ~ dunif(0,100) sd2 ~ dunif(0,100) tau <- 1/(sd*sd) tau1 <- 1/(sd1*sd1) tau2 <- 1/(sd2*sd2) } model.file <- Model file.show(model.file) #The Data list(N = 4, sm=c(9309,3253,5292,1361), a1=c(16,3,4,2), a2=c(96,31,42,3), a3=c(47,5,18,1)) inits <- function(){ list(sd = 1000, sd1 = 1000, sd2 = 1000, S1 = 0...

...)*S2*m2 a31[i]<-sm[i]*S1*(1-m1)*S2*(1-m2)*S3 sum[i]<-a11[i]+a21[i]+a31[i] } #priors #priors are dbeta(0.5,0.5), uniform is dbeta(1,1) S1~dbeta(1,1)#I(0,0.2) # S2~dbeta(1,1)#I(0.15,0.5) # S3~dbeta(1,1)#I(0.3,1) m1~dbeta(1,1)#I(0.01,1) m2~dbeta(1,1)#I(0,1) sd ~ dunif(0,100) sd1 ~ dunif(0,100) sd2 ~ dunif(0,100) tau <- 1/(sd*sd) tau1 <- 1/(sd1*sd1) tau2 <- 1/(sd2*sd2) } model.file <- Model file.show(model.file) #The Data list(N = 4, sm=c(9309,3253,5292,1361), a1=c(16,3,4,2), a2=c(96,31,42,3), a3=c(47,5,18,1)) inits <- function(){ list(sd = 1000, sd1 = 1000, sd2 = 1000, S1 = 0...

...n example of my data: mean1<-cbind(55.5,56.42,56.38,55.28) SD1<-cbind(10,9 ,7 ,3) x<-rbind(mean1,SD1) colnames(x)<-c("Autumn","Spring","Summer","Winter") rownames(x)<-c("MeanProts","SD") mean2<-cbind(10,15,9,8) SD2<-cbind(2,3 ,1 ,5) y<-rbind(mean2,SD2) colnames(y)<-c("Autumn","Spring","Summer","Winter") rownames(y)<-c("MeanDNA","SD") Thanks in advance. Nacho -- Ignacio Alborés Blanes Grupo de Investigación en Recursos Marinos...

...have 2 groups and for each group I need to compute the following steps; i) Generate 3 random numbers from normal distribution and square them. ii) Repeat step 1 for 15 times and at the end I will get 15 random numbers. I already done stage I using for loop. n1<-3 n2<-3 miu<-0 sd1<-1 sd2<-1 asim<-15 w<-rep(NA,asim) x<-rep(NA,asim) for (i in 1:asim) { print(i) set.seed(i) data1<-rnorm(n1,miu,sd1) data2<-rnorm(n2,miu,sd2) w[i]<-sum(data1^2) x[i]<-sum(data2^2) } w x Second stage is; Stage II For each group, I need to: i) Sort the group; ii) Find trimmed mean...

...run this code to get the power of the test for modified bartlett's test..but I'm not really sure that my coding is right.. #normal distribution unequal variance asim<-5000 pv<-rep(NA,asim) for(i in 1:asim) {print(i) set.seed(i) n1<-20 n2<-20 n3<-20 mu<-0 sd1<-sqrt(25) sd2<-sqrt(50) sd3<-sqrt(100) g1<-rnorm(n1,mu,sd1) g2<-rnorm(n2,mu,sd2) g3<-rnorm(n3,mu,sd3) x=c(g1,g2,g3) group=c(rep(1,n1),rep(2,n2),rep(3,n3)) N=60 k=3 v1=var(g1) v2=var(g2) v3=var(g3) #pooled variance A=((n1-1)*v1+(n2-1)*v2+(n3-1)*v3)/(N-k) #calculate B B=((N-k)*(log(A)))-((n1-1)*log(...

...us posterior to be the new prior and now I cant figure out how to do the code below in a loop. The data below is dummy data, I used a starting mu of 1, and starting precision of 0. bayes.analysis.treat<-function(mu0,p0){ n1 = 5 n2 = 10 n3 = 15 ybar1 = 12 ybar2 = 13 ybar3 = 14 sd1 = 2 sd2 = 3 sd3 = 4 #posterior 1 var1 = sd1^2 #sample variance p1 = n1/var1 #sample precision p1n = p0+p1 mu1 = ((p0)/(p1n)*mu0)+((p1)/(p1n))*ybar1 sigma1 = 1/sqrt(p1n) #posterior 2 var2 = sd2^2 #sample variance p2 = n2/var2 #sample precision p2n = p1n+p2 mu2 = ((p1n)/(p2n)*mu1)+((p2)/(p2n))*yb...

Hello I am sure this is elementary, but I can't figure it out..... I am using SAS v8.2 and R 1.4.1 on a Windows platform. I have a large (6 megabyte) file in .sd2 format. I want to import it into R. If necessary, I can create a smaller file, as I don't need all the variables that are in this file I downloaded the package foreign. I then tried to use PROC CPORT in SAS to create a transport file. It gave no error message, but I cannot find the file it...