search for: rr1

Suppose I have the following data: tmp <- data.frame(var1 = sample(c(0:10), 3, replace = TRUE), var2 = sample(c(0:10), 3, replace = TRUE), var3 = sample(c(0:10), 3, replace = TRUE)) I can run the following double loop and yield what I want in the end (rr1) as: library(statmod) Q <- 2 b <- runif(3) qq <- gauss.quad.prob(Q, dist = 'normal', mu = 0, sigma=1) rr1 <- matrix(0, nrow = Q, ncol = nrow(tmp)) L <- nrow(tmp) for(j in 1:Q){ for(i in 1:L){...

...alue. I have written R-code below library("Matching") data("lalonde") attach(lalonde) names(lalonde) Y <- lalonde$re78 Tr <- lalonde$treat glm1 <- glm(Tr~age+educ+black+hisp+married+nodegr+re74+re75,family=binomial,data=lalonde) pscore.predicted <- predict(glm1) rr1 <- Match(Y=Y,Tr=Tr,X=glm1$fitted,estimand="ATT", M=1,ties=TRUE,replace=TRUE) summary(rr1) > summary(rr1) Estimate...  2624.3  AI SE......  802.19  T-stat.....  3.2714  p.val......  0.0010702  Original number of observations..............  445  Original number of treated obs........

Hi R-users,  I have a data from multi skew t and would like to transform each of the data to uniform data.  I tried using 'pmst' but only got one output:   > rr1 <- as.vector(r1);rr1  [1]  0.7207582  5.2250906  1.7422237  0.5677233  0.7473555 -0.6020626 -2.1947872 -1.1128313 -0.6587316 -1.1409261     > pmst(rr1, xi=rep(0,10), Omega=diag(10), alpha=rep(1,10), df=5) [1] 3.676525e-09 attr(,"error") [1] 3.878226e-11 attr(,"status") [1]...

...} else { startVal <- rep(0, ncol(data)) } #qq <- gauss.quad.prob(Q, dist = 'normal', mu = 0, sigma=1) qq <- gauss.quad.prob(Q, dist = 'uniform', alpha = -5, beta=5) rr1 <- matrix(0, nrow = Q, ncol = nrow(data)) data <- as.matrix(data) L <- nrow(data) C <- ncol(data) fn <- function(b){ b <- b[1:C] for(j in 1:Q){...

...######################################## cross <- 3;years <- 13 #cross sections and years to be included frontr<-vector(length=years);ggwth <- 0.03; frontr[1]=17 for (i in 2:years) {frontr[i]=frontr[i-1]*(1+ggwth)} frontr <- rep(frontr,times=cross) atech <- matrix(0,years,cross);rr1 <- matrix(0,years,cross) agwth <- matrix(0,years,cross);cfgwth <- matrix(0,years,cross) aobs <- c(15.89,16.69,17.93,17.49,18.3,19.1,19.2,19.4,20.29,21.13,21.42,22.76,23.83, 14.1,14.4,13.4,14.9,15.23,15.4,15.55,16.7,17.8,18.87,18.99,19.24,20.59, 14.3,14.4,14.7,14.9,1...

...s what it looks like stacked. I have experimented with tapply(), by(), and some functions mentioned in archived postings, but I haven't seen anything that answers to this directly. Does anybody have any ideas? OBJECTID PolygonID SpeciesCod AbundanceP 1 15006 ANT-CBG-rr1 Leymol 5.00000 3 15008 ANT-CBG-rr1 Ambcha 5.00000 5 15010 ANT-ESH-27 Atrpat 20.00000 6 15011 ANT-ESH-27 Ambcha 10.00000 11 15016 ANT-ESH-28 Salvir 20.00000 14 15019 ANT-ESH-28 Atrpat 5.00000 18 15023 ANT...

...ever it dies not work as expected.  Hope somebody can help me to match Matlab and r codes. R code: rr <- function(r,cxn) { tol <- 1E-4; for(i in 1:n) { t1 <- (1+(i-1)*r)*log((1+(i-1)*r)) t2 <- (i-1)*(1-r)*log(1-r) rri <- ((t1+t2)/i*log(i))-cxn rr <- rri > tol } round(rr,4) } rr1 <- rr(0.5,0.0242) ; rr1 Matlab code: function F = cxncnr(r) n = 4; % terms t1 = (1+(n-1)*r)*log((1+(n-1)*r)); t2 = (n-1)*(1-r)*log(1-r); %f = term - cxn f = (t1+t2)/(n*log(n)) - 0.05011007 F = [f]; % r0 = [0.5] ; r = fsolve(@cxncnr,r0) Thank you so much for any help given. [[alternative H...

I have the following code which produces the output below it clus1 <- svydesign(ids = ~schid, data = lower_dat) items <- as.formula(paste(" ~ ", paste(lset, collapse= "+"))) rr1 <- svymean(items, clus1, deff='replace', na.rm=TRUE) > rr1 mean SE DEff W525209 0.719748 0.015606 2.4932 W525223 0.508228 0.027570 6.2802 W525035 0.827202 0.014060 2.8561 W525131 0.805421 0.015425 3.1350 W525033 0.242982 0.020074 4.5239 W525163 0.904647 0.013905...

...consider the following simple example (yes, I know that this actually is a linar model :-)): #----------------------------------------------------------------------------------- probex <- 0.05 x <- 1:10 y <- rnorm(x, x, .8) w1 <- rep(1, 10) w2 <- w1; w2[7:10] <- 0.01 * w2[7:10] rr1 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w1) rr2 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w2) yfit1 <- fitted(rr1) yfit2 <- fitted(rr2) se.fit1 <- sqrt(apply(rr1$m$gradient(), 1, function(x) sum(v...

...consider the following simple example (yes, I know that this actually is a linar model :-)): #----------------------------------------------------------------------------------- probex <- 0.05 x <- 1:10 y <- rnorm(x, x, .8) w1 <- rep(1, 10) w2 <- w1; w2[7:10] <- 0.01 * w2[7:10] rr1 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w1) rr2 <- nls(y ~ a*x + b, data = list(x = x, y = y), start = list(a = 1, b = 0), weights = w2) yfit1 <- fitted(rr1) yfit2 <- fitted(rr2) se.fit1 <- sqrt(apply(rr1$m$gradient(), 1, function(x) sum(v...

...startVal <- startVal } else { p <- colMeans(data) startVal <- as.vector(log((1 - p)/p)) } qq <- gauss.quad.prob(Q, dist = 'normal') rr1 <- matrix(0, nrow = Q, ncol = nrow(data)) data <- as.matrix(data) L <- nrow(data) C <- ncol(data) fn <- function(b){ for(j in 1:Q){ for(i in...

Hi. This patchset is preparation patches for ia64/pv_ops support. They are almost trivial and mainly make kernel paravirtualization friendly. thanks, Diffstat: arch/ia64/kernel/irq_ia64.c | 1 - include/asm-ia64/intrinsics.h | 11 +++++++++++ include/asm-ia64/mmu_context.h | 6 +----- include/asm-ia64/smp.h | 2 ++ include/asm-ia64/system.h | 10 ++++++++-- 5 files

...cted and 6 impacted). In this dataset, site is nested within harvest (H) and year is nested within before-after (BA). Also, site is considered as random by the authors. The data (fake again) can be produced with the following commands: >site<-c(rep(c("A1","A2", "RR1", "RR2", "WT1", "WT2", "WT3", "WT4"),8)) >H<-c(rep(c("exp", "exp", "prot", "pro", "exp", "exp", "exp", "exp"), 8)) >year<-c(rep(1989,8), rep(1990,8),...

...x1, x0, t0; \ @@ -251,9 +255,9 @@ __cast5_enc_blk16: movq %rdi, CTX; - vmovdqa .Lbswap_mask, RKM; - vmovd .Lfirst_mask, R1ST; - vmovd .L32_mask, R32; + vmovdqa .Lbswap_mask(%rip), RKM; + vmovd .Lfirst_mask(%rip), R1ST; + vmovd .L32_mask(%rip), R32; enc_preload_rkr(); inpack_blocks(RL1, RR1, RTMP, RX, RKM); @@ -287,7 +291,7 @@ __cast5_enc_blk16: popq %rbx; popq %r15; - vmovdqa .Lbswap_mask, RKM; + vmovdqa .Lbswap_mask(%rip), RKM; outunpack_blocks(RR1, RL1, RTMP, RX, RKM); outunpack_blocks(RR2, RL2, RTMP, RX, RKM); @@ -325,9 +329,9 @@ __cast5_dec_blk16: movq %rdi, CTX;...

Changes: - patch v2: - Adapt patch to work post KPTI and compiler changes - Redo all performance testing with latest configs and compilers - Simplify mov macro on PIE (MOVABS now) - Reduce GOT footprint - patch v1: - Simplify ftrace implementation. - Use gcc mstack-protector-guard-reg=%gs with PIE when possible. - rfc v3: - Use --emit-relocs instead of -pie to reduce

Changes: - patch v2: - Adapt patch to work post KPTI and compiler changes - Redo all performance testing with latest configs and compilers - Simplify mov macro on PIE (MOVABS now) - Reduce GOT footprint - patch v1: - Simplify ftrace implementation. - Use gcc mstack-protector-guard-reg=%gs with PIE when possible. - rfc v3: - Use --emit-relocs instead of -pie to reduce

These patches make the changes necessary to build the kernel as Position Independent Executable (PIE) on x86_64. A PIE kernel can be relocated below the top 2G of the virtual address space. It allows to optionally extend the KASLR randomization range from 1G to 3G. Thanks a lot to Ard Biesheuvel & Kees Cook on their feedback on compiler changes, PIE support and KASLR in general. Thanks to

These patches make the changes necessary to build the kernel as Position Independent Executable (PIE) on x86_64. A PIE kernel can be relocated below the top 2G of the virtual address space. It allows to optionally extend the KASLR randomization range from 1G to 3G. Thanks a lot to Ard Biesheuvel & Kees Cook on their feedback on compiler changes, PIE support and KASLR in general. Thanks to

Changes: - patch v3: - Update on message to describe longer term PIE goal. - Minor change on ftrace if condition. - Changed code using xchgq. - patch v2: - Adapt patch to work post KPTI and compiler changes - Redo all performance testing with latest configs and compilers - Simplify mov macro on PIE (MOVABS now) - Reduce GOT footprint - patch v1: - Simplify ftrace

Hi. This patchset implements ia64/pv_ops support which is the framework for virtualization support. Now all the comments so far have been addressed, but only a few exceptions. On x86 various ways to support virtualization were proposed, and eventually pv_ops won. So on ia64 the pv_ops strategy is appropriate too. Later I'll post the patchset which implements xen domU based on ia64/pv_ops.