search for: y8

...). As an illustration, below is the code: pdf("testmfrow.pdf") par(mfrow=c(4,2)) x<-seq(1:10) y1<-rnorm(10) y2<-rnorm(10,mean=2,sd=1) y3<-rnorm(10,mean=3,sd=1) y4<-rnorm(10,mean=4,sd=1) y5<-rnorm(10,mean=5,sd=1) y6<-rnorm(10,mean=6,sd=1) y7<-rnorm(10,mean=7,sd=1) y8<-rnorm(10,mean=8,sd=1) plot(x,y1,type='o', lty=1, main="I want this title is to be scaled down (resized)") plot(x,y2,type='o', lty=2, main="I want this title is to be scaled down (resized)") plot(x,y3,type='o', lty=3, main="I want this title is t...

...de ## > > library(lavaan) > > # The industrialization and Political Democracy Example > # Bollen (1989), page 332 > > model <- ' > # latent variable definitions > ind60 =~ x1 + x2 + x3 > dem60 =~ y1 + y2 + y3 + y4 > dem65 =~ y5 + y6 + y7 + y8 > > # regressions > dem60 ~ ind60 > dem65 ~ ind60 + dem60 > > # residual correlations > y1 ~~ y5 > y2 ~~ y4 + y6 > y3 ~~ y7 > y4 ~~ y8 > y6 ~~ y8 > ' > > fit <- sem(model, data=PoliticalDemocracy) > summary(fit,...

...y0 = vld1_s16(y); + y += 4; + + for (j = 0; j + 8 <= len; j += 8) + { + // Load x[0...7] + int16x8_t xx = vld1q_s16(x); + int16x4_t x0 = vget_low_s16(xx); + int16x4_t x4 = vget_high_s16(xx); + // Load y[4...11] + int16x8_t yy = vld1q_s16(y); + int16x4_t y4 = vget_low_s16(yy); + int16x4_t y8 = vget_high_s16(yy); + int32x4_t a0 = vmlal_lane_s16(a, y0, x0, 0); + int32x4_t a1 = vmlal_lane_s16(a0, y4, x4, 0); + + int16x4_t y1 = vext_s16(y0, y4, 1); + int16x4_t y5 = vext_s16(y4, y8, 1); + int32x4_t a2 = vmlal_lane_s16(a1, y1, x0, 1); + int32x4_t a3 = vmlal_lane_s16(a2, y5, x4, 1); + +...

...in problems: Say, I have a text file( named as "try"): Year C1 C2 C3 C4 C5 C6 Y1 3.5 13.8 9.5 6.8 0.4 24.2 Y2 3.8 13.9 9.9 7.6 0.7 12.8 Y3 4.5 14.5 14.2 9.2 0.6 14.5 Y4 5.9 16.2 24.6 12.7 0.2 24.3 Y5 7.2 20.4 40.6 18.2 0.8 28.2 Y6 5.9 18.6 37.4 14.5 0.3 36.9 Y7 8.0 16.1 88.6 24.1 0.1 34.6 Y8 13.6 21.1 56.3 19.0 0.7 33.3 I wish to import the file into R and make certain computations, like intercorrelation matrix. I tried the following syntax: # Inputting the data file (saved in text format) df="trial.txt" idt=read.table(df,header=T, sep="\t") idt # To generate inte...

I am new to R and have tried for a good while to figure out how to code this in R. The dataset below: FTIStandKey State County FTITract CoverType Ver_CT V_Origin V_SpGrp NAH6005-001 Texas Jasper NAH6005 PPLB-2000-U PPLB-2000-U P P NAH6005-002 Texas Jasper NAH6005 NHHX-1950-O NHHX-1950-I N H NAH6253-001 Texas Tyler NAH6253 PPLB-2001-U PPLB-2001-U P P

...e~~ y1=c(1,0.9,0.98,1.06,0.83,1.00,0.41,0.93,1.01,0.86) y2=c(0.05,0.16,0,0.26,0.15,0.61,0.33,0.31,0.33) y3=c(-0.23,0,0.16,-0.03,0.22,-0.03,-0.17,-0.05) y4=c(0.04,-0.21,-0.04,-0.26,-0.03,-0.04,-0.05) y5=c(-0.02,-0.34,0.06,-0.22,-0.11,-0.31) y6=c(0.20,0.01,0.01,-0.26,0.01) y7=c(-0.06,-0.14,0.39,0.23) y8=c(0.21,0.10,0.09) y9=c(-0.24,-0.23) y10=c(0.13) y=c(y1,y2,y3,y4,y5,y6,y7,y8,y9,y10) ## autoregressive paramters ,table 1 biometrika 1999,page 685 om2=matrix(0,nrow=55,ncol=3) om1=poly(c(1:10),degree=3) ##polynomial design matirx with level =11,cubic om2[1:10,]=t(matrix(rep(om1[1,],10)...

...full R code for fitting a SEM model: ## begin R Code ## library(lavaan) # The industrialization and Political Democracy Example # Bollen (1989), page 332 model <- ' # latent variable definitions ind60 =~ x1 + x2 + x3 dem60 =~ y1 + y2 + y3 + y4 dem65 =~ y5 + y6 + y7 + y8 # regressions dem60 ~ ind60 dem65 ~ ind60 + dem60 # residual correlations y1 ~~ y5 y2 ~~ y4 + y6 y3 ~~ y7 y4 ~~ y8 y6 ~~ y8 ' fit <- sem(model, data=PoliticalDemocracy) summary(fit, fit.measures=TRUE) ## end R code ## Please feel free to contact...

...ull R code for fitting a SEM model: ## begin R Code ## library(lavaan) # The industrialization and Political Democracy Example # Bollen (1989), page 332 model <- ' # latent variable definitions ind60 =~ x1 + x2 + x3 dem60 =~ y1 + y2 + y3 + y4 dem65 =~ y5 + y6 + y7 + y8 # regressions dem60 ~ ind60 dem65 ~ ind60 + dem60 # residual correlations y1 ~~ y5 y2 ~~ y4 + y6 y3 ~~ y7 y4 ~~ y8 y6 ~~ y8 ' fit <- sem(model, data=PoliticalDemocracy) summary(fit, fit.measures=TRUE) ## end R code ## Please feel free to...

...full R code for fitting a SEM model: ## begin R Code ## library(lavaan) # The industrialization and Political Democracy Example # Bollen (1989), page 332 model <- ' # latent variable definitions ind60 =~ x1 + x2 + x3 dem60 =~ y1 + y2 + y3 + y4 dem65 =~ y5 + y6 + y7 + y8 # regressions dem60 ~ ind60 dem65 ~ ind60 + dem60 # residual correlations y1 ~~ y5 y2 ~~ y4 + y6 y3 ~~ y7 y4 ~~ y8 y6 ~~ y8 ' fit <- sem(model, data=PoliticalDemocracy) summary(fit, fit.measures=TRUE) ## end R code ## Please feel free to contact...

...ull R code for fitting a SEM model: ## begin R Code ## library(lavaan) # The industrialization and Political Democracy Example # Bollen (1989), page 332 model <- ' # latent variable definitions ind60 =~ x1 + x2 + x3 dem60 =~ y1 + y2 + y3 + y4 dem65 =~ y5 + y6 + y7 + y8 # regressions dem60 ~ ind60 dem65 ~ ind60 + dem60 # residual correlations y1 ~~ y5 y2 ~~ y4 + y6 y3 ~~ y7 y4 ~~ y8 y6 ~~ y8 ' fit <- sem(model, data=PoliticalDemocracy) summary(fit, fit.measures=TRUE) ## end R code ## Please feel free to...

...onent: ip_conntrack AssignedTo: netfilter-buglog at lists.netfilter.org ReportedBy: philipp at redfish-solutions.com Estimated Hours: 0.0 If I send out a packet like (tcpdump output): 16:37:56.642134 IP 192.168.1.25.34699 > 239.255.255.250.ssdp: UDP, length 135 ..^....'.Y8...E..... at ................l..AQM-SEARCH * HTTP/1.1 HOST: 239.255.255.250:1900 MAN: "ssdp:discover" MX: 2 ST: urn:schemas-upnp-org:device:WANConnectionDevice:1 And a unicast response comes back to the same source tuple (UDP, 192.168.1.25.34699)... 16:37:56.645110 IP 192.168.1.1.ssdp...

...3" "A4" "A5" "A6" "A7" "A8" "A9" "A10" Now assign each element as name for a data frame: (d <- data.frame(cbind(X1=1,Y1=1:10,X2=1,Y2=1:10,X3=1,Y3=1:10,X4=1,Y4=1:10,X5=1,Y5 =1:10, X6=1,Y6=1:10,X7=1,Y7=1:10,X8=1,Y8=1:10,X9=1,Y9=1:10,X10=1,Y10=1:10))) x <- paste("X",1,sep="") y <- paste("Y",1,sep="") for (i in 2:10) {x[i] <- paste("X",i,sep="") y[i] <- paste("Y",i,sep="")} for (k in 1:10) {assign(c...

Full_Name: Joseph Haykov Version: 2.8.0 OS: Windows Submission from: (NULL) (216.189.177.202) normalizePath("C:\\DOCUME~1\\JOSEPH~1\\LOCALS~1\\Temp\\RtmpolZ4Vy\\file72ae2cd6.txt") returns: "\0354xl|\a&#144;\001?$v\001?y8" instead of returning: "C:\\Documents and Settings\\Joseph Haykov\\Local Settings\\Temp\\RtmpolZ4Vy\\file72ae2cd6.txt" By the way, this works correctly in version 2.6.2

...18 X19 X110 X111 X112 X113 X114 1 2 1 2 1 2 1 2 1 2 1 2 1 2 X115 X116 X21 X22 X23 X24 X25 X26 X27 X28 X29 X210 X211 X212 1 2 0 1 0 1 1 1 0 1 0 1 0 1 X213 X214 X215 X216 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 1 1 0 1 1 2 3 4 5 6 7 8 1 2 Y11 Y12 Y13 Y14 Y15 Y16 3 4 5 6 7 8 So, e.g. for a 3 column matrix: > matrix(do.call(c,imp), ncol=3) [,1] [,2] [,3] [1,] 1 0 1 [2,] 2 1 2 [3,] 1 0 3 [4,]...

Hello everyone, I want to transform a data.frame into an array (lets call it mydata), where: mydata[[1]] is the first imputed dataset...and for each mydata[[d]], the first p columns are covariates X, and the last one is the outcome Y. Lets assume a simple data.frame: Imputed = data.frame( X1 = c(1,2,1,2,1,2,1,2, 1,2,1,2,1,2,1,2), X2 =

...r purpose the method ?confint? will return a wrong evaluation of the confidence interval without throwing any warning. I suggest to use the fixed values that are already contained within the mle object: > fit0 at fullcoef[grep('y', names(fit0 at fullcoef))] y1 y2 y3 y4 y5 y6 y7 y8 y9 y10 y11 26 17 13 12 20 5 9 8 5 4 8 Additionally, the method ?coef? returns both the coefficient and the fixed parameters - while should return only the parameters - , while the ?fullcoef? method is not implemented - while present in the documentation of 'mle-class?. Bes...

...18 X19 X110 X111 X112 X113 X114 1 2 1 2 1 2 1 2 1 2 1 2 1 2 X115 X116 X21 X22 X23 X24 X25 X26 X27 X28 X29 X210 X211 X212 1 2 0 1 0 1 1 1 0 1 0 1 0 1 X213 X214 X215 X216 Y1 Y2 Y3 Y4 Y5 Y6 Y7 Y8 Y9 Y10 1 1 0 1 1 2 3 4 5 6 7 8 1 2 Y11 Y12 Y13 Y14 Y15 Y16 3 4 5 6 7 8 So, e.g. for a 3 column matrix: > matrix(do.call(c,imp), ncol=3) [,1] [,2] [,3] [1,] 1 0 1 [2,] 2 1 2 [3,] 1 0 3 [4,]...

...s which stands for gene expression divergence and 14 matrices which stands for gene sequence divergence. I have tried joining them by using the concatanation function giving SequenceDivergence <- c(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13,X14) ExpressionDivergence <- c(Y1,Y2,Y3,Y4,Y5,Y6,Y7,Y8,Y9,Y10,Y11,Y12,Y13,Y14) where X1,X2..X14 are the expression matrices containing r-values and Y1,Y2..Y14 are the ones with patristic distances Now, I want to plot SequenceDivergence vs. Expression Divergence Tried doing that using plot (Sequence Divergence vs. Expression Divergence) But then g...

...(stderr, "snk -w [-f keyfile] Save Key\n"); + fprintf (stderr, "snk -p [-f keyfile] [-o outfile] Change Passphrase\n"); +} + +void save_snk() { + FILE *fp; + char secret[256]; + char password[256]; + int i,x1,x2,x3,x4,x5,x6,x7,x8; + int y1,y2,y3,y4,y5,y6,y7,y8; + unsigned char data[48]; + + get_key: + strncpy(secret, getpass("Enter SNK secret key: "), sizeof(secret)); + if (sscanf(secret, "%d %d %d %d %d %d %d %d", &x1, &x2, &x3, &x4, &x5, &x6, &x7, &x8) != 8) { + fprintf (stderr, "Error: Invalid...

Following Tim's comments, here are my reworked patches for the Neon intrinsic function patches of of my Aarch64 patchset, i.e. replacing patches 5-8 of the v2 series. Patches 1-4 and 9-18 of the old series still apply unmodified. The one new (as opposed to changed) patch is the first one in this series, to add named constants for the ARM architecture variants. There are also some minor code