search for: nmiss

...hood, p. 194). I label the cells in the table as follows stim response "y" "n" total -------------------------------- y hit miss nsignal -------------------------------- n false correct nnoise alarm rejection --------------------------------- Gsens<-function(nhit,nmiss,nfa,ncr) { # Edwards's G_I, measure of interaction, is measure of sensitivity (xlogx(nhit)+xlogx(nmiss)+xlogx(nfa)+xlogx(ncr))-(xlogx(nhit+nmiss)+xlogx(nfa+ncr))-(xlogx(nhit+nfa)+xlogx(nmiss+ncr))+xlogx(nhit+nmiss+nfa+ncr) } BTW I can't figure out how to break the line at 80 cols. I tried...

...es not need any extra arguments") + error (f_"--list option does not need any extra arguments") ) | `Notes -> (match args with @@ -246,7 +246,7 @@ read the man page virt-builder(1). | [] -> error (f_"virt-builder --notes os-version\nMissing 'os-version'. Use '--list' to list available template names.") | _ -> - error (f_"virt-builder: too many parameters, expecting 'os-version'"); + error (f_"--notes: too many parameters, expecting 'os-version'");...

...monotone missing data pattern, or, if not, what the longest monotone sequence is. Here is an example, where in a dataset of 65 observations, there are 8 different patterns of missingness, with X and . representing observed and missing: Group V2 V3 V4 V5 V6 V7 V8 V9 V10 V11 nmiss 1 X X X X X X X X X X 0 2 X X X X X X . X X X 1 3 X X X X X . X X X X 1 4 X X X X X . . X X X 2 5 X X . X . X...

...the script twice with N = 1002 crashes R # 3. Running the script twice with N = 1003 crashes R (every time?) # 4. Running the script twice with N = 2000 crashes R (every time!) N <- 1003 # Create an directory with N files dest <- "dir" dir.create(dest) files <- list.files(dest) nMissing <- (N - length(files)) if (nMissing > 0) { idx <- (length(files)+1):N filename <- sapply(as.integer(idx), FUN=sprintf, fmt="file%05d.txt") pathname <- paste(dest, filename, sep="/") for (k in seq(pathname)) cat(file=pathname[k], 0) } # Create a zip...

...variables, but I don’t know how to set them as NA. R code: N <- 1000      ####number of cases n <- 12           ####number of variables X <- matrix(rnorm(N * n), N, n)    ####matrix pMiss <- 0.05     ####percent of missing values idMiss <- sample(1:N, N * pMiss)    ####sample cases nMiss <- length(idMiss) m <- 3    ####maximum number of missing variables within selected cases howmanyMiss <- sapply(idMiss, function(x) sample(1:m, 1)) howmanyMiss lapply(howmanyMiss, function(x) sample(1:n, x))   Any suggestion? Thanks, Blaž [[alternative HTML version deleted]]

...the script twice with N = 1002 crashes R # 3. Running the script twice with N = 1003 crashes R (every time?) # 4. Running the script twice with N = 2000 crashes R (every time!) N <- 1003 # Create an directory with N files dest <- "dir" dir.create(dest) files <- list.files(dest) nMissing <- (N - length(files)) if (nMissing > 0) { idx <- (length(files)+1):N filename <- sapply(as.integer(idx), FUN=sprintf, fmt="file%05d.txt") pathname <- paste(dest, filename, sep="/") for (k in seq(pathname)) cat(file=pathname[k], 0) } # Create a zip...

...the script twice with N = 1002 crashes R # 3. Running the script twice with N = 1003 crashes R (every time?) # 4. Running the script twice with N = 2000 crashes R (every time!) N <- 1003 # Create an directory with N files dest <- "dir" dir.create(dest) files <- list.files(dest) nMissing <- (N - length(files)) if (nMissing > 0) { idx <- (length(files)+1):N filename <- sapply(as.integer(idx), FUN=sprintf, fmt="file%05d.txt") pathname <- paste(dest, filename, sep="/") for (k in seq(pathname)) cat(file=pathname[k], 0) } # Create a zip...

...ile I am reading is about 20000 lines long and has a column (in the example called FILE) that contains approximately 100 unique entries. I would like to plot a subset of the data from the file and key the color from the FILE column, This is what my file looks like : CHR SNP BP NMISS BETA SE R2 T P REGION FILE RANDOM 1 rs17035189 10519610 135 0.3518 1.928 0.0002501 0.1824 0.8555 TCTX 4730341 0.284627081 6 rs3763311 32484154 109 -2.05 1.624 0.01467 -1.262 0.2096 TCTX...

I am unclear on whether to run R CMD check pgkname as user or as root on Linux. When running as user, after all the latex, html, and man files are created, I get the following error message: Rdconv(): Couldn't open '': Permission denied Has anyone dealt with that message? When I run R CMD check a second time, all latex, html, etc. are recreated which takes quite a while. Is there

...DW_BPS, 5)+ rcs(DW_BPD, 5)+ rcs(pulse3, 5)+ locat2+ (rcs(exactage, 3) * rcs(drawtimefrom8am, 3)) ) This gives rise to the following error: ---- Error in dimnames(X)[[2]] <- atr$colnames : length of 'dimnames' [2] not equal to array extent In addition: Warning message: In names(nmiss)[jz] <- fname[asm != 9] : number of items to replace is not a multiple of replacement length ---- The sessionInfo() output is below, but I think relatively unremarkable. The dataset is very large (about 550K observations, although the outcome (B_fainting) is relatively sparse with only 1...

...alloc in ncread */ double ofs, scl; /* offset and scaling factors */ double *ptr; SEXP field, dim, dimnames, lon, lat, tim, missing; printf("ncfile= %s, vname=%s\n",fname,vname); printf("\nfield->%x\nlon -> %x\nlat -> %x\ntim -> %x\nmissing -> %x\n", field,lon,lat,tim,missing); /* Call the function dealing with the netCDF data */ /* The data is placed in memeory */ status=ncread(fname, vname, &nx, &ny, &nt, &ptrs, &scl, &ofs); /* Allocate space for pointers */ /* Use PROTECT to...

Hello!   I would like to sample 30 % of cases (with at least 1 value lower than 3) and among them I want to set all values lower than 3 (within selected cases) as NA (NMAR- Not missing at random). I managed to sample cases, but I don’t know how to set values (lower than 3) as NA.   R code:   x <-

..., 3) : int [1:2] 8 9 ..$ BL_TRIGLYC.A : int 10 ..$ BP.B : int 11 ..$ BP.A : int 12 ..$ rcs(BL_HDL.A, 3):rcs(BL_CHOLEST.A, 3): int [1:4] 13 14 15 16 $ na.action :List of 3 ..$ nmiss : Named int [1:9] 780 9389 3891 2405 2404 3055 2677 10623 10328 .. ..- attr(*, "names")= chr [1:9] "srv900" "Age" "Sex" "BL_HDL.A" ... ..$ omit : Named int [1:24266] 20 29 53 75 82 84 119 160 201 230 ... .. ..- att...

Hello I just updated the kernel, dahdi-linux and dahdi-tools Im also using now asterisk 1.4.26.1 And im still with a red light (not RED/YELLOW anymore): [root at catumbela ~]# /etc/rc.d/init.d/dahdi status ### Span 1: TE4/0/1 "T4XXP (PCI) Card 0 Span 1" (MASTER) HDB3/ RED 1 PRI CAS RED 2 PRI CAS RED 3 PRI CAS RED 4 PRI

...lab[2], side = 4, at = mean(par("usr")[3:4]), line = 3, xpd = NA) } else { mtext(ylab[2], side = 4, at = 0.5 * f.row, line = 3.25, outer = TRUE, xpd = NA) } } if (length(missingrows) > 0) { cat("\nMissing rows:", missingrows, "\n") invisible(missingrows) } } ---1149173172-1804289383-983267779=:26068 Content-Type: TEXT/plain; NAME="test.asc" RDA1 5 0 9 row.names 5 names 5 class 6 levels 3 cxt 2 0 0 1 0 0 5 -1 19 0 1 4 13 0 1 90 1 1 1 1 1 1 1 1 1 1...

...[ L"format" ], Getopt.Set_string ("raw|qcow2", format), s_"Output format (default: raw)"; @@ -231,26 +231,26 @@ read the man page virt-builder(1). (match args with | [arg] -> arg | [] -> - error (f_"virt-builder os-version\nMissing 'os-version'. Use '--list' to list available template names.") + error (f_"virt-builder os-version\nMissing ‘os-version’. Use ‘--list’ to list available template names.") | _ -> - error (f_"too many parameters, expecting 'os-version...

Currently the blacklist is maintained by hand in kprobes.c which is separated from the function definition and is hard to catch up the kernel update. To solve this issue, I've tried to implement new NOKPROBE_SYMBOL() macro for making kprobe blacklist at build time. Since the NOKPROBE_SYMBOL() macros can be placed right after the function is defined, it is easy to maintain. This series

Currently the blacklist is maintained by hand in kprobes.c which is separated from the function definition and is hard to catch up the kernel update. To solve this issue, I've tried to implement new NOKPROBE_SYMBOL() macro for making kprobe blacklist at build time. Since the NOKPROBE_SYMBOL() macros can be placed right after the function is defined, it is easy to maintain. This series

Hi, Here is the version 3 of NOKPORBE_SYMBOL series. Currently the blacklist is maintained by hand in kprobes.c which is separated from the function definition and is hard to catch up the kernel update. To solve this issue, I've introduced NOKPROBE_SYMBOL() macro for making kprobe blacklist at build time. Since the NOKPROBE_SYMBOL() macros can be placed right after the function is defined

Hi, Here is the version 3 of NOKPORBE_SYMBOL series. Currently the blacklist is maintained by hand in kprobes.c which is separated from the function definition and is hard to catch up the kernel update. To solve this issue, I've introduced NOKPROBE_SYMBOL() macro for making kprobe blacklist at build time. Since the NOKPROBE_SYMBOL() macros can be placed right after the function is defined