similar to: impcat='tree'

Full_Name: Kem Phillips Version: 2.7.1 (2008-06-23) OS: Windows Xp professional Submission from: (NULL) (98.221.200.108) The Hmisc function describe fails, giving the error message: Error in formatDateTime(dd, atx, !timeUsed) : could not find function "format.dates" Loading the chron package, where function dates apparently resides, does not fix the problem. Note

Dear all, I did post this more or less identical mail in a follow up to another question I posted, but under another heading. I try again, but now under the correct header. upon running this code (from the Hmisc library-latex function) I believe the call to summary.formula is allright and produces wonderful tables, but the latex command results in a correct formatted table but where all the

Many thanks to Nick Ellis, Vadim Kutsyy, Charles Berry, and Bill Dunlap for providing thoughts and alternative solutions to the problem. I have included Nick and Charles' notes in full below and a summary of the others. Thanks also to Bill for telling me about an inconsistency in how the first argument of sample is interpreted. I had ignored this, resulting in a major bug. Each person

This seems a very natural approach but I probably am having a trouble with the iterator invalidation. However, looking at other peephole optimizers passes, I couldn't see how to do this: #define BUILD_INS(opcode, new_reg, i) \ BuildMI(*MBB, MBBI, MBBI->getDebugLoc(), TII->get(X86::opcode)) \ .addReg(X86::new_reg, kill).addImm(i) for

I'm writing a peephole pass and I'm done with the X86_64 instruction level detail work. But I'm having difficulty with the basic block surgery of replacing the old MachineInst. The peephole pass gets called per MachineFunction and then iterates over each MachineBasicBlock and in turn over each MachineInst. When it finds an instruction which should be replaced, it builds a new

Dear all, I'm trying to impute NAs by conditional medians using transcan() in conjunction with impute.transcan(). ... see and run attached example.. Everything works fine, however impute() returns saying Under WINDOWS > x.imputed <- impute(trans) Fehler in assign(nam, v, where = where.out) : unbenutzte(s) Argument(e) (where ...) Zus?tzlich: Warnmeldung: variable X1 does not

There are 11 BuildMI() functions in MachineInstrBuilder.h including four using the iterator and one using an instruction. But I just don't think that's it. The creation of the new instruction works fine (works fine with OldMI as well) and the new instruction is present in the assembly output. The problem is removing the old instruction correctly. > The loop header needs to be

On Thu, Jan 31, 2013 at 10:29 AM, Weijia Wang <wwang.nyu@gmail.com> wrote: > Hi, > > > > I have a new question about subsetting in R. > > > > Say we have this data frame: > > > > PT_ID Blood_Pressure OBS_TYPE > > 92 1900 90.0 DBP > > 94 1900 90.0 DBP > > 174 2900 140.0 SBP > > 176 2900

Hello, I'm working on a Cox Proportional Hazards model for a cancer data set that has missing values for the categorical variable "Grade" in less than 10% of the observations. I'm not a statistician, but based on my readings of Frank Harrell's book it seems to be a candidate for using multiple imputation technique(s). I understand the concepts behind imputation, but using

I made the change to the BuildMI() call. Again, I don't think that matters. #define BUILD_INS(opcode, new_reg, i) \ BuildMI(*MBB, OldMI, MBBI->getDebugLoc(), TII->get(X86::opcode)) \ .addReg(X86::new_reg, kill).addImm(i) I didn't completely understand your other proposed change: ​ for (MachineBasicBlock::iterator MBBI = MBB->begin();

I would like to impute missing data in a set of correlated variables (columns of a matrix). It looks like transcan() from Hmisc is roughly what I want. It says, "transcan automatically transforms continuous and categorical variables to have maximum correlation with the best linear combination of the other variables." And, "By default, transcan imputes NAs with "best

Dear all, I am using transcan to impute missing values (single imputation). I have several dichotomous variables in my dataset, but when I try to impute the missings sometimes values are imputed that were originally not in the dataset. So, a variable with 2 values (severe weight loss or no/limited weight loss) for example coded 0 and 1, shows 3 different values after imputation (0, 1 and 2). I

I am using RW 1.2.3. on an IBM PC 300GL. Using the data bp.dat which accompanies Helen Brown and Robin Prescott 1999 Applied Mixed Models in Medicine. Statistics in Practice. John Wiley & Sons, Inc., New York, NY, USA which is also found at www.med.ed.ac.uk/phs/mixed. The data file was opened and initialized with > dat <- read.table("bp.dat") >

Dear all; I am trying to use HMISC imputation function to perform multiple imputations on my data and I keep on getting errors for the code given in the help files. When using "aregImpute" the error is; >f <- aregImpute(~y + x1 + x2 + x3, n.impute=100) Loading required package: acepack Iteration:1 Error in .Fortran("wclosepw", as.double(w), as.double(x),

Dear colleagues, I had a question wrt the car package. How do I evaluate whether a simpler multivariate regression model is adequate? For instance, I do the following: ami <- read.table(file = "http://www.public.iastate.edu/~maitra/stat501/datasets/amitriptyline.dat", col.names=c("TCAD", "drug", "gender", "antidepressant","PR",

Hello: I have been trying to impute missing values of a data frame which has both numerical and categorical values using the function transcan() with little luck. Would you be able to give me a simple example where a data frame is fed to transcan and it spits out a new data frame with the NA values filled up? Or is there any other function that i could use? Thank you avneet ===== I believe in

Dear R help, I have the following data frame: structure(list(prochi = c("ind_1", "ind_1", "ind_1", "ind_1", "ind_1", "ind_1", "ind_1", "ind_1", "ind_1", "ind_1"), date_1st_event = structure(c(14784, 14784, 14784, 14784, 14784, 14784, 14784, 14784, 14784, 14784 ), class = "Date"),

Hello, I am trying to create subsets of grouped data (by area size), and use the area size as part of the output name. The code below works for area (xout) 1 and 50, the other files are given NA for an area. A simple example: xout <- c(1,5,10,25,50,100) for(i in xout) { print(paste("Areal_Ppt_",xout[i],"sqmi.txt", sep="")) } [1] "Areal_Ppt_1sqmi.txt"

Hello all, I'm struggling to grasp how I might use lapply() instead of looping to run approx() on a list consisting of multiple arrays - each of equal dimension. But simpler than that, I haven't been able to successfully apply approx() to an array, unless I loop through the third dimension and extract the matrix. See example code below... Any suggestions will be gratefully received. Thanks

Hi there, I had a problem when I hoped to get confidence intervals for the parameters I got using mle() of stats4 package. This problem would not appear if ``fixed'' option was not used. The following mini-example will demo the problem: x <- c(100, 56, 32, 18, 10, 1) r <- c(18, 17, 10, 6, 4, 3) n <- c(18, 22, 17, 21, 23, 20) loglik.1 <- function(alpha, beta, c) { x