similar to: Missing at random

Hi R users   Thanks in advance   Is lifting wavelet transform implemented in R? If so, which package or codes  can be used for performing that?    assaedi76@yahoo.com   Thanks     [[alternative HTML version deleted]]

R users. Thanks in advance.   I would  be glad if someone could tell me how to get a simulated data from AR(1), MA(1)   My email is : assaedi76@yahoo.com    Thanks [[alternative HTML version deleted]]

Hi, could anyone explain to me what this warning message exactly means and what the consequences are? Is it due to the fact that there are very extreme observations / outliers included or what is the reason for it? Thanks so much, Werner Machen Sie Yahoo! zu Ihrer Startseite. Los geht's:

Dear all, I want to do a logistic regression. So far I've only found out how, in a dataset of complete cases. I'd like to do logistic regression via max likelihood, using all the study cases (complete and incomplete). Can you help? I'm using glm() with family=binomial(logit). If any covariate in a study case is missing then the study case is dropped, i.e. it is doing a complete case

R users   Thanks in advance:   I need to use the function "mle.cp" in R.  When I run this function I recive   result <- mle.cp(y.hald~x.hald) Error: could not find function "mle.cp"  could some one  give me a help?   thanks [[alternative HTML version deleted]]

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 <-

Hi, I want to impute the missing values in my data set multiple times, and then combine the results (like multiple imputation, but manually) to get a mean of the parameter(s) from the multiple imputations. Does anyone know how to do this? I have the following script: y1 <- rnorm(20,0,3) y2 <- rnorm(20,3,3) y3 <- rnorm(20,3,3) y4 <- rnorm(20,6,3) y <- c(y1,y2,y3,y4) x1 <-

Hi. Can anyone point me to any module in R which implements "Little's Chi Square test" for MCAR. The problem is that i have around 60 behavioural variables on a 6 point categorical scale which i need to test for MCAR and MAR. What i can make out from preliminary analysis is that moderate (0.30 to 0.60) correlations may be present in several variable pairs leading me to suspect

I keep getting the 'memory.size' error message when I run a program I have been writing. It always it cannot allocate a vector of a certain size. I believe the error comes in the code fragement below where I have multiple arrays that could be taking up space. Does anyone know a good way around this? w1 <- outer(xk$xk1, data[,x1], function(y,z) abs(z-y)) w2 <- outer(xk$xk2,

useR's, I am trying trying to find out if there is a faster way to do a certain computation. I have successfully used FOR loops and the apply function to do this, but it can take some time to fully compute, but I was wondering if anyone may know of a different function or way to do this: > x [1] 1 2 3 4 5 > xk [1] 0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 I want to do:

Hi all I have a data frame with duplicate columns and i want to remove duplicates by adding rows in each group of duplicates, but have lots of NA's. Data: dfrm <- data.frame(a = 1:4, b= 1:4, cc= 1:4, dd=1:10, ee=1:4) names(dfrm) <- c("a", "a", "b", "b", "b") dfrm[3,2:3]<-NA dfrm a a b b b 1 1 1 1 1 1 2 2 2 2 2 2 3

Dear R-list, I'm trying to estimate the relative importance of 6 environmental variables in determining clam yield. To estimate clam yield a previous work used the function Yield = (A^a*B^b*C^c...)^1/(a+b+c+...) where A,B,C... are the values of the environmental variables and the weights a,b,c... have not been calibrated on data but taken from literature. Now I'd like to estimate the

Consider a data frame (x) with 2 variables, x1 and x2, having equal values. It looks like: x1 x2 1 1 2 2 3 3 Now, consider a second data frame (xk): xk1 xk2 0.5 0.5 1.0 0.5 1.5 0.5 2.0 0.5 0.5 1 1.0 1 1.5 1 2.0 1 0.5 1.5 1.0 1.5 1.5 1.5 2.0 1.5 0.5 2 1.0 2 1.5 2 2.0 2 I have written code to calculate some differences between these

Greetings. Here is sample code, with some comments. It shows how I can simulate data and estimate glm with binomial family when there is no individual level random error, but when I add random error into the linear predictor, I have a difficult time getting reasonable estimates of the model parameters or the variance component. There are no clusters here, just individual level responses, so

Dear all, I have discovered that there are many things that I used to do in my GIS which are easily done directly in R, for example calculating interpoint distances using geoR and pick out points inside a polygon using splancs. I now wonder, is there a function to create a line object like a watercourse and then calculate the distances between many points in space and this line? I couldn't

Dear all, let say I have following data frame: > data.frame(x=rnorm(18), y=rep(c("a", "b", "c"), each=6)) x y 1 -1.072152537 a 2 0.382985265 a 3 0.058877377 a 4 -0.006911939 a 5 -2.355269051 a 6 -0.303095553 a 7 0.484038422 b 8 0.733928931 b 9 -1.136014346 b 10 0.503552090 b 11 1.708609658 b 12 -0.294599403 b 13

I'm relatively new to R, and not particularly adept yet, but I was wondering if there was a simply way to simulate missing data that are MAR, MNAR and MCAR. I've got a good work-around for the MCAR data, but it's sort of hard to work with. Josh [[alternative HTML version deleted]]

Hi, I have a data frame with two columns. I need to remove duplicated rows in first column, but I need to do it conditionally to values of the second column. Example: Point_counts Psi_Sp 1 A 0 2 A 1 3 B 1 4 B 2 5 B 0

I have a data set with 22 fields and several thousand records in which one field (count) indicates the number of times that each specific combination of the other 21 fields occurred in a bigger and largely unavailable data set. So each record is unique in its combination of field values and has a field that identifies how many multiples of this record actually occurred. Without resorting to

Hello, I have a file which contains a column with age, which is represented in the two following patterns 1. "007/A" or ''007/a" or ''7 /a" ..... In this case A or a means year and I would like to extract only the numeric values eg 7 in the above case if this pattern exits in a line of file. 2. "004/M" or "004/m" where M or m means month