similar to: Impute missing data by regression in R

Dear all. I have data with 2 variable x,y size 10000. I want to sampling from this data with size 100. How I can do it. THANK. -- Jumlong Vongprasert Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

Dear all I have problem with calculate probability, I have data x1,...,x10, I want to calculate probability x11 given x1,...,x10 with two conditions. 1. x is normal 2. unknow distribution How I can do this. Many Thanks. Jumlong -- Jumlong Vongprasert Assist, Prof. Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani

Dear All I have 2 value assume 2.4 and 2.9 and mean = 2 variance = with n = 10 I want to find probability = 2.4 and 2.9. How I can do this. Many Thanks. Jumlong -- Jumlong Vongprasert Assist, Prof. Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

Dear All I want to calculate power of test. I want to test H0: Rho = 0 VS H1: Rho != 0. Assume, I have r=0.2 and sample size = 100. How I can do this. Many Thanks Jumlong -- Jumlong Vongprasert Assist, Prof. Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

Dear all I have data like this: x y [1,] 59.74889 3.1317081 [2,] 38.77629 1.7102589 [3,] NA 2.2312962 [4,] 32.35268 1.3889621 [5,] 74.01394 1.5361227 [6,] 34.82584 1.1665412 [7,] 42.72262 2.7870875 [8,] 70.54999 3.3917257 [9,] 59.37573 2.6763249 [10,] 68.87422 1.9697770 [11,] 19.00898 2.0584415 [12,] 60.27915 2.5365194 [13,] 50.76850

Dear all I want to create dataset with MCAR type from my dataset. I have my dataset with 100 records, and I want to create dataset from this dataset to missing 5 records. How I can do it. THX Jumlong -- Jumlong Vongprasert Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

Dear All I have this > ls() [1] "IWJR.complete" "IWJR.missing" "IWJR.reg.temp" [4] "IWJR.reg.temp.x" "IWJR.reg.temp.y" "IWJR.temp" [7] "Power.record.100.3.5" "col.delete" "correlation.100.3.5" [10] "i" "index.temp"

Dear All I want to test my data for Random Walk or Markov Process. How I can do this. Many Thanks -- Jumlong Vongprasert Assist, Prof. Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

Dear All. I have some problem with combined two data frame. .... I have first data frame .. GPAX THAI MATH SCINCE SOCIAL HEALT ART CAREER LANGUAGE 1227 2.99 3.32 2.50 2.64 3.05 3.60 3.72 3.57 2.62 1704 2.81 2.56 2.48 2.86 3.22 3.19 3.55 3.20 2.51 617 2.18 1.90 1.97 2.06 2.38 3.50 3.54 2.33 1.70 876 2.82 3.14 2.73 2.46 2.71 3.11 3.04 3.24 2.90

Dear All I have problem about calculte variance use R. I use trimm mean by mean(x, trim=.05) and I want to calculate variance with trimm=.05. How I can do this. Many Thanks. Jumlong -- Jumlong Vongprasert Assist, Prof. Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

Dear all I tried to use regression to predicted mu data, but it has error like this: > IWJR.complete x y [1,] 33.17635 2.4705021 [2,] 81.61225 3.3815620 [3,] 65.47392 1.6518975 [4,] 57.97806 1.6110785 [5,] 76.05528 2.1601246 [6,] 41.36090 1.5498132 [7,] 68.77844 2.8078691 [8,] 55.57040 2.1183063 [9,] 41.29287 1.8015709 [10,] 65.43935 2.3483183 [11,] 22.44821

Deal All I want to use MSGARCH for my data. What package I will used. Many THanks -- Jumlong Vongprasert Assist, Prof. Institute of Research and Development Ubon Ratchathani Rajabhat University Ubon Ratchathani THAILAND 34000 [[alternative HTML version deleted]]

hi, i am getting an error when calling the impute.knn function (see the screenshot below). what is the problem here and how can it be solved? screenshot: ################## *** caught segfault *** address 0x513c7b84, cause 'memory not mapped' Traceback: 1: .Fortran("knnimp", x, ximp = x, p, n, imiss = imiss, irmiss, as.integer(k), double(p), double(n), integer(p),

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

I have always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666

Hello R experts I am using Hmisc aregImpute and Impute (following example on page 105 of The Hmisc and Design Libraries). *My end goal is to have NAs physically replaced in my dataframe. I have read the help pages and example in above sited pdf file, but to no avail. Here is example of what I did. Ph, my data frame, is attached. > xt <- aregImpute (~ q5 + q22rev02 + q28a, n.impute=10,

Hi I have a dataset from biological data with forty samples whichh relate to four different treatments. Each sample has thousands of values but as usuual contains missing values I want to use knn to imput these missing values. I am doing tthis using knn.impute. Do I need to specify the various groups or can I just use the knn.impute command on the whole dataset together. Also I am setting

Dear All, I am Ricky Jacob, a project Student from India who is working on Forest Inventories. Input data: Plot(area = .1 ha) data having the following information: 1) Basal Area 2)Tree Density 3)Volume So I am applying this information to the corresponding pixels in the satellite imagery of the study area. I also have given the same values to a 3x3 window around that pixel. Inventory is taken

Dear all, I used fit.mult.impute in Dr. Harrell's Design package to fit a cox ph regression model on five imputed datasets, where all missing predictors were filled by multiple imputation using R package Mice. Are there any functions able to do bootstrapping or cross-validation for the aggregated model? I tried function 'validate' and 'calibrate' in Design package, but