similar to: how to interpolate time series data with missingness

R-help, I have a character vector whose elements are the names of matrixes. Something like this: > test <- ls(pattern="Oki") [1] "aaOki" "aOki" "bOki" "c1Oki" "c2Oki" "c3Oki" "cOki" "dOki" "eOki" "fOki" "gprsOki" "hOki"

Dear all, When calling rlm with the following data, I get an error. (R v.1.8.1, WinXP Pro 2002 with service pack 1.) > d <- na.omit(data.frame(CPRATIO, HEIGHTZ, FAMILYID)) > c <- tapply(d$CPRATIO, d$FAMILYID, mean) > h <- tapply(d$HEIGHTZ, d$FAMILYID, mean) > c 1 2 3 6 7 9 10 11 6.000000 2.500000 3.250000

Hi, I have a sparse matrix.I want to fill values into the entries whose value is 0.The new generated values should come from the interpolation of the values have existed.Does R provide such interpolation functions which operate on Matrix, for example ,such a matrix below 0 0 0 0 2.3 0 0 0 0 0 0 3.1 0 0 0 0 1.4 0 0 0 0 0 0 0 0 0 0 1.1 0 0 0 0 0 0 0 0 0 0 0 4

Le 30/09/2019 ? 16:17, Duncan Murdoch a ?crit?: > > There's a StackOverflow question > https://stackoverflow.com/q/22024082/2554330 that references this text > from ?missing: > > "Currently missing can only be used in the immediate body of the > function that defines the argument, not in the body of a nested > function or a local call. This may change in the

There is "missing with default" and "missing without default". If an argument x is missing without a default, then missing(x) is true, if you pass x to another function, it will pass the value of the "missing argument". (which is different than simply being missing!) If an argument x is missing _with_a default, then missing(x) is still true, but if you pass x to

Dear all, I generated a dataset with 500 unrelated individuals and 10 biallelic SNPs. From this dataset,I would like to create data with 5% missingness on genotype information at random and also data with 5% genotyping error. Can someone help me with how I can do it. [[alternative HTML version deleted]]

I have the R code at the end. The last command gives me "1 observation deleted due to missingness". I don't understand what this error message. Could somebody help me understand it and how to fix the problem? > summary(afit) Df Sum Sq Mean Sq F value Pr(>F) A 2 0.328 0.16382 0.1899 0.82727 B 3 2.882 0.96057 1.1136 0.34644 C

Dear R- User,   I have a dataset that looks like the following:   jh<-data.frame(  'id'=seq(1,10,1),   'time0'=c(8,5,8,8,9,NA,NA,2,4,5),   'time4'=c(NA,NA,9,8,NA,2,3,2,4,5),  'time8'=c(NA,2,8,NA,5,NA,2,3,NA,4),  'time12'=c(NA,2,NA,NA,NA,3,3,2,3,NA),  

Dear R-List, My questions concerns missing values. Specifically, is is possible to use different "types" of missingness in a dataset and not a one-size-fits-all NA? For example, data may be missing because of an outright refusal by a respondent to answer a question, or because she didn't know an answer, or because the item simply did not apply. In later analysis it is sometimes

Dear R users,   I have the following problems. My dataset (dat) is as follows: a <- c(1,2,3) id <- rep(a, c(3,2,3)) stat <- c(1,1,0,1,0,1,1,1) g <- c(0,0,0,0,0,0,1,0) stop <- c(1,2,4,2,4,1,1.5,3) dat <- data.frame(id,stat,g,stop)   I want to creat a new dataset (dat2) with missing values such that when either g = =1 or stat = =0, the remaining rows for an individual subject

There's a StackOverflow question https://stackoverflow.com/q/22024082/2554330 that references this text from ?missing: "Currently missing can only be used in the immediate body of the function that defines the argument, not in the body of a nested function or a local call. This may change in the future." Someone pointed out (in https://stackoverflow.com/a/58169498/2554330)

Dear all, I am facing a challenge when applying the outer product with a matrix by rows. What I have is a rather big matrix, which I would like to convert into a different matrix by doing something like outer(matrix_row, vector, function(x,y) x[1]+5*x[4]/y) In order to get there, I tried to get the matrix into a list using the mefa package, as subsetting the original data appears too

Readers, Is it possible to create a plot command based upon the indices of missing values in a data set? dataset1<-read.table(text=' 10 2 20 NA 30 5 40 7 50 NA 60 NA 70 2 80 6 90 NA 100 9 ') dataset2<-read.table(text=' 0.2 0.4 0.1 0.9 0.2 0.3 1.1 0.7 0.9 0.6 0.4 ') The 'approx' function is used to obtain the interpolated values for 'NA' in dataset1.

Can anybody help me write a code on the following data example, which fills out all NA values by using a linear interpolation with the two closest values? Doy is day of year (%j). Code example: yr<-c(rep(2000,14)) doy<-c(16:29) dat<-c(3.2,NA,NA,NA,NA,NA,NA,5.1,NA,NA,NA,NA,NA,4.6) ta<-cbind(yr,doy,dat) ta yr doy dat [1,] 2000 16 3.2 [2,] 2000 17 NA [3,] 2000 18 NA

Hi, I'd like to divide each element of a matrix by a specific value per column. These specific values are stored in a list. For example: > x <- c(1,2,3,4,5) > y <- matrix(c(1:30), nrow = 6) Now I want to divide each element in y[,1] by x[1], y[,2] by x[2] etc. I have tried this > my_function <- function(data, ind) data/ind > apply(y, 2, my_function, x) [,1] [,2]

Hello R-helpers: I have data like this: sample replicate height weight age A 1.00 12.0 0.64 6.00 A 2.00 12.2 0.38 6.00 A 3.00 12.4 0.49 6.00 B 1.00 12.7 0.65 4.00 B 2.00 12.8 0.78 5.00 C 1.00 11.9 0.45 6.00 C 2.00 11.84 0.44 2.00 C 3.00 11.43 0.32 3.00 C 4.00 10.24 0.84 4.00 D

reorder() is probably the best way to order the levels in a vector without manually specifying the order. But reorder() orders by default in an increasing order: "The levels are ordered such that the values returned by ?FUN? are in increasing order." Is there a way to do what reorder() does, but order the levels according to a _decreasing_ order of the values? Sverre

hello, I have this simple question. This is my dataset size 1 57 2 97 3 105 4 123 5 136 6 153 7 173 8 180 9 193 10 202 11 213 12 219 13 224 14 224 15 248 16 367 17 496 18 568 19 618 20 670 21 719 22 774 23 810 24 814 25 823 I plot it with: plot(generalstats[,1], type="b", xlab="Mesi", ylab="Numero di vertici", main=""); and try to interpolate with a

Hello, I have a xts object, I would like to fill the NA with linear interpolated data. Can anyone please help. > str(zz) An ‘xts’ object from 2010-11-24 15:59:29 to 2010-11-24 16:00:00 containing: Data: num [1:23401, 1] 312 312 312 312 312 ... Indexed by objects of class: [POSIXct,POSIXt] TZ: xts Attributes: List of 2 $ src : chr "datafeed" $ updated: POSIXct[1:1],

Dear R Users, I have some data that is very similar in form to a 3D image - ie univariate data on a regular 3D grid. I keep this as a 3D numeric array in R with attributes describing the sampling points along the 3 dimensions. I would like to interpolate this onto a new regular 3D grid that I specify (eg by supplying 3 vectors corresponding to the new grid locations on each of the 3