search for: missinges

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-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 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

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]]

Dear R-devel list, I realized that removing a predictor in lm through the "-"'s operator in formula() does not affect the complete cases that are considered. A minimal example is: summary(lm(Wind ~ ., data = airquality)) # 42 observations deleted due to missingness summary(lm(Wind ~ . - Ozone, data = airquality)) # still 42 observations deleted due to missingness, even if only 7

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 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),  

Hi all, I have a vector, most of which is missing. The data is always increasing, but may do so in jumps. I would like to interpolate the NAs with 'best guesses', using something like filter(), which doesn't work due to the NAs. Here is an example: > x <- c(2,3,NA,NA,NA,3.2,3.5,NA,NA,6,NA) > x [1] 2.0 3.0 NA NA NA 3.2 3.5 NA NA 6.0 NA I would like a function that

I want to make a multi-dimensional array. To be specific I want to make the following array results<-array(0,dim=c(2,2,64,7)) This is the code I have created but it gives no result due to the error "subscript out of bound". x<-rep(7,7) # Missingness in intervention y<-rep(7,7) # Missingness in control

Dear all, I would like to model the relationship between y and x. y is binary variable, and x is a count variable which may be possion-distribution. I think it is better to divide x into intervals and change it to a factor before calling glm(y~x,data=dat,family=binomail). I try to use rpart. As y is binary, I use "class" method and get the following result. >

There is a problem when importing an spss-file containing explicitly declared missing values in R using the read.spss function from the foreign package. I'm not sure these problems are the same in every version of spss, I am using the latest version 16.0.2. I included http://www.nabble.com/file/p18776776/missingdata.sav missingdata.sav and

Dear all, I have been attempting to use multiple imputation (MI) to handle missing data in my study. I use the mice package in R for this. The deeper I get into this process, the more I realize I first need to understand some basic concepts which I hope you can help me with. For example, let us consider two arbitrary variables in my study that have the following missingness pattern: Variable 1

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

...## indicator matrix for missingnes (1 observed, 0 missing) R<-matrix(c(1,1,1, 0,0,1, 1,1,0, 0,1,1),ncol=3,byrow=TRUE) ## probabilities for row 1, row 2, row 3 and row 4 p<-c(0.375,0.25,0.25,0.125) ## does not exactly what i want, because i get rows ## of missinges pattern which are not in R X[rbinom(100,1,p[1])==1,R[1,]==1] <- NA X[rbinom(100,1,p[2])==1,R[2,]==1] <- NA X[rbinom(100,1,p[3])==1,R[3,]==1] <- NA X[rbinom(100,1,p[4])==1,R[4,]==1] <- NA So it would be great if i can get any advice how to do this. I also tried rmultinom or sample but...

Hi, I am not a statistics expert, so I have this question. A linear model gives me the following summary: Call: lm(formula = N ~ N_alt) Residuals: Min 1Q Median 3Q Max -110.30 -35.80 -22.77 38.07 122.76 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 13.5177 229.0764 0.059 0.9535 N_alt 0.2832 0.1501 1.886 0.0739

I am unable to get the basehaz function to apply a proportional hazards model to a new data frame. I replicated my specific situation with the example for coxph in the help, where I changed the x value of the first record from 0 to 1. Is there something incorrect in the syntax that I am using? Thanks in advance! test1 <- list(time= c(4, 3,1,1,2,2,3), status=c(1,NA,1,0,1,1,0),

dear list members, there seems to be a problem with the prelim.norm function (package norm) as number of items in the dataset increases. the output of prelim.norm() is a list with different summary statistics, one of them is the missingness indicator matrix "r". it lists all patterns of missing data and a count of how often each pattern occured in the dataset. as the number of items and

Hi, A question (& possible suggestion) about function calls. Is there an R idiom to eliminate the redundancy in the following common situation? foo <- function(x, control=ComplicatedDefault) { etc. } plotfoo <- function(x, foocontrol=ComplicatedDefault, ...) { y <- foo(x, control=foocontrol) lines(x,y,...) } The idea is that there are MANY