similar to: padding specific missing values with NA to allow cbind

I am working on a function that will remove outliers for regression analysis. I am stating that a data point is an outlier if its studentized residual is above or below 3 and -3, respectively. The code below is what i have thus far for the function x = c(1:20) y = c(1,3,4,2,5,6,18,8,10,8,11,13,14,14,15,85,17,19,19,20) data1 = data.frame(x,y) rm.outliers =

Hola amigos, esta es mi primera duda, espero que no sea demasiado fácil. Tengo unos datos de dos variables y quiero mostrar recta de regresión y valor de correlación serie0 <- c(0.651, 0.712, 0.614, 0.645, 0.559, 0.647, 0.642, 0.534, 0.616, 0.621, 0.623) serie1 <- c(0.572, 0.641, 0.565, 0.596, 0.518, 0.604, 0.602, 0.501, 0.58, 0.589, 0.596) data <- cbind(serie0, serie1) colnames(data)

Bueno, ya lo he solucionado gracias a Carlos me ha enviado un correo en privado (supongo que se ha despistado, si no lo querías hacer público ya es tarde) con esta info: -- Creo que lo tienes (en formato básico) aquí: https://stat.ethz.ch/pipermail/r-help/2007-November/146285.html Aunque pensaba que si tu objetivo último es el de que usuarios "potencialmente tontos" sean capaces de

How can I get the outlier in this boxplot of "Score" to be represented by the corresponding value in "SubNo"? score=c(6,6,7,14,5,7,6,8) SubNo=1:8 mydata=data.frame(SubNo, score) boxplot(mydata$score) Thanks! Kevin [[alternative HTML version deleted]]

Hi, I am a beginner user of R. I have a trivial question ? I am almost ashamed I cannot figure it out does not matter how many times I am reading the help. I have a table in .txt format, tab delimited. I can read it with ?read.delim()? with no problems. Afterwards I would like to use boxplot function to see if there are any outliers in the column 5 of my data called TPAH16.ppm In the

Dear Community, I want to identify outliers in my data. I don't know how to use identify command in the plots obtained. I've gone through help files and use mahalanobis example for my purpose: NormalMultivarianteComparefunc <- function(x) { Sx <- cov(x) D2 <- mahalanobis(x, colMeans(x), Sx) plot(density(D2, bw=.5), main="Squared Mahalanobis distances, n=nrow(x),

Hi all, I'm trying to apply loess regression to my data and then use the fitted model to get the *standardized/studentized residuals. I understood that for linear regression (lm) there are functions to do that:* * * fit1 = lm(y~x) stdres.fit1 = rstandard(fit1) studres.fit1 = rstudent(fit1) I was wondering if there is an equally simple way to get the standardized/studentized residuals for a

I contacted John Fox about this first, because parts of the file are attributed to him. He says that he didn't write rstandard.glm(), and suggests asking r-devel. As it stands, rstandard.glm() has summary(model)$dispersion outside the sqrt(), while in rstandard.lm(), the sd is already sqrt()ed. This seems to follow stdres() in VR/MASS/R/stdres.R. Of course for the c("poisson",

#I wish to find the "next non-NA" value within each row of a data-frame. #e.g. I have a data frame mydata. Rows 1, 2 & 3 have soem NA values. mydata <- data.frame(matrix(seq(20*6), 20, 6)) mydata[1,3:5] <-  NA mydata[2,2:3] <-  NA mydata[2,5] <-  NA mydata[3,6] <-  NA mydata[1:3,] #this loop accomplishes the task; I am tryign toi learn a "better" way for(i

I am using the following command to replace all the missing values and assorted typos in a dataframe with NA: mydata[mydata>80]=NA The problem is that the first column contains values which should be more than 80, so really I want to do it just for mydata[,2:length(mydata)] I can't seem to re-write the code to fit: mydata[,2:length(mydata)>80]=NA # no error message, but doesn't

I've been searching around for a function for computing the Brown-Forsythe F* statistic which is a substitute for the normal ANOVA F statistic for when there are unequal variances, and when there is evidence of non-normality. A couple of other people have asked this question, the responses I found have been: ?oneway.test However, that function appears to use the Welch W statistic which,

Dear friends, I am trying to produce Contour Plot with R, but there are some NA in my data matrix. After I ran the following R script, I got the error message:"no proper `z' matrix specified". Does anybody know how to plot contour chart with R for the non-strict matrix? Thank you in advance!!!

I have a data which contain some NA value in their elements. What I want to do is to **perform clustering without removing rows** where the NA is present. I understand that `gower` distance measure in `daisy` allow such situation. But why my code below doesn't work? __BEGIN__ # plot heat map with dendogram together. library("gplots") library("cluster")

Thanks for your reply. I use mvrnorm from the *MASS* package and lmrob from the *robustbase* package. To further explain my data generating process, the idea is as follows. The explanatory variables are generated my a multivariate normal distribution where the covariance matrix of the variables is defined by Sigma in my code, with ones on the diagonal and rho = 0.15 on the non-diagonal. Then y

Hi folks, I am running a very simple regression using mylm <- lm(mass ~ tarsus, na.action=na.exclude) I would like the use the residuals from this analysis for more regression but I'm running into a snag when I try cbind(mylm$residuals, mydata) # where my data is the original data set The error tells me that it cannot use cbind because the length of mylm$residuals is

What is 'd'? What is 'n'? On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < christienkerbert at gmail.com> wrote: > Thanks for your reply. > > I use mvrnorm from the *MASS* package and lmrob from the *robustbase* > package. > > To further explain my data generating process, the idea is as follows. The > explanatory variables are generated my a

I have the following procedure which worked just fine for in R 2.2.0. Recently I upgraded to 2.6.1 and now get an error: > ScatterOutlier(pass_500_506[1:1000,6:12], marginal_500_506[,6:12]) Error in eval(expr, envir, enclos) : object "out" not found Note that I use the same workspace (and hence data) as in 2.2.0. When I make sure that the object "out" exists at

Hello, I'm seeking ideas on how to remove outliers from a non-normal distribution predictor variable. We wish to reset points deemed outliers to a truncated value that is less extreme. (I've seen many posts requesting outlier removal systems. It seems like most of the replies center around "why do you want to remove them", "you shouldn't remove them", "it

When you have fairly large data, _do not use the formula interface_, as a couple of copies of the data would be made. Try simply: Myforest.rf <- randomForest(Mydata[, -46], Mydata[,46], ntrees=100, mtry=7) [Note that you don't need to set proximity (not proximities) or importance to FALSE, as that's the default already.] You might also want to use

d is the number of observed variables (d = 3 in this example). n is the number of observations. 2018-03-04 11:30 GMT+01:00 Eric Berger <ericjberger at gmail.com>: > What is 'd'? What is 'n'? > > > On Sun, Mar 4, 2018 at 12:14 PM, Christien Kerbert < > christienkerbert at gmail.com> wrote: > >> Thanks for your reply. >> >> I use