similar to: Robust Statistics for Outlier Detection

Hi dear all, suppose that s is a statistic code; i have a matrix (x) which has 7 columns (1=x1,2=x23=x3,4=x4,5=x5,6=x6 and7=y) and has 20 rows. i want to do linear reggression like reg<-lm(x[,7]~1+x[,1]+x[,2]+.......+x[,6]) but i want to do delete i th row for nrows times and create regression model like above and compute each models' "s" statistics and list them. but i could

Hi dear all, It may be a simple question, i have a list output with different number of elements as following; [[1]] [1] 0.86801402 -0.82974691 0.39746666 -0.98566707 -4.96576856 -1.32056754 [7] -5.54093319 -0.07600462 -1.34457280 -1.04080125 1.62843297 -0.20473912 [13] 0.30659907 2.66908117 2.53791837 0.53788013 -0.57463077 0.27708874 [19] -2.94233200 1.54565643 -6.83694100

Hi dear all, Can aynone help me about delete-d jackknife usually normal jackknife code for my data is: n <- nrow(data) y <- data$y z <- data$z theta.hat <- mean(y) / mean(z) print (theta.hat) theta.jack <- numeric(n) for (i in 1:n) theta.jack[i] <- mean(y[-i]) / mean(z[-i]) bias <- (n - 1) * (mean(theta.jack) - theta.hat) print(bias) but how i can apply delete-d jackknife

Hi Dear All, This is a function which contains Covariance Ratio and Likelihood Distance values (CVRi, LDi). i want to compute the all row's values, that is run this function for nrow(X) times. The X and Y matrices are;

Hi dear all, i have a data (data.frame) which contain y and x coloumn(i.e. y x 1 0.58545723 0.15113102 2 0.02769361 -0.02172165 3 1.00927527 -1.80072610 4 0.56504053 -1.12236685 5 0.58332337 -1.24263981 6 -1.70257274 0.46238255 7 -0.88501561 0.89484429 8 1.14466282 0.34193875 9 0.58827457 0.15923694 10 -0.79532232 -1.44193770 ) i changed

Hi, I' am interested in differences between sample's result when samples consist of full elements and consist of only distinct elements. When sample consist of full elements it take about 120 sec., but when consist of only distinct elements it take about 4.5 or 5 times more sec. I expected that opposite of this result, because unique(sample) has less elements than full sample. Code as

Hi everyones, my function like; e <- rnorm(n=50, mean=0, sd=sqrt(0.5625)) x0 <- c(rep(1,50)) x1 <- rnorm(n=50,mean=2,sd=1) x2 <- rnorm(n=50,mean=2,sd=1) x3 <- rnorm(n=50,mean=2,sd=1) x4 <- rnorm(n=50,mean=2,sd=1) y <- 1+ 2*x1+4*x2+3*x3+2*x4+e x2[1] = 10 #influential observarion y[1] = 10 #influential observarion data.x <- matrix(c(x0,x1,x2,x3,x4),ncol=5) data.y

I have a ror project which has been productized. There are several web sites in one ror project I need to set an "enviroment variable" to run different sites, can I do that using mongrel? I tried to use mongrel''s -S option and set the enviroment variable in that file but it seems mongrel runs that file after it calls enviroment.rb I also used apache''s

Op 19-10-2016 om 13:47 schreef Matthew Broadhead: > i am not 100% sure how to give you the information you require. > > my current setup in /etc/postfix/master.cf is > flags=DRhu user=vmail:mail argv=/usr/libexec/dovecot/deliver -d > ${recipient} > so recipient would presumably be user at domain.tld? or do you want the > real email address of one of our users? is there

I have a statistical question. The data sets I am working with are right-skewed so I have been plotting the log transformations of my data. I am using a Grubbs Test to detect outliers in the data, but I get different outcomes depending on whether I run the test on the original data or the log(data). Here is one of the problematic sets: fgf2p50=c(1.563,2.161,2.529,2.726,2.442,5.047)

For the analysis of financial data wih a large variance, what is the best way to select an outlier threshold? Listed below, is there a best method to select an outlier threshold and how does R calculate it? In R, how do you find the outlier threshold through an interquartile range? In R, how do you find the outlier threshold using the hist command? In R, how do you find the outlier threshold

Hello, I need to analyse a data matrix with dimensions of 30x100. Before analysing the data there is, however, a need to remove outliers from the data. I read quite a lot about outlier removal already and I think the most common technique for that seems to be Principal Component Analysis (PCA). However, I think that these technqiue is quite subjective. When is an outlier an outlier? I uploaded

hi - if I sample from a normal distribution with something like n100<-rnorm(100,0,1) and add an outlier with n100[10]<-4 then qqnorm(n100) visually shows the point 4 as an outlier and calculating the probablity of a value of 4 or bigger in 100 samples of norm(0,1) gives > 1-exp(log(pnorm(4,0,1))*100) [1] 0.003162164 If I have more than 1 sample above outlier threshold the math is a

I have been learning about some outlier tests -- Dixon and Grubb, specifically -- for small data sets. When I try help.start() and search for outlier tests, the only response I manage to find is the Bonferroni test avaiable from the CAR package... are there any other packages the offer outlier tests? Are the Dixon and Grubb tests "good" for small samples or are others more

Hi! I'm trying to implement an outlier test once/row in a large dataframe. Ideally, I'd do this then add the Pvalue results and the number flagged as an outlier as two new separate columns to the dataframe. Grubbs outlier test requires a vector and I'm confused how to make each row of my dataframe a vector, followed by doing a Grubbs test for each row containing the vector of numbers

Dear R-users, I have a problem with randomForest{outlier}. After running the following code ( that produces a silly data set and builds a model with randomForest ): ####################### library(randomForest) set.seed(0) ## build data set X <- rbind( matrix( runif(n=400,min=-1,max=1), ncol = 10 ) , rep(1,times= 10 ) ) Y <- matrix( nrow = nrow(X), ncol = 1) for( i in (1:nrow(X))){

Dear listers: I have an idea to do the outlier detection and I need to use R to implement it first. Here I hope I can get some input from all the guru's here. I select distance-based approach--- step 1: calculate the distance of any two rows for a dataframe. considering the scaling among different variables, I choose mahalanobis, using variance as scaler. step 2: Let k be the number of

I need to detect outliers in a large data set which is highly right-skewed. I plan to use medcouple-based outlier detection. Is there any support for medcouple-based outlier detection in R? Are there any other routines in R to perform outlier detection in highly right-skewed data? Manuj Sharma See the Web&#39;s breaking stories, chosen by people like you. Check out Yahoo! Buzz.

Hi. The dprep library has at least three different methods for outlier detection: baysout, mahaout, robout. I wanted to test them on a very simple data set: vrmat<-cbind((1:22),c(8,14,14,17,21,20,27,23,25,33,31,32,30,36,37,40,42,44,52,61,81,265)) As you can see by eyeballing this, the last point is a very good outlier candidate, and maybe the second to last point, too. Anyway, I

Forescasting using predict() in an object of class arimax when there is an outlier IO in the model. Hi R users I have a problem when a use the predict() method in an object of class arimax ( These objects are the results of the implementation of the function arimax() from the TSA library) . The object is a model of a time series in which I identified an IO oulier at the element 33 of the serie