similar to: detect outliers and high levarage points

HI All, How to read SAS data directly into R ? Thank you [[alternative HTML version deleted]]

Hi All, could anybody help me to understand what is this error means ? mydata=read.table("C:/Documents and Settings/angieb/Desktop/CommercialGL/cl_ilf_claimdata.csv",header=TRUE,sep=",") > names(mydata) [1] "ILFTable" "liabLimit" "AnnAggLimit" "DedAmt" "Loss" "TIL" >

Is this book a good reference to learn R for statistical analysis ? A Handbook of Statistical Analyses Using R by Brian S. Everitt<http://www.amazon.com/exec/obidos/search-handle-url/ref=ntt_athr_dp_sr_1?%5Fencoding=UTF8&search-type=ss&index=books&field-author=Brian%20S.%20Everitt>(Author), Torsten

I have used the insurance data from R library and I have 2 questions: I use the following: >library(MASS) >data(Insurance) > m1=glm(Claims ~ District + Group + Age + offset(log(Holders)),data = Insurance, family = poisson) >summary(m1) Call: glm(formula = Claims ~ District + Group + Age + offset(log(Holders)), family = poisson, data = Insurance) Deviance Residuals: Min

What I'm trying to do is create best fit line in R for a set of data points and then remove all the outliers to re-create a best fit. I can't use IQR because the outliers I have in mind are easily within the range, but way out of line for the best fit, which is ruining the fit. I'd rather throw out those points all together. Thanks! [[alternative HTML version deleted]]

Suppose I am reading data from a file and the data contains some outliers. I want to know if it is possible in R to automatically detect outliers in a dataset and remove them -- View this message in context: http://n4.nabble.com/How-to-detect-and-exclude-outliers-in-R-tp1017285p1017285.html Sent from the R help mailing list archive at Nabble.com.

The district a is the baseline and we observe the difference between District a & b is not significant, we can choose to combine these 2 values. How to write code to combine these 2 value ? > m1=glm(Claims~District+Group+Age+log(Holders),fami ly=poisson,data=mydata) > summary(m1) Call: glm(formula = Claims ~ District + Group + Age + log(Holders), family = poisson, data = mydata)

Hi All, I am working on the dataset in which some of the variables have more than one observations with outliers . I am using below mentioned sample script library(outliers) x1 <- c(10, 10, 11, 12, 13, 14, 14, 10, 11, 13, 12, 13, 10, 19, 18, 17, 10099, 10099, 10098) outlier_tf1 = outlier(x1,logical=TRUE) find_outlier1 = which(outlier_tf1==TRUE, arr.ind=TRUE) beh_input_ro1 =

dear all, could somebody tell me how I can plot mild outliers as a circle(?) and extreme outliers as an asterisk(*) in a box-whisker plot? Thanks very much in advance -- View this message in context: http://www.nabble.com/mild-and-extreme-outliers-in-boxplot-tp25040545p25040545.html Sent from the R help mailing list archive at Nabble.com.

Dear R-users, I have two outliers related questions. I. I have a vector consisting of 69 values. mean = 0.00086 SD = 0.02152 The shape of EDA graphics (boxplots, density plots) is heavily distorted due to outliers. How to define the interval for outliers exception? Is <2SD - mean + 2SD> interval a correct approach? Or should I define 95% (or 99%) limit of agreement for data interval,

Thanks for writing this great piece of code. x = rnorm(100) boxplot(x) # you shouldn't see any outliers here although sometimes yow will # lets add some outliers intentionally x = c(21, 20, 25, x) # now 10, 15 and 20 are outliers myboxplot <- boxplot(x) # now you should see your three outliers myboxplot$out # it will print the values of the outliers How does one amend

Dear R-helpers, Very small amount of outliers can greatly affect the mean and many other statistic of a numeric variable. So, usually we must deal with the outliers properly in the process of data analysis. Here, I want to replace outliers with the group median of the variable. But, I can not construct a good way to do that efficiently, because of I am a newbie to R and programming. Can anybody

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

Estimados colegas: Entiendo que este buzón es el adecuado (me disculpan si no es así) para dar a conocer el siguiente paquete de R: *MUOD (outliers)* luisfo/muod.outliers | | | luisfo/muod.outliers | El paquete, tal y como se indica, está respaldado por un paper que hemos publicado recientemente en Scientific Reports. Detecta outliers en datos multidimensionales usando 'function

Hi Experts, I am new to R, using following sample code for capping outliers using percentile information. Working on large data (30000 observations and 150 variables), loop I am using in the below mentioned code for detecting outliers and capping to upper /lower percentile value is taking much time for the execution. Is there anything wrong with code, can anyone suggest improvement in the script

Hola! Quiero encontrar los outliers de una columna de datos que son diferencias de tiempo, aunque esta en formato numero no en horas. El paquete que encontre que me podria servir es el outliers e hice este script: library(outliers) filename<-"timediff.csv" time<-read.csv(filename, sep=";",header=TRUE,dec=",") chisq.out.test(time$TimeDiff) Pero me da error

Hallo there, greetings from Germany. I have a simple question for you. I have run a binary logistic model, but there are lots of outliers distorting the real results. I have tried to get rid of the outliers using the following commands: remove = -c(56, 303, 365, 391, 512, 746, 859, 940, 1037, 1042, 1138, 1355) MIGRATION.rebuild <- glm(MIGRATION, subset=remove)

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 =

Hi, I am new to R and I would like to get your help in finding 'outliers'. I have mvoutlier package installed in my system and added the package . But I not able find a function from 'mvoutlier' package which will identify 'outliers'. This is the sample list of data I have got which has one out-lier. 11489 11008 11873 80000000 9558 8645 8024 8371 It will

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)