similar to: error while extracting the p-value from adf.test

Dear all, I have just sent an enquiry but probably I hadn’t expressed myself properly. Could anyone help me with the following? When I run the code on my data I get a boxplot with outliers identified by numbers 200 & 201. However, what I would like is to label these outliers with their corresponding “DATA$num” values of the data frame. In this example, the outliers should be labelled as:

Hello everybody, I have got a data set with about 400 companies. Each company has a score for its enviroment comportment between 0 and 100. These companies belong to about 15 different countries. I have e.g. 70 companies from UK and 5 from Luxembourg,- so the data set is pretty unbalanced and I want to do an ANOVA. Somthing like aov(enviromentscore~country). But the aov function is just for

Hi! I've been looking for a way to calculate std errors of marginal effects when I use interaction terms, but with no success. I pretty much have two cases: continuous variable * continuous variable, and continuous variable * binary variable. In both cases, I know how to calculate the marginal effects, even with simulation. But I still can't figure out how to calculate the std errors of

Hi, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] How do I find the estimates of the coefficients in R? And also I would like to know what technique R employs to find the estimates? Any help is appreciated. Thanks,

I have a data set to be analyzed using to binary logistic regression. The data set is iin grouped form. My question is: how I can compute Hosmer-Lemeshow test and measures like sensitivity and specificity? Any suggestion will be greatly appreciated. Thank you Endy [[alternative HTML version deleted]]

Hello, How can I judge if a string is in a group of string? For example, I would like to have if (subpool in pool){ }else{ } Where > pool = c("s1","s2") > subpool = c("s1") How can I write the "subpool in pool" right in R? Thanks very much! Cheers, Rebecca ---------------------------------------------------------------------- This message,

Hi, Imagine I have the following data frame: > a <- c(1,NA,3) > b <- c(2,NA,NA) > c <- data.frame(cbind(a,b)) > c a b 1 1 2 2 NA NA 3 3 NA I want to delete the second row. If I use na.omit, that would also affect the third row. I tried to use a loop and an ifelse clause with is.na to get R identify that row in which all records are missing, as opposed to the first

Hi all, I want to fit the following model to my data: Y_t= a+bY_(t-1)+cY_(t-2) + Z_t +Z_(t-1) + Z_(t-2) + X_t + M_t i.e. it is an ARMA(2,2) with some additional regressors X and M. [Z_t's are the white noise variables] So, I run the following code: for (i in 1:rep) { index=sample(4,15,replace=T) final<-do.call(rbind,lapply(index,function(i)

Hi I'm trying to measure the effect of a policy intervention (Box and Tiao, 1975). This query has to do with the coding of the model rather than with the particulars of my dataset, so I'm not providing the actual dataset (or a simulated one) in this case, apart from some general description. The time series are of length n=34 (annual observations between 1977 and 2010). The policy

Hi all, Hope you people do not feel irritated for repeatedly sending mail on Time series. Here I got another problem on the same, and hope I would get some answer from you. I have following dataset: data[,1] [1] 4.96 4.95 4.96 4.96 4.97 4.97 4.97 4.97 4.97 4.98 4.98 4.98 4.98 4.98 4.99 4.99 5.00 5.01 [19] 5.01 5.00 5.01 5.01 5.01 5.01 5.02 5.01 5.02 5.02 5.03 5.03 5.03

Hello all! I have a problem with a double exponential equation. This are my data's> structure(list(proc = c(1870.52067384719, 766.789388745793, 358.701545859122, 237.113777545511, 43.2726259059654, 148.985133316262, 92.6242882655781, 88.4521557193262, 56.6404686159112, 27.0374477259404, 34.3347291080268, 18.3226992991316, 15.2196612445747, 5.31600719692165, 16.7015717397302,

Hi all, I was running the adf test in R. CODE 1: adf.test(data$LOSS) Augmented Dickey-Fuller Test data: data$LOSS Dickey-Fuller = -1.9864, Lag order = 2, p-value = 0.5775 alternative hypothesis: stationary CODE 2: adf.test(diff(diff(data$LOSS))) Augmented Dickey-Fuller Test data: diff(diff(data$LOSS)) Dickey-Fuller = -6.9287, Lag order = 2, p-value = 0.01 alternative

Hi, i would like to plot a few hundred .csv files. Each file contains one curve with x,y values to plot. I have been searching for "gnu r read many files qplot" and similar words. I found for loops that use assign to generate one variable containing a dataframe. When i uesed the classic "plot' command i could add the curves with something like for... {

Hi all, After running kruskal.test I have got results (p<0,005) pointing to reject the hypothesis that the samples were draw from the same population. Howerver when I run the kruskalmc there are no significant differences in any of the multiple comparisons. Is that possible? Some clarification? Thanks, Humber <https://sites.google.com/site/humberandrade> [[alternative HTML version

Hi Just a quick problem that I hope is simple to resolve. I'm doing some work with zero inflated poisson models using the pscl package. I can build models using zeroinfl and get outputs fom them with no problem, but when I try to use the predict.zeroinfl function, I get Error: could not find function "predict.zeroinfl". I was using an older version of R, but still had the same

Hello, I wrote a class like so: > rcfdpsuperclass <- setRefClass( > Class="rcfdpsuperclass", > fields = list( > RcfpdVersion = "character"), > methods = list( > initialize = function(){ > 'Populates fields with defaults and lock as appropriate' > initFields( > RcfpdVersion =

Hi: I have tried to find the appropriate clust algorithm for mixed type of data. The suggested way I see is: 1. use daisy to get the dissimilarity matrix 2. use PAM/hclust by providing the dissimilarity matrix, to get the clusters but by following this, when the data set grows bigger say 10,000 rows of data, the dissimilarity matrix will be O(n^2), and out of memory will occur. I am

Hi all, I want to test whether the MEAN of two different variables, (and different number of observations) are the same. I am trying to use the anova test but it doesn't seem to like that the number of observations are different: a=c(1:5) b=c(1:3) aov_test=aov(a~b) >>Error in model.frame.default(formula = a ~ b, drop.unused.levels = TRUE) : variable lengths differ (found for

Hello, Is there a function in R by which one can run AR model with Bayesian Information Criteria (BIC)? To my knowledge, functions ar and ar.ols could select the order only by AIC. Thanks, Miao [[alternative HTML version deleted]]

I know when I enter this into R: > x = c(15, 10, 13, 7, 9, 8, 21, 9, 14, 8) > y = c(15, 14, 12, 8, 14, 7, 16, 10, 15, 12) > t.test(x,y,alt="less",var.equal=TRUE) it shows: Two Sample t-test data: x and y t = -0.5331, df = 18, p-value = 0.3002 alternative hypothesis: true difference in means is less than 0 95 percent confidence interval: -Inf 2.027436 sample