similar to: Boxplot Labels OK

Hello all, I tried to extract the p-value from adf.test in tseries; however, I got the error message such as > ht=adf.test(list.var$aa) > ht$p-value Error in ht$p - value : non-numeric argument to binary operator > ht Augmented Dickey-Fuller Test data: list.var$aa Dickey-Fuller = -2.3147, Lag order = 4, p-value = 0.4461 alternative hypothesis: stationary > ht$data [1]

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

Hello, I have done a scatterplot and now would like to add its regression line but it does not show. Below, the code I have used. lm3 <- lm(data$S_pH_KCl2.5_BCx~data$B_OleicoPF_BCx_per) plot(data$S_pH_KCl2.5_BCx, data$B_OleicoPF_BCx_per) abline(lm3) I have been able to do the complete operation using the software STATISTICA but it would be great to do it with R. If you require more details

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,

Dear all, Could anyone help me with the following? DATA <- data.frame(rbind(c("Red1", 1, 1, 1), c("Blue1", 1, 1, 1), c("Red2", 1, 1, 1), c("Red3", 1, 1, 1))) colnames(DATA) <- c("A", "B","C", "D") #Option 1 DATA <- DATA[-2, ] #Same result I would like to achieve with Option 2 #Option 2 - I would like to do

Hello, I would like to perform a Box-Cox (“bcPower”) transformation on my data. For this, I am determining lambda using the “powerTransform” function. However, with one of my variables I get the following Warning Message: In estimateTransform(x, y, NULL, ...) : Convergence failure: return code = 52 My variable is: > x [1] 0.0001031130 0.0001029480 0.0001040010 0.0001037940 0.0001046280

From: aguitatierra@hotmail.com Sent: Friday, April 05, 2013 11:47 PM To: r-help@r-project.org ; R Help Subject: Reversing data transformation Hi everybody, I would be very grateful if you could give me your thoughts on the following issue. I need to perform Box-Cox (bcPower€) transformation on my data. To do this, I calculated lambda using the function '€powerTransform'€.

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]]

Hi everybody, I would be very grateful if you could give me your thoughts on the following issue. I need to perform Box-Cox (bcPower€) transformation on my data. To do this, I calculated lambda using the function '€powerTransform'€. powerTransform(data) However, I got an error message when performing this function: Convergence failure: return code = 52 I was told by John Fox

This is my a part of my data set > D[1:15,c(1,5:10)] X. media IE.2005 IE.2006 IE.2007 IE.2008 IE.2009 IE.2010 1 1108 22.00000 60.0 39 4.0 8.0 16.0 5.0 2 1479 110.00000 NA NA 53.0 1166.0 344.8 110.0 3 1591 86.60000 247.0 87 95.0 94.0 81.0 76.0 4 3408 807.00000 302.0 322 621.0 1071.0 1301.0 1225.0

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

Dear R users and developers, I have a dataset containing 34 variables measured in a survey, which has some missing items. I would like to conduct a factor analysis of this data. I tested mi, Amelia, and MissForest as alternative packages in order to impute the missing data. I now have 5 separate datasets with the variables I am interested in factor analysing. In my reading of the package

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, 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 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... {

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, 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

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