similar to: Plotting of the lm

Hi, I would like to know how to extract the data from Excel Spreadsheet. Thank you very much. Melissa ************************************************************ JLT Risk Solutions Ltd 6 Crutched Friars, London EC3N 2PH. Co Reg No 1536540 Tel: (44) (0)20 7528 4000 Fax: (44) (0)20 7528 4500 http://www.jltgroup.com Lloyd's Broker. Regulated by the General Insurance Standards Council

************************************************************ Important: We would draw your attention to the notices at the bottom of this e-mail, particularly before opening and reviewing any file attachment(s). ************************************************************ Here is some code we have used. a<-array(c(1,.9,.7,.9,1,.3,.7,.3,1),dim=c(3,3)) a s<-eigen(a)$vectors

************************************************************ Important: We would draw your attention to the notices at the bottom of this e-mail, particularly before opening and reviewing any file attachment(s). ************************************************************ Martin, Thank you for letting us know about posdefify. It does do exactly what the Rebonato paper recommended and

Dear All, I have attempted to install the R-Excel Interface with poor results. The version of R is 1.6.1, the version of R-Excel is 1.0, the version of COM is 0.99. All of these were downloaded Monday, 6 January 2003. R (version 1.6.1) is installed on the machine and appears to run correctly on its own. The computer is a Compaq Pentium 4 machine with 128 meg of RAM running Windows 2000 and

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 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

> On 10/11/2020 20:07 Daniel Sch?tze <dms at cwa.uk.com> wrote: > > > Dear Aki > Thank you. Unfortunately I'm struggling to get the right syntax for this as it looks like someone else was here too > https://dovecot.org/pipermail/dovecot/2018-July/112441.html > The location in my dovecot.conf is > >

Yesterday I had to recover an e-mail which a user had deleted. If I understand this correctly the message was expunged but not purged (doveadm purge had not been run). This e-mail was clearly still in the mdbox stored with zlib compression as I could tell using the doveadm dump command (doveadm dump -t dbox filename). I could however not reveal the e-mail with the normal doveadm fetch -u

Dear Luigi, Here's an option: boxplot(x, boxcol="white", whiskcol="white",medcol="white",staplecol="white") abline(h=c(-1,0,1)) grid(NA, 4, lwd = 2) boxplot(x,add=T) Regards, Jos? Iparraguirre -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Luigi Sent: 18 September 2012 09:36

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 I'm getting an error message with the roots() function in the vars package. Even the example in the help file comes up with an error: > data(Canada) > var.2c <- VAR(Canada, p = 2, type = "const") > roots(var.2c) Error in UseMethod("roots") : no applicable method for 'roots' applied to an object of class "varest" The error is odd, 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- I just upgraded R to 3.0.0 from 2.15.1 (which worked fine). When I started trying to install updated versions of the libraries, I saw the following error: > install.packages("lme4") Installing package into 'c:/Docume~1/melissa/R/win-library/3.0' (as 'lib' is unspecified) Warning in install.packages :

Hello R experts, Is there any possibility to perform exploratory structural equation modeling (ESEM) in R? Which package should I use? Thanks a lot for help, Krzysztof

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]

Is there any further development in R to identify the missing data Mechanism? Would you please help ? ======= Please find our Email Disclaimer here-->: http://www.ukzn.ac.za/disclaimer ======= [[alternative HTML version deleted]]

Hi everybody, I am quite new to data imputation, but I would like to use the R package ' Amelia II: A Program for Missing Data '. However, its unclear to me how the input for amelia should look like: I have a data frame consisting of numerous coulmns, which represent different experimental conditions, whereby each column has 3 replicates. I want amelia to perform an imputation across

In R I have 3 variables x,y and z that contain: | x y z [1,] 1 12 122 [2,] 1 13 113 [3,] 1 11 111 [4,] 3 13 123 [5,] 4 14 134 [6,] 5 15 155| I am creating a coplot with the following syntax: |given.depth<- co.intervals(z, number=3, overlap=.1) coplot(x~y|z,pch=19,col="red",cex=1.5,given.values=given.depth)| The resulting plot however has has bars in the

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]]

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