similar to: confidence interval for negatively skewed, leptokurtic sample

Hi, This problem may be very easy, but I can't think of how to do it. I have constructed histograms of various variables in my dataset. Some of them are negatively skewed, and hence need data transformations applied. I know that you first need to reflect the negatively skewed data and then apply another transformation such as log, square root etc to bring it towards normailty. How is it

Hi all: =20 I=A1=A6m trying to join a win2000 ad domain but failed. I can see my = samba server in computer list of domain controller, but the icon of the = samba server is disabled. I also got error message from my samba server: =20 Failed to set password for machine account (NT_STATUS_WRONG_PASSWORD) = Failed to join domain: Wrong Password =20 The password should be correct because I can

Hi guys Is there a way in R to simulate/generate random numbers from a negative skewed and fat tailed distribution ? I would like to simulate a set of (discrete) data. Regards, Carlos Carlos http://www.nabble.com/file/p25783889/graph.png graph.png -- View this message in context: http://www.nabble.com/Simulate-negative-skewed%2C-fat-tailed-distribution-tp25783889p25783889.html Sent from the

Hi all: I have a question on sample size calculation of 2 groups of data. If 2 groups of data are all normal distribution, then I can use the function "n.indep.t.test.eq" from samplesize package.But if 2 groups of data are all skewed distribution, but not normal distribution,how can I calculate the sample size then? I've tried many transformation (e.g. log arcsin…) in order to

Dear all (a)in SPSS PASW there is a 'split file' command is repeats the same analysis for all sub-groups. Is there a split file equivalent in R? (b) Is there a package in R that will compute immediately statistics like Hosmer and Lemeshow Test, pseudo-R squares etc without my intervention? I am mostly interested to find easy solutions for my students. They are not

Hello all, I need to simulate rating scale from skewed and kurtosis. I would like to simulate a set of (discrete) data. ( n=1000, Item = 50 and 5 choice (1-5) ) Regards, Vichr -- View this message in context: http://r.789695.n4.nabble.com/How-to-simulate-rating-scale-from-skewed-and-kurtosis-tp4363805p4363805.html Sent from the R help mailing list archive at Nabble.com.

Dear colleagues, I'm using R64 (2.13) on Mac OS 10.6.8 and I've encountered a problem with the recode function in Rcommander. The application cannot deal with apostrpohes ( ' ) do not. I've got a factor from the 2008 Canada Election study (highest level of schooling) and some of the values include "Bachelor's Degree" , "Master's Degree". I've

Hello all: Is there a way to detect in R if a dataset is normally distributed or skewed without graphically seeing it? The reason I want to be able to do this is because I have developed and application with Visual Basic where Word,Access and Excel "talk" to each other and I want to integrate R to this application to estimate confidence intervals on fish sizes (mm). I basically want to

Dear R-users, I am using R version 2.10.1 and package foreign version 0.8-39 under windows. When reading .sav-Files (PASW Statistics 18.0.1) containing string variables, these are automatically converted to factors when using option "to.data.frame = TRUE" (see example below). It's clear to me why this happens (the default behaviour of a call to as.data.frame). But this is not

Hi! I am a psychologist who suspects that the only sensible way to analyse a particular data set is to use generalised linear mixed models. I am hoping that someone might be able to point me in the right direction to find some very practical hands on documentation that might be able to talk me through actually doing such an analysis? So far in my searches the most useful document I have turned

Hi all: I have a question about the sample size calculation. It's a pilot study,which includes 2 groups(low,high),3 time point(3,6,9 monthes).Each person has 3 results which according to the 3 time points.So it's a longitudinal study. I want to calculate the minimum sample size from the pilot study, but can't find the solution since the data is highly skewed and it's a

Full_Name: Peter Fischer Hallin Version: Version 1.8.1 OS: Irix64 Submission from: (NULL) (130.225.67.236) I've made a script look like this: exp <- read.table("graph/1933672048.cluster.data") exp <- as.matrix(exp) postscript("graph/1933672048.cluster.data.ps") heatmap(exp,scale="none",cexCol=0.4,cexRow=0.2,col=custom,margins=c(5,5)) The row labels

Last week I migrated some of our servers to Asterisk 1.4.18 and we started seeing audio drops of several seconds during SIP calls. After investigating it we noticed that Asterisk was increasing the RTP timestamps abnormally during a conversation. I'm including a text file with a subset of the data collected by Wireshark that shows the problem (I have the complete packet capture if anybody

What are good ways to (automatedly) plot (or othewise present) spatial data that is very skewed? E.g. http://tinyurl.com/dn2VerySkewedSpatialData produced with fields:image.plot. There are obviously outliers :-) which may or may not be "for real." At an exploratory stage in the investigation, I don't want to prejudge, I just want to show both 1 where the outliers are. This is not

All, Can someone help me create a skewed distribution, mean = 30, with probability of selecting a random number from the distribution greater than or equal 60 equal to 10%? I need the probability density function to equal zero at zero, and have a maximum height at or near 30. Is this possible? And if it is possible, how can I adjust the distribution so that the probability of selecting a

Hi, I need to create a plot (type = "h") and then overlay a skewed-normal curve on this distribution, but I'm not finding a procedure to accomplish this. I want to use the plot function here in order to control the bin distributions. I have explored the sn library and found the dsn function. dsn uses known location, scaling and shape parameters associated with a given input

Dear list, I did simulations in which I generated 10000 independent Bernoulli(0.5)-sequences of length 100. I estimated p for each sequence and I also estimated the conditional probability that a one is followed by another one (which should be p as well). However, the second probability is significantly smaller than 0.5 (namely about 0.494, see below) and of course smaller than the direct

Dear All, I am working with skewed-t copula in my research recently, so I needed to write an mle procedure instead of using a standard fit one; I stick to the sn package. On subsamples of the entire population that I deal with, everything is fine. However, on the total sample (difference in cross-sectional dimension: 30 vs 240) things go wrong - the objective function diverges to infinity. I

When `length( skewed.probs ) > 200' uniform samples are generated in R-devel. R-3.5.1 behaves as expected. `epsilon` can be a lot bigger than illustrated and still the uniform distribution is produced. Chuck > set.seed(123) > > epsilon <- 1e-10 > > ## uniform to 200 then small > p200 <- prop.table( rep( c(1, epsilon), c(200, 999-200))) > ## uniform to 201

Dear list, I am using glm's to predict count data for a fish species inside and outside a marine reserve for three different methods of monitoring. I run glms and figured out the best model using step function for each methods used. I predicted two values for my fish counts inside and outside the reserve using means of each of the covariates (using predict() ) therefore I have only one value