similar to: Creating a specific skewed distribution

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, 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

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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

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

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

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

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

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

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

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

Dear R'ers, I would like to use barplot or a similar function to plot data demonstrating the distribution of the length of a kind of conservation in about 25000 DNA sequences. My data look like this: #Total sequences: 23873 0 19936 1 218 2 391 3 477 4 360 5 431 6 294 7 215 8 320 9 209 10 160 (.....) 99 0 100 1 101 0 102 0 103 1 104 0 105 0 106 0 107 0 108 0 109 1 Therefore, I would like

Hello everyone, Does anyone know what would cause the skewness() function (from e1071), as well as skew() from psych, to return a value of NaN? I have a vector of positively-skewed data (https://docs.google.com/file/d/0B6-m45Jvl3ZmYzlHRVRHRURzbVk/edit?usp=sharing) which these functions return a value for like normal: > skewness( data ) # returns 1.400405 but when I instead give those

Hello, I'd appreciate any ideas on how to compare the skewness of two samples. In my case, one sample is likely to be roughly normal and the other one skewed. I could run two D'Agostino tests, but then I'll have to correct for the family-wise error. What if both samples are skewed? If there are no general tests (or they can't exist), I'd like to know. Thanks, Skirmantas

Hi All; The following question is directed more to statisticians on the list. Suppose I have a normal random variable. Is there a transformation that I can apply so that the new variable is slightly positively skewed. I greatly appreciate your help. Ashraf

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.

Hello, I?ve got a statistical problem that I hope you can help me with. It doesn?t have to do directly with R, so if there?s another forum which would suit better, please tell me! Now here?s the problem: I want to derive confidence intervals for a variable X, which is - given the descriptive statistics - obviously negatively skewed and leptokurtic (i.e. peaked). My aim is to make a statement

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

I have two distributions. Both are left skewed. Is there a good statistical approach to determining if the skew of distribution 1 is statistically similar to the skew of distribution 2? Thanks, bp [[alternative HTML version deleted]]