similar to: Finding non-normal distributions per row of data frame?

All: I am using RSqlite and want to be able to update individual values in a record, such as with this simple example: library(RSQLite) drv<-dbDriver("SQLite") con<-dbConnect(drv,"test.db") my.data<-data.frame(countries=c("US","UK","Canada","Australia","NewZealand"),vals=c(52,36,74,10,98))

Dear R-users, I am working with R version 2.10.1 and package RODBC Version: 1.3-2 under windows. Say I have a table "testtable" (in an Access data base) with 3 columns and 1 row that looks like this: X Y Z 0012345 42 42,1 The columns are of these types: X - character, Y - Long Integer, Z - Decimal. I use RODBC to get these data into R: > library(RODBC) >

Dear R-users, I am working with R version 2.10.1 and package RODBC Version: 1.3-2 under windows. Say I have a table "testtable" (in an Access data base), which has many different columns, among them a character column "X" with "integer-like" data as "0012345". Using sqlFetch, I'd like to assure that column X is read in as a character variable. So what

Hi UserRs, I know that there has to be an easy way to do this in R (probably easy enough that once someone clues me in I'll smack myself on the forehead for not figuring it out myself), but my searches on my own have not yielded any hints. I have many fields in my dataset that participants entered as "free lists" - i.e., the field constitutes a varying number of names each

Hello, I'm analysing reaction time data from a linguistic experiment (a variant of a lexical decision task). To ascertain that the data was normally distributed, I used *shapiro.test *for each participant (see commands below), but only one out of 21 returns a p value above p.0 05. > f = function(dfr) return(shapiro.test(dfr$Target.RTinv)$p.value) > p = as.vector(by(newdat,

If I have data that I feed into shapio.test and jarque.bera.test yet they seem to disagree. What do I use for a decision? For my data set I have p.value of 0.05496421 returned from the shapiro.test and 0.882027 returned from the jarque.bera.test. I have included the data set below. Thank you. Kevin "Category","Period","Residual" "CHILD HATS, WIGS &

Hi everybody, somehow i dont get the shapiro wilk test for normality. i just can?t find what the H0 is . i tried : shapiro.test(rnorm(5000)) Shapiro-Wilk normality test data: rnorm(5000) W = 0.9997, p-value = 0.6205 If normality is the H0, the test says it?s probably not normal, doesn ?t it ? 5000 is the biggest n allowed by the test... are there any other test ? ( i know qqnorm

Hi, from what you're writing: "The logaritmic transformation "shapiro.test(log10(y))" says: W=0.9773, p-value= 2.512e-05." it seems the log-values are not distributed normally and so original data are not distributed like a log-normal: the p-value is extremally small! Other tests for normality are available in package: nortest compare the log-transformation of your ecdf

I have a large amount of data which I break down into a collection of vectors of 100-125 values each. I would like to test the normality of the vectors and compare them. In the interactive mode I can test any one vector using the Shapiro-Wilk test or the Kolmogorov-Smirnov test. My problem is that when I try to write out the results to a file, the term output is a mixture of alpha characters

Hi, I am trying to create a table with information from Shapiro Wilk's test of normality. However, it fails due to lack of sample size, it says, but the way I see it, this is not a problem. (See the table of sample sizes (almost) at the bottom). Applying a different function using a similar ftable call is not a problem (See the bottom table). This is R 2.1.0 on Linux (Gentoo). /Fredrik

I am having a hard time interpreting the results of the 'shapiro.test' for normality. If I do ?shapiro.test I see two examples using rnorm and runif. When I run the test using rnorm I get a wide variation of results. Most of this may be from variability of rnorm, samll sample size (limited to 5000 for the test), etc but if I repeat the test multiple times I can get: >

Hi all, I have two very large samples of data (10000+ data points) and would like to perform normality tests on it. I know that p < .05 means that a data set is considered as not normal with any of the two tests. I am also aware that large samples tend to lead more likely to normal results (Andy Field, 2005). I have a few questions to ensure that I am using them right. 1) The Shapiro-Wilk

Hi, I am looking for a normality test in R to see if a vector of data I have can be assumed to be normally distributed and hence used in a linear regression. > help.search("normality test") suggests the Shapiro test, ?shapiro.test. Now maybe I am interpreting things incorrectly (as is usually the case), am I right in assuming that this is a composite test for normality, and hence a

Hi, I have a binned data that looks like this. (8.048,18.05] (-21.95,-11.95] (-31.95,-21.95] (18.05,28.05] (-41.95,-31.95] 81 76 18 18 12 (-132,-122] (-122,-112] (-112,-102] (-162,-152] (-102,-91.95] 6 6 6 5 5 (-91.95,-81.95] (-192,-182]

Hi all T gurus, I would like to test if my dataset is indeed from N(0, 0.011908969). K.S. test gives following result: > ks.test(data, "pnorm", 0, 0.011908969) One-sample Kolmogorov-Smirnov test data: data D = 0.1092, p-value = 1.318e-05 alternative hypothesis: two-sided How ever "Shapiro-Wilk" test give following : >

Dear R users, I have a data frame in the form below, on which I would like to make normality tests on the values in the ExpressionLevel column. > head(df) ID Plant Tissue Gene ExpressionLevel 1 1 p1 t1 g1 366.53 2 2 p1 t1 g2 0.57 3 3 p1 t1 g3 11.81 4 4 p1 t2 g1 498.43 5 5 p1 t2 g2 2.14 6 6 p1 t2 g3 7.85 I

Hi I have two questions, the first perhaps dumber than the second. Firstly, I have a data set, and when I plot a histogram it looks like a normal distribution. So I want to overlay a bell-shaped normal distribution on top of it, to demonstrate how similar it is to the normal distribution. I have read the help on dnorm(), rnorm(), pnorm() etc but still can't figure out how to plot a normal

Hi all; I have a custom iproute2 table and a few rules to go along with it (CentOS 5) -- I have added the table to /etc/iproute2/rt_tables. if-up and friends doesn't seem to handle this real gracefully. How are those of you out there doing something similar managing this customization? I don't really want to muck with /etc/init.d/network or the if-up scripts to avoid breaking upgrades.

Hello, I would like to know if there is a function in R that will test for normality and handle censored data sets. Currently, I evaluate each censored data set by the extent to which a normal scores plot approximate a straight line. For complete data sets I use shapiro.test(). Below is an example of a censored data set. data1<-c(0.00, 0.00, 0.00, 5.86, 5.17, 8.17, 5.12, 4.92, 7.08,

Hi, I have a small set of data on which I have tried some normality tests. When I make a histogram of the data the distribution doesn't seem to be normal at all (rather lognormal), but still no matter what test I use (Shapiro, Anderson-Darling,...) it returns a very small p value (which as far as I know means that the distribution is normal). Am I doing something wrong here? Thanks Pieter