similar to: Problems with normality req. for ANOVA

Dear all, I am conducting a full factorial analysis. I have one factor consisting in algorithms, which I consider my treatments, and another factor made of the problems I want to solve. For each problem I obtain a response variable which is stochastic. I replicate the measure of this response value 10 times. When I apply ANOVA the assumptions do not hold, hence I must rely on non parametric

Good Night I made different test to check normality and multinormality in my dataset, but I don´t know which test is better. To verify univariate normality I checked: shapiro.test, cvm.test, ad.test, lillie.test, sf.test or jaque.bera.test and To verify multivariate normal distribution I use mardia, mvShapiro.Test, mvsf, mshapiro.test, mvnorm.e. I have a dataset with almost 1000 data and 9

library(xlsReadWrite) mydata<-read.xls("file path", header=TRUE) however if I change xls to csv it works just fine. Any ideas what I'm doing wrong? I have have also using the package gdata with the exact same error. Below is the error that pops up. Error in findPerl(verbose = verbose) : perl executable not found. Use perl= argument to specify the correct path. Error in

General problem: I have 20 projects that can be invested in and I need to decide which combinations meet a certain set of standards. The total possible combinations comes out to 2^20. However I know for a fact that the number of projects must be greater than 5 and less than 13. So far the the code below is the best I can come up with for iteratively creating a set to check against my set of

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I wish to create a matrix of all possible percentages with two decimal place percision. I then want each row to sum to 100%. I started with the code below with the intent to then subset the data based on the row sum. This works great for 2 or 3 columns, but if I try 4 or more columns the number of rows become to large. I would like to find a way to break it down into some kind of for loop, so

Hello, Gurus: I tried to test if the sample mean of a dataset is zero. The data has 1500 numbers with a lot of zeros and some small positive numbers. The data range on [0,1] but the distribution is unknown. It is zero inflated anyway. I tried to use the Wilcoxon Signed Ranks test. But I read from this website that it does assume the population pdf is symmetric.

I am just learning to use qplot and can't get the fill/colour to work. Below is the R code for a scatter plot and bar graph. library(ggplot2) x<-c(1,2,3,4,5,6,7) y<-c(1,2,3,2,5,6,3) qplot(x,y, main="Scatter Plot Test", xlab="X Label Test", ylab="Y Label Test", colour="blue")z<-c("van", "van", "van",

Hello, I investigate survival until the following year (0,1) and I wish to test if the variance in survival for two or more groups are significantly different from each other. I read that the Fligner-Killeen test is a non-parametric test which is very robust against departures from normality but is it correct (valuable technique for publication) to use it on binary data? In other

I am just starting to experiment with writing a function and have run into what seems like a limitation or more likely a lack of understanding on my part. Very Simple Example: I want to define a function that does 1+1=2. z<-1 ADD<-function(x) { x<-x+1 } ADD(z) z output for z is 1 not the expected 2. Now if I were to do print(x+1) instead of x<-x+1 it does return 2, so the

I have 4 columns and 56 rows of made up data that I want to plot as a series of bar graphs. The idea is to create one bar graph for each of the 4 columns using a for loop. I tried the following command in RStudio and when I type x in the console I get just the 4th graph instead of all four graphs. I did not define what x is before hand. I was not sure what it would be. Any suggestions on how you

I have several thousand rows of shipment data imported into R as a data frame, with two columns of particular interest, col 1 is the entry date, and col 2 is the tracking number (colname is REQ.NR). Tracking numbers should be unique but on occassion aren't because they get entered more than once. This creates two or more rows of with the same tracking number but different dates. I wrote a for

That's not an R question but a stats question, but I wouldn't do it. For one thing: The variance of binary data is a function of the mean, so the research question is dubious in the first place. Secondly, the test is based on ranking and comparing absolute differences from the group median, which for binary data is generally 0 or 1, so all absolute differences will be 1.... Put

This is a mixed question, between theory and practice. I have a dataset with a continous variable grouped by a 33 levels factor. After having log-tranformed my original data I can assume the normality of my data but I have two strong departures from the basic assumptions for anova and t tests: *unbalanced data* (some groups contain ten samples, others hundreds) and *non homogenity of variances*

Hi all, Is it possible to have a test value for assessing the normality of residuals from a linear regression model, instead of simply relying on qqplots? I've tried to use fitdistr to try and fit the residuals with a normal distribution, but fitdsitr only returns the parameters of the distribution and the standard errors, not the p-value. Am I missing something? Cheers, Federico

Hello All, I am new to R, and I am writing to seek your advice on how best to use it to run R's various normality tests in an automated way. In a nutshell, my situation is as follows. I work in an investment bank, and my team and I are concerned that the assumption we make in our models that the returns of assets are normally distributed may not be justified for certain asset classes. We are

I have two data sets, each a vector of 1000 numbers, each vector representing a distribution (i.e. 1000 numbers each of which representing a frequency at one point on a scale between 1 and 1000). For similfication, here an short version with only 5 points. a <- c(8,10,8,12,4) b <- c(7,11,8,10,5) Leaving the obvious discussion about causality aside fro a moment, I would like to see how

Can someone verify for me if the for loop below is really calculating the nonzero min for each row of a matrix? I have a bug somewhere in the is section of code. My first guess is how I am find the the nonzero min of each row of my matrix. The overall idea is to make sure I am investing all of my money, i.e. new.set is a set of indicator variables for each stock for a particular portfolio, i.e.

I took some data from an online poll about which R GUI people used most and I am messing around with it to learn how to use qplot. Specifically I am making a horizontal bar graph and I have two questions. 1. The categories are ordered in rather strange way at least to me. It is not alphabetical or ascending/descending order of votes cast so i had to manually state the order I wanted which is

Hi, I have found quite a few posts on normality checking of response variables, but I am still in doubt about that. As it is easy to understand I'm not a statistician so be patient please. I want to estimate the possible effects of some predictors on my response variable that is nº of males and nº of females (cbind(males,females)), so, it would be: