similar to: runs.test question

I'm sorry, maybe the question was bad posed. Ista has well described my problem. Thanks Massimo >----Messaggio originale---- >Da: izahn at psych.rochester.edu >Data: 28/07/2011 17.52 >A: "David Winsemius"<dwinsemius at comcast.net> >Cc: "m.fenati at libero.it"<m.fenati at libero.it>, <r-help at r-project.org> >Ogg: Re: [R]

Hello wizards, First of all, I'm a physicist and therefore I'm not much used to use statistics. So, please forgive me if this is a FAQ or stupid, but I failed to find the answer by myself. I have used "runs.test" (Package tseries) for computes the runs test for randomness , but I get this result: Runs test -1.8732 P-value = 0.0610 Alternative Hypothesis : Two sided How can

Hi everybody I am a recent convert from SAS so please excuse me if this is all very obvious: I want to use the runs test {runs.test() in package tseries} to test the randomness of a certain variable in a survey for each interviewer. I tried to us the by() statement but it doesn't seem to work with runs.test() as the function. Here is what I have: Consider a data frame with two variables and

Hello Everyone,   This is my first attempt to do something in R. As a precursor to a Willingness to Pay analysis, I want to conduct a Mixed Conditional Logit analysis but am unsure how to proceed because of some nesting within my data.   Below is some data and code that illustrate what I’m trying to do. The data are based on responses to a conjoint survey obtained during pilot testing. In the

Hi all, I apologize for the ignorance implicit in this question, but I'm having a hard time figuring out how R functions work. For example, if I wanted to write a function to compute a variance, I would do something like >my.var <- function(x) (sum(((x-mean(x)))^2))/(length(((x-mean(x))) ^2)-1) And this seems to work, e.g., > my.var(V1) [1] 116.1 > var(V1) [1] 116.1

Dear R helpers, I am trying to understand how to use the independence_test function in the coin package. I think I suffer from a misunderstanding about what the package does. Either that or I do not understand how to use it properly. Specifically, I cannot understand if I can test independence of arbitrary statistics. Take the following example: set.seed(10) d <- data.frame(y = c(rnorm(10,

I've been attempting to understand the one-sample run test for randomness. I've found run.test{tseries} and run.test{lawstat}. Both use a large sample approximation for distribution of the total number of runs in a sample of n1 observations of one type and n2 observations of another type. I've been unable to find R code to generate the exact distribution and would like to see how

Hi all, I think I'm cracking up. Please help me understand why I'm getting different results with m.test and m.test2 in the example below. > library(reshape) Loading required package: plyr > > m.test <- data.frame(id = factor(rep(1:10, 2)), variable=rep(c("var1","var2"),10), value=rnorm(20)) > cast(m.test, ...~variable, value="value") ## cast

Hi helpeRs, I have inherited a set of data files that use the file system as a sort of poor man's database, i.e., the data files are nested in directories that indicate which city they come from. For example: dir.create("deleteme") for(i in paste("deleteme", c("New York", "Los Angeles"), sep="/")) { dir.create(i) for(j in

Hi everyone, I'm sure this has been asked before, but I could not find the right search terms to find it. If I want to see what summarizing a model fit with lm() does, I can write "summary.lm". But what if I want to see what summarizing a model fit with lmer() (lme4 package) does? showMethods(summary) Function: summary (package base) object="ANY"

Hi everyone, I'm building a website (http://yourpsyche.org) using Jeffrey Horner's awesome Rapache module. I want to take user input, and pass it to an R script. At first I was simply using if else statements, but after a while I had so many nested if else's in my code that my head was spinning. So then I started using cat() and source() to write temporary files and read them back in

Hallo List, I'm trying to implemement a restricted permutation scheme in permutest(). More precisely I have dependence in my data that should be allowed for in the permutation - I simulated the problem in the example of the vegan documentation p.24: library(vegan) data(varespec) ## Bray-Curtis distances between samples dis <- vegdist(varespec) ## First 16 sites grazed, remaining 8 sites

Hi, I've run into a difficulty with qplot function (in the ggplot2 package). I can facet histograms even when the faceting variable contains missing values, but only so long as the faceting variable is not a factor. Example: y1 <- rnorm(10) x1 <- c(rep(1,5), rep(2,4), NA) x2 <- factor(c(rep(1,5), rep(2,4), NA)) library(ggplot2) qplot(y1, geom = "histogram", facets =

I am confused by obtaining different results when testing for unit root when using different packages. I have 2625 price entries for which I want to determine whether they exhibit unit root. First I test using adf.test from tseries package by running: > adf.test(P, k=30) Augmented Dickey-Fuller Test data: P Dickey-Fuller = -4.685, Lag order = 30, p-value = 0.01 alternative hypothesis:

Dear R users The following function is R code for the main compuations in the article: M. Eliasziw, S Lorraine Young, M Gail Woodbury and Karen Fryday-Field (1994): Statistical Methodology for the Concurrent Assessment of Intrarater and Intrarater Reliability: Using Goniometric Measurements as an Example. Physical Therapy 74 (8); 777-788 The function gives the estimated inter- and intrarater

I would like to see R packages arranged by topic. CRAN Task Views are at http://lib.stat.cmu.edu/R/CRAN/src/contrib/Views/ , but I'd like something more detailed. For example, the IMSL Fortran library, version 4 is easy to navigate and has procedures arranged according to following topics: Basic Statistics Regression Correlation Analysis of Variance Categorical and Discrete

Hi there, I'm trying to solve 2 nonlinear equations in 2 unknowns using the BB package. The first part of my program solves 3 ODEs using the deSolve package. This part works. The output is used as parameter values in the functions I need to solve. The second part is to solve 2 equations in 2 unknowns. This does not work. I get the error message "unexpected end of input". So what

>> I am trying to compile rseries from Whit Armstrong and a colleague of mine found a problem with using GCC4 I get the following error when compiling rseries g++-sjlj -Ic:/R/include -O2 -Wall -c Rutils.cpp -o Rutils.o Rutils.cpp: In function 'double* getColPointer(SEXPREC*, int)': Rutils.cpp:406: warning: deprecated conversion from string constant to 'char*'

I have R 2.6.2, and have tried downloading and installing the package "tseries". I get the same error when I use two different mirror sites: > utils:::menuInstallPkgs() trying URL 'http://cran.mirrors.hoobly.com/bin/windows/contrib/2.6/tseries_0.10-14.zip' Content type 'application/zip' length 400799 bytes (391 Kb) opened URL downloaded 391 Kb package

Hi, What other information should i post? I now have the two versions on the same machine. With R 2.3.1 and tseries 0.10-7 it works fine. With R 2.6.0 and tseries 0.10-11 the function garch doesnt work. The test was made with the same data, in the same machine. The maintainer did not say anything yet. Regards, Jos? Augusto Jr. ---------- Cabe?alho original ----------- De: "Prof Brian