similar to: Maximum likelihood estimation of Regression parameters

I'm wondering how to choose an appropriate linear model for a given problem. I have been reading Applied Linear Regression Models by John Neter, Michael H Kutner, William Wasserman and Christopher J. Nachtsheim. I'm still not clear how to choose an appropriate linear model. For multi-factor ANOVA, shall I start with all the interaction terms and do an F-test to see with interaction terms

Dear friends, I found the thread on this subject this summer but wonder whether it has been taken any further. I have an important medical problem where X is computed from a three independent and complicated measurements (exchangeable sodium and potassium and total body water - i.e. X = (Nae+Ke)/TBW ) and Y is serum sodium concentration (all data in Edelman, JCI 1958). I have the individual

If I'm not mistaken, partial R-squared is the R^2 of the quantities plotted in a partial residual plot, so you can base the computation on that. Prof. Fox's `car' package on CRAN has a function for creating those plots, but you need to figure out the way to extract the quantities being plotted. [In any case, the basic tools for doing such computations are all in R, and it

Dear Sir/Madam Hi. I am a general paediatrician, and I have read *some* chapters of the following books(1-3). I think SPSS lacks some features that may be important in data analysis (for example: interval of correlation coefficient in bivariate normal distribution, PRESS, and MSPR in cross-validation). I am thinking about changing SPSS to R: 1. SPSS is very expensive for me to update.

Hello everyone I appologize for my lack of experience in statistical methods. I am an R user begginer and I am running a logit model using "zelig" and "pcse" packages. I will go to the point and is that Im having problems with interpreting the results of my models.. It is really simple (I guess for the most advanced scholars) however I really dont understand how to interpret

> From: "Alan T. Arnholt" <arnholt at math.appstate.edu> > To: Bill Venables <William.Venables at cmis.CSIRO.AU> > Cc: r-help at stat.math.ethz.ch, arnholt at math.appstate.edu > Subject: Re: [R] Lack of Fit test > Date: Wed, 23 Feb 2000 09:40:21 -0500 (EST) > X-Authentication: none > > > I guess my question was not adequately stated when I sent

Hello, I'm looking for a textbook that can explain some of the math behind the intro-to-intermediate stuff like ANOVA, multiple regression, non- parametric tests, etc. A little background: I took an intro stats course last year and would like to further my education. Being as that was the highest (and only) stats class the local community college offers, it looks like I'm on

I hope this question is sufficiently different from the other requests for book recommendations that it's not repetitious. If not, I apologize in advance. I'm curious what standard reference books working statisticians, or biostatisticians, have within easy reach of their desk. I'm a computer systems administrator, and have a two-foot bookshelf directory under my monitor that contains

I've been trying to download from CRAN the floppy versions of the R source files: R-release-1.tar.gz, R-release-2.tar.gz I tried the servers in Seattle, Madison, and the Vienna Technical University. In each case, the file(s) were unavailable. Anne ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Anne E. York National Marine Mammal Laboratory Seattle WA 98115-0070 USA e-mail: anne.york at noaa.gov

Hi, I am trying a two way anova with unequal sample sizes but results are not as expected: I take the example from Applied Linear Statistical Models (Neter et al. pp889-897, 1996) growth rate gender bone development 1.4 1 1 2.4 1 1 2.2 1 1 2.4 1 2 2.1 2 1 1.7 2 1 2.5 2 2 1.8 2 2 2 2 2 0.7 3 1 1.1 3 1 0.5 3 2 0.9 3 2 1.3 3 2 expected results are

I am still a little unclear in the difference between randomized block design and two-way factorial design after consulting a few books, including John Rice Mathematical Statistics and Data Analysis. Both put observations in cells corresponding to two factors of many levels. Both use the same computer program to analyze data. It seems that randomized block design can have only one observation

Simple question. For a simple linear regression, I obtained the "standard error of predicted means", for both a confidence and prediction interval: x<-1:15 y<-x + rnorm(n=15) model<-lm(y~x) predict.lm(model,newdata=data.frame(x=c(10,20)),se.fit=T,interval="confidence")$se.fit 1 2 0.2708064 0.7254615

Dear R users, I am trying to fit the following ANCOVA model in R2.4.0 Y_ij=mu+alpha_i+beta*(X_ij-X..)+epsilon_ij Particularly I am interested in obtaining estimates for mu, and the effects alpha_i I have this data (from the book Applied Linear Statistical Models by Neter et al (1996), page 1020) y<-c(38,43,24,39,38,32,36,38,31,45,27,21,33,34,28)

Hi, I would like to know how to assign values to a whole vector while keeping its attributes. For example, say I have a <- structure(1:3,x=3) and I want to change the values to 2:4. If I do, a <- 2:4, the attribute x will be lost. I have a workaround for this case, which is to use subset assignment a[1:3] <- 2:4. However, what if I want to also change the length of a? Then this workaround

hi all i would like to know if anyone has a reference on how one would place the "bands" on the pp plot. i want to test whether or not a certain data set comes from a particular distribution (not normal). i've already plotted F(X(j)) vs j/(n+1) where F(x) is the cum dist function, X(j) is the j'th order statistic and n is the sample size. a goole search gave arb references

In short: I didn't take enough stats courses in college. Now I am working on scientific research and I feel somewhat lost when it comes to designing the statistical framework. I have looked through the books at: http://www.r-project.org/doc/bib/R-books.html I even tried to read [17] Julian J. Faraway. Linear Models with R. This book is too advanced. It helped a little bit but I still

What is available to help design experiments with non-standard requirements? I have a recurring need to solve these kinds of problems, with deadlines of next Wednesday for two sample cases. The first of the two is "mission impossible", while the second is merely difficult. The following outlines briefly the two problems and the approach I'm currently considering. I'd

Hi, this should be an easy one, but I can't figure it out. I have a vector of tests, with their units between brackets (if they have units). eg tests <- c("pH", "Assay (%)", "Impurity A(%)", "content (mg/ml)") Now I would like to hava a function where I use a test as input, and which returns the units like: f <- function (x) sub("\\)",

I am using a two-factor ANOVA model with random factor effects including the interaction, i.e. the factors are crossed. I would like to be able to generate all four variance components along with approximate confidence intervals using the NLME package. However, I do not know how to specify the random option because of two problems. First, I do not know how to enter the interaction term into the

I have been trying to analyse an unreplicated repeated measures 2-level factorial design with 11 factors using aov() with Error(), similar to that described in "Notes on the use of R for psychology experiments and questionnaires" by Jonathan Baron and Yuelin Li (see the example of Hay's) I have found that there seems to be a limit, in the number of factors in Error() . For