similar to: two way ANOVA with unequal sample sizes

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

Hi, I want to use Maximum likelihood to estimate the parameters from my regression line. I have purchased the book "Applied linear statistical models" from Neter, Kutner, nachtsheim & Wasserman, and in one of the first chapters, they use maximum likelihood to estimate the parameters. Now I want to tried it for my self, but couldn't find the right function. In the book, they give

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

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)

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

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

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

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

Hi there, we are beginners in R and we are trying to fit the following time series using ar(2): > x <- c(1.89, 2.46, 3.23, 3.95, 4.56, 5.07, 5.62, 6.16, 6.26, 6.56, 6.98, > 7.36, 7.53, 7.84, 8.09) The reason of choosing the present time series is that the we have previously calculated analitically the autoregressive coefficients using the direct inversion method as 1.1, 0.765, 0.1173.

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

Hi all, I have an issue with the lm() function regarding the listing of the coefficients. My data are below, showing a list of hours (HR) relating to the time spent resting (R) by an individual animal. Simply i want to run a lm() to run in an anova() to see if there is a significant difference in resting between hours. HR R 1 2 0.6666667 2 2 0.4666667 3 2 0.8000000 4 2

Hi, sorry by this off. I'm still try to understand nested design. I have the follow example (fiction): I have 12 plots in 4 sizes in 3 replicates (4*3 = 12) In each plot I put 2 species (A and B) to reproduce. After a period I make samples in each board and count the number of individuals total (tot) and individuals A and B (nsp). Others individuals excepts A and B are in total of

Hi all, I have managed to get the r-scale usage algorithm to work, but I need to obtain the final results from this. As I understand it, this code is designed to generate a matrix after processing and store it somewhere????? Here is the code..... I get this part of the code, it all makes sense. ## if(nchar(Sys.getenv("LONG_TEST")) != 0) {R=1000} else {R=5} { data(customerSat)

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

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.

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

> 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

I forgot to mention that this analysis was carried out using the Windows version . Perhaps this problem is windows specific. Peter Peter Ho wrote: > Prof. Ripley, > > I am sending this to you again, as the first one was undelivered. > > > Peter > > ripley at stats.ox.ac.uk wrote: > > >>> There is no intended limit, but there was a bug fix in 1.5.0. If