similar to: fitted values are different from manually calculating

The normality of the residuals is important in the inference procedures for the classical linear regression model, and normality is very important in correlation analysis (second moment)... Washington S. Silva > Thank you all for your replies.... they have been more useful... well > in my case I have chosen to do some parametric tests (more precisely > correlation and linear regressions

Hi,everyone. I use the following program calculates Fisher's alpha from counts of individuals and species. The program is wrote by Prof. Kyle Harm. However, when I run the program, it can work very quickly sometimes, but it can not work very well sometimes. It depends on the counts of individuals and species. For example, > calc.alpha(1000,70) [1] 17.14375 > calc.alpha(10000,70) [1]

I'm running a huge number of regressions in a loop, so I tried lm.fit for a speedup. However, I would like to be able to calculate the t-stats for the coefficients. Does anyone have some functions for calculating the regression summary stats of an lm.fit object? Thanks, Whit

Hi all, apologies for seeking advice on a general stats question. I ve run normality tests using 8 different methods: - Lilliefors - Shapiro-Wilk - Robust Jarque Bera - Jarque Bera - Anderson-Darling - Pearson chi-square - Cramer-von Mises - Shapiro-Francia All show that the null hypothesis that the data come from a normal distro cannot be rejected. Great. However, I don't think it looks

Dear all, I need to calculate tr(A B), tr(A B A B) and similar quantities **fast** where the matrices A, B are symmetrical. I've searched for built-in functions for that purpose, but without luck. Can anyone help? Thanks in advance Søren [[alternative HTML version deleted]]

Hello, I am using polr(...) to generate a model. The summary shows the coefficients and the intercepts. For example: coefficient for x1 = c1 coefficient for x2 = c2 intercept A|B = i1 intercept B|C = i2 I can then run predict(..., type="p") with the model and see the odds for each factor. For example: A B C 1 0.3 0.5 0.2 2 0.4

Dear list, I would like to know how the function residuals.lm calculates the partial residuals from an lm object with more than one predictor variable. In other words what is residuals.lm(...,type="partial") doing behind the scenes? According to the help file for residuals.lm (?residuals.lm), "The partial residuals are a matrix with each column formed by omitting a term from

hello, sorry for posting what may be a simple question: i do have a matrix of coordinates (positional judgments, see below) and now want to calculate and plot the corresponding error ellipse. can anyone help me with the exact steps/syntax? > xyDat X Y 1 -0.49 -2.13 2 0.91 0.48 3 0.20 -2.80 4 -0.76 -3.23 5 -0.36 2.50 6 1.38 1.24 7

Hello, I am new to R and I am attempting to use the ProbForecastGOP package for some research I am conducting. The package works fine when I call the functions from the command line as the examples instruct. However, I am attempting to step through some of the functions so that I can obtain a better idea of what is going on. I have come to the following command:

when i run ./configure in fedora 9 64 bit i get this error checking build system type... x86_64-unknown-linux-gnu checking host system type... x86_64-unknown-linux-gnu checking whether make sets $(MAKE)... yes checking for gcc... gcc -m32 checking for C compiler default output file name... configure: error: C compiler cannot create executables See `config.log' for more details. this is

Hi, I can not comprehend the linear fitting results of polynoms. For example, given the following data (representing y = x^2): > x <- 1:3 > y <- c(1, 4, 9) performing a linear fit > f <- lm(y ~ poly(x, 2)) gives weird coefficients: > coefficients(f) (Intercept) poly(x, 2)1 poly(x, 2)2 4.6666667 5.6568542 0.8164966 However the fitted() result makes sense: >

Dear all, I have been stuck on this problem, am rather struggling and would appreciate some advice if anyone can help. I apologise if this is a bit long-winded, but I've tried to limit it to the bare essentials, but don't know how to make it more generic! I have some slightly odd real world data that I'm looking at representing number of positive diagnoses for different diseases,

I have some calculations that require persistent state. For example, they retain most of the data across calls with different parameters. They retain parameters across calls with different subsets of the cases (this is for distributed computation). They retain early analysis of the problem to speed later computations. I've created an S4 object, and the stylized code looks like this calc

Hi, among the many (5) methods that I found in the list to do partial correlation in the following two that I had a look I am getting different t-values. Does anyone have any clues on why is that? The source code is below. Thanks. pcor3 <- function (x, test = T, p = 0.05) { nvar <- ncol(x) ndata <- nrow(x) conc <- solve(cor(x)) resid.sd <- 1/sqrt(diag(conc)) pcc <-

Folks I have modified an existing function to calculate 'ec/ld/lc' 50 values and their associated Fieller's confidence limits. It is based on EC50.calc (writtien by John Bailer) - but also borrows from the dose.p (MASS) function. My goal was to make the original EC50.calc function flexible with respect to 1) probability at which to calculate the expected dose, and 2) the link

Hi all I installed *wine 9.6 on fedora4, IE6* , and *APPs* for *CALC TI83 plus * i tried to use the *USB *cable to connect to the *CALC* but did`t work then i used *serial cable* after i make *? ln -s /dev/ttyS0 ~/dosdevices/com1? *then i run the "TI Connect APPs" to coonect to the *CALC *it finds the *COM1* when it tried connect to the* CALC* but connection filed. any one

Hi, is this list only related to R issues or it has a broader context regarding questions and discussions about statistics. Is there any other email list or forum for that? For example, I have a question regarding variance. It is defined as: variance = sum(sq(Xi-mean)) / (N-1) and I never understood why not define it as variance = sum(absolute(Xi-mean) / (N-1) I read somewhere that this cannot

I am fitting individual growth models using nlme (multilevel models with repeated measurements nested within the individual), and I am trying to calculate the Pseudo R-squared for the models (an overall summary of the total outcome variability explained). Singer and Willett (2003) recommend calculating Pseudo R-squared in multilevel modeling by squaring the sample correlation between observed and

I have done additional testing and I haven't been able to figure out why it's failing. Since my original testing we now set the realm on the authentication section to match what we receive from NEC. It's of the format abc at xyz.com I have verified the md5_cred several times and it matches the user:realm:password formula 3016:insiph at something0a646666.com:3016 where username is

2008/11/21 David Flynn <davidf+nntp at woaf.net>: > On 2008-11-21, Conrad Parker <conrad at metadecks.org> wrote: >> 2008/11/15 David Flynn <davidf+nntp at woaf.net>: >>> On 2008-11-14, Conrad Parker <conrad at metadecks.org> wrote: >>>> It seems oggz chop, merge and sort will need some attention to deal >>>> with the Dirac