similar to: coefficient constraints

Hi, I was trying to evaluate the pearson correlation and the p-values for an nxm matrix, where each row represents a vector. One way to do it would be to iterate through each row, and find its correlation value( and the p-value) with respect to the other rows. Is there some function by which I can use the matrix as input? Ideally, the output would be an nxn matrix, containing the p-values

Hi all, I have a multiple-linear regression problem. There are 13 columns of data, the whole data matrix is: n x 13, where n is the number of samples. Now I want to regress EACH of the first 12 columns onto the 13th column, with 2-parameter linear model y_i = b0 + b1 * x_i, where i goes from 1 to n, and b0 is the intercept. How do I create a design matrix to do the 12-column regression

Dear list members, In sem, std.coef() will give me standardized coefficients from a sem model. But is there a trick so that path.diagram can use these coefficients rather than unstandardized ones? Thanks Steve Powell From: John Fox <jfox_at_mcmaster.ca> Date: Wed 28 Feb 2007 - 14:37:22 GMT Dear Tim, See ?standardized.coefficients (after loading the sem package). Regards, John John

Dear useRs, I very much like the effect display of the proportional odds model on page 29 (Figure 8) of the following paper by John Fox: http://socserv.mcmaster.ca/jfox/Papers/logit-effect-displays.pdf It really gives a very concise overview of the model. I would like to use it to illustrate the proportional odds mixed models we fit here for a project on Diabetes but I can't seem to reproduce

I am new to R and would like to get some practice analyzing multilevel data. I wonder if anyone can point me to a sample data set and command lines that I might replicate for a sample session. I would then compare my output with HLM output. Any help is appreciated. ------ Harold C. Doran Director of Research and Evaluation New American Schools 675 N. Washington Street, Suite 220 Alexandria,

Full_Name: Robert Baer Version: 2.6.1 OS: Windows XP Submission from: (NULL) (198.209.172.95) It would be wonderful if the CRAN mirror had an easy way to set a "default CRAN mirror". Current behavior is to have to choose a CRAN mirror the first time a package is installed in a session. The closest mirror site is always the same for me, and I wouldn't wish to change it unless it

In this years biostat teaching I will include Rcommander (it indeed simplifies syntax problems that makes students frequently miss the core statistical problems). But I could not find how to make a simple chisquare comparison between observed frequencies and expected frequencies (eg in genetics where you expect phenotypic frequencies corresponding to 3:1 in standard dominant/recessif

Hi all, Im a little bit confused concerning the effect() command, effects package. I have done several glm models with family=quasipoisson: model <-glm(Y~X+Q+Z,family=quasipoisson) and then used results.effects <-effect("X",model,se=TRUE) to get the "adjusted means". I am aware about the debate concerning adjusted means, but you guys just have to trust me - it

I hope this time I'm using the "iris" dataset correctly: ir <- rbind(iris3[,,1], iris3[,,2], iris3[,,3]) lir <- data.frame(log(ir)) names(lir) <- c("a","b","c","d") I'm trying to understand the meaning of expressions like "~ a+b+c+d", used with princomp, e.g. princomp(~ a+b+c+d, data=lir, cor=T) By inspection, it

I am trying to evaluate a model by using the commands durbin.watson and cr.plot. However, I keep getting errors that I can't figure out. A description follows. Does anyone have a hint as to what may be wrong? 1)The Durbin Watson Test. In running the command I kept getting the message "residuals include missing values" when actually this was NOT the case. Example:

Dear Helplist, I want to produce forecasts from a structural equation model. With the SEM package the model setup and its estimation is possible. However, I have not figured out how to obtain ex ante forecasts, i.e. applying the Gauss-Seidel algorithm to the estimated structural equations for provided values of the exogenous variables (i.e.: y_t = -inv(A)*B*x_t). Does anyone know if the there is

Hello, I'm using R 2.0.0 with the latest Rcmdr package installed from CRAN, on Windows XP Professional. When trying to copy some commands or results, either from the upper or lower text window, this causes Rcmdr to crash: "R for Windows GUI front-end has encountered a problem and needs to close" Did anyone have the same problem? I don't think it's my system, as it

Hello, I'm using R 2.0.0 with the latest Rcmdr package installed from CRAN, on Windows XP Professional. When trying to copy some commands or results, either from the upper or lower text window, this causes Rcmdr to crash: "R for Windows GUI front-end has encountered a problem and needs to close" Did anyone have the same problem? I don't think it's my system, as it

hi, i am need to plot two or more sets of data in a 3d scatterplot, each set with different color. i tried Rcmdr, and the 3d scatterplot facility, based on rgl. that is what i need. but i cannot seem to code different sets of data differently. any help will be very helpful. i tried scatterplot3d, but it is difficult to get the right angle in it. i need to be able to rotate the axes, and

Dear all, Is there any simple way in R that can I put the all the interactions of the variables in the OLS model? e.g. I have a bunch of variables, x1,x2,.... x20... I expect then to have interaction (e.g. x1*x2, x3*x4*x5... ) with some combinations(2 way or higher dimensions). Is there any way that I can write the model simpler? Thanks! Leaf

I've used the scatterplot3d function to graph some data and had it graph a "smooth" fit. Is there a way to actualy find out the function of the surface? I've looked through the help and figured out how to get it to report the following: Family: gaussian Link function: identity Formula: y ~ s(x, z) Parametric coefficients: Estimate Std. Error t value Pr(>|t|)

Hi! I'm trying to do some ploytomous logistic regression using multinom() in the nnet package, but am a bit confused about interpretation of the results Is it possible to get the following quantities: I: maximum likelihood estimates to test for fit of model and significance of each predictor (I would like to produce a table of the following type) Analysis of Variance: MLE (values are

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

Hi, I am running glm logit regressions with R and I would like to test a linear combination of coefficients (H0: beta1=beta2 against H1: beta1<>beta2). Is there a package for such a test or how can I perform it otherwise (perhaps with logLik() ???)? Additionally I was wondering if there was no routine to calculate pseudo R2s for logit regressions. Currently I am calculating the pseudo R2

Dear all, is there a way to extract individual likelihoods from a glm/lrm object? By individual likelihoods, I mean the likelihoods whose product give the overall likelihood of the model. I guess the code in the base package, used to compute the Akaike Information Criterion may help me. However, I couldn't figure it out, probably because I'm rather new to likelihood theory and ML