similar to: density

Hi, I recently changed from uw imap to dovecot on the sound recommendation of a friend and have mostly succeeded in getting all of my clients up and running, but am really stuck with the iPhone which is failing to make connections. I run certificates on all of my clients and thunderbird happily connects both locally and remotely. I installed the certificate on the iPhone after great pain (pk12

Dear all, this is probably more a statistics question than an R question but probably there is somebody who can help me nevertheless. I'm running a regression with four predictors (a, b, c, d) and all their interaction effects using lm. Based on theory I assume that a influences y positively. In my output (see below) I see, however, a negative regression coefficient for a. But several of the

Hi Everyone, I'm continuing to run into trouble with polyfit. I'm using the fitting function of the form; fit <- lm(y ~ poly(x,degree,raw=TRUE)) and I have found that in some cases a polynomial of certain degree can't be fit, the coefficient won't be calculated, because of a singularity. If I use orthogonal polynomials I can fit a polynomial of any degree, but I don't get

I have a data set with 35 observations and 4 variables. I have removed 4 observations as they were skewing the data analysis but I want to dummy them back in, (not entirely sure on the reason, but have been told I should try). I have used this in order to do it, but I am not getting any statistical analysis with my dummy it's all NA. > dummy15=rep(0, times=n) > dummy15[15]=1 >

Hi! I'm running an anova on a lm and I get the message Error: invalid dimnames given for data frame Any idea where it comes from? Many thanks. Here is the fit and the attempted anova: > fit Call: lm(formula = as.formula(paste(chemical, "a~", cmd, sep = "")), singular.ok = TRUE) Coefficients: (Intercept) Odom Displace Weight

Dear friends, In R, the help of "bic.glm" tells the difference between postmean(the posterior mean of each coefficient from model averaging) and condpostmean(the posterior mean of each coefficient conditional on the variable being included in the model), But it's still unclear about the results explanations, and the artile of Rnews in 2005 on BMA still don't give more detail on

For mean() and sd() you need to convert the data frame to a matrix (I'm guessing here since you did not show us the structure of your data). The min() and max() functions should work on the data frame just fine. If you have other columns in the data frame, extract the monthly columns first. > set.seed(42) > x <- data.frame(matrix(rnorm(100), 20, 5)) > str(x) 'data.frame':

Hi, I am working on problem 2 of Chapter 8 in Data Analysis and Graphics Using R and don't know how to approach the second half of the question: In the data set (an artificial one of 3121 patients, that is similar to a subset of the data analyzed in Stiell et al., 2001) head.injury, obtain a logistic regression model relating clinically.important.brain.injury to other variables. Patients

Dear all,   I want to calculate AIC values of PLSR models. But I find that AIC and extractAIC functions in R could not be used to calculate AIC values of PLSR models. Now I write a section of code(below) to calculate it. But I don't known whether the result is right or not. If I am wrong, please give me some suggestions. Thanks a lot.   Rong Huang   data<-data.frame(

Hi there, I have been having trouble running negative binomial regression (glm.nb) using library MASS in R v2.15.0 on Mac OSX. I am running multiple models on the variables influencing the group size of damselfish in coral reefs (count data). For total group size and two of my species, glm.nb is working great to deal with overdispersion in my count data. For two of my species, I am getting a

Dear Spencer and Prof. Fox, Thank you for your replies. I'll very appreciate, if you have any ideas concerning the problem described below. First, I'd like to describe the model in brief. In general I consider a model with three equations. First one is for annual GRP growth - in general it looks like: 1) GRP growth per capita = G(investment, migration, initial GRP per

Dear all, I'm writing my manuscript to publish after analysis my final data with ANOVA, ANCOVA, MANCOVA. In a section of my result, I did correlation of my data (2 categirical factors with 2 levels: Quantity & Quality; 2 dependent var: Irid.area & Casa.PC1, and 1 co-var: SL). But as some traits (here Irid.area) are significantly influenced by the covariate (standard length, SL), I

(The list appears to have eaten my previous attempt to send this. Apologies if you receive multiple copies.) The attached patches allow you to use paravirtualised network and block interfaces from fully virtualised domains, based on Intel''s patches from a few months ago. These are significantly faster than the equivalent ioemu devices, sometimes by more than an order of magnitude.

Hi, I wonder if the function "density" outputs the gaussian mixture formula that is estimated from the input data, assuming a gaussian model is used at each data point ? I want to take the derivative of the finally estimated gaussian mixture formula for further analysis. Thanks in advance for any help that you can offer me! Hui

Hi, I was using a density estimation function as follows: > est <- KernSmooth::bkde(x3, bandwidth=10) When setting bandwidth less than 5, I got the error "longer object length, is not a multiple of shorter object length in: kappa * gcounts ". I wonder if there is anybody who can explain the error for me? Thanks! Hui