similar to: Newbie on functions

Hi R users, I have a couple of questions about some problems that I am facing with regard to numerical integration and optimization of likelihood functions. Let me provide a little background information: I am trying to do maximum likelihood estimation of an econometric model that I have developed recently. I estimate the parameters of the model using the monthly US unemployment rate series

Dear R community, I've been writing simple functions for the past year and half and have come across a similar problem several times. The execution of a function within my own function produces NaN's or fails to execute as intended. My conundrum is that I can execute the function outside of my function without error, so it's difficult for me, as a novice functioneer, to figure out

I am looking for examples of code that demonstrates the fine tuning of the strip panels in lattice graphics and uses plotmath characters. The code for the graphic is as follows: xyplot(lagy ~ n | rho1 * rho2, data= data, layout=c(2,6), span = 1, xlab = "Sample Size", ylab = "Bias in the Coefficient for the Lag of X", type = "o") rho1 is a four level factor

[Apologies in advance if this is too "statistics" and not enough "R".] I've got an experiment with two sets of treatments. Each subject either received all treatments from set A or all treatments from set B. I can compute the N pairwise correlations for all treatments in either set using cor(). If I take the mean of these N pairwise correlations, I see that the effects

#I wish to create a "list of three dataframes" ("results2") from a "list of two dataframes" (temp) and a dataframe ("c")? #Please advise. a <- data.frame(x=c(1,2,3),y=c(5,7,9)) b <- data.frame(x=c(2,4,7,9),y=c(2,3,5,4)) c <- data.frame(x=c(22,34,7,9),y=c(52,63,5,4)) results1 <- list(a,b,c) #what I want #but this is how I need to get there

Just to inform: I posted that before in R-sig-ecology but as it might be interesting also for other useRs, I post it also to the general r-user list: Hello Alexandre, thank you very much. I also found another way to extract summarizing information from lm results over e.g. 1000 repeated model runs: results2 <- t(as.data.frame(results)) summary(results2) Although some questions popped up in

Hello All, Okay i think I''m finally getting all of what i want out of ferret working, thanks mostly to reading this forum and also getting ALOT of questions answered, thanks alot everyone. Anyway my last ferret task is too get the results sorted by a field called date_registered and have this working with pagination. here is what i''m doing at the moment:

Hi,there, I am using R package "boot" to bootstrap. I have one question here: does anybody possibly know how the boot package generates the "indices" which is used in the statistic function? I thought "indices = sample(data, replace=TRUE)", but when I replaced "indices" with this command and used "boot", I got different results. Specifically,

Hi All, I am doing econometric modeling of panel data (fixed effects). We currently use Eviews to do this, but I have discovered a bug in Eviews 7 and am exploring the use of R to build panel data models / longitudinal data models. I looked at the plm package but do not see how I can incorporate AR terms in the model using the plm package. I have an Eviews model with two AR terms, AR(1) and

Hello everybody, I recently wrote a "program" that to generate AR1 time series, here the code: #By Jomopo. Junio-2007, Leioa, Vizcaya #This program to create the AR1 syntetic series (one by one) #Where the mean is zero, but the variance of the serie AR1 and #the coef. of AR1 are be changed. If var serie AR1 = 1 then is standarized! #Final version for AR1 time series program #Mon Jul

Hi, this is more a general statistics question I think. I am working on a system which automatically answers user questions (such systems are commonly called "Question Answering systems"). I evaluated different versions of the same system on a publicly available test set. This set contains 500 question. Naturally, for each question the answer can be wrong or right, which is coded as

I am calculating the mean of each column grouped by the variable 'id'. I do this using aggregate, data.table, and plyr. My aggregate results do not match the other two, and I am trying to figure out what is incorrect with my syntax. Any suggestions? Thanks. Here is the data. myData <- structure(list(var1 = c(31.59, 32.21, 31.78, 31.34, 31.61, 31.61, 30.59, 30.84, 30.98, 30.79, 30.79,

Stephen, thanks for your effort. The more appropriate list for this discussion is probably R-devel (as far as I understand it) so I've moved the discussion there. Related topics have already been discussed in the past. Specifically, I remember contributions by Paul Johnson ("rockchalk" package) and John Fox ("effects" and "car" package) as their packages

Dear All: I am trying to fit a special case of a 2-banded Toeplitz correlation structure. A 2-banded Toeplitz has ones on the diagonal, a correlation, RHO1, on the first off-diagonal, and a correlation, RHO2, on the second off-diagonal, with zeros on all subsequent off-diagonals. After reading relevant sections in Mixed-Effects Models in S and S-PLUS (Pinheiro & Bates, 2000) and searching

Dear All: I am trying to fit a special case of a 2-banded Toeplitz correlation structure. A 2-banded Toeplitz has ones on the diagonal, a correlation, RHO1, on the first off-diagonal, and a correlation, RHO2, on the second off-diagonal, with zeros on all subsequent off-diagonals. After reading relevant sections in Mixed-Effects Models in S and S-PLUS (Pinheiro & Bates, 2000) and searching

Dear All: I am trying to fit a special case of a 2-banded Toeplitz correlation structure. A 2-banded Toeplitz has ones on the diagonal, a correlation, RHO1, on the first off-diagonal, and a correlation, RHO2, on the second off-diagonal, with zeros on all subsequent off-diagonals. After reading relevant sections in Mixed-Effects Models in S and S-PLUS (Pinheiro & Bates, 2000) and searching

Hello r-help, I am using the excellent np package to conduct a nonparametric kernel regression and am having some trouble plotting the results. I have 2 covariates, x1 and x2, and a continuous outcome variable y. I am conducting a nonparametric regression of y on x1 and x2. The one somewhat unusual feature of these data is that, to be included in the dataset, x1 must be at least as large as x2.

I believe this may be more related to analysis than it is to R, per se. Suppose I have the following function that I wish to integrate: ff <- function(x) pnorm((x - m)/sigma) * dnorm(x, observed, sigma) Then, given the parameters: mu <- 300 sigma <- 50 m <- 250 target <- 200 sigma_i <- 50 I can use the function integrate as: > integrate(ff, lower= -Inf, upper=target)

Hello, I have a data set which is similar to the following data mice <- rep(letters[1:4],10) outcome <- sample(c(0,1),length(mice),replace=T) group <- c(rep("A",length(mice)/2),rep("B",length(mice)/2)) my.data <- data.frame(mice,outcome,group) my.sort.data <- my.data[order(my.data[,1]),] I would like to test wether there is a different between group A and B

Dear all, Is there a fairly general R implementation of the delta method that uses numerical derivatives? I realise that the delta method has been implemented using symbolic derivatives (e.g. alr3::delta.method, emdbook::deltamethod, msm::deltamethod and survey:::nlcon), however possibly non-linear estimators using the delta method with numerical derivatives can be quite useful (e.g. predictnl