similar to: about loop, 10 times, 10 matrix

dear professor: thank you for your help,witn your help i develop the nomogram successfully. after that i want to do the internal validation to the model.i ues the bootpred to do it,and then i encounter problem again,just like that.(´íÎóÓÚerror to :complete.cases(x, y, wt) : ²»ÊÇËùÓеIJÎÊý¶¼Ò»Ñù³¤(the length of the augment was different)) i hope you tell me where is the mistake,and maybe i have

Hello All, I have searched many help forums, message boards, etc. and I just can't apply the comments to what I need my program to do. I am running R 2.5.1 on an XP system, and my desire is to produce replicate datasets for a simulation study I am running. Essentially, I have sets of parameters (a's, b's, and c's) that define a function which produces a decimal value. This

I have constructed the function mml2 (below) based on the likelihood function described in the minimal latex I have pasted below for anyone who wants to look at it. This function finds parameter estimates for a basic Rasch (IRT) model. Using the function without the gradient, using either nlminb or optim returns the correct parameter estimates and, in the case of optim, the correct standard

Hi all-- We are planning an intervention study for adolescent alcohol use, and I am planning to use simulations based on a hurdle model (using the hurdle() function in package pscl) for sample size estimation. The simulation code and power code are below -- note that at the moment the "power" code is just returning the coefficients, as something isn't working quite right. The

I'm solving 4 complex equations simultaneously. Code is below. The code returns only zero's for the solution though there should also be a non-zero result. I'm pretty confident that the equations are correct because they are straight from a published paper and I checked them pretty thoroughly. The parameter values I used are from the published paper as well. Any suggestions for how

Dear all, I want to min "integrate( (p1*dnorm+p2*dnorm+p3*dnorm)^(1.3))" for p, mu, and sigma. So, I have to estimate 8 parameters(p3=1-p1-p2). I got this warning-"Error in integrate(numint, lower = -Inf, upper = Inf) : non-finite function value." My questions are How could I fix it? I tried to divide into several intervals and sum up, but I got same message. My code is

I'm completely stumped and seem to be going around for 2 days in the wrong circles. Here's what I have in 2 dataframes: > dim(params.data); head(params.data, n=2) [1] 284 6 item a1 a2 b1 b2 c 1 1 0.6671587 0.9588642 -3.5 -3.437961 0.2 2 2 1.1296298 1.4899307 -3.5 -4.146118 0.2 > dim(examinees.data); head(examinees.data, n=2) [1] 1600 3 examinee

This is a very basic question, but I am a bit confused with optim. I want to get the MLEs using optim which could replace the newton-raphson code I have below which also gives the MLEs. The function takes as input a vector x denoting whether a respondent answered an item correctly (x=1) or not (x=0). It also takes as input a vector b_vector, and these are parameters of test items (Rasch estimates

Hi - I am doing a monte carlo experiment that requires to do a linear regression of a matrix of vectors of dependent variables on a fixed set of covariates (one regression per vector). I am wondering if anyone has any idea of how to speed up the computations in R. The code follows: #regression function #Linear regression code qreg <- function(y,x) { X=cbind(1,x) m<-lm.fit(y=y,x=X)

Full_Name: Matthias von Davier Version: 1.1.1 OS: nt4.0 Submission from: (NULL) (144.81.31.148) sim3pl <- function(theta,i) { p2 <- p3pl(theta,i) p1 <- runif(1) temp <- response(p1,p2) return(p1) } when calling outer(theta,items,sim3pl), where theta = rnorm(100,m,s) and items = seq(1:nvar) runif(1) is only called once (instead of 100*nvar times), even though if calling sim3pl

i=1 for(rate in c(2,4) ){ for(shape in c(1,3,5) ){ curve(dgamma(x,rate,shape),xlim=c(0,3),ylab="",col=i,lty=i,add=T) i=i+1 } } How can I add some legend to represent these lines? i.e. the legend is displayed as col=1 lty=1 lambda=2 theta=1 col=2 lty=2 lambda=2 theta=3 col=3 lty=3 lambda=2 theta=5 col=4 lty=4 lambda=4 theta=1 col=5 lty=5 lambda=4 theta=3 col=6 lty=6 lambda=4 theta=5

Dear all, In order to be sure I understand glmm correctly, I try to reproduce by hand a simple result. Here is a reproducible code. The questions are in _________________ Of course I have tried to find the solution using internet but I was not able to find a solution. I have also tried to follow glmer but it is very complicated code! Thanks for any help. Marc # Generate set of df with nb

I have a problem integrating the 'standard map' ( http://en.wikipedia.org/wiki/Standard_map http://en.wikipedia.org/wiki/Standard_map ) with deSolve: By using the modulo-operator '%%' with 2*pi in the ODEs (standardmap1), the resulting values of P and Theta, should not be greater than 2pi. Because this was not the case, i was thinking that the function 'ode' has some

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 all, In an attempt to draw fill patterns in grid graphics, I have encountered a behavior of grobX that I cannot understand from the documentation. Consider this, library(grid) ## gTree g1 <- gTree(children=gList( rectGrob(0.5,0.5, width=unit(0.8,"npc"), height=unit(2,"cm")), circleGrob(r=0.3)), vp=viewport(0.5,0.5)) ##

Dear list, I'm a bit perplexed why the 95% confidence bands for the predicted probabilities for units where x=0 and x=1 overlap in the following instance. I've simulated binary data to which I've then fitted a simple logistic regression model, with one covariate, and the coefficient on x is statistically significant at the 0.05 level. I've then used two different methods to

Greetings all, I have two logistic plots coming from two calls to plogis. The code is .x <- seq(-7.6, 7.6, length=100) plot(.x, plogis(.x, location=0, scale=1), xlab="x", ylab="Density", main="Logistic Distribution: location = 0, scale = 1", type="l") abline(h=0, col="gray") .y <- seq(-7.6, 7.6, length=100) plot(.x, plogis(.x,

Hello All, I'm trying to print specific row and column for Observed_Scores matrix, however, when I execute the command "Observed_Scores[1,1]", I get the message "Error in Observed_Scores[1, 1] : incorrect number of dimensions". Could you please help and let me know where is the mistake? Here is my program: library(ltm) library(psych) # Settting the working directory

I added "plogis" to the derivative table in the development version of R; the patch against yesterday's R-devel src/deriv/main.c is available at http://www.zoology.ufl.edu/bolker/deriv_patch.txt . I pretty much followed the framework of the other symbols; here was my incantation - } else if (CAR(expr) == PlogisSymbol) { - ans = simplify(TimesSymbol, -

Hi, When running z <- plot(fit, age=NA, cholesterol=NA, perim=boundaries, method='image') Legend(z, fun=plogis, at=qlogis(c(.01,.05,.1,.2,.3,.4,.5)), zlab='Probability') And after pointing the cursor to the plot() screen in R, I obtain the following message: Using function "locator(2)" to place opposite corners of image.legend Error in