similar to: HowTo derive a correct likelihood-ratio chi-squared statistic from lrm() with a rsc() ?

Dear R list, From the lrm() binary logistic model we derived the G2 value or the likelihood-ratio chi-squared statistic given as L.R. model, in the output of the lrm(). How can this value be penalized for non-linearity (we used splines in the lrm function)? lrm.iRVI <- lrm(arson ~ rcs(iRVI,5), penalty=list(simple=10,nonlinear=100,nonlinear.interaction=4)) This didn’t work

Dear, When fitting the following model knots <- 5 lrm.NDWI <- lrm(m.arson ~ rcs(NDWI,knots) I obtain the following result: Logistic Regression Model lrm(formula = m.arson ~ rcs(NDWI, knots)) Frequencies of Responses 0 1 666 35 Obs Max Deriv Model L.R. d.f. P C Dxy Gamma Tau-a R2 Brier 701 5e-07 34.49

Hi, I have generated a profile likelihood for a parameter (x) and am trying to get 95% confidence limits by calculating the two points where the log likelihood (LogL) is 2 units less than the maximum LogL. I would like to do this by linear interpolation and so I have been trying to use the function approxfun which allows me to get a function to calculate LogL for any value of x within

Hello, The likelihood includes two parameters to be estimated: lambda (=beta0+beta1*x) and alpha. The algorithm for the estimation is as following: 1) with alpha=0, estimate lambda (estimate beta0 and beta1 via GLM) 2) with lambda, estimate alpha via ML estimation 3) with updataed alpha, replicate 1) and 2) until alpha is converged to a value I coded 1) and 2) (it works), but faced some

Thanks to Gabor for setting me right. My code is as follows. I found it useful for learning optim(), and you might find it similarly useful. I will be most grateful if you can guide me on how to do this better. Should one be using optim() or stats4::mle? set.seed(101) # For replicability # Setup problem X <- cbind(1, runif(100)) theta.true <- c(2,3,1) y <- X

Hello, I have a question about error handling. I run simulation studies and often the program stops with an error, for example during maximum likelihood. I would like the program not to stop but to continue and I would like to ask how the error handling can be set up for this (if it can). I tried to look through manuals etc but unfortunately did not get closer to the solution. Below is a

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Hello, My name is Julia and I'm doing my phd on roc analysis. I'm trying to write a maximization function for the likelihood attached in the document. For some reason it's not working I keep getting \this error: Error: unexpected symbol in: " +log(v_pred)) return" > } Error: unexpected '}' in "}" > >

Hello- I'm new to R, coding and stats. (Oh no.) Anyway, I have about 12000 data points in a data.frame (dealing with dimensions and geological stage information for fossil protists) and have plotted them in a basic scatter plot. I also added a boxplot to overlay these points. Each worked fine independently, but when I attempt to superimpose them with add=true, I get a different scale for

Hi r-helpers, When I issue the command latex(anova(raw1.lmer0, raw1.lmer, raw1.lmerI), file = 'raw1.tex', rownamesTexCmd = c('baR', 'addit', 'multip'), longtable = F, dcolumn = T, booktabs = T, t able.env = F, colheads = NULL, colnamesTexCmd = c ('', 'df', 'aic', 'bic', 'logl', 'chisq', 'chisqdf',

Hello, I tried to fit a lognormal distribution by using optim. But sadly the output seems to be incorrect. Who can tell me where the "bug" is? test = rlnorm(100,5,3) logL = function(parm, x,...) -sum(log(dlnorm(x,parm,...))) start = list(meanlog=5, sdlog=3) optim(start,logL,x=test)$par Carsten. [[alternative HTML version deleted]]

Hi R-friends, Attached is the SAS XPORT file that I have imported into R using following code library(foreign) mydata<-read.xport("C:\\ctf.xpt") print(mydata) I am trying to maximize logL in order to find Maximum Likelihood Estimate (MLE) of 5 parameters (alpha1, beta1, alpha2, beta2, p) using NLM function in R as follows. # Defining Log likelihood - In the function it is noted as

Dear R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've tried using

hi all - is there a package or library that contains a function for partitioning the chi-square statistic of an I X J contingency table into its respective independent parts? i looked around for this, but i didn't find anything. perhaps there's another name for this sort of analysis? i know it as "g-squared". thanks, chris. [[alternative HTML version deleted]]

Hello, I am struggling for some time now to estimate AR(1) process for commodity price time series. I did it in STATA but cannot get a result in R. The equation I want to estimate is: p(t)=a+b*p(t-1)+error Using STATA I get 0.92 for a, and 0.73 for b. Code that I use in R is: p<-matrix(data$p) # price at time t lp<-cbind(1,data$lp) # price at time t-1

I wrote this: # Setup problem x <- runif(100) y <- 2 + 3*x + rnorm(100) X <- cbind(1, x) # True OLS -- lm(y ~ x) # OLS likelihood function -- ols.lf <- function(theta, K, y, X) { beta <- theta[1:K] sigma <- exp(theta[K+1]) e <- (y - X%*%beta)/sigma logl <- sum(log(dnorm(e))) return(logl) } optim(c(2,3,0), ols.lf, gr=NULL, method="BFGS",

What's up fellows... I am a begginer in R and i am trying to find the parameters of one likelihood function, but when i otimize it, always appers a error or advertisement and the solve does not occur. The problem seems like that: "lMix<-function(pars,y){ beta1<-pars[1] beta2<-pars[2] beta3<-pars[3] beta4<-pars[4] beta5<-pars[5] alfa1<-pars[6]

Dear R-list I have been trying to make survreg fit a normal regression model with left truncated data, but unfortunately I am not able to figure out how to do it. The following survreg-call seems to work just fine when the observations are right censored: library(survival) n<-100000 #censored observations x<-rnorm(n) y<-rnorm(n,mean=x) d<-data.frame(x,y) d$ym<-pmin(y,0.5)

R-help, I 'm trying to optimize a model to data using log-likelihoods but I encounter the following error message: > l= c(49.4, 57.7,64.8,70.9,78.7,86.6,88.3,91.6,99,115) > t=3:12 > fn <- function(params, l=l, t=t) { Linf <- params[1] k <- params[2] t0 <- params[3] sigma <- params[4]

Hi guys, I am afraid I am stuck with an estimation problem. I have two variables, X and Y. Y is explained by the weighted sum of n lagged values of X. My aim is to estimate the two parameters c(alpha0,alpha1) in: Yt = Sum from j=1 to n of ( ( alpha0 + alpha1 * j ) * Xt-j ) Where Xt-j denotes the jth lag of X. I came up with this approach because I thought it would be a good idea to estimate