similar to: Censored Poisson Data

Dear Rxperts, Running the following code: ======================================================= twlo=10; twhi=20; wt=154; vd=0.5; cl=0.046; tau=6; t=3; F=1; wtkg <- wt/2.2 # convert lbs to kg vd.pt <- wtkg * vd # compute weight-based vd (L) cl.pt <- wtkg * cl # compute CL (L/hr) k <- cl.pt/vd.pt # compute k (hr^-1) cave <-

I wonder if there is a package that will estimate a Zero Inflated Poisson Model (ZIP), and also if there is a package that will estimate what is called the Tobit model: that is a combination of censored and observed values in the same sample. Georgina Bermann Biostatistics AstraZeneca R&D M?lndal -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing

I'm trying to convert an S-Plus program to R.  Since I'm a SAS programmer I'm not facile is either S-Plus or R, so I need some help.  All I did was convert the underscores in S-Plus to the assignment operator <-.  Here are the first few lines of the S-Plus file:   sshc _ function(rc, nc, d, method, alpha=0.05, power=0.8,              tol=0.01, tol1=.0001, tol2=.005, cc=c(.1,2),

dear R-users, I have to model counts where all counts above some threshold have been censored. In the same dataset I have too many zeroes for a Poisson or even a negative binomial distribution to make sense, so I would need a zero-inflated-censored negative binomial family for use in glmer (or glmmADMB?). That seems not to exist. my question is : how could I add a custom-built family of

Hi R users, I have a question about adding uncertainty bars to stacked bar plots. DF: year A B C Amin Amax Bmin Bmax Cmin Cmax 2009 40 45 15 30 61 23 56 14 17 2010 36 41 23 26 54 22 51 22 24 I use the code below: DF.refm = melt(subset(DF[,c(1:4)]),id.vars='year',variable_name='Legend') fig1 =

On 18/06/17 12:10, lily li wrote: > Hi R users, > > I have a question about adding uncertainty bars to stacked bar plots. > > DF: > year A B C Amin Amax Bmin Bmax Cmin Cmax > 2009 40 45 15 30 61 23 56 14 17 > 2010 36 41 23 26 54 22 51 22 24 > > I use the code below: > >

Dear all, I'm looking for someone that help me to write an R function to simulate survival data under complex situations, namely time-varying hazard ratio, marginal distribution of survival times and covariates. The algorithm is described in the reference below and it should be not very difficult to implement it. However I tried but without success....;-( Below there the code that I used; it

Dear R users, I would like to show the estimated density of a (0, 1) uniformly distributed random variable. The density curve, however, goes beyond 0 and 1 because of the kernel smoothing. Example: x = runif(10000) plot(density(x)) Is there a way to estimate the density curve strictly within (0, 1) and still use some sort of smoothing? Any help would be greatly appreciated. Best regards,

One set of data has censored (less-than detection limits) water chemistry concentrations for 80-100% of all observations. My initial trial-and-error attempts to apply the cenboxplot() method suggests that it has an upper limit to the percentage of censored observations. I do not see this limit in Dennis Helsel's second edition. Has anyone experience plotting censored data and can provide

Hi Everyone, This example code results in R 'crashing'; that is the R application closes with no warnings or error messages. #----------------------- myD <- read.table(stdin(), header=TRUE, nrows=20) Broth Salt pH Temp N Y Growth 1 310 9.0 2.92 10 90.0 NA 0 2 615 6.0 7.82 30 1.0 2 1 3 217 2.0 7.34 10 7.0 8

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

I start by doing a simple gaussian tobit by MLE: x1 <- runif(1000) # E() = 0.5 x2 <- runif(1000)*2 # E() = 1 x3 <- runif(1000)*4 # E() = 2 ystar <- -7 + 4*x1 + 5*x2 + rnorm(1000) # is mean 0 y <- ystar censored <- ystar <= 0 y[censored] <- 0 library(AER) m <- tobit(y ~ x1 + x2, left=0, data=D) summary(m) Which gives: Call:

Hi, I am wondering how to specify interval-censored data in coxph? The example in the help page summary(coxph(Surv(start, stop, event) ~ x, data = test2)) is for counting process data, is the counting process data the same as interval-censored data? Thanks __________________________________

Dear Listers, I have a general statistical question. Are hurdle logit-poisson model and posson model nested? Thank you so much?

Dear list, I have the following problem : I want to model a series of observations of a given hospital activity on various days under various conditions. among my "outcomes" (dependent variables) is the number of patients for which a certain procedure is done. The problem is that, when no relevant patient is hospitalized on said day, there is no observation (for which the "number

Dear all, Are there functions or packages in R that can handle interval-censored data? I have looked in various packages (such as survival5 or event), but it seems that only right-censored data can be analysed. More generally, are there methods to analyse both interval-censored observations and right-censored observations in the same data set? Thanks in advance. Emmanuel Paradis

I am using a binomial mixture model to estimate abundance (N) and detection probability (p) using simulated count data: -Each site has a simulated abundance that follow a Poisson distribution with lambda = 5 -There are 200 simulated sampled sites -3 repeated counts at each site - only 50 percent of the animals are counted during each count (i.e, detection probability p =0.5, see codes) We removed

Can anybody give me a neat example of interval censored data analysis codes in R? Given that suvreg(Surv(c(1,1,NA,3),c(2,NA,2,3),type="interval2")~1) works why does survreg(Surv(data[,1],data[,2],type="interval2")~1) not work where data is : T.1 T.2 Status 1 0.0000000 0.62873036 1 2 0.0000000 2.07039068 1 3 0.0000000

Hi all! I have two survival data sets looking at similar effects in different settings. One data set is only right censored, but the other is interval censored. In the right censored data set, there is an effect of one factor that causes very different shapes in survival curves (and non-proportional hazards) and I'd really like to say that the same factor has a similar effect in the other

Dear R users, I tried to analysis the hazard function of some data by coxph function in survival package. The type of the data include "left-censored", "right-censored", "both right-censored and left-censored" (btw, does this has a technical term?), and "complete" ones. I noticed that event (one parameter in "Surv()") might be an indicator for the