similar to: Confidence intervals of log transformed data

Hi there fellow R-users, Can anyone tell me how to build a prediction interval for a gaussian log-link model for the reponse variable?? I can find the standard error of the predictions but I cant seem to find the prediction interval. Is there a way I can calculate the prediction interval from the standard errors?? Here's the example: logX<-rnorm(100)

Dear R users, My question is that how it is possible to generate some random numbers using rnorm( ) function but in log transformed values. Thank you, Tobias --------------------------------- [[alternative HTML version deleted]]

Hi R help list I'm simulating logistic regression data with a specified odds ratio (beta) and have a problem/unexpected behaviour that occurs. The datasets includes a lognormal exposure and diseased and healthy subjects. Here is my loop: ors <- vector() for(i in 1:200){ # First, I create a vector with a lognormally distributed exposure: n <- 10000 # number of study subjects

On Tue, Jan 10, 2017 at 08:13:21PM -0600, Dirk Eddelbuettel wrote: > > On 10 January 2017 at 17:48, frederik at ofb.net wrote: > | Hi R Devel, > | > | I just ran into a corner case with 'strptime'. Recall that the "%OS" > | conversion accepts fractional seconds: > | > | > strptime("17_35_14.01234.mp3","%H_%M_%OS.mp3")$sec > |

1. Emma is performing an experiment that requires individual handling of some animals. The sizes of the animals are lognormally distributed: The natural logarithms of their sizes has a normal distribution with mean 3 and standard deviation 0.4. The time (in minutes) it takes to handle each animal is given by 10 + s · 1.5 + eε for animals with s ≤ 20 20 + s · 0.8 + eε for animals with s > 20

Hi R Devel, I just ran into a corner case with 'strptime'. Recall that the "%OS" conversion accepts fractional seconds: > strptime("17_35_14.01234.mp3","%H_%M_%OS.mp3")$sec [1] 14.01234 Unfortunately for my application it seems to be "greedy", in that it tries to parse a decimal point which might belong to the rest of the format: >

Hi, Dear R-help I know there are some R package to deal with zero-inflated count data. But I am now looking for R package to deal with zero-inflated continuous data. The response variable (Y) in my dataset contains a larger mount of zero and the Non-zero response are quite right skewed. Now what i am doing is first to use a logistic regression on covariates (X) to estimate the probability of Y

Hello, I was wondering if you could tell me how to calculate 95% confidence intervals for lambda for a box-cox power transformation. Best wishes Eoin [[alternative HTML version deleted]]

Hi I would like to plot bar charts in a particular way. The intervals are not evenly distributed and are in a data frame column called size and the relative frequencies are in a second column called mass. Both size and mass are continuous ratio data to all intents and purposes. The data actually represents sieving of sand where $size is sieve aperture and where $mass is the amount remaining on

Hi, I would like to write a function that finds parameters of a log-normal distribution with a 1-alpha CI of (x_lcl, x_ucl): However, I don't know how to optimize for the two unknown parameters. Here is my unsuccessful attempt to find a lognormal distribution with a 90%CI of 1,20: prior <- function(x_lcl, x_ucl, alpha, mean, var) { a <- (plnorm(x_lcl, mean, var) - (alpha/2))^2 b

I know that at the end of an R session I'm given the option to save the current *state* of the session. But I would like to save the entire sequence of inputs that took place during the session, so that I can play them back later, and not only be left in the same state I was at the time of saving the session, but be able to see the entire history of the session (inputs and outputs). (This is

Dear guRus: I am using lmer for a mixed model that includes a random intercept for a set of effects that have the same distribution, Normal(0, sig2b). This set of effects is of variable size, so I am using an as.formula statement to create the formula for lmer. For example, if the set of random effects has dimension 8, then the lmer call is: Zs<-

I am trying to optimize custom likelyhood with nlminb() Arguments h and f are meant to be fixed. example.R: compute.hyper.log.likelyhood <- function(a, h, f) { a1 <- a[1] a2 <- a[2] l <- 0.0 for (j in 1:length(f)) { l <- l + lbeta(a1 + f[j], a2 + h - f[j]) - lbeta(a1, a2) } return(l) } compute.optimal.hyper.params <- function(start, limits, h_, f_) { result

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

This should be simple but I am struggling. I like to easily switch in xyplot between a linear or logarithmic y-axis by setting a logical flag logY to False or True. This switch changes the scales argument of xyplot. I found out that the original two-dimentional data (Conc vs Time in my case) are converted to log10(Conc) if log=TRUE in scales, but it appears that functions like panel.curve need to

Hi R lovers! I'd like to know how to use the parameter 'start' in the function fitdistr() obviously I have to provide the initial value of the parameter to optimize except in the case of a certain set of given distribution Indeed according to the help file for fitdistr " For the following named distributions, reasonable starting values will be computed if `start'

Dear R-users, I am using R version 2.10.1 under windows. In a barplot, I want to mark one of the bars with a special border color. For example: barplot(c(3, 7, 11), border = c(NA, "red", NA)) But how to do this when the bars are stacked? for example: barplot(matrix(1:6, ncol=3)) # border of second bar (i.e. the one with total height = 7) should be red again, I try: barplot(matrix(1:6,

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

Dear List, I have read that a lognormal mixture model having a pdf of the form f(x)=w1*f1(x)+(1-w1)*f2(x) fits most data sets quite well, where f1 and f2 are lognormal distributions. Any pointers on how to create a function that would produce the 5 parameters of f(x) would be greatly appreciated. > version _ platform i386-pc-mingw32 arch i386 os

Besides the primary citation, "Kennedy, W. J. Jr and Gentle, J. E. (1980) Statistical Computing Marcel Dekker." (which is $300 and my library doesn't have it), is there any other documentation on how to take a poly() object and predict "by hand" new data? E.g. What do those coefficients actually mean ("The orthogonal polynomial is summarized by the coefficients, which