search for: risked

Dear all, I have a quasipoisson glm for which I need confidence bands in a graphic: gm6 <- glm(num_leaves ~ b_dist_min_new, family = quasipoisson, data = beva) summary(gm6) library('VIM') b_dist_min_new <- as.numeric(prepare(beva$dist_min, scaling="classical", transformation="logarithm")). My first steps for the solution are following: range(b_dist_min_new)

I'm trying to write a function that takes a vector of length n and then takes the first value of the vector i.e j=1 and forms a new vector of length n (i.e replicate the first value n times). This function will then calculate the absoulte difference of the original vector and the new vector and store the results omitting the difference between the value and itself. This function should

Dear all, I'm using the "survival" package with R 2.4.0 on Mac OS X 10.4.8. I have two core statistics books (one of which is Altman's medical stats book) which suggest showing the number of individuals at risk at different time intervals on the Kaplan-Meier curve. My plot shows two curves that later cross, because of one significant outlier. I have two queries: Is there an

Hi, when I generated a survfit() object, I can get number of patients at risk at various time points by using summary(): fit<-survfit(Surv(time,status)~class,data=mtdata) summary(fit) class=1 time n.risk n.event survival std.err lower 95% CI upper 95% CI 9.9 78 1 0.987 0.0127 0.963 1 41.5 77 1 0.974 0.0179 0.940 1 54.0 76

Dear R-help How can one get survplot() to place the number at risk just below the survival curve as opposed to the default which is just above the x-axis? I tried the code bellow but the result is not satisfactory as some numbers are repeated several times at different y coordinates and the position of the n.risk numbers corresponds to the x-axis tick marks not the survival curve time of

Good afternoon, I would like to ask for help in controlling y-axis limits and labels in lattice doplots. Unfortunately, the problem is somewhat convoluted, please bear with the long explanation. I would like to create a 3x3 lattice of dotplots, say subject ~ count. The plot is conditioned on variables treatment and risk: subject ~ count | treatment + risk. In the experiment, not all subjects

hello i have a mixed effect model which gives slope and intercept terms for 6 groups (diagnosis (3 levels) by risk group(2 levels)). the fixed part of the model is -- brain volume ~ Diagnosis + Risk Group + (Risk Group * age : Diagnosis) - 1 thus allowing risk group age/slope terms to vary within diagnosis and omitting a nonsignificant diagnosis by risk group intercept (age was centered)

Hi Rs, I am analyzing a time to event dataset with several competing risks. 0 = Active by end of study 1 = Stopped treatment to start another treatment 2 = Lost 3 = Dead My event of interest in Lost to Followup but starting a different treatment and dying are competing risks. All 1,2,3 events are events of exiting the study, but it's only 2-LTFU that we are concerned with (I know I am

Dear all, I am using Windows and R 2.9.2 for my analyses. I have a large dataset and I am particularly interested in looking at time to an event for a continuous variable. I would like to produce a plot of log(relative risk) or relative risk (also known as hazard ratio) against the continuous variable. I have spent a long time looking for advice on how to do this but my search has proved

I have a competing risk data where a patient may die from either AIDS or Cancer. I want to compare the cox model for each of the event of interest with a competing risk model. In the competing risk model the cumulative incidence function is used directly. I used the jackknife (pseudovalue) of the cumulative incidence function for each cause (AIDS or Cancer) in a generalized estimating equation. I

Noble is looking for a Market Risk Manager/Analyst for its London Office, prefer Candidates who have R programming skills ================================================================================== Noble Noble is a market leader in managing the global supply chain of agricultural, industrial and energy products. Our "hands on" approach to business has seen us grow to become a

The concrete problem is that I am refereeing a paper where a confidence interval is presented for the risk ratio and I do not find it credible. I show below my attempts to do this in R. The example is slightly changed from the authors'. I can obtain a confidence interval for the odds ratio from fisher.test of course === fisher.test example === > outcome <- matrix(c(500, 0, 500, 8),

Quantitative Risk Analysis with R May 1-4, 2018 Fort Collins, Colorado, USA Join us this spring for our QRA with R training. Our class will focus on applied risk modeling methods using the R statistical language and will cover the core principles of QRA and Monte Carlo simulation modeling. Both Bayesian and frequentist methods will be discussed. This class is very popular with a variety of

Does anyone know of an R function for computing the Greenland-Robins variance for Mantel-Haenszel relative risks? Thanks Frank -- Frank E Harrell Jr Professor and Chair School of Medicine Department of Biostatistics Vanderbilt University

Dear R-users Has anybody implemented a function/package that will compute an individual's risk of an event in the presence of competing risks, adjusted for the individual's covariates? The only thing that seems to come close is the cuminc function from cmprsk package, but I would like to adjust for more than one covariate (it allows you to stratify by a single grouping vector). Any

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Hi, I want to calculate the Value at Risk with using some distirbutions and a volatility model. I use the following data(http://uploadeasy.net/upload/cdm3n.rar) which are losses (negative returns) of a company of approx. the last 10 years. So I want to calculated the Value at Risk, this is nothing else than the quantile. Since I have losses I consider the right tail of the distribution. Consider

Hi Has someone got a package/script at hand to do a historical simulation to calculate the Value at Risk? If your not sure what Historical Simulation is: In simple terms, Historical Simulation (HS) is just taking sample percentiles over a moving sample. Suppose we want to use HS to predict a portfolio's Value-at-Risk at a confidence level of 99 percent and the window size is chosen to be 250

Hi, I am working on problem 2 of Chapter 8 in Data Analysis and Graphics Using R and don't know how to approach the second half of the question: In the data set (an artificial one of 3121 patients, that is similar to a subset of the data analyzed in Stiell et al., 2001) head.injury, obtain a logistic regression model relating clinically.important.brain.injury to other variables. Patients