similar to: question about bootstrap and z-score

Dear R Help list, My question is regarding extracting the standard error or Z-score from a cph or coxph call. My Cox model is: - modz=cph(Surv(TSURV,STATUS)~RAGE+DAGE+REG_WTIME_M+CLD_ISCH+POLY_VS, data=kidneyT,method="breslow", x=T, y=T) I've used names(modz) but can't see anything that will let me extract the Z scores for each coefficient or the standard errors in the same

Hi folks, I'm trying to estimate bias-corrected percentile (BCP) confidence intervals on a vector from a simple for loop used for resampling. I am attempting to follow steps in Manly, B. 1998. Randomization, bootstrap and monte carlo methods in biology. 2nd edition., p. 48. PDF of the approach/steps should be available here: https://wyocoopunit.box.com/s/9vm4vgmbx5h7um809bvg6u7wr392v6i9 If

In R, what is the best way to convert z-scores (Normal distribution) to a percentiles, and vice-versa. (I'll be looking to do the same with other distributions eventually.) === Peter Petto <ppetto at ppetto.com> Bay Village, OH 440.249.4289

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence

I tried to reproduce a result from a former colleague which he got with S-plus bootstrap method. I don't have S-plus at hand. In R, there are 2 packages related to bootstrap method, bootstrap and boot. The former has a function called 'bootstrap' but this does not seem to conform either to the function used in S-plus nor to that described in MASS, 3d ed., p.144. The latter seems to be

There appear to be reports in the literature that transform continuous independent variablea by the use of splines, e.g., assume the dependent variable is hot dogs eaten per week (HD) and the independent variable is waistline (WL), a normal linear regression model would be: nonconfusing_regression <- lm(HD ~ WL) One might use a spline, confusion_inducing_regression_with_spline <- lm(HD

I have distilled my bootstrap problem down to this bit of code, which calculates an estimate of the 95th percentile of 7500 random numbers drawn from a standard normal distribution: library(boot) per95 <- function( annual.data, b.index) { sample.data <- annual.data[b.index] return(quantile(sample.data,probs=c(0.95))) } m <- 10000 x <- rnorm(7500,0,1) B <-

Dear R-experts, Here below, my R code working BUT I get a strange result I was not expecting! Indeed, the?95% percentile bootstrap CIs is (-54.81, -54.81 ). Is anything going wrong? Best, ########################################## Score=c(345,564,467,675,432,346,476,512,567,543,234,435,654,411,356,658,432,345,432,345, 345,456,543,501) ? Country=c("Italy", "Italy",

I am new to R. Used to use FORTRAN. R is so different from FORTRAN. The following codes would work in FOTRAN. I am trying to put an upper limit at 120. If the score is > 120, it is assigned 120. Or else, keep the original values. version 1: equated<-11 result<-11 equated<-c(111.0,112.06, 112.9, 113.8, 115.0, 116.2, 117.0, 118.0, 120.5, 120.5, 120.5) for (i in 1:11){ if (equated >

The rms ("Regression Modeling Strategies") package has undergone a massive update. The entire list of updates is at the bottom of this note. CRAN has the update for linux and will soon have it for Windows and Mac - check http://cran.r-project.org/web/packages/rms/ for availability. This rms update relies on a major update of the Hmisc package. The most user-visible changes are:

I was told to go to packages on the tool bar and select 'install packages'. Then choose library (equate). My R is version 2.11. I tried multiple times with different locations of R within US. Yet, I didn't see library(equate) being offered in the packages. Thanks. Grace [[alternative HTML version deleted]]

Folks, I have this code fragment: set.seed(1001) x <- c(0.79211363702017, 0.940536712079832, 0.859757602692931, 0.82529998629531, 0.973451006822, 0.92378802164835, 0.996679563355802, 0.943347739494445, 0.992873542980045, 0.870624707845108, 0.935917364493788) range(x) # from 0.79 to 0.996 e <- function(x,d) { median(x[d]) } b <- boot(x, e, R=1000)

Hello, I have a population of 2000+ zoo time series (but my environment also contains objects that are not zoo time series). I'm trying to calculate the latest 90 days Z-Score of all zoo time series, using the following code: LZS<-function(ser) { temp<-window(ser,start=Sys.Date()-90) last((temp-mean(temp))/sd(temp)) } sapply(ls(), LZS ) The LZS function works on individual zoo time

Does anyone know how to get cumulative frequency percentage and how to access library (equate)? Thanks. Grace [[alternative HTML version deleted]]

Hi, I am analysing gene expression data using the heatmap.2 function in R and I was wondering what is the formula of the "raw z-score" bar which shows the colors for each pixel. According to that post: https://mailman.stat.ethz.ch/pipermail/r-help/2006-September/113598.html, it is the (actual value - mean of the group) / standard deviation. But, mean of which group? Mean of the gene

Hi, Can anyone tell me how to set Z-score according to my own requirement as the below code is taking as per the file entries. Any help would be appreciable. library(gplots) x=read.table("final.txt", header=TRUE) mat=data.matrix(x) heatmap.2(mat, col=colorRampPalette(c("green","white","red"))(256), #col=greenred(75), Rowv=TRUE, Colv=FALSE, distfun = dist,

We have a project on which we need to compare various methods for computing factor scores. Are there any R routines available that do parametric bootstrap or compute factor scores?

Hi all, I'm reviewing a paper that employs a strange (to me) approach to a non-parametric significance testing. I'm familiar with permutation tests and bootstrapping confidence intervals around parameter estimates, but here they seem to be bootstrapping CIs for a manufactured Null F-value. They have a simple 3 groups between-Ss design and compute the F-value of the observed data. Then,

Dear R-Users, I am resending this message just to reminder my question regarding the calculation of a bootstrap confidence intervals for a GAM plot. I am trying to apply a bootstrap to a GAM in order to calculate the 95% confidence intervals for a smooth curve obtained by the ?plot.gam? function of the mgcv package. Nonetheless, I am getting some difficulties in transposing the results for

Dear R-Users, I am trying to apply a bootstrap to a GAM in order to calculate the 95% confidence intervals for a smooth curve obtained by the ?plot.gam? function of the mgcv package. Nonetheless, I am getting some difficulties in transposing the results for the graphs. I used the following commands in R, ?mgcv? and ?boot? packages: *> attach(bbvc_11Jul08)* *>