similar to: Defining reference category for a cph model summary inside of a "for" loop

Hi all, I''ve got a simple contingency table produced with table(), and the upper and lower quartiles (quantile25 and quantile75) for the same dataset. Is there a function that tells me in which category does the value of the quartile falls into? for example: tabsp <- table(fam$I[fam$I>0]) 1 3 6 6 1 1 and for the cumulative frequency distribution tabcum <- cumsum(tabsp) 1 3

I've just started using R and am still a neophyte, but I found the following curious result. I'm using the current version of R (2.5.1 (2007-06-27) ). Why are the results for the third quartile different in the output from the summary and fivenum commands? For the following data set 457 514 530 530 538 560 687 745 745 778 786 790 792

Hello, I am constructing a nomogram using cph and nomogram commands in Dr. Harrell's Design/RMS package. The HR that I obtain for dichotomous and categorical variables are identical to those that I obtain using STATA stcox. However, the inter-quartile HR I obtain for continuous variables is obviously different, since STATA gives me HR for each unit (year, centimeter, etc) like coxph would

Could someone explain the summary(cph.object)? The example is in the help file of cph. n <- 1000 set.seed(731) age <- 50 + 12*rnorm(n) label(age) <- "Age" sex <- factor(sample(c('Male','Female'), n, rep=TRUE, prob=c(.6, .4))) cens <- 15*runif(n) h <- .02*exp(.04*(age-50)+.8*(sex=='Female')) dt <- -log(runif(n))/h label(dt)

Hello All, Using the standard "summary" function in 'R', I ran across some odd behavior that I cannot understand. Easy to reproduce: Typing: summary(c(6,207936)) Yields:: Min. *1st Qu. Median Mean 3rd Qu. Max.* 6 *51990 104000 104000 156000 207900* None of these values are correct except for the minimum. If I perform "quantile(c(6,

Greetings R users, My goal is to generate quartile groups of each variable in my data set. I would like each experiment to have its designated group added as a subsequent column. I can accomplish this individually with the following code: brks <- with(data_variables, cut2(var2, g=4)) #I don't want the actual numbers, I need a numbered group data$test1=factor(brks,

Dear all, I used cph() function from Frank harrell's Design package to create a survival model, then used functions 'Function' and 'sascode' to generate prediction equation based on the saved survival model. But it failed. I included a stratified variable in the model. If I removed the stratification, they were working well. Does that mean that function 'Function'

Hi I need some help getting results from multiple linear models into a dataframe. Let me explain the problem. I have a dataframe with ejection fraction results measured over a number of quartiles and grouped by base_study. My dataframe (800 different base_studies) looks like > afvtprelvefs basestudy quartile ef ef_std entropy CBP0908020 1 21.6 0.53 3.27

I have data that looks like lake,loglength,logweight 1,2.369215857,1.929418926 1,2.426511261,2.230448921 1,2.434568904,2.298853076 1,2.437750563,2.298853076 1,2.442479769,2.230448921 1,2.445604203,2.356025857 ... 102,2.722633923,3.310268367 102,2.781755375,3.502153893 102,2.836324116,3.683407299 102,2.802773725,3.583312152 102,2.790285164,3.546419267 102,2.806179974,3.599118565

Hi,everyone i need to calculate quartile values of a variable grouped by the other variable . same as in aggregate function(only median,mean or functions is possible-i think so) Could you please help me to achieve the same for other quartile values(5,10,25,75,90) as for median using aggregate. Thanks in advance. data : zip price 60000 567000 60001 478654 60004 485647 60001

Why am I getting a wrong result for quartiles? here is my code: > cbiomass = c(910, 1058, 929, 1103, 1056, 1022, 1255, 1121, 1111, 1192, > 1074, 1415) > summary(cbiomass) > IQR(cbiomass) The result R gives me is: For the summary > Min. 1st Qu. Median Mean 3rd Qu. Max. 910 1048 1088 1104 1139 1415 For IQR > 91.25 ********* The true Q1 is 1039

I have read and reread the boxplot and the boxplot stats page, and I still cannot understand how and what boxplot shows. I realize that this might be due to me not knowing enough statistics, but anyway... First, how does boxplot determine the size of the box? And is the line inside the box the mean or the median (or something completely different?) And how does it determine how long out the

Hi every one, I am trying to generate a normally distributed random variable with the following descriptive statistics, min=1, max=99, variance=125, mean=38.32, 1st quartile=38, median=40, 3rd quartile=40, skewness=-0.274. I know the "rnorm" will allow me to simulate random numbers with mean 38.32 and Sd=11.18(sqrt(125)). But I need to have the above mentioned descriptive

Hello everybody, This is a bug involving functions in core R package: graphics::hist.default, grDevices::nclass.FD, and base::pretty.default. It is not yet on Bugzilla. I cannot submit it myself, as I do not have an account. Could somebody else add it for me, perhaps? That would be much appreciated. Kind regards, Sietse Sietse Brouwer Summary ------- Floating point errors can cause a data

> On Oct 13, 2017, at 2:51 AM, PIKAL Petr <petr.pikal at precheza.cz> wrote: > > Hi > > You expect us to solve your problem but you ignore advice already recieved. > > Your data are unreadable, use dput(yourdata) instead. see ?dput > >> test<-read.table("clipboard", heade=T) > Error in scan(file = file, what = what, sep = sep, quote = quote,

Hemant's problem is that the indicators are not distributed uniformly. With a uniform distribution, categorization gives a reasonably optimal separation of cases. One approach would be to drop categorization and calculate the overall score as the mean of the standardized indicator scores. Whether this is an option I do not know. I did offer an "eyeball" set of breaks in a previous

hello, I'm confused what you guys are talking about. i just want to set ideal threshold values for my RFM scores which can be done using Quantiles but i don't want to use quantiles because my data is not normally distributed so it will lead to wrong ranges of breaks. to fix this problem I'm looking for an approach which can define the ideal range to breaks to categorize RFM scores into

I am very new to R so forgive me if this seems basic but I have done extensive searching and failed to come up with the answer for myself. I am trying to label a boxplot I have created with the values for the median, upper and lower quartiles and max and min values. I have been unable to do this or find anything on the net to say how it might be done. Is this possible and if so how? Regards,

Hi, I want to remove the outliers of my database with the following program (an observation is considered an outlier if it is bigger than second quartile + 1,5* distance interquartiles or less than second quartile - 1,5*distance interquartiles): for(i in 1:length(dados)){ q3=quantile(dados[i], probs=.75) q3=quantile(dados[i], probs=.50) q1=quantile(dados[i], probs=.25) d=q3-q1 for(i2 in

Hello, 1) What is the difference between a "data frame" (J H Maindonald, Using R, p. 12) and a "vector"? In Using R, the author asks the reader to enter the following data in a data frame, which I will call "mydata": year snow.cover 1970 6.5 1971 12.0 1972 14.9 1973 10.0 1974 10.7 1975 7.9 ... mydata=data.frame(year=c(1970,...),snow.cover=c(6.5,...)) 2) How to