search for: interquartiles

Hi, I have two non-normal distributions and use interquartile ranges as a dispersion measure. Now I am looking for a test, which tests whether the interquartile ranges from the two distributions are significantly different. Any idea? Thanks, joerg [[alternative HTML version deleted]]

Hi, I am trying to show an interquartile range while grouping values using the function ddply(). So my function call now is like groupedAll <- ddply(data ,~groupColumn ,summarise ,col1_mean=mean(col1) ,col2_mode=Mode(col2) #Function I wrote for getting the mode shown below

Hi Michael, At a guess, try this: iqr<-function(x) { return(paste(round(quantile(x,0.25),0),round(quantile(x,0.75),0),sep="-") } .col3_Range=iqr(datat$tenure) Jim On Tue, Apr 19, 2016 at 11:15 AM, Michael Artz <michaeleartz at gmail.com> wrote: > Hi, > I am trying to show an interquartile range while grouping values using > the function ddply(). So my function

Are you aware that there *already is* a function that does this? ?IQR (also your "function" iqr" is just a character string and would have to be parsed and evaluated to become a function. But this is a TERRIBLE way to do things in R as it completely circumvents R's central functional programming paradigm). Cheers, Bert Bert Gunter "The trouble with having an open mind

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

That didn't work Jim! Thanks anyway On Mon, Apr 18, 2016 at 9:02 PM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi Michael, > At a guess, try this: > > iqr<-function(x) { > return(paste(round(quantile(x,0.25),0),round(quantile(x,0.75),0),sep="-") > } > > .col3_Range=iqr(datat$tenure) > > Jim > > > > On Tue, Apr 19, 2016 at

HI that did not work for me either. The value I got returned from that function was "<rounded mean> - <rounded mean>" :(. thanks for the reply through On Tue, Apr 19, 2016 at 10:34 AM, William Dunlap <wdunlap at tibco.com> wrote: > > That didn't work Jim! > > It always helps to say how the suggestion did not work. Jim's > function had a typo

> That didn't work Jim! It always helps to say how the suggestion did not work. Jim's function had a typo in it - was that the problem? Or did you not change the call to ddply to use that function. Here is something that might "work" for you: library(plyr) data <- data.frame(groupColumn=rep(1:5,1:5), col1=2^(0:14)) myIqr <- function(x) {

Dear all, I am looking for ways to compute standardized logistic regression coefficients. I found papers describing at least 6 different ways to standardize logistic regression coefficients. I also found a very old (Thu May 12 21:50:36 CEST 2005) suggestion by Frank E Harrell (one of the colleagues who frequently contribute on this list) saying... Design doesn't implement those because they

NO NO -- I am wrong! The paste() expression is of course evaluated. It's just that a character string is returned of the form "something - something". I apologize for the confusion. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County"

Hi i am doing an environmental research The equation is as follow: gam(y1 ~ x1 + s(x2) + s(x3) + s(x4), family = gaussian, fit = true) I would like to obtain the beta coefficient and 95CI of x4 (or s(x4)), what should I do? Thanks, Lung -- View this message in context: http://r.789695.n4.nabble.com/mgcv-beta-coefficient-and-95-CI-tp3320491p3320491.html Sent from the R help mailing list

To be precise: paste(round(quantile(x,0.25),0),round(quantile(x,0.75),0),sep="-") is an expression that evaluates to a character string: "round(quantile(x,.25),0) - round(quantile(x,0.75),0)" no matter what the argument of your function, x. Hence return(paste(...)) will return this exact character string and never evaluates x. Cheers, Bert Bert Gunter "The

Oh thanks for that clarification Bert! Hope you enjoyed your coffee! I ended up just using the transform argument in the ddply function. It worked and it repeated, then I called a mode function in another call to ddply that summarised. Kinda hacky but oh well! On Tue, Apr 19, 2016 at 12:31 PM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > ... and I'm getting another cup of

... and I'm getting another cup of coffee... -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Tue, Apr 19, 2016 at 10:30 AM, Bert Gunter <bgunter.4567 at gmail.com> wrote: > NO NO -- I am wrong! The paste() expression is

Hi, Here is what I am doing notGroupedAll <- ddply(data ,~groupColumn ,summarise ,col1_mean=mean(col1) ,col2_mode=Mode(col2) #Function I wrote for getting the mode shown below ,col3_Range=myIqr(col3) ) groupedAll <- ddply(data ,~groupColumn ,summarise

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 1:length(dados$quest_1)) { if (dados[i2,i] < q2-1.5*d) dados[i2,i]=NULL else if (dados[i2,...

If you show us, not just tell us about, a self-contained example someone might show you a non-hacky way of getting the job done. (I don't see an argument to plyr::ddply called 'transform'.) Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Apr 19, 2016 at 12:18 PM, Michael Artz <michaeleartz at gmail.com> wrote: > Oh thanks for that clarification Bert! Hope you enjoyed

Hi, Jumping into this thread mainly on the point of the mode of the distribution, while also supporting Bert's comments below on theory. If the vector 'x' that is being passed to this function is an integer vector, then a tabulation of the integers can yield a 'mode', presuming of course that there is only one unique mode. You may have to decide how you want to handle a

Hi, Is it possible to produce box-and-whisker plots given that I have the median, interquartile and 5/95th centile values, but not the data from which they come? It seems that it ought to be possible to coerce bxp to do what I want, but I can't quite see how. Thanks, Matthew -- Matthew Vernon, Research Fellow Ecology and Epidemiology Group, University of Warwick

Well, instead of your functions try: Mode <- function(x) { tabx <- table(x) tabx[which.max(tabx)] } and use R's IQR function instead of yours. ... so I still don't get why you want to return a character string instead of a value for the IQR; and the mode of a sample defined as above is generally a bad estimator of the mode of the distribution. To say more than that would