similar to: Bug in ci.plot(HH Package) (PR#11163)

Thanks for the reproducable example. I can confirm that it fails on my machine using survival 2-37.5, the next soon-to-be-released version, The issue is with NextMethod, and my assumption that the called routine inherited everything from the parent, including the environment chain. A simple test this AM showed me that the assumption is false. It might have been true for Splus. Working this

Dear R help, This seems to be a commonly asked question and I am able to run examples that have been proposed, but I can't seems to get this to work with my own data. Reproducible code is below. Thank you in advance for any help you can provide. The main problem is that I can not get the confidence lines to plot correctly. The secondary problem is that predict is not able to find my object

fit lwr upr 1 218.4332 90.51019 346.3561 2 218.3906 90.46133 346.3198 3 218.3906 90.46133 346.3198 4 161.3982 44.85702 277.9394 5 192.4450 68.39903 316.4909 6 179.8056 56.49540 303.1158 7 219.5406 91.52707 347.5542 8 162.6761 46.65760 278.6945 9 193.8506 70.59838 317.1029 10 181.3816 58.11305 304.6502 11 221.2871 92.14366 350.4305 12 164.2947 47.91081 280.6785 13

Hi all I have run into a case where I don't understand why predict.lrm and predict.glm don't yield the same results. My data look like this: set.seed(1) library(Design); ilogit <- function(x) { 1/(1+exp(-x)) } ORDER <- factor(sample(c("mc-sc", "sc-mc"), 403, TRUE)) CONJ <- factor(sample(c("als", "bevor", "nachdem",

In modelling functions some people like to use a weight of 0 to drop an observation instead of using a subset value of FALSE. E.g., weights=c(0,1,1,...) instead of subset=c(FALSE, TRUE, TRUE, ...) to drop the first observation. lm() and summary.lm() appear to treat these in the same way, decrementing the number of degrees of freedom for each dropped observation. However, predict.lm() does

Hello, I am trying to generate a confidence interval (90 or 95%) of a regression line. This is primarily just for illustration on a scatter plot (i.e. I am trying to make this http://www.ast.cam.ac.uk/~rgm/scratch/statsbook/graphics/anima4.gif). I have been trying to use the predict.lm function, with interval set as "confidence", but this still seems to be giving me a prediction

Dear All, When I plot the values and linear regression line for one data set, it is fine. But for another one I see zigzags, when I plot the confidence interval >cd Depth CHAOsep12RNA 9,94 804 25,06 1476,833333 40,04 1540,561404 50,11 1575,166667 52,46 349,222222 54,92 1941,5 57,29 1053,507042 60,11 1535,1 70,04 2244,963303 79,97 1954,507042 100,31 2679,140625 >

For a given linear regression, I wish to find the 2-tailed t-dist probability that Y-hat <= newly observed values. I generate prediction intervals in predict() for plotting, but when I calculate my t-dist probabilities, they don't agree. I have researched the issues with variance of individual predictions and been advised to use the variance formula below (in the code). I presume my

I'm trying to plot a time series (quarterly observations, seasonal effect, T=56),regression line and predicted point on the same plot. I'm using following commands: > plot(qdts,xlim=c(1982,1997)) > lines(fitted(m2)~time,data=qd,col="red") > points(predict(m2,newdata=nqd),col="blue") where: qdts<-ts(qd$durables,start=c(1982,1),freq=4)

Dear R users, I cannot display ylab with the following code while trying to stack 7 rows of 3 histograms each. Thanks in advance! par(mfrow=c(7,3)) par(mar=c(2,2,2,2)) par(oma=c(5,5,0,0)) with (newdata <- subset(table, Month1 == "1"), hist(newdata$CarapWid, breaks=5*(0:80), col="gray80", main="All individuals", xlab="", xlim =c(15,85), ylab="April

Hi Maria If you have problems just start with a small model with predictions and then plot with xyplot the same applies to xyplot Try library(gamm4) spring <- dget(file = "G:/1/example.txt") str(spring) 'data.frame': 11744 obs. of 11 variables: $ WATERBODY_ID : Factor w/ 1994 levels "GB102021072830",..: 1 1 2 2 2 3 3 3 4 4 ... $ SITE_ID

I'm pasting below a working R file featuring a function I'd like to polish up. I'm teaching regression this semester and every time I come to something that is very difficult to explain in class, I try to simplify it by writing an R function (eventually into my package "rockchalk"). Students have a difficult time with predict and newdata objects, so right now I'm

The data set in R is wrong. I've found mistakes on 2 lines in a quick look. I don't know if the data is incorrect in the Appendix of Fleming and Harrington as well (someone seems to have borrowed my copy), which is where the data set appears to have been taken from, given all the "-9" codes in it. (Note, Tom Fleming originally got the data from me, so I'm fairly

Enun ejemplo real estoy viendo como el intervalo de confianza usando lo que me comentas me ha salido mucho más pequeño de lo que la realidad luego refleja. ¿Cómo es esto posible?? Es decir, veo que para valores de 2,70 obtengo una respuesta de entre 2,69 y 2,90 y sin embargo luego en la realidad tengo valores entre 2,20 y 3 Gracias Jesús From: jorgeivanvelez en gmail.com Date: Thu, 21 Apr

Hello r-experts, I sure could us a little help. I have an ever updating text file with timestamped data in it. I can reformat in anyway I want if need be but currently I have chosen to make columns of date, time and measuresed value (comma delimeted and with the dates and times in quotes to interpret them as strings). Here is a small section of my text data file:

Dear R-list Suppose I have a data set stored in hmet, for which I did get confidence limit on a linear regression as shown below. My question is how I can subset only data points which are within the confidence limit. Thank you. Keun-Hyung --------------------------------------------------------------- Al=rnorm(100, 3) Cd=rnorm(100, 0.2) hmet=data.frame(Al=Al, Cd=Cd) plot(Al,

Dear all,? I hope that you can help me on this. I have been struggling to figure this out but I haven't found any solution. I am running a generalised mixed effect model, gamm4, for an ecology project. Below is the code for the model: model<-gamm4(LIFE.OE_spring~s(Q95, by=super.end.group)+Year+Hms_Rsctned+Hms_Poaching+X.broadleaved_woodland? ? ? ? ? ? ?+X.urban.suburban+X.CapWks,

Como usas la función image puedes consultar la ayuda ?image o help(image) y encontrarás el siguiente ejemplo donde se usa un diferente color Palette (mencionada por pepeceb en su respuesta). x <- 10*(1:nrow(volcano)) y <- 10*(1:ncol(volcano)) image(x, y, volcano, col = terrain.colors(100), axes = FALSE) # O puedes usar directamente el número para indicar el color image(x, y, volcano, col =

Hi, There is something I don't get with object of class "mle" returned by a function I wrote. More precisely it's about the behaviour of method "confint" and "profile" applied to these object. I've written a short function (see below) whose arguments are: 1) A univariate sample (arising from a gamma, log-normal or whatever). 2) A character string

Dear All, I'd like to fit a "kind" of logistic model to small data-set using nonlinear least-squares regression. A transcript of R-script are reproduced below. Estimated B and T (the model's coeff, herein B=-8,50 and T=5,46) seem appropriate (at least visually) but are quite diff from those obtained w/ SPSS (Levenberg-Marquardt): B=-19,56 and T=2,37. Am I doing something wrong in