similar to: How does predict() calculate prediction intervals?

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

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

With this data x <- c(0,0,1,1,2,2) y <- c(5,6,4,3,2,6) lwr <- y-1 upr <- y+1 xlab <- c("Low","Low","Med","Med","High","High") mydata <- data.frame(x,xlab,y,lwr,upr) I would like to make a dot plot and use lwr and upr as error bars. Above 0=Low. I would like there to be some space between the 5 and the 6 corresponding

Hello I'm having trouble figuring out how to use the output of "segmented()" with a new set of predictor values. Using the example of the help file: ??set.seed(12) xx<-1:100 zz<-runif(100) yy<-2+1.5*pmax(xx-35,0)-1.5*pmax(xx-70,0)+15*pmax(zz-.5,0)+rnorm(100,0,2) dati<-data.frame(x=xx,y=yy,z=zz) out.lm<-lm(y~x,data=dati) o<-## S3

Hi R-users, I have a question relating to the HoltWinters() function. I am trying to forecast a series using the Holt Winters methodology but I am getting some unusual results. I had previously been using R for Windows version 2.7.2 and have just started using R 2.9.1. While using version 2.7.2 I was getting reasonable results however upon changing versions I found I started to see unusual

Hi, 1. How do I construct 95% prediction interval for new x values, for example - x = 30000? 2. How do I construct 95% confidence interval? my dataframe is as follows : >dt structure(list(y = c(26100000, 60500000, 16200000, 30700000, 70100000, 57700000, 46700000, 8600000, 10000000, 61800000, 30200000, 52200000, 71900000, 55000000, 12700000 ), x = c(108000, 136000,

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

In the course of exploring response prediction, I stumbled upon a small discrepancy between the CIs produced by predict.lm() and all.effects() require(mlmRev) require(effects) hsb.lm <- lm(mAch ~ minrty * sector, Hsb82) hsb.new <- data.frame( minrty = rep(c('No', 'Yes'), 2), sector = rep(c('Public', 'Catholic'), each = 2)) hsb.eff <-

Windows 98 R : Copyright 2001, The R Development Core Team Version 1.2.1 (2001-01-15) Dear friends. How comes this works and produce a single prediction: x <- rnorm(15) y <- x + rnorm(15) predict(lm(y ~ x)) new <- data.frame(x = seq(-3, 3, 0.5)) predict(lm(y ~ x), new, se.fit = TRUE) pred.w.plim <- predict(lm(y ~ x), new, interval="confidence") new1 <- data.frame(x=3)

Dear all, I have a quite basic questions about anova analysis in R, sorry for this, but I have no clue how to explain this result. I have two datasets which are named: nmda123, nmda456. Each dataset has three samples which were measured three times. And I would like to compare means of them with Posthoc test using R, following please see the output: (CREB, mCREB and No virus are the name of

Dear all, I've fitted this model with train data lms <- lm(vd ~ log(v1) + fv2+ fv5+ fv7 ) and predicted over test data using plms <- predict.lm(lmsub, new=test,interval="predict", level=0.95,se.fit=TRUE) I've two questions: q1: what's the difference between writing interval "predict" or "confidence" ? How are they computed? Any

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,

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 >

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

Full_Name: Yasuhiro Nakajima Version: 2.6.1 OS: WinXP SP2 Submission from: (NULL) (202.237.255.13) Dear all, I noticed the following behaviour of ci.plot in HH Package(ver.2.1-9): > library(HH) > data(women, package="datasets") > attach(women) > ft <- lm(height~weight) > windows() > ci.plot(ft,conf.level=0.95) > windows() > ci.plot(ft,conf.level=0.999) I

I am studying statistics using R and a book "Understandable Statistics", by Brase and Brase. The book has two worked examples for calculating a confidence interval around a predicted value from a linear model. The answers to the two examples in the book differ from those I get from R. The regression line, the standard error, and the predicted value in R and the book all agree for the

Dear all, I am studying a dataset using the aov() function. The independant variable 'cds' is a factor() with 8 levels and here is the result in studying the dependant variable 'rta' with aov() : > summary(aov(rta ~ cds)) Df Sum Sq Mean Sq F value Pr(>F) cds 7 0.34713 0.04959 2.3807 0.02777 Residuals 92 1.91635 0.02083 The dependant variable

Dear useRs, Recently I've discorved ggplot2 and I must say that I really like it, although the documentation still is a working in progress. My first question: How can I change the position of the labels and the text of the labels? With a basic plot I would use axis(2, at = position.of.the.ticks, labels = text.at.the.ticks). Could someone provide me with an example of how to do this with

Dear R People: In the DASL library, there is a story about hot dogs. Here are the data: Beef 186 495 Beef 181 477 Beef 176 425 Beef 149 322 Beef 184 482 Beef 190 587 Beef 158 370 Beef 139 322 Beef 175 479 Beef 148 375 Beef 152 330 Beef 111 300 Beef 141 386 Beef 153 401 Beef 190 645 Beef 157 440 Beef 131 317 Beef 149 319 Beef 135 298 Beef 132 253 Meat 173 458 Meat 191 506 Meat 182 473 Meat 190

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