similar to: One sided confidence limits for the regression line

Hi all, For the simple linear regression, I want to find the input "x" value so that the lower confidnece limit is a specific number, say 0.2. In other words, I want to find the value of x so that the lower confidence bound crosses the horizontal line 0.2. Is there a simple way (an R function) that can do this? Thanks. Hanna [[alternative HTML version deleted]]

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 >

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,

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 list, I am using glm's to predict count data for a fish species inside and outside a marine reserve for three different methods of monitoring. I run glms and figured out the best model using step function for each methods used. I predicted two values for my fish counts inside and outside the reserve using means of each of the covariates (using predict() ) therefore I have only one value

I am trying to find a confidence band for a fitted non-linear curve. I see that the predict.nls function has an interval argument, but a previous post indicates that this argument has not been implemented. Is this still true? I have tried various ways to extract the interval information from the model object without success. My code is: Model.predict <- predict(My.nls.model,

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

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

Data from Fisher's paper: Confidence Limits for a Cross-Product Ratio. > y col1 col2 [1,] 10 3 [2,] 2 15 fisher.test(y) Fisher's Exact Test for Count Data data: y p-value = 0.0005367 alternative hypothesis: true odds ratio is not equal to 1 95 percent confidence interval: 2.753438 300.682787 sample estimates: odds ratio 21.30533 The crude odds

Hi, I trying to determine the best way to compute the power for a one-sample one-sided binomial test. Specifically I need to sample a population of individuals and ask whether a sample rate of 0% is compatable with a minimum threshold of 3% and how many samples are needed. I have made use of power.prop.test but I am not sure if a) that is the correct (or best) function to use and b) if the

Hi all, I am doing a two-sided DW test: H0: rho = 0 H1: rho =/= 0 My understanding is that most test statistics tables are one-sided. It's the way they created the table. So from online, by doing Googling, I found a bunch of DW tables for Confidence Level 5%. Those tables can answer my two-sided question at 5x2 = 10% confidence level. But what if I want two-sided test at 1% and 5%

Dear R experts: I need to get this plot, but also with 95% confidence interval bands: hour <- c(1, 2, 3, 4, 5, 6) millivolts <- c(3.5, 5, 7.5, 13, 40, 58) plot(hour, millivolts, xlim=c(1,10), ylim=c(0,1000)) pm <- lm(millivolts ~ poly(hour, 3)) curve(predict(pm, data.frame(hour=x)), add=TRUE) How can the 95% confidence interval band curves be plotted too? Sincerely,

Hello, I drew a plot of weight and height of people and fitted it with a polynominal regression x^2. (using curve()) Now I would like to draw the confidence interval line for the fitted curve. Please kindly advise the code for the purpose. Thank you. Elaine [[alternative HTML version deleted]]

The problem occurs when the sample odds ratio is Inf, such as in the following example. Given the fact that both upper bounds of the two 95% confidence intervals are Inf, I would have expected that the two lower bounds be equal, but they aren't. x <- matrix(c(9,4,0,2),2,2) x # [,1] [,2] #[1,] 9 0 #[2,] 4 2 rbind("two.sided.95CI"=fisher.test(x)$conf.int,

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

Dear R, I am using this R version:R version 2.11.1 (2010-05-31)(Cran Mirror Berlin) It seems to me, that R constructs a wrong confidence intervall if you try to get a one sided t-test. If the true mean is 1 and my alternative hypothesis (H1) says that mu is smaller ("less")than zero the conf. intervall should reach +∞ and not -∞ if it is constructed for the H0 saying that mu is

hi, I don't know what I am doing wrong, but with that code; x1 <- c(1.60, 0.27, 0.17, 1.63, 1.37, 2.00, 0.90, 1.07, 0.89, 0.43, 0.37, 0.59, 0.47, 1.83, 1.79, 0.90, 0.72, 1.83, 0.23, 1.97, 2.03, 2.19, 2.03, 0.86) x2 <- c(1.30, 0.24, 0.20, 0.50, 1.33, 1.87, 1.30, 0.75, 1.07, 0.43, 0.37, 0.87, 1.40, 1.37, 1.63, 0.80, 0.57, 1.60, 0.39, 2.03, 1.90, 2.07, 1.93, 0.93) model <-

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

Hello, I'm trying to fit some points with a 8-degrees polynom (result of lm is stored in pfit). In most of the case, it is ok but for some others, some coefficients are "NA". I don't really understand the meaning of these "NA". And the problem is that I can't perform a derivation (pderiv<-as.function((deriv(polynomial(pfit$coefficients))))) on pfit due to the

Also, it is possible to change "ylim" also? 2012/7/1 li li <hannah.hlx@gmail.com> > Dear all, > I have a few questions regarding the boxplot output from the > "geom_boxplot" function. > Attached is the output I get. Below are my questions: > > 1. How can I define the xlab and ylab myself? > Also I would like to remove