similar to: Unexpected behavior of predict and interval="confidence"

Hi I have measured the UV absorbance (abs) of 10 solutions of a substance at known concentrations (conc) and have used a linear model to plot a calibration graph with confidence limits. I now want to predict the concentration of solutions with UV absorbance results given in the new.abs data.frame, however predict.lm only appears to work for new "conc" variables not new "abs"

Hello, All: I am unable to manually replicate predict.lm, specifically comparing se.fit with (fit[,3]-fit[,2]): I think their ratio should be 2*qnorm((1-level)/2), and that's not what I'm getting. Consider the following slight modification of the first example in help('predict.lm'): set.seed(1) x <- rnorm(15) y <- x + rnorm(15) predict(lm(y ~ x)) new <-

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,

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

Hi R People: If I have a fitted values from a model, how do I plot the (1-alpha)100% confidence intervals along with the fitted values, please? Also, if the intervals are "shaded" gray, that would be nice too, please? I check confint, but that doesn't seem to do what I want. Thanks in advance, Sincerely, Erin -- Erin Hodgess Associate Professor Department of Computer and

(I'm reposting this message because the original has not appeared after about 2 days. Sorry if it shows up twice.) Hello. First, thanks to those who responded to my recent inquiry about using contour() over arbitrary (x,y) by mentioning the interp() function in the akima package. That worked nicely. Now for a new question: I would like to use a pair of prediction intervals to

Dear R Users, I am encountering a problem when reading nc files into R using the ncdf and ncdf4 libraries. The nc files are too large to attach an example (but if someone is interested in helping out I could send a file privately via an online drive), but the code is basic: for(i in 1:length(thesenames[,1])){ data <- nc_open(paste(INDIR, thesenames[i,c("wholename")],

On Fri, Apr 22, 2016 at 1:32 AM, Louise Mair <louise.mair at slu.se> wrote: > Dear R Users, > > I am encountering a problem when reading nc files into R using the ncdf > and ncdf4 libraries. The nc files are too large to attach an example (but > if someone is interested in helping out I could send a file privately via > an online drive), but the code is basic: > ?[...]?

R-helpers, I am using the package "drc" to fit a 4 parameter logistic model. When I use the predict function to get prediction on a new dataset, I am not getting the requested confidence or prediction intervals. Any idea what is going on? Here is code to reproduce the problem: --- library(drc) # Fit model to existing dataset in package spinach.model <- drm(SLOPE~DOSE, data =

Hi all, I'm building a plot of the values in tmeant (below) against positions 1 to 5, using matplot. tmeant looks like this: case1 case2 pos1 861.8466 818.5909 pos2 961.2841 976.3466 pos3 878.6080 1262.8523 pos4 950.8011 1129.6080 pos5 968.1080 1063.3920 I also have lower (object tl) and upper (object tu) bounds on the confidence intervals as follows: tl: pos1

Dear List,   I am new to ROC analysis and the package ROCR. I want to compute the confidence intervals of sensitivity and specificity for a given cutoff value. I have used the following to calculate sensitivity and specificity:   data(ROCR.simple) pred <- prediction(ROCR.simple$predictions, ROCR.simple$labels)   se.sp <- function (cutoff, performance) {     sens <-

Dear all, I have in my possession a netcdf from which I want to extract some data files. I have used the "ncdf" package to read the netcdf file and used the "get.var.ncdf" function to identify the variable i wish to use. The data is in the form of a time-series of geographical data points that relate to climatology variables. As such I have a large data frame,

Hi all, I have played a bit with the "reshape" package and function along with "melt" and "cast", but I feel I still don't have a good handle on how to use them efficiently. Below I have included a application of "reshape" that is rather clunky and I'm hoping someone can offer advice on how to use reshape (or melt/cast) more efficiently. #For this

n <- 63 a <- 1:n x <- a-1 y <- outer(x,a) matplot(x,y,type='l') Warning message: In matplot(x, y, type = "l") : default 'pch' is smaller than number of columns and hence recycled Why is it complaining if I specifically ask for type="l", so no pch involved? Annoyance or feature? The fix (if needed) is simple. In the matplot code change: if

Hello! I have 2 variables - predictor "pred" and response variable "DV": pred<-c(439635.053, 222925.718, 668434.755, 194242.330, 5786.321, 115537.344, 100835.368, 7133.206, 159058.286, 4079991.629, 3380078.060, 2661279.136, 2698324.478, 1245213.965, 1901815.503, 1517019.451, 1396857.736, 1034030.988, 432249.574, 342329.325, 1831335.792, 2209578.859, 1641709.754,

Dear all, I'm trying to construct confidence intervals for a LOWESS estimation (by not using bootstrapping). I have checked previous posts and other material online and I understand that the main procedure is: my.count<- seq(...) fit<- loess (y ~ x, data=z) pred<- pred(fit, my.count, se=TRUE) and then the plotting. However, it's not working; as confidence

I have an irregular time series, which cannot easily be converted to a ts object (long stretches with NA). I have calculated the climatological mean and st.dev, but need help on how to calculate the anomalies from the climatology and my original data set. Below is an example, where I have indicated the basic idea of what I want to do under Question 1 and 2. I assume I will have to create a custom

Dear list, I want to calculate SPE (squared prediction error) in x-space, can someone help? Here are my codes: fit.pls<- plsr(Y~X,data=DAT,ncomp=3,scale=T,method='oscorespls',validation="CV",x= T) actual<-fit.pls$model$X pred<-fit.pls$scores %*% t(fit.pls$loadings) SPE.x<-rowSums((actual-pred)^2) Am I missing something here? Thanks in advance. Stella Sim

Hi, I often use the mod() and instring() functions that are available in the clim.pact package. This package has a lot of dependencies, including installation of netCDF, and I haven't yet been able to get library(clim.pact) to work on a Mac OS 10.4.6. A previous request for help with the Mac problem yielded no results, so now I wonder if I could just extract the definitions for the couple

Dear All I am quite new to R and would appreciate some help fitting 95% confidence intervals to a nls function. I have the data DOY CET 90 5.9 91 8 92 8.4 93 7.7 95 6.6 96 6.8 97 7.1 98 9.7 99 12.3 100 12.8 102 11 103 9.3 104 9.8 105 9.9 107 7.7 110 6.2 111 5.9 112 5.9 113 3.4 114 3.5 116 3.3 117 5.4 118 6.3 119 9.7 120 11.2 121 7.3 124 7.8 etc I am trying to use some code that has been