similar to: Help with predict.lm

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 <-

Hello, I'm new here, but will try to be as specific and complete as possible. I'm trying to use “lm“ to first estimate parameter values from a set of calibration measurements, and then later to use those estimates to calculate another set of values with “predict.lm”. First I have a calibration dataset of absorbance values measured from standard solutions with known concentration of

Hi, I have to do a four point logistics for a dataset. All I have is the absorbance value for different proteins and need to get the four Point values. I have no idea where to start. Any suggestions would be much helpful. Thanks Ramya -- View this message in context: http://r.789695.n4.nabble.com/Four-paramete-logistics-tp3265251p3265251.html Sent from the R help mailing list archive at

I am having a problem with a gantt chart since moving to R2.4.0. from 2.3.1 I made some adaptations to the code from http://addictedtor.free.fr/graphiques/RGraphGallery.php?graph=74 and successful produced a simple gantt chart. However when I upgraded to 2.4.0 it no longer works as desired. See http://ca.geocities.com/jrkrideau/R/gantproblem.pdf for the two charts. The charts were produced

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

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 everyone, Sorry for the newbie question but whenever I enter the following code into r it gives me an unexpected string constant in "boxplot(Abs~Conc,ylab='Absorbency',xlab'Ethanol(%)'" error. I have tried everything to eliminate it and have searched these forums to no avail, can anyone tell me what to do to allow r to generate my boxplot for me? Here is my code:

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

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)

Based on some recent r-help discussions, I have been trying out plotting confidence intervals using predict and matplot. Matplot appeared to not be plotting the linear regression when using the default column names generated by read.table (V1, V2, etc). On further inspection, the error seemed to be with predict and vector names (V1, V2) rather than with matplot. I was using some textbook

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")],

Hi, I seem to be having a problem adding the following two raster objects together - one is a rasterLayer, the other is a rasterBrick. The extent, resolution, and origin are the same, so according to my understand it should work. The objects look like so: > obs.clim class : RasterLayer dimensions : 60, 200, 12000 (nrow, ncol, ncell) resolution : 0.5, 0.5 (x, y) extent : -70,

I need to modify the graph of the autocorrelation. I tried to do it through plot.acf but with no success. 1. I would like to get rid of the lag zero 2. I would like to have numbers on the x-axis only at lags 12, 24, 36, 48, 60, ... Could anybody help me in this? Any help will be appreciated Thank you for your attention Stefano [[alternative HTML version deleted]]

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

Hello all, I am trying to use "clim.pact" package for my work, but since this is the beginning for me to use gridded datasets in "R", I am having some trouble. I want to do seasonal analyses like trends, anomalies, variograms, EOF and probably kriging too to downscale my 1 degree gridded data to 0.5.  So, as a first step, I compiled my entire dataset (with 25

Dear all, I wanted to plot the mean and standard error on the regression equation (instead of individual data points) in the following code, but I could not find the right code in the help files. Could someone please show how to do this. Thank you very much. temp <- c(16,16,16,16,16, 20,20,20,20,20, 24,24,24,24,24, 28,28,28,28,28, 32,32,32,32,32) dev1hr <- c(36.2, 34, 32.2, 36.4, 36,

Some years ago when I used S-PLUS, I seem to remember, there was a discussion about a simple method of removing trailing spaces from vector elements. I'd be fairly sure the same would work in R, but I can't find any mention of anything like it in the R archives or with help.search(). There are ways I could do it with substring(), but I seem to remember there was something more elegant.

Hello all R users, My simulation function works correctly, but I have problems with plot function. You will find the following code using it. Thank you for your help ##################################################" simulation <- function(k, n){ conc <- seq(0,10,by=0.5) #choixg <- seq(1, length(conc)) choixg <- rep(0,length(conc)) for (i in 1:length(conc)){ choixg[i]

Hello everyone, I am trying to solve 2D differential equations using finite difference scheme in R. I have been able to work with the equations with only one spatial dimensions but I want to extend it to the two dimensional problem. For example i can simulate one dimensional diffusion using a code like the following. But I want to write a similar code for,say, a two dimensional diffusion

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: > ?[...]?