similar to: t-test confidence interval

Dear all, I am trying to write an R function that can estimate Transfer functions *with additive noise* i.e. Y_t = \delta^-1(B)\omega(B)X_{t-b} + N_t where B is the backward shift operator, b is the delay and N_t is a noisy component that can be modelled as an ARMA process. The parameters to both the impulse response function and the ARMA noisy component need to be estimated simultaneously. I

Hi, I have been using R for a few months now and I am confident that the language has everything I will need to complete my PhD. I can create functions, script files and packages, but I would like to write my programs more efficiently (maybe using OO). Can anyone recommend a good book on the "art" of good R programming? Kind Regards, Sam.

Hi, I am trying to use the stl function in the ts package. It requires that the data is a univariant time series at the moment my data is in a vector. I have coerced it to a time series using.... crimets <- ts(crimeData) However, this does not work. Does anyone have any suggestions? Cheers, Sam. p.s. I am fairly new to R so apologies if this is a stupid posting.

Dear R-users! I?m faced with following problem: Given is a sample where the sample size is 12, the sample mean is 30, and standard deviation is 4.1. Based on a Student-t distribution i?d like to simulate randomly 500 possible mean values within a two-tailed 95% confidence interval. Calculation of the lower and upper limit of the two-tailed confidence interval is the easy part. m <- 30 #sample

I am sure there is something simple here I am missing, so please bear with me. It concerns the computation of the confidence interval for a population mean. The data are 125 measurements of Cs137 radation, a sample data set from Davis "Statistics and Data Analysis in Geology" 3rd ed. (CROATRAD.TXT) ------------------ method 1: using textbook definitions: mean \pm se_mean * t-value mu

Hi, I want to set up a dynamic link between a library e.g. myLibrary.a and a C++ file myProgram.cc to use in R. Is this possible? If so how does one go about doing it? Any help will be greatly appreciated. Cheers, Sam.

Hi, I am trying to "do" a dyn.load(), but I get the following error... > dyn.load("fileGT.dll") Error in dyn.load(x, as.logical(local), as.logical(now)) : unable to load shared library "C:/R_Files/fileGT.dll": LoadLibrary failure: The specified module could not be found. It states it can't find the dll but it is in that directory. I have

Hello, I have a question regarding bootstrap confidence intervals. Suppose we have a data set consisting of single measurements, and that the measurements are independent but the distribution is unknown. If we want a confidence interval for the population mean, when should a bootstrap confidence interval be preferred over the elementary t interval? I was hoping the answer would be

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

I would like to obtain Confidence Intervals for the estimates (unstandardized beta weights) of each predictor in a WLS regression: m1 = lm(x~ x1+x2+x3, weights=W, data=D) SPSS offers that output by default, and I am not able to find a way to do this in R. I read through predict.lm, but I do not find a way to get the CIs for multiple independent variables. Thank you Torvon [[alternative HTML

Hello, I'm looking to calculate a 95% confidence interval about my estimate for a sample's weighted mean, where the calculated confidence interval would equal the t-test confidence interval of the sample in the case when all of the weights are equal. My initial thought was to simply implement a modified version of the t-test function but substituting the weighted variance and mean for the

Hi, I'm using R for some undergraduate lectures, reaching the t tests. No matter what conf.level one specifies in the syntax, the output always shows the 95 percent confidence interval. Is it possible to alter the function somehow, to report the CL percent confidence interval? TIA, Adrian -- Adrian Dusa Romanian Social Data Archive Bd. Schitu Magureanu nr.1 Tel./Fax: +40 21 3126618 \

Classification: UNCLASSIFIED Caveats: NONE A similar question has been posted in the past but never answered. My question is this: for probit analysis, how do you program a 95% confidence interval for the LD50 (or LC50, ec50, etc.), including a heterogeneity factor as written about in "Probit Analysis" by Finney(1971)? The heterogeneity factor comes into play through the chi-squared

Dear R-users, In a randomized placebo-controlled within-subject design, subjects recieved a psycho-active drug and placebo. Subjects filled out a questionnaire containing 15 scales on four different time points after drug administration. In order to detect drug effects on each time point, I compared scale values between placebo and drug for all time conditions and scales, which sums up to

Hi, I am again asking a generic question and the general response for such questions is cold. I am a beginner but use and write simple R scripts. I am looking for some ideas to calculate the confidence intervals based on this excerpt from the paper. Moreover it would help if someone points to material to read about degrees of freedom and any related concepts. Thanks,

Sorry if this has been discussed here before, but anyway: The are differences between the confidence intervals of the t.test as done by R and Splus: *** R R> x<-rnorm(1000)+1 R> t.text(x) Error: couldn't find function "t.text" R> t.test(x) One Sample t-test data: x t = 31.4062, df = 999, p-value = 0 alternative hypothesis: true mean is not equal to 0 95

Hello, I am interested in using nlme to model repeated measurements, but I don't seem to get good CIs. With the code below I tried to generate data sets according to the model given by equations (1.4) and (1.5) on pages 7 and 8 of Pinheiro and Bates 2000 (having chosen values for beta, sigma.b and sigma similar to those estimated in the text). For each data set I used lme() to fit a model,

I am trying to recreate an analysis that has been done by another group (in SAS I believe). I'm stuck on one part, I think because my stats knowledge is lacking, and while it's OT, I'm hoping someone here can help. Given this dataframe; foo*<-*structure(list(OBS = structure(1:18, .Label = c("1", "2", "3", "4", "5",

This is a follow-up mail of "the effect of blocking on the size of confidence intervals - analysis using aov". In both mails I pursue the idea of using blocking factors in order to reduce the width of confidence intervals. My dataset comprises, a quantitative response variable, namely: "response", and three categorical eplanatory variables, namely: "method",

Hello, I would like to calculate the 95% confidence intervals for the estimates of a linear model and I just wanted to check that I am doing it correct. Is it just: Estimate + 1.95996*Std.Error to Estimate - 1.95996*Std.Error or is there another approach that doesn't assume a normal distrbution? Thanks. Apologies for my naiivity Dan --