similar to: Student-t distributed random value generation within a confidence interval?

Hello R-Users, I'm new to R so I apologize in advance for any big mistake I might be doing. I'm trying to fit a set of samples with some probabilistic curve, and I have an important question to ask; in particular I have some data, from which I calculate manually the CDF, and then I import them into R and try to fit: I have the x values (my original samples) and the y values

I have a sample of lung capacities from a population measured against height. I need to know the 95% CI of the lung capacity of a person of average height. I have fitted a regression line. How do I get a minimum and maximum values of the 95% CI? My thinking was that this has something to do with covariance, but how? My other thinking was that I could derive the 0.975 (sqrt 0.95) CI for the

Dear R experts Sorry for this question M1 <- 1:10 lcd1 <- c(11, 22, 33, 44, 11, 22, 33, 33, 22, 11) lcd2 <- c(22, 11, 44, 11, 33, 11, 22, 22, 11, 22) lcd3 <- c(12, 12, 34, 14, 13, 12, 23, 23, 12, 12) #generating variables through sampling pvec <- c("PR1", "PR2", "PR3", "PR4", "PR5", "PR6", "PR7",

For a given linear regression, I wish to find the 2-tailed t-dist probability that Y-hat <= newly observed values. I generate prediction intervals in predict() for plotting, but when I calculate my t-dist probabilities, they don't agree. I have researched the issues with variance of individual predictions and been advised to use the variance formula below (in the code). I presume my

Can anyone please tell me if there is a function to calculate confidence intervals for the results of the quantile function. Some of my data is normally distributed but some is also a squewed distribution or a capped normal distribution. Some of the data sets contain about 700 values whereas others are smaller with about 100-150 values, so I would like to see how the confidence intervals change

Dear List, I am not sure if A) this is possible or B) the correct place to ask. I am looking to find the mean - i have n, and the two-tailed confidence intervals 0.95 & 0.25 with a p-value of 0.05. Can i find the mean from this data ? Thanks Peter

Hello all, could you please tell me how to create a 95 percent confidence interval using R, if I have the next data: > blue [1] 4 69 87 35 39 79 31 79 65 95 68 62 70 80 84 79 66 75 59 77 36 86 39 85 74 [26] 72 69 85 85 72 > red [1] 62 80 82 83 0 81 28 69 48 90 63 77 0 55 83 85 54 72 58 68 88 83 78 30 58 [26] 45 78 64 87 65 Build a confidence interval of 95 % for the difference of the

Hello, I am trying to calculate and plot mean and confidence intervall for a set of data. This is the code that I am currently using: means <- sapply(data, mean, na.rm=TRUE) n <- sapply(data,length) stdev <- sqrt(sapply(data, var, na.rm=TRUE)) ciw <- qt(0.98, n) * stdev / sqrt(n) par(mgp=c(2,0.6,0), las=2, fin=c(7,3), mai=c(1,0.5,0.2,0.2), cex=0.8) plotCI(x=means, uiw=ciw,

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,

Hi, All: I only have 4 samples. I wish to get a confidence interval around the mean. Is it reasonable? If not, is there a way to compute a confidence interval for such small sample size's mean? Many thanks, U [[alternative HTML version deleted]]

Dear R gurus, I have the following code, but I still not know how to estimate and extract confidence intervals (95%CI) from resampling. Thanks! ~Adriana #data penta<-c(770,729,640,486,450,410,400,340,306,283,278,260,253,242,240,229,201,198,190,186,180,170,168,151,150,148,147,125,117,110,107,104,85,83,80,74,70,66,54,46,45,43,40,38,10) x<-log(penta+1) plot(ecdf(x),

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver another confidence interval, e.g. when I set

What's the current best wisdom on how to construct confidence bounds on something like a normal probability plot? I recall having read a suggestion to Monte Carlo something like 201 simulated lines with the same number of points, then sort the order statistics, and plot the 6th and 196th of these. [I use 201 not 200 because quantile(1:201, c(0.025, 0.975)) = 6 and 196 while

Hi! With the following script, I'm trying to make a demonstration of a Confidence Interval, but I'm observing some differences on tails. # Teste de média entre uma amostra e uma população normal # Autor: Raphael de Freitas Saldanha # Agosto de 2008 n <- 200 # Sample size xbar <- 100 # Sample mean s <- 2 # Sample SD nc <- 0.95 # Confidence level (95%

The first thing you are missing is the documentation -- try ?survfit.object. fit <- survfit(Surv(time,status)~1,data) fit$std.err will contain the standard error of the cumulative hazard or -log(survival) The standard error of the survival curve is approximately S(t) * std(hazard), by the delta method. This is what is printed by the summary function, because it is what user's

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

Dear list, I'm a bit perplexed why the 95% confidence bands for the predicted probabilities for units where x=0 and x=1 overlap in the following instance. I've simulated binary data to which I've then fitted a simple logistic regression model, with one covariate, and the coefficient on x is statistically significant at the 0.05 level. I've then used two different methods to

Dear Sir/Madam, I am trying to use calculation for two-tailed inverse of the student`s t-distribution function presented by Excel functions like =TINV(probability, deg_freedom). For instance: The Excel function =TINV(0.0000408831,1221) = returns 4.0891672. Would you like to show me a manual calculation for this? Appreciate your helps in advance. Cheers! Andre [[alternative HTML version

Based on simulations, I've come up with a simple function to compute the confidence interval for the variance of the binomial variance, where the true variance is v = rho*(1-rho)/n where rho = true probability of success and n = # of trials. For x = # successes observed in n trials, p = x / n as usual. For p < 0.25 or p > 0.75, I use the proportion-based transformed confidence

Hello, every one, Using the qt() function in R, I got a different Student t-value with the Student t table. e.g. the return value of function qt(0.95,9) is 1.833, while in table it is 2.262. I do not know why the difference exists. I guess it may be a bug in R. -- Íõ»¯Èå ²©Ê¿Ñо¿Éú ±±¾©Ê¦·¶´óѧ¾°¹ÛÉú̬Óë¿É³ÖÐøÐÔ¿ÆѧÑо¿ÖÐÐÄ ±±¾©Êк£µíÇøнֿÚÍâ´ó½Ö19ºÅ±±¾©Ê¦·¶´óѧÉúÃü¿ÆѧѧԺ 100875 Huaru