I looked into pROC, but as far as I understood it, there you need the raw data for each ROC curve (which I don't have). One can observe that it is quite simple to obtain the confidence interval directly. I have calculated different proportion estimates with 95% confidence intervals. Link, C. L. (1984). Biometrics 40, 601-610. Although there is a weighted.mean function in R, so far I couldn’t find a implementation of weighted.var and weighted.t.test – here they are (the weighted variance is from Gavin Simpson, found on the R malining list): ?View Code RSPLUS# weighted … Continue reading → Note. By using nboot =10000 (or any other number that can easily be divided) it makes it quite simple to find the confidence interval by merely taking the alpha/2 and (1-alpha/2) percentiles; in this case below the 50 and 9950 positions. (2) Using the model to predict future values. Once SE(k) is calculated, a 100(1 – alpha)% confidence interval for kappa may be computed using the standard normal distribution as follows: For example, the formula of the 95% confidence interval is: k +/- 1.96 x SE. Further detail of the predict function for linear regression model can be found in the R documentation. I calculated the weighted average for the areas with . Also, when doing so, i want to weight the three different proportions according to my own liking. T1 - Confidence intervals for weighted proportions. R code to compute step by step the Cohen’s kappa: Weighted mean with confidence interval 22 Sep 2016, 15:35. [R] Weighted Mean Confidence Interval; McGehee, Robert. Woodruff RS (1952) Confidence intervals for medians and other position measures. Now i want to calculate a mean of these different proportions with 95% confidence intervals. A linear regression model can be useful for two things: (1) Quantifying the relationship between one or more predictor variables and a response variable. Aug 9, 2006 at 7:23 pm: 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 … I used weighted regression because the variance of my original OLS model increases with age. The 95% confidence interval of the mean eruption duration for the waiting time of 80 minutes is between 4.1048 and 4.2476 minutes. When using the predict function to generate prediction intervals for a … Y1 - 1994/5/30. The point estimate of the proportion, with the confidence interval as an attribute References Rao, JNK, Scott, AJ (1984) "On Chi-squared Tests For Multiway Contingency Tables with Proportions Estimated From Survey Data" Annals of Statistics 12:46-60. Williams RL (1995) "Product-Limit Survival Functions with Correlated Survival Times" Lifetime Data Analysis 1: 171--186. weighted.mean(a, n) Is there a way in R to also calculate the 95% confidence intervals of the weighted mean, based on the information I have? In regards to (2), when we use a regression model to predict future values, we are often interested in predicting both an exact value as well as an interval that contains a range of likely values. Confidence intervals for the survival function using Cox's proportional hazards model with covariates. Hello! AU - Waller, Jennifer L. AU - Addy, Cheryl L. AU - Jackson, Kirby L. AU - Garrison, Carol Z. PY - 1994/5/30. I fitted a weighted regression model to predict age as a function of several DNA methylation markers (expressed in percentages). Weighted least squares (WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which the errors covariance matrix is allowed to be different from an identity matrix.WLS is also a specialization of generalized … A function for calculating confidence/prediction intervals of (weighted) nonlinear models for the supplied or new predictor values, by using first-/second-order Taylor expansion and Monte Carlo simulation.


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