confidence-interval

问题 I'm working on doing a logistic regression using MATLAB for a simple classification problem. My covariate is one continuous variable ranging between 0 and 1, while my categorical response is a binary variable of 0 (incorrect) or 1 (correct). I'm looking to run a logistic regression to establish a predictor that would output the probability of some input observation (e.g. the continuous variable as described above) being correct or incorrect. Although this is a fairly simple scenario, I'm

问题 I'm working on doing a logistic regression using MATLAB for a simple classification problem. My covariate is one continuous variable ranging between 0 and 1, while my categorical response is a binary variable of 0 (incorrect) or 1 (correct). I'm looking to run a logistic regression to establish a predictor that would output the probability of some input observation (e.g. the continuous variable as described above) being correct or incorrect. Although this is a fairly simple scenario, I'm

问题 I have model prediction for mean ans confidence intervals from my data that I want to add on the graph. I know how to plot the data, but how can I add the model fitted mean and confidence intervals? For the latter geom_ribbon does not seem to do the job. df <- data.frame( fertilizer = c("N","N","N","N","N","N","N","N","N","N","N","N","P","P","P","P","P","P","P","P","P","P","P","P","N","N","N","N","N","N","N","N","N","N","N","N","P","P","P","P","P","P","P","P","P","P","P","P"), level = c("low"

问题 I would like to plot the line and the 95% confidence interval from a linear model where the response has been logit transformed back on the original scale of the data. So the result should be a curved line including the confidence intervals on the original scale, where it would be a straight line on the logit transformed scale. See code: # Data dat <- data.frame(c(45,75,14,45,45,55,65,15,3,85), c(.37, .45, .24, .16, .46, .89, .16, .24, .23, .49)) colnames(dat) <- c("age", "bil.") # Logit

问题 I would like to plot the line and the 95% confidence interval from a linear model where the response has been logit transformed back on the original scale of the data. So the result should be a curved line including the confidence intervals on the original scale, where it would be a straight line on the logit transformed scale. See code: # Data dat <- data.frame(c(45,75,14,45,45,55,65,15,3,85), c(.37, .45, .24, .16, .46, .89, .16, .24, .23, .49)) colnames(dat) <- c("age", "bil.") # Logit

问题 I am wondering how to assign a value to a variable? For example I perform a simple t.test " One Sample t-test data: FirstExample t = 19.3645, df = 599, p-value < 2.2e-16 alternative hypothesis: true mean is not equal to 0 90 percent confidence interval: 0.3522468 0.4177532 sample estimates: mean of x 0.385" and I would like to assign the lower confidence interval to a variable: LowerConf= 0.3522468 Is there a way to automatically do it? 回答1: In general you assign a value exactly the way you

问题 When plotting two regression curves using geom_smooth() in ggplot2 , for the fill color, the legend picks the one where the confidence intervals intersect. I do think this behaviour arises when the overlapping area is proportionally bigger that the other, however I find this quite undesired because the reader is capable of deducing that the "darkened" area is the one where the CI intersect. It is IMHO a bit harder or unintuitive to assign the same color for both the curves. How can I correct

来源： https://stackoverflow.com/questions/34816702/confidence-interval-for-standard-deviations-from-bootstrapping-in-r

来源： https://stackoverflow.com/questions/34816702/confidence-interval-for-standard-deviations-from-bootstrapping-in-r

来源： https://stackoverflow.com/questions/34816702/confidence-interval-for-standard-deviations-from-bootstrapping-in-r