How To Calculate Confidence Interval
For large sample size n, the sample mean is normally distributed, and one can calculate its confidence interval using st.norm.interval() (as suggested in Jaime's If you're looking to compute the confidence interval of the regression parameters, one way is to manually compute it using the results of LinearRegression from
A practical example: If I had an RMSE value of 0.3 and 1000 samples were used to calculate that value, I can then do. rmse_interval(0.3, 1000) which would return: # A tibble: 1 x 2 My initial thought is to divide the 95th percentile with the 84th percentile as a way to find the scaling factor between the widths of the 90% and 68% confidence
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For large sample size n, the sample mean is normally distributed, and one can calculate its confidence interval using st.norm.interval() (as suggested in Jaime's
If you're looking to compute the confidence interval of the regression parameters, one way is to manually compute it using the results of LinearRegression from
A practical example: If I had an RMSE value of 0.3 and 1000 samples were used to calculate that value, I can then do. rmse_interval(0.3, 1000) which would return: # A tibble: 1 x 2
My initial thought is to divide the 95th percentile with the 84th percentile as a way to find the scaling factor between the widths of the 90% and 68% confidence
Is there an R function for computing 95% confidence interval of interpolated X from an nls model? 3 How to perform nonlinear least squares with shared parameters in R?
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