First of all, I am not a statistician. I only how to interpret stats and to do them with R, my understanding of the math/formulas behind them is virtually zero.

With this said, I am looking for a laymen's explanation of the way percentile bootstrap confidence intervals are calculated for mixed-effects models with the confint() function of the R package lme4 (that is, I would like to gain a basic understanding of how the math works)

Would it be correct to state that this function selects a user-defined number of subsamples from the original data, applies the regression model to them and then calculates the range within which we can be 95% sure that the true population effect of a level of a predictor falls? How does this calculation work? Would it be correct to portray it as averaging over a large number of coefficients?

Thanks in advance!

First of all, I am not a statistician. I only how to interpret stats and to do them with R, my understanding of the math/formulas behind them is virtually zero.

With this said, I am looking for a laymen's explanation of the way percentile bootstrap confidence intervals are calculated for mixed-effects models with the confint() function of the R package lme4 (that is, I would like to gain a basic understanding of how the math works)

Would it be correct to state that this function selects a user-defined number of subsamples from the original data, applies the regression model to them and then calculates the range within which we can be 95% sure that the true population effect of a level of a predictor falls? How does this calculation work? Would it be correct to portray it as averaging over a large number of coefficients?

First of all, I am not a statistician. I only how to interpret stats and to do them with R, my understanding of the math/formulas behind them is virtually zero.

With this said, I am looking for a laymen's explanation of the way percentile bootstrap confidence intervals are calculated for mixed-effects models with the confint() function of the R package lme4 (that is, I would like to gain a basic understanding of how the math works)

Would it be correct to state that this function selects a user-defined number of subsamples from the original data, applies the regression model to them and then calculates the range within which we can be 95% sure that the true population effect of a level of a predictor falls? How does this calculation work? Would it be correct to portray it as averaging over a large number of coefficients?

Thanks in advance!

P.S. If you think this is a dumb/noob/silly/shitty post, go out and have a beer instead of calling me names and this post a waste of your time. You don't have to be on this site.

First of all, I am not a statistician. I only how to interpret stats and to do them with R, my understanding of the math/formulas behind them is virtually zero.

With this said, I am looking for a laymen's explanation of the way percentile bootstrap confidence intervals are calculated for mixed-effects models with the confint() function of the R package lme4 (that is, I would like to gain a basic understanding of how the math works)

Would it be correct to state that this function selects a user-defined number of subsamples from the original data, applies the regression model to them and then calculates the range within which we can be 95% sure that the true population effect of a level of a predictor falls? How does this calculation work? Would it be correct to portray it as averaging over a large number of coefficients?

Thanks in advance!