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Results tagged with bootstrap
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user 11849
The bootstrap is a resampling method to estimate the sampling distribution of a statistic.
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vote
Accepted
How do I randomize a larger population, from an existing population in R?
To get this answered:
What you want to do is essentially bootstrap resampling: You sample with replacement in order to create a bigger sample (and possibly infer properties of the distribution). …
- 7,076
3
votes
Why could data bootstraping modifiy the slope of a population comming from the same distribu...
Now, what is the purpose of the bootstrap? … Well, by using the t.test functions with the bootstrap resamples, you have an additional division by the square root of the bootstrap n. …
- 7,076
1
vote
Bootstrapping if the estimate of interest equal to zero in the available data?
You can't use bootstrap to infer anything about this rare event. … You'd need to increase your sample size so that the rare event is actually part of the sample if you want to use bootstrap and even then you shouldn't use bootstrap for samples with extremely low number …
- 7,076
3
votes
Which randomization test is equivalent to bootstrapped CIs
You could bootstrap the difference of means:
library(boot)
set.seed(1)
grp <- sample(c("a","b"), 100, replace = TRUE)
#some data with an actual difference in means
value <- rnorm(100, mean = as.integer … <- data.frame(grp, value)
b <- boot(df, function(DF, i) {
DF <- DF[i,]
mean(DF[DF$grp == "a", "value"]) -
mean(DF[DF$grp == "b", "value"])
}, R = 1e4, strata = as.integer(factor(df$grp)))
#bootstrap …
- 7,076
3
votes
How to calculate the confidence interval of the x-intercept in a linear regression?
I would recommend bootstrapping the residuals:
library(boot)
set.seed(42)
sims <- boot(residuals(fit), function(r, i, d = data.frame(x, y), yhat = fitted(fit)) {
d$y <- yhat + r[i]
fitb <- lm( …
- 7,076