boot_ci {sjstats} | R Documentation |
Standard error and confidence intervals for bootstrapped estimates
Description
Compute nonparametric bootstrap estimate, standard error, confidence intervals and p-value for a vector of bootstrap replicate estimates.
Usage
boot_ci(data, select = NULL, method = c("dist", "quantile"), ci.lvl = 0.95)
boot_se(data, select = NULL)
boot_p(data, select = NULL)
boot_est(data, select = NULL)
Arguments
data |
A data frame that containts the vector with bootstrapped estimates, or directly the vector (see 'Examples'). |
select |
Optional, unquoted names of variables (as character vector)
with bootstrapped estimates. Required, if either |
method |
Character vector, indicating if confidence intervals should be
based on bootstrap standard error, multiplied by the value of the quantile
function of the t-distribution (default), or on sample quantiles of the
bootstrapped values. See 'Details' in |
ci.lvl |
Numeric, the level of the confidence intervals. |
Details
The methods require one or more vectors of bootstrap replicate estimates as input.
-
boot_est()
: returns the bootstrapped estimate, simply by computing the mean value of all bootstrap estimates. -
boot_se()
: computes the nonparametric bootstrap standard error by calculating the standard deviation of the input vector. The mean value of the input vector and its standard error is used by
boot_ci()
to calculate the lower and upper confidence interval, assuming a t-distribution of bootstrap estimate replicates (formethod = "dist"
, the default, which ismean(x) +/- qt(.975, df = length(x) - 1) * sd(x)
); formethod = "quantile"
, 95\ confidence intervals (quantile(x, probs = c(0.025, 0.975))
). Useci.lvl
to change the level for the confidence interval.P-values from
boot_p()
are also based on t-statistics, assuming normal distribution.
Value
A data frame with either bootstrap estimate, standard error, the lower and upper confidence intervals or the p-value for all bootstrapped estimates.
References
Carpenter J, Bithell J. Bootstrap confdence intervals: when, which, what? A practical guide for medical statisticians. Statist. Med. 2000; 19:1141-1164
See Also
[]bootstrap()
] to generate nonparametric bootstrap samples.
Examples
data(efc)
bs <- bootstrap(efc, 100)
# now run models for each bootstrapped sample
bs$models <- lapply(
bs$strap,
function(.x) lm(neg_c_7 ~ e42dep + c161sex, data = .x)
)
# extract coefficient "dependency" and "gender" from each model
bs$dependency <- vapply(bs$models, function(x) coef(x)[2], numeric(1))
bs$gender <- vapply(bs$models, function(x) coef(x)[3], numeric(1))
# get bootstrapped confidence intervals
boot_ci(bs$dependency)
# compare with model fit
fit <- lm(neg_c_7 ~ e42dep + c161sex, data = efc)
confint(fit)[2, ]
# alternative function calls.
boot_ci(bs$dependency)
boot_ci(bs, "dependency")
boot_ci(bs, c("dependency", "gender"))
boot_ci(bs, c("dependency", "gender"), method = "q")
# compare coefficients
mean(bs$dependency)
boot_est(bs$dependency)
coef(fit)[2]