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Bootstrap Standard Errors and Confidence Intervals for Parameters

Source: R/boot.seci.R
boot.seci.Rd

Computes standard errors and confidence intervals for cosinor and post-hoc parameters via non-parametric bootstrap

Usage

boot.seci(object, ci_level = 0.95, n = 500, digits = 2)

Arguments

object

A fitted `CosinorM` or `CosinorM.KDE` model object.

ci_level

Numeric scaler. The threshold for the confidence interval. Default: 0.95

n

Numeric scaler. Numbers of bootstraps required to estimate the standard errors and confidence intervals. Default: 500

digits

Numeric scaler. Integer indicating the number of decimal places (round) to be used. Default: 2

Value

A data.frame with one row per cosinor coefficient and columns:

  • Estimate: Mean of bootstrap coefficient values.

  • Std Error: Bootstrap standard deviation of each coefficient across n resamples.

  • t value: Ratio of the observed estimate to its bootstrap standard error, analogous to a signal-to-noise measure: $$t = \hat{\theta}_{obs} / SE_{boot}$$

  • lower CI label: Percentile lower bound at \(\frac{\alpha}{2}\), where \(\alpha = 1 - ci_{level}\).

  • upper CI label: Percentile upper bound at \(1 - \frac{\alpha}{2}\).

See also

boot

Examples

if (FALSE) { # \dontrun{
# Import data
FlyEast

BdfList <-
    BriefSum (
        df = FlyEast,
        SR = 1 / 60,
        Start = "2017-10-24 13:45:00"
    )

# Let's extract actigraphy data from a single day
df <- BdfList$df
df <- subset (df, df$Date == "2017-10-27")

# Multicomponent Cosinor Model
fit <- CosinorM (
    time = df$Time,
    activity = df$Activity,
    tau = c (12, 24),
    method = "OLS"
)

# inspect coefficients

boot.seci (
    object = fit,
    ci_level = 0.95,
    n = 500
)


# Gaussian Kernel Density Estimation
fit2 <- CosinorM.KDE (
    time = df$Time,
    activity = df$Activity
)

# inspect coefficients

boot.seci (
    object = fit2,
    ci_level = 0.95,
    n = 500
)
} # }