ccor_posim: Credible interval estimation for curve correlation based on...

In ctmva: Continuous-Time Multivariate Analysis

Credible interval estimation for curve correlation based on posterior simulation

Description

Inputs raw data representing two curves, and computes a credible interval for the curve correlation between them simulating from the approximate posterior distribution of the joint spline coefficient vector.

Usage

ccor_posim(
  y,
  time,
  curve = NULL,
  method,
  k = 15,
  conf = 0.95,
  ndraw = 999,
  min.overlap = 0
)

Arguments

y, time, curve, k, min.overlap	see ccor
method	"indep" (curves fitted independently) or "mvn" (curves fitted together, with error correlation estimated based on multivariate normal likelihood)
conf	confidence level
ndraw	number of draws from posterior distribution of spline coefficient vector

Value

A list with components

cor	curve correlation
model	the model for the two curves (if method=="mvn"), or a list of the two curves' models (if method=="indep")
bsb	B-spline basis (from package fda) used for the curves
Vc.fda	corrected posterior covariance matrix for the coefficients with respect to the B-spline basis bsb (see the component $Vc in gamObject)
sims	curve correlations for the ndraw draws from the posterior distribution
ci	credible interval for the curve correlation

Author(s)

Philip Tzvi Reiss <[e-mail]>, Noemi Foa, Dror Arbiv and Biplab Paul <[e-mail]>

See Also

ccor, ccor_boot, mvn

Examples

## Not run:
if (interactive () &&
    requireNamespace("wbwdi", quietly = TRUE) &&
    requireNamespace("ggplot2", quietly = TRUE) &&
    requireNamespace("dplyr", quietly = TRUE)) {

    # Curve correlation of per capita GDP and fertility rate in Paraguay
    wdi_dat <- wbwdi::wdi_get(entities = c("PRY"), start_year=1960, end_year=2023,
                       indicators = c("NY.GDP.PCAP.KD","SP.DYN.TFRT.IN"), format="wide") |>
        dplyr::rename(percapitaGDP = NY.GDP.PCAP.KD, fertility = SP.DYN.TFRT.IN)
    ggplot2::ggplot(wdi_dat, aes(percapitaGDP, fertility, color=year)) + geom_point()

    y <- as.matrix(wdi_dat[ , c("percapitaGDP", "fertility")])

    set.seed(345)

    ci <- list()
    ci[[1]] <- ccor_posim(y=y, time=wdi_dat$year, method="indep")
    ci[[2]] <- ccor_posim(y=y, time=wdi_dat$year, method="mvn")
    ci[[3]] <- ccor_boot(y=y, time=wdi_dat$year, ndraw=399)

    tabl <- matrix(NA, 3, 3)
    for (k in 1:3) tabl[k, ] <- c(ci[[k]]$cor, ci[[k]]$ci)
    dimnames(tabl) <- list(c("Posim_indep", "Posim_MVN", "Bootstrap"), c("Est","Lower95","Upper95"))
    round(tabl, 4)
}
## End(Not run)

ctmva documentation built on Nov. 20, 2025, 5:06 p.m.