fingerprint {dacc}  R Documentation 
Optimal Fingerprinting via total least square regression.
Description
This function estimates the signal factors and corresponding confidence interval via the estimating equation or total least squares.
Usage
fingerprint(
Xtilde,
Y,
mruns,
ctlruns.sigma,
ctlruns.bhvar,
S,
T,
B,
method = c("EE", "PBC", "TS"),
cov.method = c("l2", "mv"),
conf.level = 0.9,
missing = FALSE,
cal.a = TRUE,
ridge = 0
)
Arguments
Xtilde 

Y 

mruns 
number of ensembles to estimate the corresponding pattern.
It is used as the scale of the covariance matrix for 
ctlruns.sigma 

ctlruns.bhvar 

S 
number of locations for the observed responses. 
T 
number of time steps for the observed responses. 
B 
number of replicates in bootstrap procedure. 
method 
for estimating the scaling factors and corresponding confidence interval 
cov.method 
method for estimation of covariance matrix in confidence interval estimation of PBC method. (only for PBC method) 
conf.level 
confidence level for confidence interval estimation. 
missing 
indicator for whether missing values present in Y. 
cal.a 
indicator for calculating the a value, otherwise use default value a = 1. (only for EE method) 
ridge 
shrinkage value for adjusting the method for missing observations if missing = TRUE. (only for EE method) 
Value
a list of the fitted model including point estimate and interval estimate of coefficients and corresponding estimate of standard error.
Author(s)
Yan Li
References
Gleser (1981), Estimation in a Multivariate "Errors in Variables" Regression Model: Large Sample Results, Ann. Stat. 9(1) 24–44.
Golub and Laon (1980), An Analysis of the Total Least Squares Problem, SIAM J. Numer. Anal. 17(6) 883–893.
Pesta (2012), Total least squares and bootstrapping with applications in calibration, Statistics 47(5), 966–991.
Li et al (2021), Uncertainty in Optimal Fingerprinting is Underestimated, Environ. Res. Lett. 16(8) 084043.
Sai et al (2023), Optimal Fingerprinting with Estimating Equations, Journal of Climate 36(20), 7109–7122.
Li et al (2024), Detection and Attribution Analysis of Temperature Changes with Estimating Equations, Submitted to Journal of Climate.
Examples
## load the example dataset
data(simDat)
Cov < simDat$Cov[[1]]
ANT < simDat$X[, 1]
NAT < simDat$X[, 2]
## generate the simulated data set
## generate regression observation
Y < MASS::mvrnorm(n = 1, mu = ANT + NAT, Sigma = Cov)
## generate the forcing responses
mruns < c(1, 1)
Xtilde < cbind(MASS::mvrnorm(n = 1, mu = ANT, Sigma = Cov / mruns[1]),
MASS::mvrnorm(n = 1, mu = NAT, Sigma = Cov / mruns[2]))
## control runs
ctlruns < MASS::mvrnorm(100, mu = rep(0, nrow(Cov)), Sigma = Cov)
## ctlruns.sigma for the point estimation and ctlruns.bhvar for the interval estimation
ctlruns.sigma < ctlruns.bhvar < ctlruns
## number of locations
S < 25
## number of year steps
T < 10
## call the function to estimate the signal factors via EE
fingerprint(Xtilde, Y, mruns,
ctlruns.sigma, ctlruns.bhvar,
S, T, B = 10,
method = "EE",
conf.level = 0.9,
cal.a = TRUE,
missing = FALSE, ridge = 0)