clikcorr (version 1.0)

clikcorr: Censoring data and LIKelihood-based CORRelation estimation and inference

Description

A profile likelihood based method of estimation and hypothesis testing on the correlation coefficient of bivariate data with different types of cencoring.

Usage

clikcorr(data, lower1, upper1, lower2, upper2, cp = 0.95, dist = "n",  df = 4, sv = NA, nlm = FALSE, ...)
"clikcorr"(data, lower1, upper1, lower2, upper2, cp = 0.95, dist = "n",  df = 4, sv = NA, nlm = FALSE, ...)
"print"(x, ...)
"summary"(object, ...)

Arguments

data

a data frame name.

lower1

the lower bound of the first of the two variables whose correlation coefficient to be calculated.

upper1

the upper bound of the first of the two variables whose correlation coefficient to be calculated.

lower2

the lower bound of the second of the two variables whose correlation coefficient to be calculated.

upper2

the upper bound of the second of the two variables whose correlation coefficient to be calculated.

cp

confidence level for the confidence interval.

dist

working distribution. By default, dist="n" assuming the data from a bivariate normal distribution. Set dist="t" if the data are assumed generated from a bivariate t-distribution.

df

degree of freedom of the bivariate t-distribution when dist="t". By default df=4.

sv

user specified starting values for the vector of (mean1, mean2, var1, corr, var2).

nlm

use nlm as the optimization method to minimize the negative log (profile) likelihood. By default nlm=FALSE and optim is used to maximize the log (profile) likelihood.

x

an object of class "clikcorr", i.e., a fitted model.

object

an object of class "clikcorr", i.e., a fitted model.

...

not used.

Value

A list with components:

pairName

variable names for the input paired data structure in the clikcorr class.

pairData

a paired data structure in the clikcorr class.

dist

Normal or t distribution.

df

degree of freedom for t distribution.

coefficients

maximum likelihood estimate (MLE) of the correlation coefficient.

Cov

estimated variance covariance matrix.

Mean

estimated means.

CI

unsymmetric profile confidence interval for the estimated correlation coefficient.

P0

p-value for likelihood ratio test with null hypothesis says that the true correlation coefficient equals zero.

logLik

the value of the log likelihood at MLE.

Details

clikcorr conducts point estimation and hypothesis testing on the correlation coefficient of bivariate data with different types of cencoring.

References

Yanming Li, Kerby Shedden, Brenda W. Gillespie and John A. Gillespie (2016). Calculating Profile Likelihood Estimates of the Correlation Coefficient in the Presence of Left, Right or Interval Censoring and Missing Data.

Examples

data(ND)
logND <- log(ND)
logND1 <- logND[51:90,]

obj <- clikcorr(logND1, "t1_OCDD", "t2_OCDD", "t1_HxCDF_234678", "t2_HxCDF_234678")

## Not run: 
# clikcorr(logND, "t1_OCDD", "t2_OCDD", "t1_HxCDF_234678", "t2_HxCDF_234678")
# 
# clikcorr(logND, "t1_OCDD", "t2_OCDD", "t1_HxCDF_234678", "t2_HxCDF_234678",
#  nlm=TRUE)
# 
# clikcorr(logND, "t1_OCDD", "t2_OCDD", "t1_HxCDF_234678", "t2_HxCDF_234678",
#  method="BFGS")
# 
# clikcorr(logND, "t1_OCDD", "t2_OCDD", "t1_HxCDF_234678", "t2_HxCDF_234678",
#  sv=c(5,-0.5,0.6,0.5,0.6))
# 
# clikcorr(logND, "t1_OCDD", "t2_OCDD", "t1_HxCDF_234678", "t2_HxCDF_234678",
#  dist="t", df=10, nlm=TRUE)
# ## End(Not run)

print(obj)
summary(obj)