CSDfit {FMCCSD}

Analysing clustered current status data

Description

CSDfit is used to analyze clustered current status data. The function provides parameter estimates, the maximum log likelihood value and the corresponding AIC value.

Usage

CSDfit(Rawdata, n_subject.raw, n_within.raw, r, n_quad = 30,
  lambda = 0, tolerance = 0.5, knots.num = 2,
  degree = 2, scale.numr = TRUE, clustering=TRUE)

Arguments

Rawdata

This is a dataframe of the current status data. The first column should be the index of the subject (cluster). The second column is the inspection time. The next n_subjec.raw columns are the subject (cluster-specifie) level covariates. Then the next n_within.raw columns are the within subject covariates. The last column is the indicator of the event where 1 or 0 indicate if the event has or has not happened by the inspection time, respectively. All the covariates are assumed to be either numerical or binary, and our program automatically detects if a covariate is a binary or numerical variable.

n_subject.raw

The number of subject (cluster-specifie) level covariates.

n_within.raw

The number of within cluster covariates.

r

The index of the Generalized odds ratio (GOR) model. This index is a non-negative number and it must be specified by the user. Here r=0 and 1 imply the proportional hazard and the proportional odds model, respectively.

n_quad

The number of Gauss-Hermite quadrature nodes used in numerical integration. The default value is 30.

lambda

The tuning parameter of the roughness penalty used for estimating the non-parametric component of the GOR model. The default value is 0. One must use the roughness penalty when the number of basis functions in the non-parametric component of the GOR model is large.

tolerance

This denotes the summation of the absolute values of the relative tolerance of all parameters in the model. It is used to define the convergence of the parameter estimates. The default value is 0.5.

knots.num

The number of equidistant interior knots for the integrated B-spline approximation of the nonparametric component of the GOR model. The default value is 2.

degree

The degree of integrated B-splines. The default value is 2.

scale.numr

logical. If TRUE, then all numeric covariates (cluster specifie and within cluster) are scaled with mean zero and standard deviation one. The default value is TRUE.

clustering

logical. TRUE and FALSE indicate assume there is clustering effect or not, respectively. The default value is TRUE.

Value

Function CSDfit returns a list containing the following components:

parameter.est

It is a matrix. The column "par.est" contains the estimate of the regression parameters. The column "SE" contains the standard error of these estimators. The columns "Z" and "p-value" are the corresponding Z-statistic and p-value. The last two columns are the lower and upper bound of the 95% Wald's confidence interval for the parameters.

log_likelihood

The maximum log likelihood value. This includes the logarithm of the roughness penalty and the Cauchy penalty if Cauchy.pen=TRUE.

AICvalue

The AIC value.

coefs

The estimates of the coefficients of the integrated B-spline basis functions.

Examples

data(PD)
PDfit=CSDfit(PD,3,1,0,n_quad=5)