ccrFit: Fit the correspondence curve regression

In FPZhang2015/CCR: Fit the correspondence curve regression

Description

Fit the correspondence curve regression

Usage

ccrFit(formula, data, model = TRUE, link = c("loglog", "logitlogit"),
  is.slope = FALSE, is.ht = FALSE, par.ini, tm = seq(0.01, 0.9999,
  length.out = 50), method = "ccr.fit", sig.level = 0.05)

Arguments

formula	object of class 'formula' (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under 'Details'.
data	an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which ccrFit is called.
model	logical. If TRUE the corresponding components of the fit are returned.
link	The link function to be used for fitting, currently only link = 'loglog' and 'logitlogit'.
is.slope	logical. Only for link 'logitlogit'. Default is FALSE.
is.ht	logical. Only for link 'logitlogit'. Default is FALSE.
par.ini	the initial values for the estimate parameters. If is.null(par.ini) == TRUE, par.ini is set in the the details.
tm	The vector of tm.
method	The method to be used for fitting, currently only method = 'ccr.fit'.
sig.level	The significant level. Default is 0.05.

Details

Please refer to Li, Q. and Zhang, F. (2017).

Value

A list with the elements:

coefficients

a named vector of coefficients.

model

if requested (the default), the model frame used.

call

the matched call.

std.error

the estimated standard errors.

CI

the confidence intervals.

Author(s)

Feipeng Zhang and Qunhua Li

References

Li, Q. and Zhang, F. (2017). A regression framework for assessing covariate effects on the reproducibility of high-throughput experiments. Biometrics, http://dx.doi.org/10.1111/biom.12832.

Examples

## The example of ChIP data
## Not run: 
data(data_ChIP)
## estimate
par.ini = c(1, 0, -0.1, 0, 0, 0.5)  # initial value
fit <- ccrFit(cbind(y1, y2)~x, data = data_ChIP,
             link = 'loglog', par.ini = par.ini)
             
             
## The example of microarray data
data(data_microarray)
## with slope without ht
par.ini = c(-1, 1, 0.1, 0.1)    # initial value
fit <- ccrFit(cbind(y1,y2)~x1+x2, 
              data = data_microarray,
              link = 'logitlogit', 
              is.slope = TRUE, is.ht = FALSE, 
              par.ini = par.ini)

## with slope with ht, including iteractions
par.ini = c(-1, 0.1, 0.1, 0, 1,  0.1, 0.1, 0)
fit <- ccrFit(cbind(y1,y2)~x1*x2, 
              data = data_microarray,
              link = 'logitlogit', 
              is.slope = TRUE, is.ht = TRUE, 
              par.ini = par.ini)

## End(Not run)

FPZhang2015/CCR documentation built on May 28, 2019, 12:38 p.m.