MRCV (version 0.2-0)

predict.genloglin: Calculate Observed and Model-Predicted Odds Ratios for MRCV Data

Description

The predict.genloglin method function calculates observed and model-predicted odds ratios and their confidence intervals using results from genloglin. It offers an asymptotic normal approximation for estimating the confidence intervals for the observed and model-predicted odds ratios, and a bootstrap approach for estimating the confidence intervals for the model-predicted odds ratios.

Usage

## S3 method for class 'genloglin':
predict(object, alpha = 0.05, pair = "WY", print.status = TRUE, ...)

Arguments

object

An object of class 'genloglin' produced by the genloglin function.

alpha

The desired alpha level. The predict.genloglin function provides two-sided (1-alpha)x100% confidence intervals.

pair

For the case of three MRCVs, a character string specifying the pair of items for which odds ratios will be calculated: "WY" indicates odds ratios should be calculated for each (Wi, Yj) pair conditional on the response for each Zk, "WZ"<

print.status

A logical value indicating whether bootstrap progress updates should be provided.

...

Additional arguments passed to or from other methods.

Value

• --- A list containing at least original.arg, OR.obs, and OR.model.asymp. original.arg is a list containing the following objects:
  • data:
  {The original data frame supplied to the data argument.}
• I:
• {The original value supplied to the I argument.}
• J:
• {The original value supplied to the J argument.}
• K:
• {The original value supplied to the K argument.}
• nvars:
• {The number of MRCVs.}
• alpha:
• {The original value supplied to the alpha argument.}

code

OR.obs

itemize

• B.use:

item

• B.discard:
• OR.model.BCa:

Details

Wald confidence intervals are estimated for both model-based (see Appendix A of Bilder and Loughin, 2007) and observed (see Agresti, 2013, p. 70) odds ratios. A bootstrap method is also available which provides bias-corrected accelerated (BCa) confidence intervals for the model-predicted odds ratios. See Efron (1987) for more information about BCa intervals. The predict.genloglin function uses a jackknife approximation for estimating the empirical influence values. The bootstrap confidence intervals are available only when boot = TRUE in the original call to the genloglin function.

References

Agresti, A. (2013) Categorical data analysis (3rd ed.). Hoboken, New Jersey: John Wiley & Sons. Bilder, C. and Loughin, T. (2007) Modeling association between two or more categorical variables that allow for multiple category choices. Communications in Statistics--Theory and Methods, 36, 433--451. Efron, B. (1987) Better bootstrap confidence intervals. Journal of the American Statistical Association, 82, 171--185.

Examples

## For examples see help(genloglin).