Statistics and Probability Commons^™

Pglr-Sas Data, Joseph M. Hilbe

Joseph M Hilbe

SAS data files for Practical Guide to Logistic Regression

R Code For Practical Guide To Logistic Regression, Joseph M. Hilbe

Joseph M Hilbe

R code for Practical Guide to Logistic Regression

Pglr-Stata Data Files, Joseph M. Hilbe

Joseph M Hilbe

Stata data files for Practical Guide to Logistic Regression

Mcd - Stata Commands, Joseph M. Hilbe

Joseph M Hilbe

Stata commands and affiliated files for examples in book. Text file explanation of command names is included. 103 files in total

Mcd - 11 R Data Files From Book, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data: ZIP file with 11 R data files from book

Mcd - 11 Stata Data Files, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data: 11 Stata files from book

Hilbe-Mcd-Cvs-Data, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data, data files from book in CVS format

Mcd-Data-Sas, Joseph M. Hilbe

Joseph M Hilbe

Modeling Count Data, 11 SAS data files. SAS users

Sas Macro: Testing Marginal Homogeneity In Clustered Matched-Pair Data, Zhao Yang

Zhao (Tony) Yang, Ph.D.

The SAS Macro and simulated data example are used to demonstrate the application of tests for marginal homogeneity in clustered matched-pair data.

Sas Macro: Weighted Kappa Statistic For Clustered Matched-Pair Ordinal Data, Zhao Yang

Zhao (Tony) Yang, Ph.D.

This SAS macro calculate the weighted kappa statistic and its corresponding non-parametric variance estimator for the clustered matched-pair ordinal data.

Sas Macro: Kappa Statistic For Clustered Physician-Patients Polytomous Data, Zhao Yang

Zhao (Tony) Yang, Ph.D.

This SAS macro calculate the kappa statistic and its semi-parametric variance estimator for the clustered physician-patients polytomous data. The proposed method depends on the assumption of conditional independence for the clustered physician-patients data structure.

R Codes For " Hypothesis Testing For An Extended Cox Model With Time-Varying Coefficients" (Biometrics), Chongzhi Di

Chongzhi Di

No abstract provided.

Hamamatsu Flash4.0 Scmos Exposure Time Series, George Mcnamara

George McNamara

Hamamatsu FLASH4.0 scientific cMOS camera exposure time series are pairs of images of:

1 millisecond (00,001ms series)

10 millisecond (00,010ms series)

100 millisecond (00,100ms series)

1,000 millisecond (01,000ms series)

4,000 millisecond (04,000ms series)

10,000 millisecond (10,000ms series)

I also included:

* difference images (exposure 2 minus exposure 1 plus 100 intensity values).

* a series of eleven 1 second (1,000 ms) exposure time images in a multi-plane TIFF file (different images than the pair of 1,000ms images above).

* Stack Arithmetic: Median, Average, Minimum, Maximum, of the eleven plane series (Stack Arithmetic is a MetaMorph command).

These images were acquired …

Gee-2 R Data Files, Joseph Hilbe

Joseph M Hilbe

Generalized Estimating Equations, 2nd edition Publsihed: 10 December, 2012 R Data Files

Gee-2 R Scripts And Functions, Joseph Hilbe

Joseph M Hilbe

Generalized Estimating Equations, 2nd edition Published: 10 December, 2012 R scripts and functions

Sas Macro: Kappa Statistic For Clustered Matched-Pair Data, Zhao Yang

Zhao (Tony) Yang, Ph.D.

The SAS macro was developed to calculate the kappa statistic for the clustered matched-pair data.

Glme3_Ado_Do_Files, Joseph Hilbe

Joseph M Hilbe

GLME3 ado and do files (116 in total)

R Code: A Non-Iterative Implementation Of Tango's Score Confidence Interval For A Paired Difference Of Proportions, Zhao Yang

Zhao (Tony) Yang, Ph.D.

For matched-pair binary data, a variety of approaches have been proposed for the construction of a confidence interval (CI) for the difference of marginal probabilities between two procedures. The score-based approximate CI has been shown to outperform other asymptotic CIs. Tango’s method provides a score CI by inverting a score test statistic using an iterative procedure. In the developed R code, we propose an efficient non-iterative method with closed-form expression to calculate Tango’s CIs. Examples illustrate the practical application of the new approach.

Nbr2 Stata Ado-Do Files, Joseph Hilbe

Joseph M Hilbe

No abstract provided.

Software For Assumption Weighting For Meta-Analysis Of Genomic Data, Debashis Ghosh, Yihan Li

Debashis Ghosh

This is the software that accompanies Li and Ghosh, "Assumption weighting for incorporating heterogeneity into meta-analysis of genomic data."

Code For Fitting Bdsacgh, Veera Baladandayuthapani

Veera Baladandayuthapani

No abstract provided.

R Package For Bayesian Ensemble Methods For Survival Prediction In Gene Expression Data, Veera Baladandayuthapani

Veera Baladandayuthapani

This is the R package for the methods described in Bayesian ensemble methods for survival prediction in gene expression data by Vinicius Bonato , Veerabhadran Baladandayuthapani, Kim-Anh Do, Bradley M. Broom, Erik P. Sulman, and Kenneth D. Aldape Submitted to Bioinformatics (2010)

R Codes For "Multilevel Functional Principal Component Analysis" (Aoas), Chongzhi Di

Chongzhi Di

No abstract provided.

Software For Fitting Hierarchical Spatial Functional Models, Veera Baladandayuthapani

Veera Baladandayuthapani

No abstract provided.

Matlab Code For Bayesian Fitting Of Adaptive P-Splines In Regression, Veera Baladandayuthapani

Veera Baladandayuthapani

No abstract provided.

Poicen.Sas : Censored Poisson Regression, Joseph Hilbe, Gordon Johnston

Joseph M Hilbe

SAS Macro to estimate censored Poisson data, using method of Hilbe. See Hilbe, Joseph M (2011), Negative Binomial Regression, 2nd ed (Cambridge University Press)