186,019 research outputs found
The R Package geepack for Generalized Estimating Equations
No full textThis paper describes the core features of the R package geepack, which implements the generalized estimating equations (GEE) approach for fitting marginal generalized linear models to clustered data. Clustered data arise in many applications such as longitudinal data and repeated measures. The GEE approach focuses on models for the mean of the correlated observations within clusters without fully specifying the joint distribution of the observations. It has been widely used in statistical practice. This paper illustrates the application of the GEE approach with geepack through an example of clustered binary data.
Gee, R E, QX58128
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/428012Surname: Gee. Given Name(s) or Initials: R E. Military Service Number or Last Known Location: QX58128. Prisoner of War Enquiry Card Index Number: K.82. Division Enquiry: Qld. Rank: CPL. Unit: [No Unit]326769
Item: [2016.0049.60274] "Gee, R E, QX58128
Gee, H R, VX33247
No full textThis record was harvested from a previous catalogue system and will be withdrawn in 2025. Information in this record may be superseded or incomplete. Visit this record in UMA's new catalogue at: https://archives.library.unimelb.edu.au/nodes/view/387135Surname: GEE. Given Name(s) or Initials: H R. Military Service Number or Last Known Location: VX33247. Missing, Wounded and Prisoner of War Enquiry Card Index Number: 3722.208905
Item: [2016.0049.19428] "Gee, H R, VX33247
Granville R-2 Gee Bee
No full text1/4 right side view of a Granville R-2, a racing plane, on the ground. The plane\u27s tail is marked, Gee Bee.https://corescholar.libraries.wright.edu/special_ms344_photographs/1326/thumbnail.jp
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PGEE: An R Package for Analysis of Longitudinal Data with High-Dimensional Covariates
No full textWe introduce an R package PGEE that implements the penalized generalized estimating equations (GEE) procedure proposed byWang et al. (2012) to analyze longitudinal data with a large number of covariates. The PGEE package includes three main functions: CVfit, PGEE, and MGEE. The CVfit function computes the cross-validated tuning parameter for penalized generalized estimating equations. The function PGEE performs simultaneous estimation and variable selection for longitudinal data with high-dimensional covariates; whereas the function MGEE fits unpenalized GEE to the data for comparison. The R package PGEE is illustrated using a yeast cell-cycle gene expression data set
GEE-based Bell model for longitudinal count outcomes
No full textLongitudinal count models are usually constructed based on Poisson and negative binomial distributions. Recently, a single-parameter discrete Bell distribution has been presented as an alternative to well-known count distributions. In this study, a new marginal model is proposed for longitudinal count responses based on Bell distribution to handle overdispersion and dependency structure. Bell distribution is more practical in that it has fewer parameters than the negative binomial distribution and still handle overdispersion with a single parameter. Focusing on demonstrating that regression diagnostics supplement the Bell marginal model based on GEE to serve as sensitivity analysis. The Bell marginal model is used to analyze the number of accidents caused injuries in Greece during the 5-year time period. The half-normality plots indicate that the Bell marginal model provides better fit than other marginal models for the accident dataset. The common working covariance selection criterias and properties of parameter estimations are investigated for the Bell marginal model in the simulation study. Parameter estimations of the new model based on GEEs are obtained by geeM R package with the user-defined function. Diagnostic measures and simulated envelope algorithm are also provided for the proposed model.</p
Ernest Gee in group, unknown location
No full textHandwritten on back: "L to R, Stan Mitchell (son in law of Ernest Gee), Union Rep, Ernest Gee (Engineer), Ernest Gee's son Kenneth"
Monthly NEE, GEE and R<sub>eco</sub> over the study period.
No full text<p>Monthly NEE, GEE and R<sub>eco</sub> over the study period.</p
%QLS SAS Macro: A SAS Macro for Analysis of Correlated Data Using Quasi-Least Squares
No full textQuasi-least squares (QLS) is an alternative computational approach for estimation of the correlation parameter in the framework of generalized estimating equations (GEE). QLS overcomes some limitations of GEE that were discussed in Crowder (1995). In addition, it allows for easier implementation of some correlation structures that are not available for GEE. We describe a user written SAS macro called %QLS, and demonstrate application of our macro using a clinical trial example for the comparison of two treatments for a common toenail infection. %QLS also computes the lower and upper boundaries of the correlation parameter for analysis of longitudinal binary data that were described by Prentice (1988). Furthermore, it displays a warning message if the Prentice constraints are violated. This warning is not provided in existing GEE software packages and other packages that were recently developed for application of QLS (in Stata, MATLAB, and R). %QLS allows for analysis of continuous, binary, or count data with one of the following working correlation structures: the first-order autoregressive, equicorrelated, Markov, or tri-diagonal structures.
PENERAPAN GENERALIZED ESTIMATING EQUATION (GEE) BERBASIS WEB INTERAKTIF DENGAN R-SHINY UNTUK RESPON MULTINOMIAL BERSKALA ORDINAL
No full textGeneralized Estimating Equation (GEE) merupakan salah satu metode
statistika yang digunakan untuk menganalisa data berkorelasi salah satunya karena
pengukuran berulang (repeated measurement). Data dengan respon berkorelasi
disebut sebagai data longitudinal. Metode GEE dalam penelitian ini, diterapkan pada
respon multinomial berskala ordinal.
Salah paket dalam R yang digunakann untuk analisis data dengan metode
Generalized Estimating Equation (GEE) berskala ordinal adalah paket multgee
dengan fungsi ordLORgee(). Namun, dalam penggunaanya paket tersebut tidak
mudah terutama bagi peneliti yang kurang menguasai pemrograman yang dalam hal
ini program R. Selain itu, untuk program GEE baik itu GEE binomial, GEE2 maupun
GEE multinomial belum ada yang menggunakan sistem GUI. Sehingga, dalam
penelitian ini akan dibuat program GEE multinomial berskala ordinal berbasis web
interaktif menggunakan R-shiny. Program dibuat dalam bentuk tutorial yang meliputi
ringkasan teori, aplikasi dan hasil analisis data.
Web interaktif program GEE multinomial berskala ordinal ini dapat diakses di
alamat http://statslab-rshiny.fmipa.unej.ac.id/JORS/MultGEEOrd/. Analisis data
menggunakan web interaktif program GEE multinomial berskala ordinal ini dapat
dilakukan dengan pilihan data yang tersedia dalam menu atau mengimpor data milik
pengguna. Untuk data respon multinomial dengan skala ordinal berkorelasi dipilih
dua jenis struktur rasio odds lokal yaitu uniform dan category exchangreability.
Goodness of fit untuk model GEE multinomial berskala ordinal dilihat berdasarkan
nilai root mean square error (RMSE) untuk memilih model yang lebih baik antara
dua model dengan struktur berbeda. Untuk uji signifikansi parameter digunakan pvalue.
Aplikasi data Lapharoscopic Cholecystectomy menggunakan GEE
multinomial ordinal yaitu menganalisa tentang tingkat Lapharoscopic
Cholecystectomy yang terdiri dari lima tingkatan. Variabel prediktornya meliputi
terapi, jenis kelamin, umur, dan waktu pengukuran. Dari hasil analisis data diperoleh
model dengan struktur rasio odds lokal Uniform merupakan model yang lebih baik
dibandingkan dengan model dengan struktur rasio odds lokal Category
Exchangeability dan dari uji signifikansi diperoleh model bahwa terapi tidak aktif
(Terapi[TA]) dan waktu pengukuran keenam (Waktu[F]) signifikan terhadap respon.
Bentuk persamaan yang diperoleh yaitu
1 1 = 0,76260 − 1,893221 + 1,039804
2 2 = 1,61849 − 1,893221 + 1,039804
3 3 = 2,59122 − 1,893221 + 1,039804
4 4 = 3,94952 − 1,893221 + 1,039804
dengan adalah peluang respon pada kategori +
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