TY - CHAP

T1 - Symbolic Formulae for Linear Mixed Models

AU - Tanaka, Emi

AU - Hui, Francis K.C.

N1 - Publisher Copyright:
© Springer Nature Singapore Pte Ltd 2019.

PY - 2019

Y1 - 2019

N2 - A statistical model is a mathematical representation of an often simplified or idealised data-generating process. In this paper, we focus on a particular type of statistical model, called linear mixed models (LMMs), that is widely used in many disciplines e.g. agriculture, ecology, econometrics, psychology. Mixed models, also commonly known as multi-level, nested, hierarchical or panel data models, incorporate a combination of fixed and random effects, with LMMs being a special case. The inclusion of random effects in particular gives LMMs considerable flexibility in accounting for many types of complex correlated structures often found in data. This flexibility, however, has given rise to a number of ways by which an end-user can specify the precise form of the LMM that they wish to fit in statistical software. In this paper, we review the software design for specification of the LMM (and its special case, the linear model), focusing in particular on the use of high-level symbolic model formulae and two popular but contrasting R-packages in lme4 and asreml.

AB - A statistical model is a mathematical representation of an often simplified or idealised data-generating process. In this paper, we focus on a particular type of statistical model, called linear mixed models (LMMs), that is widely used in many disciplines e.g. agriculture, ecology, econometrics, psychology. Mixed models, also commonly known as multi-level, nested, hierarchical or panel data models, incorporate a combination of fixed and random effects, with LMMs being a special case. The inclusion of random effects in particular gives LMMs considerable flexibility in accounting for many types of complex correlated structures often found in data. This flexibility, however, has given rise to a number of ways by which an end-user can specify the precise form of the LMM that they wish to fit in statistical software. In this paper, we review the software design for specification of the LMM (and its special case, the linear model), focusing in particular on the use of high-level symbolic model formulae and two popular but contrasting R-packages in lme4 and asreml.

KW - Fixed effects

KW - Hierarchical model

KW - Model API

KW - Model formulae

KW - Model specification

KW - Multi-level model

KW - Random effects

UR - http://www.scopus.com/inward/record.url?scp=85078477417&partnerID=8YFLogxK

U2 - 10.1007/978-981-15-1960-4_1

DO - 10.1007/978-981-15-1960-4_1

M3 - Chapter

AN - SCOPUS:85078477417

SN - 9789811519598

T3 - Communications in Computer and Information Science

SP - 3

EP - 21

BT - Statistics and Data Science - Research School on Statistics and Data Science, RSSDS 2019, Proceedings

A2 - Nguyen, Hien

PB - Springer

T2 - 3rd Research School on Statistics and Data Science, RSSDS 2019

Y2 - 24 July 2019 through 26 July 2019

ER -