2 Multivariate analysis of variance of repeated measurements

Timm, Neil H.

doi:10.1016/s0169-7161(80)01004-8

Cited by 111 publications

(60 citation statements)

References 26 publications

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“…where p is overall mean, al, is the effect of ith level of BSF (FE doses), BJ is the effect of jth level of WSF (week), aBIJ is interaction effect of ith level of BSF (FE doses) and jth level of WSF (week), y(l)k is the effect of kth experimental unit in ith level of the BSF (FE doses) (Error 1) and E ,~ is error term (Error 2) (Timm, 1980;Gill, 1981). For testing the BSF, WSF and interaction effects Ustatistics were used (Gill, 1981).…”

Section: Methodsmentioning

confidence: 99%

Ferula eleaochytrisPowder Effect in Layer Diet on Feed Intake and Some Egg Parameters Using a Multivariate Analysis Method for Repeated Measures

Sahinler

Şahi̇n

Görgülü

2005

Journal of Applied Animal Research

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Section: Methodsmentioning

confidence: 99%

Ferula eleaochytrisPowder Effect in Layer Diet on Feed Intake and Some Egg Parameters Using a Multivariate Analysis Method for Repeated Measures

Sahinler

Şahi̇n

Görgülü

2005

Journal of Applied Animal Research

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“…These reflect different geometric properties of the "size" of the H ellipsoid relative to the size of the E ellipsoid. The statistical and geometric details of this relationship are described in Friendly (2007) and Brien and Kaiser (1985), Timm (1980), Hand and Taylor (1987) for details on the multivariate approach to repeated measure designs; Friendly (2010) gives details and examples of the application of the HE plot framework for these models.…”

Section: Brief Review Of the Mlmmentioning

confidence: 99%

Graphical methods for multivariate linear models in psychological research: An R tutorial

Friendly¹,

Sigal²

2017

TQMP

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This paper is designed as a tutorial to highlight some recent developments for visualizing the relationships among response and predictor variables in multivariate linear models (MLMs), and implemented in convenient packages for R. These models include multivariate multiple regression analysis (MMRA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA). The methods we describe go well beyond what can be understood and explained from simple univariate graphical methods for the separate response variables. We describe extensions of these methods for the case of more than just a few response variables, where the important relationships can be readily seen in the low-dimensional (2D) space that accounts for most of the relevant information. As befits the tutorial nature of this paper, we analyze some sample psychological research studies utilizing these multivariate designs, showing examples in R. In the process, we also take up several practical problems related to the assumptions of MLMs, and how these can be dealt with using graphical methods. Finally, we provide guidelines to aid researchers in conducting multivariate research, pertaining to the analysis, visualization, and reporting of such designs. The graphical and statistical methods described here are all freely available and implemented in the R packages candisc, car, heplots, and mvinfluence.

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“…(3). An upper limit for the standard deviation of D c , r Dc , is obtained from (U , r U ) and (DP, r DP ) following the logarithmic differentiation method as 16 r D c D c 1 2…”

Section: Estimation Of Minimum Constriction Area From Aerodynamic Datamentioning

confidence: 99%

Estimation of minimum oral tract constriction area in sibilant fricatives from aerodynamic data

Fujiso

Nozaki

Hirtum

2015

The Journal of the Acoustical Society of America

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2 Multivariate analysis of variance of repeated measurements

Cited by 111 publications

References 26 publications

Ferula eleaochytrisPowder Effect in Layer Diet on Feed Intake and Some Egg Parameters Using a Multivariate Analysis Method for Repeated Measures

Ferula eleaochytrisPowder Effect in Layer Diet on Feed Intake and Some Egg Parameters Using a Multivariate Analysis Method for Repeated Measures

Graphical methods for multivariate linear models in psychological research: An R tutorial

Estimation of minimum oral tract constriction area in sibilant fricatives from aerodynamic data

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