2013
DOI: 10.32614/rj-2013-004
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Hypothesis Tests for Multivariate Linear Models Using the car Package

Abstract: The multivariate linear model is

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Cited by 560 publications
(518 citation statements)
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“…Mixed models were analyzed using R package lme4 (Bates et al ). We report tests of coefficients using Wald χ 2 statistics from Type II ANOVAs that assess effects after accounting for all other non‐interaction effects using R package car (Fox #8954). For treatments with significant ( p < 0.05) Wald χ 2 tests for the treatment as a whole, we show results of Tukey multiple comparisons on the graphs using letters that indicate which treatment levels differ significantly from each other.…”
Section: Methodsmentioning
confidence: 99%
“…Mixed models were analyzed using R package lme4 (Bates et al ). We report tests of coefficients using Wald χ 2 statistics from Type II ANOVAs that assess effects after accounting for all other non‐interaction effects using R package car (Fox #8954). For treatments with significant ( p < 0.05) Wald χ 2 tests for the treatment as a whole, we show results of Tukey multiple comparisons on the graphs using letters that indicate which treatment levels differ significantly from each other.…”
Section: Methodsmentioning
confidence: 99%
“…Finally, when modelling the number of discrete looks within a trial (a count response), we used a Poisson distribution. Across analyses, graphs showing predicted effects and confidence intervals (CIs) from these models were calculated using the effects package in R (Fox et al, 2016). …”
Section: Methodsmentioning
confidence: 99%
“…Once the associated R in Eq. (4) is constructed, under the sphericity assumption its SS term and the corresponding SS term for errors can be obtained (Fox et al, 2013) as tr ( H ( R T R ) −1 ) and tr ( E ( R T R ) −1 ). Under alternative coding schemes that render an orthonormal transformation matrix R , unique portions of variance among the transformed response variables can be captured, and the SS terms simplify to tr ( H ) and tr ( E ).…”
Section: Univariate Testing (Uvt) Under the Mvm Platformmentioning
confidence: 99%
“…The two correction methods above, UVT-SC and HT, adopted at the voxel level in , are similar to statistical packages such as car in R (Fox et al, 2013), in IBM SPSS Statistics (IBM Corp., 2012) and REPEATED statement in of SAS (SAS Institute Inc., 2011) except that contingent schemes are adopted here. In addition, instead of directly adjusting the degrees of freedom for sphericity correction, we opt to keep the original degrees of freedom (constant across the brain) but change the F -value to match the adjusted p -value, and this allows us to simplify the bookkeeping and visualization of the output.…”
Section: Implementation Of Mvm In Afnimentioning
confidence: 99%