MANOVA?

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This topic contains 1 reply, has 2 voices, and was last updated by  henrik 9 months ago.

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  • #211

    MattMcMullen
    Participant

    Greetings, is it possible to conduct a MANOVA with afex? Thanks.

  • #212

    henrik
    Keymaster

    In principle yes, but potentially not in the way you want it.

    Let me explain: To calculate the ANOVA afex uses car::Anova which per default calculates both univariate (i.e., repeated-measures ANOVA) and multivariate tests for the repeated-measures factors. Per default, only the univariate tests are reported. But the multivariate tests can be obtained easily via summary(model$Anova). For example:

    
    library("afex")
    data(md_12.1)
    a1 <- aov_ez("id", "rt", md_12.1, within = c("angle", "noise"))
    
    summary(a1$Anova)
    # Type III Repeated Measures MANOVA Tests:
    # 
    # ------------------------------------------
    #  
    # Term: (Intercept) 
    # 
    #  Response transformation matrix:
    #            (Intercept)
    # X0_absent            1
    # X0_present           1
    # X4_absent            1
    # X4_present           1
    # X8_absent            1
    # X8_present           1
    # 
    # Sum of squares and products for the hypothesis:
    #             (Intercept)
    # (Intercept)   116553960
    # 
    # Multivariate Tests: (Intercept)
    #                  Df test stat approx F num Df den Df     Pr(>F)    
    # Pillai            1   0.98518 598.4492      1      9 1.5266e-09 ***
    # Wilks             1   0.01482 598.4492      1      9 1.5266e-09 ***
    # Hotelling-Lawley  1  66.49435 598.4492      1      9 1.5266e-09 ***
    # Roy               1  66.49435 598.4492      1      9 1.5266e-09 ***
    # ---
    # Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    # 
    # ------------------------------------------
    #  
    # Term: angle 
    # 
    #  Response transformation matrix:
    #            angle1 angle2
    # X0_absent       1      0
    # X0_present      1      0
    # X4_absent       0      1
    # X4_present      0      1
    # X8_absent      -1     -1
    # X8_present     -1     -1
    # 
    # Sum of squares and products for the hypothesis:
    #         angle1 angle2
    # angle1 1128960 403200
    # angle2  403200 144000
    # 
    # Multivariate Tests: angle
    #                  Df test stat approx F num Df den Df     Pr(>F)    
    # Pillai            1  0.887597 31.58624      2      8 0.00015963 ***
    # Wilks             1  0.112403 31.58624      2      8 0.00015963 ***
    # Hotelling-Lawley  1  7.896559 31.58624      2      8 0.00015963 ***
    # Roy               1  7.896559 31.58624      2      8 0.00015963 ***
    # ---
    # Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    # 
    # ------------------------------------------
    #  
    # Term: noise 
    # 
    #  Response transformation matrix:
    #            noise1
    # X0_absent       1
    # X0_present     -1
    # X4_absent       1
    # X4_present     -1
    # X8_absent       1
    # X8_present     -1
    # 
    # Sum of squares and products for the hypothesis:
    #         noise1
    # noise1 1713960
    # 
    # Multivariate Tests: noise
    #                  Df test stat approx F num Df den Df     Pr(>F)    
    # Pillai            1  0.789552 33.76596      1      9 0.00025597 ***
    # Wilks             1  0.210448 33.76596      1      9 0.00025597 ***
    # Hotelling-Lawley  1  3.751773 33.76596      1      9 0.00025597 ***
    # Roy               1  3.751773 33.76596      1      9 0.00025597 ***
    # ---
    # Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    # 
    # ------------------------------------------
    #  
    # Term: angle:noise 
    # 
    #  Response transformation matrix:
    #            angle1:noise1 angle2:noise1
    # X0_absent              1             0
    # X0_present            -1             0
    # X4_absent              0             1
    # X4_present             0            -1
    # X8_absent             -1            -1
    # X8_present             1             1
    # 
    # Sum of squares and products for the hypothesis:
    #               angle1:noise1 angle2:noise1
    # angle1:noise1        416160        171360
    # angle2:noise1        171360         70560
    # 
    # Multivariate Tests: angle:noise
    #                  Df test stat approx F num Df den Df     Pr(>F)    
    # Pillai            1  0.918223 44.91353      2      8 4.4722e-05 ***
    # Wilks             1  0.081777 44.91353      2      8 4.4722e-05 ***
    # Hotelling-Lawley  1 11.228381 44.91353      2      8 4.4722e-05 ***
    # Roy               1 11.228381 44.91353      2      8 4.4722e-05 ***
    # ---
    # Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    # 
    # Univariate Type III Repeated-Measures ANOVA Assuming Sphericity
    # 
    #                   SS num Df Error SS den Df       F    Pr(>F)    
    # (Intercept) 19425660      1   292140      9 598.449 1.527e-09 ***
    # angle         289920      2    64080     18  40.719 2.087e-07 ***
    # noise         285660      1    76140      9  33.766  0.000256 ***
    # angle:noise   105120      2    20880     18  45.310 9.424e-08 ***
    # ---
    # Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    # 
    # 
    # Mauchly Tests for Sphericity
    # 
    #             Test statistic p-value
    # angle              0.96011 0.84972
    # angle:noise        0.89378 0.63814
    # 
    # 
    # Greenhouse-Geisser and Huynh-Feldt Corrections
    #  for Departure from Sphericity
    # 
    #              GG eps Pr(>F[GG])    
    # angle       0.96164  3.402e-07 ***
    # angle:noise 0.90398  3.454e-07 ***
    # ---
    # Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
    # 
    #               HF eps   Pr(>F[HF])
    # angle       1.217564 2.086763e-07
    # angle:noise 1.117870 9.424093e-08
    # Warning message:
    # In summary.Anova.mlm(a1$Anova, multivariate = TRUE) :
    #   HF eps > 1 treated as 1

    Hope that helps!

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