Author: Øystein Olav Skaar
Date: 2022-02-22

# Predict Metric

#### Øystein Olav Skaar

#### 2022-02-22

## Predict Metric

Enjoy this brief demonstration of the predict metric module

First, we steal Field’s (2017) dancing cat example (please see Cats.R)

    # Define data
    data <- bfw::Cats
    # Aggregate data
    aggregate.data <- stats::aggregate(list(Ratings = data$Ratings),
                                       by=list(Reward = data$Reward ,
                                               Dance = data$Dance ,
                                               Alignment = data$Alignment),
                                       FUN=function(x) c(Mean = mean(x), SD = sd(x)))
    # Describe data
    describe.data <- psych::describe(data)[,c(2:5,10:12)]
    describe.data
    #>               n mean   sd median range  skew kurtosis
    #> Reward*    2000 1.81 0.39   2.00     1 -1.58     0.49
    #> Dance*     2000 1.38 0.49   1.00     1  0.49    -1.76
    #> Alignment* 2000 1.35 0.48   1.00     1  0.63    -1.61
    #> Ratings    2000 3.37 1.92   2.69     6  0.38    -1.40

    # Print data
    print(aggregate.data, digits = 3)
    #>      Reward Dance Alignment Ratings.Mean Ratings.SD
    #> 1      Food    No      Evil        5.078      0.991
    #> 2 Affection    No      Evil        1.785      0.602
    #> 3      Food   Yes      Evil        4.887      0.925
    #> 4 Affection   Yes      Evil        1.692      0.604
    #> 5      Food    No      Good        3.789      0.934
    #> 6 Affection    No      Good        5.528      0.857
    #> 7      Food   Yes      Good        3.898      1.097
    #> 8 Affection   Yes      Good        5.734      0.809

### Next we’ll run the Bayesian model to analyze the cats

    # Use the three categorical variables and mixed contrast.
    mcmc <- bfw::bfw(project.data = data,
                y = "Ratings",
                x = "Reward,Dance,Alignment",
                saved.steps = 50000,
                jags.model = "metric",
                run.contrasts = TRUE,
                use.contrast = "mixed",
                contrasts = "1,2,3",
                jags.seed = 100,
                silent = TRUE)

    # ... and just show the most likely parameter estimate of effect sizes.
    round(normal$summary.MCMC[grep("Effect size:",
                             rownames(normal$summary.MCMC)), c(2,5:7)],3)
    #                                                      Median  HDIlo  HDIhi    n
    # Effect size: Food/Affection                          -0.832 -0.992 -0.667 2000
    # Effect size: No/Yes                                  -0.012 -0.163  0.148 2000
    # Effect size: Evil/Good                               -1.600 -1.775 -1.419 2000
    # Effect size: Food/Affection @ No                     -0.893 -1.151 -0.632 1240
    # Effect size: Food vs. No/Yes                         -0.079 -0.248  0.100  380
    # Effect size: Food/Affection vs. No/Yes               -0.830 -1.015 -0.650 2000
    # Effect size: Affection/Food vs. No/Yes                0.836  0.571  1.110 2000
    # Effect size: Affection vs. No/Yes                     0.035 -0.194  0.274 1620
    # Effect size: Food/Affection @ Yes                    -0.773 -0.968 -0.582  760
    # Effect size: Food/Affection @ Evil                   -4.007 -4.458 -3.541 1299
    # Effect size: Food vs. Evil/Good                      -5.320 -5.696 -4.952  380
    # Effect size: Food/Affection vs. Evil/Good            -2.500 -2.811 -2.186 2000
    # Effect size: Affection/Food vs. Evil/Good            -0.725 -0.940 -0.506 2000
    # Effect size: Affection vs. Evil/Good                  1.134  0.882  1.393 1620
    # Effect size: Food/Affection @ Good                    1.911  1.663  2.154  701
    # Effect size: No/Yes @ Evil                            0.168 -0.082  0.401 1299
    # Effect size: No vs. Evil/Good                        -1.445 -1.712 -1.169 1240
    # Effect size: No/Yes vs. Evil/Good                    -1.573 -1.831 -1.323 2000
    # Effect size: Yes/No vs. Evil/Good                    -1.631 -1.878 -1.380 2000
    # Effect size: Yes vs. Evil/Good                       -1.752 -1.974 -1.532  760
    # Effect size: No/Yes @ Good                           -0.164 -0.357  0.033  701
    # Effect size: Food/Affection @ No @ Evil              -3.971 -4.708 -3.192 1063
    # Effect size: Food vs. No/Yes @ Evil                   0.147 -0.148  0.442  230
    # Effect size: Food/Affection vs. No/Yes @ Evil        -3.969 -4.301 -3.634 1299
    # Effect size: Food @ No vs. Evil/Good                 -5.040 -5.530 -4.549  100
    # Effect size: Food/Affection @ No vs. Evil/Good       -2.543 -2.964 -2.095 1240
    # Effect size: Food vs. No/Yes vs. Evil/Good           -5.530 -5.811 -5.253  380
    # Effect size: Food/Affection vs. No/Yes vs. Evil/Good -2.381 -2.734 -1.999 2000
    # Effect size: Affection/Food vs. No/Yes @ Evil         4.049  3.216  4.892 1299
    # Effect size: Affection vs. No/Yes @ Evil              0.181 -0.153  0.508 1069
    # Effect size: Affection/Food @ No vs. Evil/Good       -0.499 -0.879 -0.135 1240
    # Effect size: Affection @ No vs. Evil/Good             1.301  0.888  1.735 1140
    # Effect size: Affection/Food vs. No/Yes vs. Evil/Good -0.735 -1.073 -0.376 2000
    # Effect size: Affection vs. No/Yes vs. Evil/Good       1.103  0.709  1.494 1620
    # Effect size: Food/Affection @ Yes @ Evil             -4.059 -4.539 -3.586  236
    # Effect size: Food vs. Yes/No vs. Evil/Good           -5.120 -5.792 -4.475  380
    # Effect size: Food/Affection vs. Yes/No vs. Evil/Good -2.636 -3.147 -2.119 2000
    # Effect size: Food @ Yes vs. Evil/Good                -5.624 -6.197 -5.065  280
    # Effect size: Food/Affection @ Yes vs. Evil/Good      -2.468 -2.913 -2.031  760
    # Effect size: Affection/Food vs. Yes/No vs. Evil/Good -0.718 -0.944 -0.482 2000
    # Effect size: Affection vs. Yes/No vs. Evil/Good       1.171  0.865  1.479 1620
    # Effect size: Affection/Food @ Yes vs. Evil/Good      -0.970 -1.157 -0.788  760
    # Effect size: Affection @ Yes vs. Evil/Good            0.972  0.699  1.230  480
    # Effect size: Food/Affection @ No @ Good               1.923  1.554  2.297  177
    # Effect size: Food vs. No/Yes @ Good                  -0.242 -0.446 -0.036  150
    # Effect size: Food/Affection vs. No/Yes @ Good         1.649  1.317  1.971  701
    # Effect size: Affection/Food vs. No/Yes @ Good        -2.209 -2.565 -1.843  701
    # Effect size: Affection vs. No/Yes @ Good             -0.102 -0.402  0.200  551
    # Effect size: Food/Affection @ Yes @ Good              1.899  1.586  2.196  524

### Uhm. That’s a lot of obscure output

Let’s try to break it down. For instance, the effect size is an approximation of Cohen’s d. Now, if we take a look at Effect size: Food/Affection vs. No/Yes vs. Evil/Good, it clearly indicate a large, negative effect of some sort. From the aggregate table at the beginning of the vignette, we can try to interpret the result.

    # Let's print the aggregate table again.
    print(aggregate.data, digits = 3)
    #>      Reward Dance Alignment Ratings.Mean Ratings.SD
    #> 1      Food    No      Evil        5.078      0.991
    #> 2 Affection    No      Evil        1.785      0.602
    #> 3      Food   Yes      Evil        4.887      0.925
    #> 4 Affection   Yes      Evil        1.692      0.604
    #> 5      Food    No      Good        3.789      0.934
    #> 6 Affection    No      Good        5.528      0.857
    #> 7      Food   Yes      Good        3.898      1.097
    #> 8 Affection   Yes      Good        5.734      0.809

First, we can see that regardless of whether the evil cats dance or not, they prefer food (M = 4.98) as reward over affection (M = 1.73). Second we can see that good cats prefer affection (M = 5.63) over food (M = 2.43). Furthermore, we can also infer that evil cats that dance (M = 2.02) rate their owners about the same as evil cats that do not dance (M = 2.11). Good cats, similarly have fairly equal ratings regardless of whether they dance (M = 2.88) or not (M = 2.77). Finally, evil cats (M = 2.07) rate their owners somewhat lower than good cats (M = 2.83), as seen by Effect size: Evil/Good = -1.60.

From the results we can claim that evil cats, in general, rate their owners higher if they get food rather than affection (d = -4.01), and that the opposite is true for good cats (d = -1.91).

Please note that by conducting mixed contrasts results will include both between and within contrasts, in addition to any possible combination (including ones that does not necessarily give any meaning).

## References

- Field, A. (2017). Discovering statistics using IBM SPSS statistics (5th edition). Thousand Oaks, CA: SAGE Publications.