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Unadjusted and Adjusted Change scores in lmBF

I am interested in presenting both the unadjusted and adjusted change scores. The values are quite similar to that of the frequentist model (so i have some confidence), but I wanted to see if someone might be able to confirm that I am interpreting the posterior output correctly when using a co-varate.

design: 3 separate groups

a single change score from each group

a single covariate


Unadjusted Model [ lmBF (changescore ~ grouping, data = d)

2. Quantiles for each variable:

                  2.5%     25%     50%     75%   97.5%
mu             1.44679  1.6349  1.7345  1.8364  2.0326
grouping-con1 -1.61912 -1.3539 -1.2126 -1.0691 -0.7859
grouping-con2  0.17807  0.4405  0.5768  0.7174  0.9838
grouping-con3  0.21945  0.4906  0.6316  0.7690  1.0432
sig2           2.63573  3.0324  3.2776  3.5509  4.1465
g_grouping     0.08697  0.2220  0.3978  0.7851  4.1571

i am making the assumption that the change score (95% credible interval) for each group is as follows

grouping con 1: 0.52 (-0.17, 1.25)

grouping con 2: 2.3 (1.61, 3.01)

grouping con 2: 2.36 (1.65, 3.07)


calculated by taking [grand mean (mu) + each value noted for each group).

i.e. 1.44+ - 1.61 (group 1 for lower bound)

1.44 + 0.17 (group 2 for lower bound)

1.44 + 0.21 (group 3 for lower bound)


assuming i did that correctly...i wanted to then determine the adjusted change score following the addition of a co-variate. It seems to check out given that the credible interval shrinks as one might expect.


Adjusted Model [ lmBF (changescore ~ grouping + covariate, data = dd)

2. Quantiles for each variable:

                        2.5%      25%      50%      75%    97.5%
mu                   1.44973  1.63613  1.73322  1.83431  2.03040
grouping-con1       -1.63824 -1.35344 -1.21518 -1.07134 -0.79084
grouping-con2        0.18455  0.44114  0.57468  0.71100  0.97161
grouping-con3        0.23938  0.49846  0.63718  0.77577  1.04333
covariate-covariate -0.09323 -0.05407 -0.03412 -0.01479  0.02411
sig2                 2.61519  3.00498  3.24478  3.52477  4.11191
g_grouping           0.09003  0.22584  0.39645  0.78175  4.31550
g_continuous         0.01906  0.05050  0.10161  0.24308  2.58558

to get the adjusted value, i took the [ grand mean (mu) - the covariate] to calculate a new grand mean for the 2.5%, 50%, and 97.5% quantiles.

2.5%: 1.44 - - 0.09 = 1.53

50%: 1.73 - - 0.03 = 1.76

97.5% 2.03 - 0.02 = 2.01


using same procedure as earlier [ new grand mean + each value noted for each group] i calculated the adjusted change scores

grouping con 1: 0.55 (-0.1, 1.22)

grouping con 2 :2.33 (1.71, 2.98)

grouping con 3: 2.39 (1.76, 3.05)


e.g. adjusted lower bound calculated as

1.53 + - 1.63 = -0.1

1.53 + 0.18 = 1.71

1.53 + 0.23 = 1.76


does anyone have any idea if I am on the right track? Thanks in advance.

Comments

  • Since your code is based on the BayesFactor package, and Richard knows more about change scores than I do, I've forwarded your question to him (sorry for the tardy response, just had kid #2, makes it difficult to keep up)

    E.J.

  • Thank you for the response and Congratulations!!!

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