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The viztest() function in the VizTest
pacakge will calculate all pairwise tests using normal theory tests
(potentially adjusted for multiplicity if the user elects to do so). We
imagine there are use cases where users derive the significance of
differences using some procedure that is not anticipated by the workflow
in the package. We give users who do this the option to input a vector
indicating which pairs are significantly different with the
sig_diffs argument. This gets somewhat complex, though, so
we walk through an example below. Consider the following regression
model:
#>
#> Call:
#> lm(formula = Petal.Width ~ Species, data = iris)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -0.626 -0.126 -0.026 0.154 0.474
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.24600 0.02894 8.50 1.96e-14 ***
#> Speciesversicolor 1.08000 0.04093 26.39 < 2e-16 ***
#> Speciesvirginica 1.78000 0.04093 43.49 < 2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.2047 on 147 degrees of freedom
#> Multiple R-squared: 0.9289, Adjusted R-squared: 0.9279
#> F-statistic: 960 on 2 and 147 DF, p-value: < 2.2e-16
#>
#> Species Estimate Std. Error z Pr(>|z|) S 0.0 % 100.0 %
#> setosa 0.246 0.0289 8.5 <0.001 55.5 0.133 0.359
#> versicolor 1.326 0.0289 45.8 <0.001 Inf 1.213 1.439
#> virginica 2.026 0.0289 70.0 <0.001 Inf 1.913 2.139
#>
#> Type: responseLet’s imagine, for the sake of argument that we had a different way
of identifying whether there are significant differences between the
estimates presented above. We could use
make_diff_template() to create a template for inputting
these differences. Note that this also taked
include_intercept and include_zero logical
arguments that must be the same as the ones you will specify in
viztest(). Let’see how it works. The first thing we need to
do is make a vector of the estimates and apply the appropriate
names:
Next, we give that vector to make_diff_template().
#> Larger Smaller
#> 1 30+ 20-29
#> 2 30+ 10-19
#> 3 30+ 0-9g/day
#> 4 20-29 10-19
#> 5 20-29 0-9g/day
#> 6 10-19 0-9g/dayAt this point, we could make a vector of zeros and ones indicating whether there is a significant difference between the two stimuli.
You could add that to the template and print it to ensure you did it right.
#> Larger Smaller sig
#> 1 30+ 20-29 1
#> 2 30+ 10-19 1
#> 3 30+ 0-9g/day 1
#> 4 20-29 10-19 0
#> 5 20-29 0-9g/day 0
#> 6 10-19 0-9g/day 0Alternatively, you could export tmpl to a CSV file or
similar, input by hand the significant differences and read the
completed file back in. You would then use the vector of zeros and ones
from the imported CSV file as the sig_diffs argument to
viztest().
If everything looks alright, you can use this in the
viztest() function:
#>
#> Correspondents of PW Tests with CI Tests
#> level psame pdiff easy method
#> 1 0.78 1 0.5 -0.06401344 Lowest
#> 2 0.87 1 0.5 -0.02784027 Middle
#> 3 0.96 1 0.5 -0.03875646 Highest
#> 4 0.92 1 0.5 -0.00135610 Easiest
#>
#> All 6 tests properly represented for by CI overlaps.What we see is that any confidence level between \(78\%\) and \(96\%\) will have (non-)overlaps that
correspond with the pairwise differences we specified. Just so you can
see this is different, let’s do it without the sig_diffs
argument:
#>
#> Correspondents of PW Tests with CI Tests
#> level psame pdiff easy method
#> 1 0.78 1 0.5 -0.06401344 Lowest
#> 2 0.87 1 0.5 -0.02784027 Middle
#> 3 0.96 1 0.5 -0.03875646 Highest
#> 4 0.92 1 0.5 -0.00135610 Easiest
#>
#> All 6 tests properly represented for by CI overlaps.These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.