[R] Is manova() correct for this analysis?

[Bert Gunter]

This list is about R programming. Yours is a statistical question.
Although there is certainly a nonempty intersection (and someone may
attempt a response), statistical questions are better directed to a
statistical list like stats.stackexchange.com.

Cheers,
Bert


Bert Gunter

"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
   -- Clifford Stoll


On Mon, Nov 23, 2015 at 3:58 PM, angelo.arcadi at virgilio.it
<angelo.arcadi at virgilio.it> wrote:
> Dear list members,
> I have to perform the following analysis but I do not know which function in R
> must be used. My guess is to use manova().
>
> During an experiment I presented participants with some sound stimuli and I asked them to
> modify two parameters of the sound (Centroid and Sound_Level_Peak) to reach a given
> experimental goal. The initial sounds (preset sounds) had a value for those two parameters.
> What I am interested in is whether participants' modifications of the two parameters of the
> sound stimuli resulted in values actually different from the initial values of the parameters
> of the preset sounds.
>
> To give an idea, some rows of my data set are the following:
>
>> head(scrd)
> Stimulus_Type   Centroid        Sound_Level_Peak    Preset
> Stimulus_A      1960.2          -20.963                           no
> Stimulus_A      5317.2          -42.741                           no
> .....
> Stimulus_B      11256.0        -16.480                           no
> Stimulus_B      9560.3          -19.682                           no
> .....
> .....
> Stimulus_A      1900.2          -18.63                             yes
> Stimulus_A      5617.6          -44.41                             yes
> Stimulus_B      12056.0        -17.80                             yes
> Stimulus_B      8960.5          -21.82                             yes
>
>
> This is the analysis I performed with manova():
>
>> fit <- manova(cbind(Centroid,Sound_Level_Peak)~ Stimulus_Type*Preset, data=scrd)
>> summary(fit, test="Pillai")
>                        Df  Pillai approx F num Df den Df  Pr(>F)
> Stimulus_Type          11 0.91888  106.629     22   2760 < 2e-16 ***
> Preset                  1 0.00343    2.371      2   1379 0.09378 .
> Stimulus_Type:Preset   11 0.01155    0.729     22   2760 0.81348
> Residuals            1380
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>>
>
> If I am not wrong, these results say that for each stimulus type there is no difference
> between the patterns of the two parameters in the preset and modified conditions.
>
> Can anyone please tell me if I am correct or suggest how to perform in R such an analysis?
>
> Thanks in advance
>
> Best
>
> Angelo
>
>         [[alternative HTML version deleted]]
>
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