# R's stats::aov() and nlme::lme() functions for repeated measurement anova

I am running an experiment testing reaction times under different conditions. I have a data sample located here and I have added a graphical plot of my data below in order to ease your understanding:

I would like to check whether weak emotion recognition has significantly higher error rates than all other conditions. I was told that the proper way to do this was a repeated measurement ANOVA. I have found out that this can be done via R's stats::aov() function.

I am interfacing with R via RPy and you may see my exact code under this notebook.

I am getting the following resulting summary:

Error: ID
Df Sum Sq  Mean Sq F value Pr(>F)
Residuals  6  0.022 0.003666

Error: Within
Df  Sum Sq  Mean Sq F value Pr(>F)
COI        6 0.02628 0.004379   3.468 0.0083 **
Residuals 36 0.04547 0.001263
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1


How does this help me address my issue?

Additionally, in a discussion resulting from this other question I have been told that while anova is acceptable in some cases (such as this one) linear models such as the one generated by nlme::lme() are preferable.

I have used that function in this other notebook, and the output reads as follows:

Fixed effects: ER ~ COI
Value  Std.Error DF  t-value p-value
(Intercept) 0.01928571 0.01514819 36 1.273137  0.2111
COIem-hard  0.07028571 0.01899579 36 3.700069  0.0007
COIsc-11    0.00403687 0.01899579 36 0.212514  0.8329
COIsc-15    0.01700000 0.01899579 36 0.894935  0.3768
COIsc-19    0.00417857 0.01899579 36 0.219974  0.8271
COIsc-23    0.00432488 0.01899579 36 0.227676  0.8212
COIsc-27    0.00417857 0.01899579 36 0.219974  0.8271


how am I to interpret those p-values in the context of the point I'm trying to make? Also, why is my first COI (COIem-easy) absent from the list?

As a general point I would also be very happy to hear which of these 2 approaches you advise I should use.