Dear R-helpers,
I have a dataset named "qu", organized as follows:
Sample Run Replicate Value
1 1 1 25
1 1 2 40
1 1 3 33
1 1 4 29
1 2 1 37
1 2 2 44
1 2 3 45
1 3 1 25
1 3 2 40
1 4 1 33
1 4 2 29
1 4 3 25
2 ...
Basically, a sample was run on an assay multiple times within a single
day. Each of these results is "Replicate". Then run was repeated several
times in consecutive days - variable "Run". There are 210 such samples.
I need to actually calculate the CV for each sample:
- within run (between replicates) - that's easy to do in Excel
- between run - that's the problem.
I was thinking of using either 'aov' or 'lme' to solve this. However, I
don't know how to interpret the output. For example, a summary output
from "aov(Value~Run+Replicate, subset(qu,Sample==79))' for one sample was:
Df Sum Sq Mean Sq F value Pr(>F)
Run 1 4.000 4.000 0.3214 0.6104
Replicate 1 73.500 73.500 5.9062 0.0933 .
Residuals 3 37.333 12.444
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Do you guys think this is correct approach?
How do I extract these numbers (sum of squares) to store in a separate
dataframe for further calculations?
And how should I interpret the "Residual" in this setting?
I will appreciate your comments.
--
Michal J. Figurski
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