Sorry, I could write Dummy and not Gummy.
Regards
--- El jue, 20/9/12, Eva Prieto Castro <evapcastro@yahoo.es> escribió:
De: Eva Prieto Castro <evapcastro@yahoo.es>
Asunto: Gummy Variable : Doubt
Para: R-help@r-project.org
Fecha: jueves, 20 de septiembre, 2012 11:13
Hi,
I have a system in which I analyze 2 subjects and 1 variable, so I have
2 models as follow:
y ~ x_1[, 1] + x_2[, 1] + x_1[, 2] + x_2[, 2]
Where
x_1[, i] = cos(2 * pi * t / T_i)
x_2[, i] = sin(2 * pi * t / T_i)
i = 1, 2
Data have two columns: t and y.
As you can see, I have a multiple components model, with rithm and
without trends, and I have a fundamental period (T_1 = 24 hour; T_2 = 12 hour).
I have to compare the parameters between the two models (one for each
subject), using a parametric test as described in the doc I adjunt (page 500,
Parametric solution):
I have to reach results as follow:
______________________________________________________
H0: Equality of... df F p
______________________________________________________
MESOR ( 1, 171) 224.0246 <0.0001
(A,phi) 24h ( 2, 171) 7.6332 0.0007
(A,phi) 24h ( 2, 171) 5.8370 0.0035
Rhythmic
components ( 4, 171)
6.3568 <0.0001
Whole model (
5, 171) 51.6583
<0.0001
I know how to obtain df values and I know how to obtain F and p for the
whole model, because whole model means that all parameters of the two series
are equal, so it means that all values are in the same serie, so I construct a
unique serie concatenating the respective t’s and y’s vectors.
The problem is that I don’t know how to obtain F in the other cases (H1:
equal mesor, H2.x: equal amplitude and acrophase, H3: equal rhythmic
components). I suppose I have to use dummy variables, but I don’t know how to
do it.
I could access something similar in a solution manual of a Weisberg
book (1985), chapter 6, problem 9, as follows:
m1 <- lm(Yvar~ Xvar + Fvar + Fvar:Xvar, na.action=na.omit,
weights=theWeights) # this is model 1 the most general
m2 <- lm(Yvar~ Xvar + Fvar , na.action=na.omit,
weights=theWeights) # this is model 2 parallel
m3 <- lm(Yvar~ Xvar + Fvar:Xvar , na.action=na.omit,
weights=theWeights) # this is model 3 common intercept
m4 <- lm(Yvar~ Xvar , na.action=na.omit,
weights=theWeights) # this is model 4 the least general (all the same)
Please could you help me?.
Thank you in advance.
Eva
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