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 tâs and yâs vectors.  The problem is that I donât 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 donât 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  [[alternative HTML version deleted]]
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