I apologize in advance for my question that can seem redundant, but I am still struggling to interpret the outcome of my lme model, as the other posts mainly deal with several categorical variables and I get really confused...
I have three continuous variables,
Delta (my measurement),
Light and two categorical variables,
Identity (names) and
Treatment applied to the subjects, which has 3 non ordered levels.
I understand how to interpret betas when the parameters are all categorical/all continuous - but the mix makes me perplex.
I use the lme model:
model.lme = lme(Delta ~ Treatment + Light * Date, random =~1|Identity, na.action = na.omit, control = lmeControl(optimum ="opt"))
and I get :
Random effects: Formula: ~1 | ID (Intercept) Residual StdDev: 0.08049669 0.3757045 Fixed effects: Delta ~ Treatment + Light * Date Value Std.Error DF t-value p-value (Intercept) 0.27590485 0.03358550 350400 8.214999 0.0000 Treatment2 -0.06606907 0.04418992 350400 -1.495116 0.1349 Treatment3 -0.01027265 0.04861505 16 -0.211306 0.8353 Light 0.00088466 0.00009181 350400 9.635306 0.0000 Date -0.00083441 0.00004336 350400 -19.241967 0.0000 Light:Date 0.00000134 0.00000050 350400 2.673734 0.0075 Correlation: (Intr) Trtmn2 Trtmn3 Light DOY Treatment2 -0.709 Treatment3 -0.654 0.490 Light -0.109 0.000 0.000 DOY -0.235 0.004 0.007 0.459 Light:DOY 0.109 0.000 0.000 -0.995 -0.462 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -5.28753179 -0.23911026 -0.09499650 0.05150716 43.17908373 Number of Observations: 350422 Number of Groups: 18
If I am right, Intercept is the base effect, the value of
Delta when the others parameters = 0. But somehow in what I have read I understand that I should take intercept as
Does it mean that Treatment1 is taken as a reference to which other parameters are compared ? It happens that Treatment1 is a control condition, but I am not sure whether or not this is scientifically valid as I would like all treatments to be considered 'equals'.
If this is not right, what exactly is
Treatment1 here ?
Also, should I interpret the value as a linear relation, as the model assumes so ? eg.
Date has a significant effect on
Delta, and its value is negative. So the further we are in time the lower is
Delta ? Is it correct to say that Delta decreases following a slope of 0,0008 ? Or is it more accurate to say that the slope is (0,2759 - 0,0008)?
I guess that if I have no proof that there is a linear relation (which is not very likely here I think) the only thing I can say with this model is 'Delta is significantly affected by Light and Date' ?
Many, many thanks in advance for any help on this,