Hi there,
I want to do a nonparametric covariance analysis and I have tried to use the
package "sm" function "sm.ancova" but it didn't work for me because I have more
then one covariates (I have 18 covariates and 3 factors).
I want to analyse for one factor (who has 13 levels) where the differences for
my response are, using the explanatory (covariates). (The other two factors
are:
Temperature and force needed).
I mean, I want to create groups for the levels of the factor, where within each
group no significant difference exist.
My residuals haven't normal distribution and aren't homoscedastic. I have tried
already the boxcox transformation and glm procedures, but whitout success. So I
am looking for these methos for nonparametrics analysis of covariance.
My model is:
mod<-lm(y ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 + x11 + x12 + x13
+
x14 + x15 + x16 + x17 + x18 + F1+ F2+ F3, data = dds, weights = erro.y,
na.action = na.exclude)
For this model, I haven't normal distribution on residuals and they aren't
homoscedastic. So I tried a boxcox transformation:
residuo<-residuals(mod)
lillie.test(residuo)
leveneTest(residuo~F1,data=dds)
leveneTest(residuo~F2,data=dds)
leveneTest(residuo~F3,data=dds)
boxcox(model)
modnew<-lm((y^1.3-1)/1.3 ~ x1 + x2 + x3 + x4 + x5 + x6 + x7 + x8 + x9 + x10 +
x11 + x12 + x13 + x14 + x15 + x16 + x17 + x18 + F1+ F2+ F3, data = dds,
weights = erro.y, na.action = na.exclude)
But I still haven't success with Anova assumptions.
My factor F1 is the one of interest, it is about som materials used on the
analysis and has 13 levels. I want to study, for which of my levels (F1 levels)
I haven't any statistic difference on my y.
The factors F2 and F3 are temperature and force respectively.
I know that I have a lot of variables, but they are the most important (after a
stepwise regression). The initial number of variables was something about 30.
Thanks for any help.
--
Daniela Rodrigues Recchia
Master Student of Statistics - Technische Universität Dortmund.
"Like dreams, statistics are a form of wish fulfillment."
Jean Baudrillard.
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