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Thread: [R] Non-parametric test to multi factorial ANOVA

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    [R] Non-parametric test to multi factorial ANOVA




    Hi,

    I've spent hours searching online (by "non parametric factorial ANOVA"), reading in books (Zar's "Biostatistical analysis") and manuals (R), but I can't find any clear solution, so I'll bite the bullet and I whine to you.

    First, sorry for my English and my lack of Statistical knowledge.

    Data:


    http://imgur.com/23toaot

    saccharo.csv.txt

    Dependent variable: Ci
    Independent variables (nominal): Sex, Age or Mature and Location.

    Goal:

    Find if the dependent variable follows/is affected significatively by any independent variable of interactions of the latter. I assumed I needed a multi-factorial ANOVA.

    Problem:

    Dependent variable doesn't follow a normal distribution (neither its residuals after fitting it to an ANOVA).

    I tried using Friedman, but I'm not sure if it's only for the two-ways ANOVA. In any case, I believe I can't analyze the possible interactions, so I'm not sure if it would be right choice.

    Questions:

    Any non-parametric alternative to multi-factorial ANOVA?

    If I'd need to transform the data, what transformation should I apply?

    How can I deal with uncompleted design where I don't have data for every block?


    Thanks, I'd really appreciate your help.

    Note: I'm using R.

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    Re: [R] Non-parametric test to multi factorial ANOVA

    Why do you think it needs to be non-parametric? Have you done an initial ANOVA perhaps and gone through model validation techniques?
    The earth is round: P<0.05

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    Re: [R] Non-parametric test to multi factorial ANOVA

    I did an ANOVA:

    Code: 
    saccharo <- read.csv ("saccharo.csv.txt")
    saccharo.aov <- (aov(glm(Ci ~ Sex*Age*Mature*Location, data=saccharo)))
    shapiro.test (saccharo.aov$residuals)
    Shapiro-Wilk normality test

    data: saccharo.aov$residuals
    W = 0.5304, p-value = 6.06e-07

    Maybe I'm wrong, but it indicates the residuals are not normally distributed, right?

    Any tip in model validation techniques?

    Thanks!

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    Re: [R] Non-parametric test to multi factorial ANOVA


    Well, I'm not sure if it's right or not, but I've log-transformed the dependent variable:

    Code: 
    saccharo$Ci <- log (saccharo$Ci)
    Remove the Inf:

    Code: 
    NA <- saccharo [16,11]
    and run an ANOVA:

    Code: 
    saccharo.aov <- aov (glm (Ci.log ~ Sex * Location * Age, data= saccharo)
    None of the independent variables or interactions are significantly affecting the dependent variable, but the residuals are normally distributed:

    Code: 
    shapiro.test (saccharo.aov$residuals)
    Any tip?

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