Assumptions for ANOVA: the right way to check the normality
A lot of this depends on what question you are really trying to answer. For one way anova replacing y-values with their ranks essentially transforms the distribution to uniform (under the null) and the Central Limit Theorem kicks in for the uniform with samples larger than about 5, so the normal approximations are pretty good and the theory works, but what are you actually testing? The most meaningful null that is being tested is that all data come from the exact same distribution. So what does it mean when you reject that null? It means that all the groups are not representing the same distribution, but is that because the means differ? Or the variances? Or the shapes? It can be any of those. Some point out that if you make certain assumptions such as symmetry or shifts of the same distributions, then you can talk about differences in means or medians, but usually if I am using non-parametrics it is because I don't believe that things are symmetric and the shift idea doesn't fit in my mind. Some alternatives include bootstrapping or permutation tests, or just transforming the data to get something closer to normal. Now what does replacing by ranks do in 2-way anova where we want to test the difference in one factor without making assumptions about whether the other factor has an effect or not? I'm not sure on this one. I have seen regression on ranks, it basically tests for some level of relationship, but regression is usually used for some type of prediction and predicting from a rank-rank regression does not seem meaningful to me. Fitting the regression model does not require normality, it is the tests on the coefficients and confidence and prediction intervals that assume normality (again the CLT helps for large samples (but not for prediction intervals)). Bootstrapping is an option for regression without assuming normality, transformations can also help. -- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.snow at imail.org 801.408.8111
-----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r- project.org] On Behalf Of Ben Ward Sent: Thursday, January 06, 2011 2:00 PM To: r-help at r-project.org Subject: Re: [R] Assumptions for ANOVA: the right way to check the normality On 06/01/2011 20:29, Greg Snow wrote:
Some would argue to always use the kruskal wallis test since we never
know for sure if we have normality. Personally I am not sure that I understand what exactly that test is really testing. Plus in your case you are doing a two-way anova and kruskal.test does one-way, so it will not work for your case. There are other non-parametric options. Just read this and had queries of my own and comments on this subject: Would one of these options be to rank the data before doing whatever model or test you want to do? As I understand it makes the place of the data the same, but pulls extreme cases closer to the rest. Not an expert though. I've been doing lm() for my work, and I don't know if that makes an assumption of normality (may data is not normal). And I'm unsure of any other assumptions as my texts don't really discuss them. Although I can comfortably evaluate a model say using residual vs fitted, and F values turned to P, resampling and confidence intervals, and looking at sums of squares terms add to explanation of the model. I've tried the plot() function to help graphically evaluate a model, and I want to make sure I understand what it's showing me. I think the first, is showing me the models fitted values vs the residuals, and ideally, I think the closer the points are to the red line the better. The next plot is a Q-Q plot, the closer the points to the line, the more normal the model coefficients (or perhaps the data). I'm not sure what the next two plots are, but it is titled Scale-Location. And it looks to have the square root of standardized residuals on y, and fitted model values on x. Might this be similar to the first plot? The final one is titled Residuals vs Leverage, which has standardized residuals on y and leverage on x, and something called Cooks Distance is plotted as well. Thanks, Ben. W
Whether to use anova and other normality based tests is really a
matter of what assumptions you are willing to live with and what level of "close enough" you are comfortable with. Consulting with a local consultant with experience in these areas is useful if you don't have enough experience to decide what you are comfortable with.
For your description, I would try the proportional odds logistic
regression, but again, you should probably consult with someone who has experience rather than trying that on your own until you have more training and experience.
-- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.snow at imail.org 801.408.8111 From: Frodo Jedi [mailto:frodo.jedi at yahoo.com] Sent: Thursday, January 06, 2011 12:57 PM To: Greg Snow; r-help at r-project.org Subject: Re: [R] Assumptions for ANOVA: the right way to check the
normality
Ok, I see ;-) Let?s put in this way then. When do I have to use the kruskal wallis
test? I mean, when I am very sure that I have
to use it instead of ANOVA? Thanks Best regards P.S. In addition, which is the non parametric methods corresponding
to a 2 ways anova?..or have I to
repeat many times the kruskal wallis test?
________________________________ From: Greg Snow<Greg.Snow at imail.org> To: Frodo Jedi<frodo.jedi at yahoo.com>; Robert Baer<rbaer at atsu.edu>;
"r-help at r-project.org"<r-help at r-project.org>
Sent: Thu, January 6, 2011 7:07:17 PM Subject: RE: [R] Assumptions for ANOVA: the right way to check the
normality
Remember that an non-significant result (especially one that is still
near alpha like yours) does not give evidence that the null is true. The reason that the 1st 2 tests below don't show significance is more due to lack of power than some of the residuals being normal. The only test that I would trust for this is SnowsPenultimateNormalityTest (TeachingDemos package, the help page is more useful than the function itself).
But I think that you are mixing up 2 different concepts (a very
common misunderstanding). What is important if we want to do normal theory inference is that the coefficients/effects/estimates are normally distributed. Now since these coefficients can be shown to be linear combinations of the error terms, if the errors are iid normal then the coefficients are also normally distributed. So many people want to show that the residuals come from a perfectly normal distribution. But it is the theoretical errors, not the observed residuals that are important (the observed residuals are not iid). You need to think about the source of your data to see if this is a reasonable assumption. Now I cannot fathom any universe (theoretical or real) in which normally distributed errors added to means that they are independent of will result in a finite set of integers, so an assumption of exact normality is not reasonable (some may want to argue this, but convincing me will be very difficult). But looking for exact normality is a bit of a red herring because, we also have the Central Limit Theorem that says that if the errors are not normal (but still iid) then the distribution of the coefficients will approach normality as the sample size increases. This is what make statistics doable (because no real dataset entered into the computer is exactly normal). The more important question is are the residuals "normal enough"? for which there is not a definitive test (experience and plots help).
But this all depends on another assumption that I don't think that
you have even considered. Yes we can use normal theory even when the random part of the data is not normally distributed, but this still assumes that the data is at least interval data, i.e. that we firmly believe that the difference between a response of 1 and a response of 2 is exactly the same as a difference between a 6 and a 7 and that the difference from 4 to 6 is exactly twice that of 1 vs. 2. From your data and other descriptions, I don't think that that is a reasonable assumption. If you are not willing to make that assumption (like me) then means and normal theory tests are meaningless and you should use other approaches. One possibility is to use non-parametric methods (which I believe Frank has already suggested you use), another is to use proportional odds logistic regression.
-- Gregory (Greg) L. Snow Ph.D. Statistical Data Center Intermountain Healthcare greg.snow at imail.org<mailto:greg.snow at imail.org> 801.408.8111
-----Original Message----- From: r-help-bounces at r-project.org<mailto:r-help-bounces at r-
project.org> [mailto:r-help-bounces at r-
project.org<http://project.org>] On Behalf Of Frodo Jedi Sent: Wednesday, January 05, 2011 3:22 PM To: Robert Baer; r-help at r-project.org<mailto:r-help at r-project.org> Subject: Re: [R] Assumptions for ANOVA: the right way to check the normality Dear Robert, thanks so much!!! Now I understand! So you also think that I have to check only the residuals and not
the
data directly. Now just for curiosity I did the the shapiro test on the residuals.
The
problem is that on fit3 I don?t get from the test that the data are normally distribuited. Why? Here the data:
shapiro.test(residuals(fit1))
Shapiro-Wilk normality test data: residuals(fit1) W = 0.9848, p-value = 0.05693 #Here the test is ok: the test says that the data are distributed normally (p-value greather than 0.05)
shapiro.test(residuals(fit2))
Shapiro-Wilk normality test data: residuals(fit2) W = 0.9853, p-value = 0.06525 #Here the test is ok: the test says that the data are distributed normally (p-value greather than 0.05)
shapiro.test(residuals(fit3))
Shapiro-Wilk normality test data: residuals(fit3) W = 0.9621, p-value = 0.0001206 Now the test reveals p-value lower than 0.05: so the residuals for
fit3
are not distributed normally.... Why I get this beheaviour? Indeed in the histogram and Q-Q plot for fit3 residuals I get a normal distribution.
________________________________ From: Robert Baer<rbaer at atsu.edu<mailto:rbaer at atsu.edu>> Sent: Wed, January 5, 2011 8:56:50 PM Subject: Re: [R] Assumptions for ANOVA: the right way to check the normality Someone suggested me that I don?t have to check the normality of
the
data, but
the normality of the residuals I get after the fitting of the
linear
model.
I really ask you to help me to understand this point as I don?t
find
enough
material online where to solve it.
Try the following: # using your scrd data and your proposed models fit1<- lm(response ~ stimulus + condition + stimulus:condition, data=scrd) fit2<- lm(response ~ stimulus + condition, data=scrd) fit3<- lm(response ~ condition, data=scrd) # Set up for 6 plots on 1 panel op = par(mfrow=c(2,3)) # residuals function extracts residuals # Visual inspection is a good start for checking normality # You get a much better feel than from some "magic number" statistic hist(residuals(fit1)) hist(residuals(fit2)) hist(residuals(fit3)) # especially qqnorm() plots which are linear for normal data qqnorm(residuals(fit1)) qqnorm(residuals(fit2)) qqnorm(residuals(fit3)) # Restore plot parameters par(op)
If the data are not normally distributed I have to use the kruskal
wallys test
and not the ANOVA...so please help me to understand.
Indeed - Kruskal-Wallis is a good test to use for one factor data
that
is ordinal so it is a good alternative to your fit3. Your "response" seems to be a discrete variable rather than a continuous variable. You must decide if it is reasonable to approximate it with a normal distribution which is by definition continuous.
I make a numerical example, could you please tell me if the data in
this table
are normally distributed or not? Help! number stimulus condition response 1 flat_550_W_realism A 3 2 flat_550_W_realism A 3 3 flat_550_W_realism A 5 4 flat_550_W_realism A 3 5 flat_550_W_realism A 3 6 flat_550_W_realism A 3 7 flat_550_W_realism A 3 8 flat_550_W_realism A 5 9 flat_550_W_realism A 3 10 flat_550_W_realism A 3 11 flat_550_W_realism A 5 12 flat_550_W_realism A 7 13 flat_550_W_realism A 5 14 flat_550_W_realism A 2 15 flat_550_W_realism A 3 16 flat_550_W_realism AH 7 17 flat_550_W_realism AH 4 18 flat_550_W_realism AH 5 19 flat_550_W_realism AH 3 20 flat_550_W_realism AH 6 21 flat_550_W_realism AH 5 22 flat_550_W_realism AH 3 23 flat_550_W_realism AH 5 24 flat_550_W_realism AH 5 25 flat_550_W_realism AH 7 26 flat_550_W_realism AH 2 27 flat_550_W_realism AH 7 28 flat_550_W_realism AH 5 29 flat_550_W_realism AH 5 30 bump_2_step_W_realism A 1 31 bump_2_step_W_realism A 3 32 bump_2_step_W_realism A 5 33 bump_2_step_W_realism A 1 34 bump_2_step_W_realism A 3 35 bump_2_step_W_realism A 2 36 bump_2_step_W_realism A 5 37 bump_2_step_W_realism A 4 38 bump_2_step_W_realism A 4 39 bump_2_step_W_realism A 4 40 bump_2_step_W_realism A 4 41 bump_2_step_W_realism AH 3 42 bump_2_step_W_realism AH 5 43 bump_2_step_W_realism AH 1 44 bump_2_step_W_realism AH 5 45 bump_2_step_W_realism AH 4 46 bump_2_step_W_realism AH 4 47 bump_2_step_W_realism AH 5 48 bump_2_step_W_realism AH 4 49 bump_2_step_W_realism AH 3 50 bump_2_step_W_realism AH 4 51 bump_2_step_W_realism AH 5 52 bump_2_step_W_realism AH 4 53 hole_2_step_W_realism A 3 54 hole_2_step_W_realism A 3 55 hole_2_step_W_realism A 4 56 hole_2_step_W_realism A 1 57 hole_2_step_W_realism A 4 58 hole_2_step_W_realism A 3 59 hole_2_step_W_realism A 5 60 hole_2_step_W_realism A 4 61 hole_2_step_W_realism A 3 62 hole_2_step_W_realism A 4 63 hole_2_step_W_realism A 7 64 hole_2_step_W_realism A 5 65 hole_2_step_W_realism A 1 66 hole_2_step_W_realism A 4 67 hole_2_step_W_realism AH 7 68 hole_2_step_W_realism AH 5 69 hole_2_step_W_realism AH 5 70 hole_2_step_W_realism 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hole_2_combination_W_realism A 3 156 hole_2_combination_W_realism A 5 157 hole_2_combination_W_realism A 2 158 hole_2_combination_W_realism A 5 159 hole_2_combination_W_realism A 5 160 hole_2_combination_W_realism A 1 161 hole_2_combination_W_realism AH 7 162 hole_2_combination_W_realism AH 5 163 hole_2_combination_W_realism AH 3 164 hole_2_combination_W_realism AH 1 165 hole_2_combination_W_realism AH 6 166 hole_2_combination_W_realism AH 4 167 hole_2_combination_W_realism AH 7 168 hole_2_combination_W_realism AH 5 169 hole_2_combination_W_realism AH 5 170 hole_2_combination_W_realism AH 2 171 hole_2_combination_W_realism AH 6 172 hole_2_combination_W_realism AH 2 173 hole_2_combination_W_realism AH 4 Thanks in advance [[alternative HTML version deleted]]
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______________________________________________ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-
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