Thanks a lot Greg, you have been very helpful. All the best
________________________________ From: Greg Snow <greg.s...@imail.org> <r-help@r-project.org> Sent: Thu, January 6, 2011 9:29:36 PM Subject: RE: [R] Assumptions for ANOVA: the right way to check the normality 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. 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 âclose enoughâ you are comfortable with. Consulting with a local consultant with experience in these areas is useful if you donât 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.s...@imail.org 801.408.8111 Sent: Thursday, January 06, 2011 12:57 PM To: Greg Snow; r-help@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.s...@imail.org> "r-help@r-project.org" <r-help@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.s...@imail.org 801.408.8111 > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r- > project.org] On Behalf Of Frodo Jedi > Sent: Wednesday, January 05, 2011 3:22 PM > To: Robert Baer; r-help@r-project.org > Subject: Re: [R] Assumptions for ANOVA: the right way to check the > normality > > Dear Robert, [[elided Yahoo spam]] > 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 <rb...@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 AH 1 > > 71 hole_2_step_W_realism AH 5 > > 72 hole_2_step_W_realism AH 5 > > 73 hole_2_step_W_realism AH 5 > > 74 hole_2_step_W_realism AH 2 > > 75 hole_2_step_W_realism AH 6 > > 76 hole_2_step_W_realism AH 5 > > 77 hole_2_step_W_realism AH 5 > > 78 hole_2_step_W_realism AH 6 > > 79 bump_2_heel_toe_W_realism A 3 > > 80 bump_2_heel_toe_W_realism A 3 > > 81 bump_2_heel_toe_W_realism A 3 > > 82 bump_2_heel_toe_W_realism A 2 > > 83 bump_2_heel_toe_W_realism A 3 > > 84 bump_2_heel_toe_W_realism A 3 > > 85 bump_2_heel_toe_W_realism A 4 > > 86 bump_2_heel_toe_W_realism A 3 > > 87 bump_2_heel_toe_W_realism A 4 > > 88 bump_2_heel_toe_W_realism A 4 > > 89 bump_2_heel_toe_W_realism A 6 > > 90 bump_2_heel_toe_W_realism A 5 > > 91 bump_2_heel_toe_W_realism A 4 > > 92 bump_2_heel_toe_W_realism AH 7 > > 93 bump_2_heel_toe_W_realism AH 3 > > 94 bump_2_heel_toe_W_realism AH 4 > > 95 bump_2_heel_toe_W_realism AH 2 > > 96 bump_2_heel_toe_W_realism AH 5 > > 97 bump_2_heel_toe_W_realism AH 6 > > 98 bump_2_heel_toe_W_realism AH 4 > > 99 bump_2_heel_toe_W_realism AH 4 > > 100 bump_2_heel_toe_W_realism AH 4 > > 101 bump_2_heel_toe_W_realism AH 5 > > 102 bump_2_heel_toe_W_realism AH 2 > > 103 bump_2_heel_toe_W_realism AH 6 > > 104 bump_2_heel_toe_W_realism AH 5 > > 105 hole_2_heel_toe_W_realism A 3 > > 106 hole_2_heel_toe_W_realism A 3 > > 107 hole_2_heel_toe_W_realism A 1 > > 108 hole_2_heel_toe_W_realism A 3 > > 109 hole_2_heel_toe_W_realism A 3 > > 110 hole_2_heel_toe_W_realism A 5 > > 111 hole_2_heel_toe_W_realism A 2 > > 112 hole_2_heel_toe_W_realism AH 5 > > 113 hole_2_heel_toe_W_realism AH 1 > > 114 hole_2_heel_toe_W_realism AH 3 > > 115 hole_2_heel_toe_W_realism AH 6 > > 116 hole_2_heel_toe_W_realism AH 5 > > 117 hole_2_heel_toe_W_realism AH 4 > > 118 hole_2_heel_toe_W_realism AH 4 > > 119 hole_2_heel_toe_W_realism AH 3 > > 120 hole_2_heel_toe_W_realism AH 3 > > 121 hole_2_heel_toe_W_realism AH 1 > > 122 hole_2_heel_toe_W_realism AH 5 > > 123 bump_2_combination_W_realism A 4 > > 124 bump_2_combination_W_realism A 2 > > 125 bump_2_combination_W_realism A 4 > > 126 bump_2_combination_W_realism A 1 > > 127 bump_2_combination_W_realism A 4 > > 128 bump_2_combination_W_realism A 4 > > 129 bump_2_combination_W_realism A 2 > > 130 bump_2_combination_W_realism A 4 > > 131 bump_2_combination_W_realism A 2 > > 132 bump_2_combination_W_realism A 4 > > 133 bump_2_combination_W_realism A 2 > > 134 bump_2_combination_W_realism A 6 > > 135 bump_2_combination_W_realism AH 7 > > 136 bump_2_combination_W_realism AH 3 > > 137 bump_2_combination_W_realism AH 4 > > 138 bump_2_combination_W_realism AH 1 > > 139 bump_2_combination_W_realism AH 6 > > 140 bump_2_combination_W_realism AH 5 > > 141 bump_2_combination_W_realism AH 5 > > 142 bump_2_combination_W_realism AH 6 > > 143 bump_2_combination_W_realism AH 5 > > 144 bump_2_combination_W_realism AH 4 > > 145 bump_2_combination_W_realism AH 2 > > 146 bump_2_combination_W_realism AH 4 > > 147 bump_2_combination_W_realism AH 2 > > 148 bump_2_combination_W_realism AH 5 > > 149 hole_2_combination_W_realism A 5 > > 150 hole_2_combination_W_realism A 2 > > 151 hole_2_combination_W_realism A 4 > > 152 hole_2_combination_W_realism A 1 > > 153 hole_2_combination_W_realism A 5 > > 154 hole_2_combination_W_realism A 4 > > 155 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]] > > > > > > > > > ______________________________________________ > > R-help@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- > guide.html > > and provide commented, minimal, self-contained, reproducible code. > > > > > > > [[alternative HTML version deleted]] [[alternative HTML version deleted]]
______________________________________________ R-help@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-guide.html and provide commented, minimal, self-contained, reproducible code.
