Topic 29: Three-Way ANOVA

Transcription

1 Topic 29: Three-Way ANOVA

2 Outline Three-way ANOVA Data Model Inference

3 Data for three-way ANOVA Y, the response variable Factor A with levels i = 1 to a Factor B with levels j = 1 to b Factor C with levels k = 1 to c Y ijkl is the l th observation in cell (i,j,k), l = 1 to n ijk A balanced design has n ijk =n

4 KNNL Example KNNL p 1005 Y is exercise tolerance, minutes until fatigue on a bicycle test A is sex, a=2 levels: male, female B is percent body fat, b=2 levels: high, low C is smoking history, c=2 levels: light, heavy n=3 persons aged per (i,j,k) cell

5 Read and check the data data a1; infile 'c:\...\ch24ta04.txt'; input extol sex fat smoke; proc print data=a1; run;

6 Obs extol sex fat smoke

7 Define variable for a plot data a1; set a1; if (sex eq 1)*(fat eq 1)*(smoke eq 1) then gfs='1_mfs'; if (sex eq 1)*(fat eq 2)*(smoke eq 1) then gfs='2_mfs'; if (sex eq 1)*(fat eq 1)*(smoke eq 2) then gfs='3_mfs'; if (sex eq 1)*(fat eq 2)*(smoke eq 2) then gfs='4_mfs'; if (sex eq 2)*(fat eq 1)*(smoke eq 1) then gfs='5_ffs'; if (sex eq 2)*(fat eq 2)*(smoke eq 1) then gfs='6_ffs'; if (sex eq 2)*(fat eq 1)*(smoke eq 2) then gfs='7_ffs'; if (sex eq 2)*(fat eq 2)*(smoke eq 2) then gfs='8_ffs'; run;

8 Obs extol sex fat smoke gfs _Mfs _Mfs _Mfs _MfS _MfS _MfS _MFs _MFs _MFs _MFS _MFS _MFS

9 Plot the data title1 'Plot of the data'; symbol1 v=circle i=none c=black; proc gplot data=a1; plot extol*gfs/frame; run;

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11 Find the means proc sort data=a1; by sex fat smoke; proc means data=a1; output out=a2 mean=avextol; by sex fat smoke;

12 Define fat*smoke data a2; set a2; if (fat eq 1)*(smoke eq 1) then fs='1_fs'; if (fat eq 1)*(smoke eq 2) then fs='2_fs'; if (fat eq 2)*(smoke eq 1) then fs='3_fs'; if (fat eq 2)*(smoke eq 2) then fs='4_fs';

13 Obs sex fat smoke FR avextol fs _fs _fs _fS _fS _Fs _Fs _FS _FS

14 Plot the means proc sort data=a2; by fs; title1 'Plot of the means'; symbol1 v='m' i=join c=black; symbol2 v='f' i=join c=black; proc gplot data=a2; plot avextol*fs=sex/frame; run;

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16 Cell means model Y ijkl = μ ijk + ε ijkl where μ ijk is the theoretical mean or expected value of all observations in cell (i,j,k) the ε ijkl are iid N(0, σ 2 ) Y ijkl ~ N(μ ijk, σ 2 ), independent

17 Estimates Estimate μ ijk by the mean of the observations in cell (i,j,k), Y ijk ˆ Y Y n ijk ijk l ijkl ijk For each (i,j,k) combination, we can get an estimate of the variance 2 2 s ijk Yijkl Y l ijk nijk 1 We need to combine these to get an estimate of σ 2

18 Pooled estimate of σ 2 We pool the s ijk2, giving weights proportional to the df, n ijk -1 The pooled estimate is s n ijk 1 s ijk nijk ijk ijk

19 Factor effects model Model cell mean as μ ijk = μ + α i + β j + γ k + (αβ) ij + (αγ) ik + (βγ) jk + (αβγ) ijk μ is the overall mean α i, β j, γ k are the main effects of A, B, and C (αβ) ij, (αγ) ik, and (βγ) jk are the two-way interactions (first-order interactions) (αβγ) ijk is the three-way interaction (second-order interaction) Extension of the usual constraints apply

20 ANOVA table Sources of model variation are the three main effects, the three two-way interactions, and the one three-way interaction With balanced data the SS and DF add to the model SS and DF Still have Model + Error = Total Each effect is tested by an F statistic with MSE in the denominator

21 Run proc glm proc glm data=a1; class sex fat smoke; model extol=sex fat smoke sex*fat sex*smoke fat*smoke sex*fat*smoke; means sex*fat*smoke; run;

22 Run proc glm proc glm data=a1; class sex fat smoke; model extol=sex fat smoke; means sex*fat*smoke; run; Shorthand way to express model

23 SAS Parameter Estimates Solution option on the model statement gives parameter estimates for the glm parameterization These are as we have seen before; any main effect or interaction with a subscript of a, b, or c is zero These reproduce the cell means in the usual way

24 ANOVA Table Source DF Sum of Squares Mean Square F Value Pr > F Model Error Corrected Total Type I and III SS the same here

25 Factor effects output Source DF Type I SS Mean Square F Value Pr > F sex fat sex*fat smoke sex*smoke fat*smoke sex*fat*smoke

26 Analytical Strategy First examine interactions highest order to lowest order Some options when one or more interactions are significant Interpret the plot of means Run analyses for each level of one factor, eg run A*B by C (lsmeans with slice option) Run as a one-way with abc levels Define a composite factor by combining two factors, eg AB with ab levels Use contrasts

27 Analytical Strategy Some options when no interactions are significant Use a multiple comparison procedure for the main effects Use contrasts When needed, rerun without the interactions

28 Example Interpretation Since there appears to be a fat by smoke interaction, let s run a two-way ANOVA (no interaction-note:pooling not necessary here) using the fat*smoke variable Note that we could also use the interaction plot to describe the interaction

29 Run glm proc glm data=a1; class sex fs; model extol=sex fs; means sex fs/tukey; run;

30 ANOVA Table Source DF Sum of Squares Mean Square F Value Pr > F Model <.0001 Error Corrected Total

31 Factor effects output Source DF Type I SS Mean Square F Value Pr > F sex fs <.0001 Both are significant as expected compare means

32 Means for sex Mean N sex A B

33 Tukey comparisons for fs Mean N fs A _fs B _fS B B _FS B B _Fs

34 Conclusions sex difference with males having a roughly 5.5 minute higher exercise tolerance beneficial to add CI here There was a smoking history by body fat level interaction where those who were low body fat and had a light smoking history had a significantly higher exercise tolerance than the other three groups

35 Last slide Read NKNW Chapter 24 We used program topic29.sas to generate the output for today