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Workshop 9.3a: Randomized block designs Murray Logan November - PDF document

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. . . . . . . . . . . . . . . . . . . . . . . . . . -1- Workshop 9.3a: Randomized block designs Murray Logan November 23, 2016 Table of contents 1 Randomized Block (RCB) designs 1 2 Worked Examples 12 1.

  1. . . . . . . . . . . . . . . . . . . . . . . . . . . -1- Workshop 9.3a: Randomized block designs Murray Logan November 23, 2016 Table of contents 1 Randomized Block (RCB) designs 1 2 Worked Examples 12 1. Randomized Block (RCB) designs 1.1. RCB design Simple A1 C4 B1 A4 B2 C1 C3 A3 C2 A2 B4 B3 Randomized block design A1 B1 C1 B4 C4 B3 C3 A4 C2 A3 B2 A2 1.2. RCB design y ij = µ + β i + α j + ε ij µ - the mean of the first treatment group

  2. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -2- β - the random (Block) effect α - the main within Block effect e.g. abundance = base + block + treatment 1.3. Repeated measures designs Subject 1 T0 T5 T50 T500 T10 20 T100 T200 T1000 Subject 2 T0 T5 T50 T500 T10 20 T100 T200 T1000 Subject 3 T0 T5 T50 T500 T10 20 T100 T200 T1000 Subject 4 T0 T5 T50 T500 T10 20 T100 T200 T1000 1.4. Assumptions • Normality, homogeneity of variance • No Block by within-block interaction

  3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . -3- Subject 1 Block 3 Block 1 Block 2 Q4 Q4 T0 T5 T50 T500 Q1 T10 20 T100 T200 T1000 Q3 Q3 Q4 Q1 Subject 2 Q3 Q1 Q2 Q2 Q2 T0 T5 T10 20 T50 T100 T200 T500 T1000 Subject 3 Block 4 Block 5 Block 6 T0 T5 T50 T500 T10 20 T100 T200 T1000 Q4 Q3 Q4 Subject 4 Q3 Q4 Q1 Q2 T0 T5 T10 20 T50 T100 T200 T500 T1000 Q3 Q1 Q2 Q1 Q2 1.5. Assumptions • Normality, homogeneity of variance • No Block by within-block interaction • Independence – (variance-covariance structure) 1.6. Var-cov structure T1 T2 T3 T4 T1 0.15 0.00 0.00 0.00 T2 0.00 0.15 0.00 0.00 T3 0.00 0.00 0.15 0.00 T4 0.00 0.00 0.00 0.15 T1 T2 T3 T4 Block B Block D T1 T2 T3 T4 T1 0.15 0.05 0.05 0.05 T2 0.05 0.15 0.05 0.05 T3 0.05 0.05 0.15 0.05 Block A Block C T4 0.05 0.05 0.05 0.15 T1 T2 T3 T4 Subject C T1 T2 T3 T4 T1 0.15 0.60 0.30 0.10 Subject B T2 0.60 0.15 0.60 0.30 T3 0.30 0.60 0.15 0.60 T4 0.10 0.30 0.60 0.15 Subject A 10 20 30 40 Time (mins)

  4. -4- 1.7. Assumptions • Normality, homogeneity of variance • No Block by within-block interaction • Independence – (variance-covariance structure) ∗ RCB - usually met ∗ Repeated measures - rarely met 1.8. Example > data.rcb1 <- read.csv('../data/data.rcb1.csv', strip.white=TRUE) > head(data.rcb1) y A Block 1 37.39761 A B1 2 61.47033 B B1 3 78.07370 C B1 4 30.59803 A B2 5 59.00035 B B2 6 76.72575 C B2 1.9. Exploratory data analysis Normality and homogeneity of variance > boxplot(y~A, data.rcb1) ● 100 80 60 40 20 A B C 1.10. Exploratory data analysis No block by within-block interaction

  5. -5- > library(ggplot2) > ggplot(data.rcb1, aes(y=y, x=A, group=Block,color=Block)) + + geom_line() + + guides(color=guide_legend(ncol=3)) 100 Block B1 B20 B31 B10 B21 B32 B11 B22 B33 80 B12 B23 B34 B13 B24 B35 B14 B25 B4 y B15 B26 B5 60 B16 B27 B6 B17 B28 B7 B18 B29 B8 B19 B3 B9 40 B2 B30 20 A B C A 1.11. Exploratory data analysis No block by within-block interaction > library(car) > residualPlots(lm(y~Block+A, data.rcb1)) ● 5 5 Pearson residuals Pearson residuals 0 0 −5 −5 B1 B14 B19 B23 B28 B32 B5 B9 A B C Block A ● ● ● ● ● ● ● ● ● ● ● ● 5 ● ● ● ● ● Pearson residuals ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −5 ● ● ● ● ● ● ● 20 40 60 80 100 Fitted values Test stat Pr(>|t|) Block NA NA A NA NA Tukey test -0.885 0.376

  6. -6- 1.12. Sphericity > library(nlme) > data.rcb1.lme <- lme(y~A, random=~1|Block, + data=data.rcb1) > acf(resid(data.rcb1.lme)) Series resid(data.rcb1.lme) 1.0 0.8 0.6 0.4 ACF 0.2 0.0 −0.2 −0.4 0 5 10 15 20 Lag 1.13. Model fitting > #Assuming sphericity > data.rcb1.lme <- lme(y~A, random=~1|Block, data=data.rcb1, + method='REML') > data.rcb1.lme1 <- lme(y~A, random=~A|Block, data=data.rcb1, + method='REML') > AIC(data.rcb1.lme, data.rcb1.lme1) df AIC data.rcb1.lme 5 722.1087 data.rcb1.lme1 10 727.2001 > anova(data.rcb1.lme, data.rcb1.lme1) Model df AIC BIC logLik Test L.Ratio p-value data.rcb1.lme 1 5 722.1087 735.2336 -356.0544 data.rcb1.lme1 2 10 727.2001 753.4499 -353.6001 1 vs 2 4.908574 0.4271 1.14. Model fitting > #Assuming sphericity > data.rcb1.lme.AR1 <- lme(y~A, random=~1|Block, data=data.rcb1, + correlation=corAR1(),method='REML') > data.rcb1.lme1.AR1 <- lme(y~A, random=~A|Block, data=data.rcb1, + correlation=corAR1(),method='REML') > AIC(data.rcb1.lme, data.rcb1.lme1,data.rcb1.lme.AR1, data.rcb1.lme1.AR1)

  7. -7- df AIC data.rcb1.lme 5 722.1087 data.rcb1.lme1 10 727.2001 data.rcb1.lme.AR1 6 723.3178 data.rcb1.lme1.AR1 11 729.2001 > anova(data.rcb1.lme, data.rcb1.lme1,data.rcb1.lme.AR1, data.rcb1.lme1.AR1) Model df AIC BIC logLik Test L.Ratio p-value data.rcb1.lme 1 5 722.1087 735.2336 -356.0544 data.rcb1.lme1 2 10 727.2001 753.4499 -353.6001 1 vs 2 4.908574 0.4271 data.rcb1.lme.AR1 3 6 723.3178 739.0676 -355.6589 2 vs 3 4.117606 0.3903 data.rcb1.lme1.AR1 4 11 729.2001 758.0748 -353.6001 3 vs 4 4.117606 0.5326 1.15. Model validation > plot(data.rcb1.lme) ● 2 ● ● ● ● ● ● ● ● ● ● ● ● ● Standardized residuals 1 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −1 ● ● ● ● ● ● ● 20 40 60 80 100 Fitted values > plot(resid(data.rcb1.lme, type='normalized') ~ + data.rcb1$A) ● 2 resid(data.rcb1.lme, type = "normalized") 1 0 −1 A B C data.rcb1$A 1.16. Effects plots > library(effects) > plot(Effect('A',data.rcb1.lme))

  8. -8- A effect plot ● 80 ● 70 y 60 50 ● 40 A B C A 1.17. Parameter estimates > summary(data.rcb1.lme) Linear mixed-effects model fit by REML Data: data.rcb1 AIC BIC logLik 722.1087 735.2336 -356.0544 Random effects: Formula: ~1 | Block (Intercept) Residual StdDev: 11.51409 4.572284 Fixed effects: y ~ A Value Std.Error DF t-value p-value (Intercept) 43.03434 2.094074 68 20.55053 0 AB 28.45241 1.092985 68 26.03185 0 AC 40.15556 1.092985 68 36.73936 0 Correlation: (Intr) AB AB -0.261 AC -0.261 0.500 Standardized Within-Group Residuals: Min Q1 Med Q3 Max -1.78748258 -0.57867597 -0.07108159 0.49990644 2.33727672 Number of Observations: 105 Number of Groups: 35

  9. -9- 1.18. Parameter estimates > intervals(data.rcb1.lme) Approximate 95% confidence intervals Fixed effects: lower est. upper (Intercept) 38.85568 43.03434 47.21300 AB 26.27140 28.45241 30.63343 AC 37.97455 40.15556 42.33658 attr(,"label") [1] "Fixed effects:" Random Effects: Level: Block lower est. upper sd((Intercept)) 8.964236 11.51409 14.78925 Within-group standard error: lower est. upper 3.864944 4.572284 5.409077 1.19. Parameter estimates > VarCorr(data.rcb1.lme) Block = pdLogChol(1) Variance StdDev (Intercept) 132.57434 11.514093 Residual 20.90578 4.572284 1.20. ANOVA table > anova(data.rcb1.lme) numDF denDF F-value p-value (Intercept) 1 68 1089.3799 <.0001 A 2 68 714.0295 <.0001 1.21. R 2 [1] 0.6516126 [1] 0.300933 [1] 0.04745443 [1] 0.9525456

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