diff --git a/10-MultipleRegression-FactorialDesigns.html b/10-MultipleRegression-FactorialDesigns.html
index 064ad2e..d389693 100644
--- a/10-MultipleRegression-FactorialDesigns.html
+++ b/10-MultipleRegression-FactorialDesigns.html
@@ -700,15 +700,15 @@
Verwijderen van
Linear Hypotheses:
Estimate Std. Error t value Pr(>|t|)
-poison: II - I == 0 0.04686 0.01744 2.688 0.07352 .
+poison: II - I == 0 0.04686 0.01744 2.688 0.07355 .
poison: III - I == 0 0.19964 0.01744 11.451 < 0.001 ***
poison: III - II == 0 0.15278 0.01744 8.763 < 0.001 ***
treat: B - A == 0 -0.16574 0.02013 -8.233 < 0.001 ***
-treat: C - A == 0 -0.05721 0.02013 -2.842 0.05085 .
+treat: C - A == 0 -0.05721 0.02013 -2.842 0.05088 .
treat: D - A == 0 -0.13583 0.02013 -6.747 < 0.001 ***
treat: C - B == 0 0.10853 0.02013 5.391 < 0.001 ***
-treat: D - B == 0 0.02991 0.02013 1.485 0.61546
-treat: D - C == 0 -0.07862 0.02013 -3.905 0.00282 **
+treat: D - B == 0 0.02991 0.02013 1.485 0.61547
+treat: D - C == 0 -0.07862 0.02013 -3.905 0.00274 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
(Adjusted p values reported -- single-step method)
@@ -721,21 +721,21 @@ Verwijderen van
Fit: lm(formula = 1/time ~ poison + treat, data = rats)
-Quantile = 2.8497
+Quantile = 2.8499
95% family-wise confidence level
Linear Hypotheses:
Estimate lwr upr
-poison: II - I == 0 0.0468641 -0.0028208 0.0965491
-poison: III - I == 0 0.1996425 0.1499575 0.2493274
-poison: III - II == 0 0.1527784 0.1030934 0.2024633
-treat: B - A == 0 -0.1657402 -0.2231115 -0.1083690
-treat: C - A == 0 -0.0572135 -0.1145848 0.0001577
-treat: D - A == 0 -0.1358338 -0.1932051 -0.0784626
-treat: C - B == 0 0.1085267 0.0511554 0.1658979
-treat: D - B == 0 0.0299064 -0.0274648 0.0872777
-treat: D - C == 0 -0.0786203 -0.1359915 -0.0212490
+poison: II - I == 0 0.0468641 -0.0028243 0.0965526
+poison: III - I == 0 0.1996425 0.1499540 0.2493310
+poison: III - II == 0 0.1527784 0.1030899 0.2024668
+treat: B - A == 0 -0.1657402 -0.2231155 -0.1083649
+treat: C - A == 0 -0.0572135 -0.1145888 0.0001618
+treat: D - A == 0 -0.1358338 -0.1932091 -0.0784585
+treat: C - B == 0 0.1085267 0.0511514 0.1659020
+treat: D - B == 0 0.0299064 -0.0274689 0.0872817
+treat: D - C == 0 -0.0786203 -0.1359956 -0.0212450
plot(comparisons,yaxt="none")
contrastNames <- c("II-I","III-I","III-II","B-A","C-A","D-A","C-B","D-B","D-C")
axis(2,at=c(length(contrastNames):1), labels=contrastNames,las=2)
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diff --git a/10-Recap-AlgemeenLineairModel-AdditiefModel.pdf b/10-Recap-AlgemeenLineairModel-AdditiefModel.pdf
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diff --git a/10-Recap-AlgemeenLineairModel-OverigeConcepten.html b/10-Recap-AlgemeenLineairModel-OverigeConcepten.html
index 7ff182d..45e1231 100644
--- a/10-Recap-AlgemeenLineairModel-OverigeConcepten.html
+++ b/10-Recap-AlgemeenLineairModel-OverigeConcepten.html
@@ -905,14 +905,14 @@ Besluitvorming
##
## Fit: lm(formula = BPSysAve ~ Age * Gender, data = bpData, weights = 1/mSd$fitted^2)
##
-## Quantile = 2.3167
+## Quantile = 2.317
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
## Age == 0 0.4400 0.4050 0.4750
-## Age + Age:Gendermale == 0 0.2484 0.2049 0.2919
+## Age + Age:Gendermale == 0 0.2484 0.2048 0.2919
## Age:Gendermale == 0 -0.1916 -0.2475 -0.1357
diff --git a/10-Recap-AlgemeenLineairModel-OverigeConcepten.pdf b/10-Recap-AlgemeenLineairModel-OverigeConcepten.pdf
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diff --git a/Examenvoorbeeld_oefening_1.html b/Examenvoorbeeld_oefening_1.html
index ce2c9ab..a53a320 100644
--- a/Examenvoorbeeld_oefening_1.html
+++ b/Examenvoorbeeld_oefening_1.html
@@ -743,7 +743,7 @@
Hypotheses testen
## Estimate Std. Error t value Pr(>|t|)
## bacon == 0 -1.19081 0.13182 -9.034 <0.001 ***
## bacon + bacon:sexmale == 0 -0.87572 0.06286 -13.930 <0.001 ***
-## bacon:sexmale == 0 0.31509 0.14603 2.158 0.0734 .
+## bacon:sexmale == 0 0.31509 0.14603 2.158 0.0736 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
@@ -754,15 +754,15 @@ Hypotheses testen
##
## Fit: lm(formula = permeat ~ bacon + length + sex + bacon:sex, data = pigs)
##
-## Quantile = 2.3172
+## Quantile = 2.3178
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
-## bacon == 0 -1.19081 -1.49627 -0.88535
-## bacon + bacon:sexmale == 0 -0.87572 -1.02139 -0.73005
-## bacon:sexmale == 0 0.31509 -0.02329 0.65347
+## bacon == 0 -1.19081 -1.49634 -0.88528
+## bacon + bacon:sexmale == 0 -0.87572 -1.02142 -0.73001
+## bacon:sexmale == 0 0.31509 -0.02337 0.65355
Conclusie
diff --git a/Examenvoorbeeld_oefening_1.pdf b/Examenvoorbeeld_oefening_1.pdf
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diff --git a/Examenvoorbeeld_oefening_2.html b/Examenvoorbeeld_oefening_2.html
index 9694934..8bddf11 100644
--- a/Examenvoorbeeld_oefening_2.html
+++ b/Examenvoorbeeld_oefening_2.html
@@ -692,7 +692,7 @@
Hypotheses testen
## Estimate Std. Error t value Pr(>|t|)
## tissueV == 0 7.9680 0.4532 17.580 <0.001 ***
## tissueV + locationR:tissueV == 0 5.5038 0.4532 12.143 <0.001 ***
-## locationR:tissueV == 0 -2.4641 0.6410 -3.844 0.0193 *
+## locationR:tissueV == 0 -2.4641 0.6410 -3.844 0.0195 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
@@ -703,24 +703,24 @@
Hypotheses testen
## Fit: lm(formula = intensity %>% log2 ~ location * tissue + patient,
## data = hart)
##
-## Quantile = 3.0262
+## Quantile = 3.0167
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
-## tissueV == 0 7.9680 6.5963 9.3396
-## tissueV + locationR:tissueV == 0 5.5038 4.1322 6.8755
-## locationR:tissueV == 0 -2.4641 -4.4039 -0.5244
+## tissueV == 0 7.9680 6.6006 9.3353
+## tissueV + locationR:tissueV == 0 5.5038 4.1365 6.8711
+## locationR:tissueV == 0 -2.4641 -4.3978 -0.5304
## Estimate lwr upr
-## tissueV 250.3757829 96.85309171 647.2486482
-## tissueV + locationR:tissueV 45.3753769 17.55259829 117.3002876
-## locationR:tissueV 0.1812291 0.04730366 0.6943223
+## tissueV 250.3757829 96.81778511 647.4846807
+## tissueV + locationR:tissueV 45.3753769 17.54619970 117.3430636
+## locationR:tissueV 0.1812291 0.04727928 0.6946804
## attr(,"conf.level")
## [1] 0.95
## attr(,"calpha")
-## [1] 3.023148
+## [1] 3.024308
Conclusie:
@@ -729,10 +729,10 @@
Conclusie:
0.001).
Het geometrisch gemiddelde van de expressie van Myosin3L is een
factor 250 hoger in het linkerventrikel dan in het linkeratrium (p <
-0.001, 95% BI [96.5, 649.4]).
+0.001, 95% BI [96.8, 647.9]).
Het geometrisch gemiddelde van de expressie van Myosin3L is een
factor 45 hoger in het rechterventrikel dan in het rechteratrium (p <
-0.001, 95% BI [17.6, 117.2]).
+0.001, 95% BI [17.5, 117.4]).
De opregulatie van Myosin3L in ventrikel vs het atrium is gemiddeld
een factor 6 hoger in de linker- dan in de rechterzijde (p = 0.018, 95%
BI [1.4, 21.1] ).
diff --git a/Examenvoorbeeld_oefening_2.pdf b/Examenvoorbeeld_oefening_2.pdf
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diff --git a/Exp_design_fast_1_sol.html b/Exp_design_fast_1_sol.html
index 05de769..2eaa93a 100644
--- a/Exp_design_fast_1_sol.html
+++ b/Exp_design_fast_1_sol.html
@@ -677,9 +677,9 @@
Simulation
alpha <- 0.05
power <- simFast(form, rodents, betas, sd, contrasts = contrast, alpha = alpha, nSim = nSim)
power
-## [1] 0.2137
+## [1] 0.2239
We observe that the experiment is severly underpowered. We only have
-a power of 21.4% to pick up the treatment effect.
+a power of 22.4% to pick up the treatment effect.
@@ -696,7 +696,7 @@
Power for a balanced
predictorData <- data.frame(group = rep(c("ctrl","treat"),c(n1,n2)) %>% as.factor)
powerBalanced <- simFast(form, predictorData, betas, sd, contrasts = contrast, alpha = alpha, nSim = nSim)
powerBalanced
-## [1] 0.2462
+## [1] 0.2596
We observe that the power is larger for the balanced design. We could
also have known this from formula of the standard error from the
two-sample t-test.
diff --git a/Exp_design_fast_1_sol.pdf b/Exp_design_fast_1_sol.pdf
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index 6872e1c..1ac51cb 100644
--- a/Exp_design_fast_2_sol.html
+++ b/Exp_design_fast_2_sol.html
@@ -606,10 +606,10 @@ Power for
power <- simFast(form, Puromycin, betas, sd, contrasts = contrast, alpha = alpha, nSim = nSim)
power
-[1] 0.4223
+[1] 0.4285
The power to pick up a slope of \(\beta_1=10\) for this experiment is only
-42.2%.
+42.9%.
Optimal design with 12
powerOpt <- simFast(form, simData, betas, sd, contrasts = contrast, alpha = alpha, nSim = nSim)
powerOpt
-[1] 0.6081
+[1] 0.6047
Note that the power for a design where we repeat each concentration 1
time and the minimum and maximum concentration 4 times is considerably
higher than that for the designs where we repeat all data points.
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diff --git a/Exp_design_fast_3_sol.html b/Exp_design_fast_3_sol.html
index f672456..d06155e 100644
--- a/Exp_design_fast_3_sol.html
+++ b/Exp_design_fast_3_sol.html
@@ -683,9 +683,9 @@ Import KPNA2 data in
## Estimate Std. Error t value Pr(>|t|)
## grade3 == 0 1.6675 0.1827 9.127 < 0.001 ***
## grade3 + grade3:node1 == 0 0.8928 0.1827 4.886 < 0.001 ***
-## node1 == 0 0.6577 0.1827 3.600 0.00712 **
-## node1 + grade3:node1 == 0 -0.1170 0.1827 -0.640 0.89823
-## grade3:node1 == 0 -0.7748 0.2584 -2.998 0.02653 *
+## node1 == 0 0.6577 0.1827 3.600 0.00728 **
+## node1 + grade3:node1 == 0 -0.1170 0.1827 -0.640 0.89822
+## grade3:node1 == 0 -0.7748 0.2584 -2.998 0.02662 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
@@ -744,7 +744,7 @@ power of current
power1 <- simFastMultipleContrasts(form, kpna2, betas, sd, contrasts, nSim = nSim)
power1
## graden0 graden1 nodeg1 nodeg3 interaction
-## 1.00000 0.97115 0.77105 0.02580 0.57105
+## 1.00000 0.97210 0.77340 0.02430 0.57225
We observe large powers for all contrasts, except for contrast
nodeg3, which has a small effect size.
@@ -780,15 +780,15 @@ Power for increasing
}
powers
## n graden0 graden1 nodeg1 nodeg3 interaction
-## [1,] 2 0.70525 0.21170 0.11090 0.01155 0.07710
-## [2,] 3 0.99170 0.56765 0.29185 0.01600 0.19135
-## [3,] 4 0.99995 0.80185 0.47860 0.01945 0.31810
-## [4,] 5 1.00000 0.92250 0.64605 0.02170 0.45465
-## [5,] 6 1.00000 0.96890 0.76020 0.02395 0.56840
-## [6,] 7 1.00000 0.98930 0.85155 0.02610 0.66720
-## [7,] 8 1.00000 0.99650 0.90705 0.03195 0.74735
-## [8,] 9 1.00000 0.99895 0.94380 0.03340 0.81375
-## [9,] 10 1.00000 0.99940 0.96920 0.03760 0.86985
+## [1,] 2 0.70425 0.21590 0.10945 0.01315 0.07565
+## [2,] 3 0.99330 0.56650 0.29390 0.01475 0.19335
+## [3,] 4 0.99985 0.80505 0.48135 0.01875 0.31890
+## [4,] 5 1.00000 0.91870 0.63915 0.02080 0.44875
+## [5,] 6 1.00000 0.97270 0.76385 0.02415 0.57265
+## [6,] 7 1.00000 0.98935 0.85140 0.02930 0.67180
+## [7,] 8 1.00000 0.99680 0.90725 0.03055 0.75520
+## [8,] 9 1.00000 0.99900 0.94380 0.03385 0.81975
+## [9,] 10 1.00000 0.99960 0.96910 0.03955 0.86520
powers %>%
as.data.frame %>%
gather(contrast, power, -n) %>%
@@ -818,7 +818,7 @@ Power when FC for
power3 <- simFastMultipleContrasts(form, kpna2, betas2, sd, contrasts,nSim = nSim)
power3
## graden0 graden1 nodeg1 nodeg3 interaction
-## 0.63860 0.05100 0.76620 0.02570 0.56395
+## 0.63630 0.05205 0.76120 0.02380 0.56895
It is clear that only the power of the contrasts containing \(\beta_g\) change when the effect size of
\(\beta_g\) changes.
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@@ -769,9 +769,9 @@ Post-hoc analyse
##
## Linear Hypotheses:
## Estimate Std. Error t value Pr(>|t|)
-## linseed - casein == 0 -104.83 23.28 -4.504 0.000201 ***
-## soybean - casein == 0 -77.15 22.43 -3.440 0.004220 **
-## soybean - linseed == 0 27.68 22.43 1.234 0.441428
+## linseed - casein == 0 -104.83 23.28 -4.504 < 0.001 ***
+## soybean - casein == 0 -77.15 22.43 -3.440 0.00432 **
+## soybean - linseed == 0 27.68 22.43 1.234 0.44141
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## (Adjusted p values reported -- single-step method)
@@ -805,15 +805,15 @@ Post-hoc analyse
##
## Fit: lm(formula = weight ~ feed, data = chickwts)
##
-## Quantile = 2.4459
+## Quantile = 2.4472
## 95% family-wise confidence level
##
##
## Linear Hypotheses:
## Estimate lwr upr
-## linseed - casein == 0 -104.8333 -161.7640 -47.9026
-## soybean - casein == 0 -77.1548 -132.0146 -22.2950
-## soybean - linseed == 0 27.6786 -27.1812 82.5384
+## linseed - casein == 0 -104.8333 -161.7946 -47.8720
+## soybean - casein == 0 -77.1548 -132.0440 -22.2655
+## soybean - linseed == 0 27.6786 -27.2107 82.5678
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@@ -668,13 +668,13 @@ Post-hoc
confDiet <- confint(multComp)
confDiet$confint
## Estimate lwr upr
-## 2 - 1 20.33333 12.79575 27.87091
-## 3 - 1 44.00000 36.46242 51.53758
-## 3 - 2 23.66667 16.12909 31.20425
+## 2 - 1 20.33333 12.79917 27.86750
+## 3 - 1 44.00000 36.46584 51.53416
+## 3 - 2 23.66667 16.13250 31.20083
## attr(,"conf.level")
## [1] 0.95
## attr(,"calpha")
-## [1] 2.793755
+## [1] 2.792487
Conclusion
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