Activity 5
Leocint Consistente
4/24/2022
MANOVA in R – How To Implement and Interpret One-Way MANOVA
MANOVA in R: Implementation
As with most of the things in R, performing a MANOVA statistical test boils down to a single function call. But we’ll need a dataset first. The Iris dataset is well-known among the data science crowd, and it is built into R:
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosaDependent variables
library(ggplot2)
library(gridExtra)
box_sl <- ggplot(iris, aes(x = Species, y = Sepal.Length, fill = Species)) +
geom_boxplot() +
theme(legend.position = "top")
box_sw <- ggplot(iris, aes(x = Species, y = Sepal.Width, fill = Species)) +
geom_boxplot() +
theme(legend.position = "top")
box_pl <- ggplot(iris, aes(x = Species, y = Petal.Length, fill = Species)) +
geom_boxplot() +
theme(legend.position = "top")
box_pw <- ggplot(iris, aes(x = Species, y = Petal.Width, fill = Species)) +
geom_boxplot() +
theme(legend.position = "top")
grid.arrange(box_sl, box_sw, box_pl, box_pw, ncol = 2, nrow = 2)
One-way MANOVA in R
We can now perform a one-way MANOVA in R. The best practice is to separate the dependent from the independent variable before calling the manova() function. Once the test is done, you can print its summary:
dependent_vars <- cbind(iris$Sepal.Length, iris$Sepal.Width, iris$Petal.Length, iris$Petal.Width)
independent_var <- iris$Species
manova_model <- manova(dependent_vars ~ independent_var, data = iris)
summary(manova_model)## Df Pillai approx F num Df den Df Pr(>F)
## independent_var 2 1.1919 53.466 8 290 < 2.2e-16 ***
## Residuals 147
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1While we’re here, we could also measure the effect size. One metric often used with MANOVA is Partial Eta Squared. It measures the effect the independent variable has on the dependent variables. If the value is 0.14 or greater, we can say the effect size is large. Here’s how to calculate it in R:
library(effectsize)## Registered S3 method overwritten by 'parameters':
## method from
## format.parameters_distribution datawizardeta_squared(manova_model)## # Effect Size for ANOVA (Type I)
##
## Parameter | Eta2 (partial) | 95% CI
## -----------------------------------------------
## independent_var | 0.60 | [0.54, 1.00]
##
## - One-sided CIs: upper bound fixed at (1).Interpret MANOVA in R With a Post-Hoc Test
Linear Discriminant Analysis (LDA), which finds a linear combination of features that best separates two or more groups.
library(MASS)
iris_lda <- lda(independent_var ~ dependent_vars, CV = F)
iris_lda## Call:
## lda(independent_var ~ dependent_vars, CV = F)
##
## Prior probabilities of groups:
## setosa versicolor virginica
## 0.3333333 0.3333333 0.3333333
##
## Group means:
## dependent_vars1 dependent_vars2 dependent_vars3 dependent_vars4
## setosa 5.006 3.428 1.462 0.246
## versicolor 5.936 2.770 4.260 1.326
## virginica 6.588 2.974 5.552 2.026
##
## Coefficients of linear discriminants:
## LD1 LD2
## dependent_vars1 0.8293776 0.02410215
## dependent_vars2 1.5344731 2.16452123
## dependent_vars3 -2.2012117 -0.93192121
## dependent_vars4 -2.8104603 2.83918785
##
## Proportion of trace:
## LD1 LD2
## 0.9912 0.0088The snippet below uses the predict() function to get the linear discriminants and combines them with our independent variable:
lda_df <- data.frame(
species = iris[, "Species"],
lda = predict(iris_lda)$x
)
lda_df## species lda.LD1 lda.LD2
## 1 setosa 8.0617998 0.300420621
## 2 setosa 7.1286877 -0.786660426
## 3 setosa 7.4898280 -0.265384488
## 4 setosa 6.8132006 -0.670631068
## 5 setosa 8.1323093 0.514462530
## 6 setosa 7.7019467 1.461720967
## 7 setosa 7.2126176 0.355836209
## 8 setosa 7.6052935 -0.011633838
## 9 setosa 6.5605516 -1.015163624
## 10 setosa 7.3430599 -0.947319209
## 11 setosa 8.3973865 0.647363392
## 12 setosa 7.2192969 -0.109646389
## 13 setosa 7.3267960 -1.072989426
## 14 setosa 7.5724707 -0.805464137
## 15 setosa 9.8498430 1.585936985
## 16 setosa 9.1582389 2.737596471
## 17 setosa 8.5824314 1.834489452
## 18 setosa 7.7807538 0.584339407
## 19 setosa 8.0783588 0.968580703
## 20 setosa 8.0209745 1.140503656
## 21 setosa 7.4968023 -0.188377220
## 22 setosa 7.5864812 1.207970318
## 23 setosa 8.6810429 0.877590154
## 24 setosa 6.2514036 0.439696367
## 25 setosa 6.5589334 -0.389222752
## 26 setosa 6.7713832 -0.970634453
## 27 setosa 6.8230803 0.463011612
## 28 setosa 7.9246164 0.209638715
## 29 setosa 7.9912902 0.086378713
## 30 setosa 6.8294645 -0.544960851
## 31 setosa 6.7589549 -0.759002759
## 32 setosa 7.3749525 0.565844592
## 33 setosa 9.1263463 1.224432671
## 34 setosa 9.4676820 1.825226345
## 35 setosa 7.0620139 -0.663400423
## 36 setosa 7.9587624 -0.164961722
## 37 setosa 8.6136720 0.403253602
## 38 setosa 8.3304176 0.228133530
## 39 setosa 6.9341201 -0.705519379
## 40 setosa 7.6882313 -0.009223623
## 41 setosa 7.9179372 0.675121313
## 42 setosa 5.6618807 -1.934355243
## 43 setosa 7.2410147 -0.272615132
## 44 setosa 6.4144356 1.247301306
## 45 setosa 6.8594438 1.051653957
## 46 setosa 6.7647039 -0.505151855
## 47 setosa 8.0818994 0.763392750
## 48 setosa 7.1867690 -0.360986823
## 49 setosa 8.3144488 0.644953177
## 50 setosa 7.6719674 -0.134893840
## 51 versicolor -1.4592755 0.028543764
## 52 versicolor -1.7977057 0.484385502
## 53 versicolor -2.4169489 -0.092784031
## 54 versicolor -2.2624735 -1.587252508
## 55 versicolor -2.5486784 -0.472204898
## 56 versicolor -2.4299673 -0.966132066
## 57 versicolor -2.4484846 0.795961954
## 58 versicolor -0.2226665 -1.584673183
## 59 versicolor -1.7502012 -0.821180130
## 60 versicolor -1.9584224 -0.351563753
## 61 versicolor -1.1937603 -2.634455704
## 62 versicolor -1.8589257 0.319006544
## 63 versicolor -1.1580939 -2.643409913
## 64 versicolor -2.6660572 -0.642504540
## 65 versicolor -0.3783672 0.086638931
## 66 versicolor -1.2011726 0.084437359
## 67 versicolor -2.7681025 0.032199536
## 68 versicolor -0.7768540 -1.659161847
## 69 versicolor -3.4980543 -1.684956162
## 70 versicolor -1.0904279 -1.626583496
## 71 versicolor -3.7158961 1.044514421
## 72 versicolor -0.9976104 -0.490530602
## 73 versicolor -3.8352593 -1.405958061
## 74 versicolor -2.2574125 -1.426794234
## 75 versicolor -1.2557133 -0.546424197
## 76 versicolor -1.4375576 -0.134424979
## 77 versicolor -2.4590614 -0.935277280
## 78 versicolor -3.5184849 0.160588866
## 79 versicolor -2.5897987 -0.174611728
## 80 versicolor 0.3074879 -1.318871459
## 81 versicolor -1.1066918 -1.752253714
## 82 versicolor -0.6055246 -1.942980378
## 83 versicolor -0.8987038 -0.904940034
## 84 versicolor -4.4984664 -0.882749915
## 85 versicolor -2.9339780 0.027379106
## 86 versicolor -2.1036082 1.191567675
## 87 versicolor -2.1425821 0.088779781
## 88 versicolor -2.4794560 -1.940739273
## 89 versicolor -1.3255257 -0.162869550
## 90 versicolor -1.9555789 -1.154348262
## 91 versicolor -2.4015702 -1.594583407
## 92 versicolor -2.2924888 -0.332860296
## 93 versicolor -1.2722722 -1.214584279
## 94 versicolor -0.2931761 -1.798715092
## 95 versicolor -2.0059888 -0.905418042
## 96 versicolor -1.1816631 -0.537570242
## 97 versicolor -1.6161564 -0.470103580
## 98 versicolor -1.4215888 -0.551244626
## 99 versicolor 0.4759738 -0.799905482
## 100 versicolor -1.5494826 -0.593363582
## 101 virginica -7.8394740 2.139733449
## 102 virginica -5.5074800 -0.035813989
## 103 virginica -6.2920085 0.467175777
## 104 virginica -5.6054563 -0.340738058
## 105 virginica -6.8505600 0.829825394
## 106 virginica -7.4181678 -0.173117995
## 107 virginica -4.6779954 -0.499095015
## 108 virginica -6.3169269 -0.968980756
## 109 virginica -6.3277368 -1.383289934
## 110 virginica -6.8528134 2.717589632
## 111 virginica -4.4407251 1.347236918
## 112 virginica -5.4500957 -0.207736942
## 113 virginica -5.6603371 0.832713617
## 114 virginica -5.9582372 -0.094017545
## 115 virginica -6.7592628 1.600232061
## 116 virginica -5.8070433 2.010198817
## 117 virginica -5.0660123 -0.026273384
## 118 virginica -6.6088188 1.751635872
## 119 virginica -9.1714749 -0.748255067
## 120 virginica -4.7645357 -2.155737197
## 121 virginica -6.2728391 1.649481407
## 122 virginica -5.3607119 0.646120732
## 123 virginica -7.5811998 -0.980722934
## 124 virginica -4.3715028 -0.121297458
## 125 virginica -5.7231753 1.293275530
## 126 virginica -5.2791592 -0.042458238
## 127 virginica -4.0808721 0.185936572
## 128 virginica -4.0770364 0.523238483
## 129 virginica -6.5191040 0.296976389
## 130 virginica -4.5837194 -0.856815813
## 131 virginica -6.2282401 -0.712719638
## 132 virginica -5.2204877 1.468195094
## 133 virginica -6.8001500 0.580895175
## 134 virginica -3.8151597 -0.942985932
## 135 virginica -5.1074897 -2.130589999
## 136 virginica -6.7967163 0.863090395
## 137 virginica -6.5244960 2.445035271
## 138 virginica -4.9955028 0.187768525
## 139 virginica -3.9398530 0.614020389
## 140 virginica -5.2038309 1.144768076
## 141 virginica -6.6530868 1.805319760
## 142 virginica -5.1055595 1.992182010
## 143 virginica -5.5074800 -0.035813989
## 144 virginica -6.7960192 1.460686950
## 145 virginica -6.8473594 2.428950671
## 146 virginica -5.6450035 1.677717335
## 147 virginica -5.1795646 -0.363475041
## 148 virginica -4.9677409 0.821140550
## 149 virginica -5.8861454 2.345090513
## 150 virginica -4.6831543 0.332033811The final step in this post-hoc test is to visualize the above lda_df as a scatter plot. Ideally, we should see one or multiple groups stand out:
ggplot(lda_df) +
geom_point(aes(x = lda.LD1, y = lda.LD2, color = species), size = 4) +
theme_classic()