How to perform ANOVA in R?
This recipe helps you perform ANOVA in R
Recipe Objective
ANOVA which is short for ANalysis Of VAriance can determine whether the means of two or more sample groups are different from each other or not. It uses F-test to statistically test equality of means.
ANOVA uses both between group variability and within group variability to test whether the population mens are significantly different from each other or not.
F-statistic is the ratio of between group variability to within group variability. Large F signifies greater dispersion.
Hypothesis testing with ANOVA includes the following:
- Null Hypothesis: There is no difference in the means
- Alternate Hypothesis: At least one pair of samples is significantly different
In this recipe, we learn how to perform one-way ANOVA test in R.
STEP 1: Reading the sample and hypothesis testing
Example: A study to test the effects of 3 types of fertilizer on crop yield.
- Null Hypothesis: No significantly effect on the crop yield
- Alternate Hypothesis: At least one pair fertilizers has a significant effect on crop yield
# data manipulation
library(tidyverse)
sample = read.csv("R_205_crop_sample.csv", colClasses = c("factor", "factor", "factor", "numeric"), header = TRUE)
glimpse(sample)
Observations: 96 Variables: 4 $ density1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1,... $ block 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3, 4, 1, 2, 3,... $ fertilizer 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,... $ yield 177.2287, 177.5500, 176.4085, 177.7036, 177.1255, 176.77...
STEP 2: Carrying out ANOVA test
We use aov() function to run the test and summary() to print the results of the model.
Syntax: aov(y ~ X1+X2+X3+..., data = )
where:
- y = dependent variable
- X1,X2,X3 = independent variables
anova_one_way = aov(yield ~ fertilizer, data = sample)
summary(anova_one_way)
Df Sum Sq Mean Sq F value Pr(>F) fertilizer 2 6.07 3.0340 7.863 7e-04 *** Residuals 93 35.89 0.3859 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Result: After checking the Pr(>F) which is the p-value of the F-statistic, we see that it's lower than 0.05. This means that atleast one pair of fertilizers used has a real impact on the final crop yield.
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