![]() $$PPV=$$ (where sens=Sensitivity, spec=Specificity and prev=Prevalence). Then the predicted values cannot be estimated and you should ignore those values.Īlternatively, when the disease prevalence is known, then the predictive valuesĬan be calculated using the following formula's based on Bayes' theorem 05, this means there is no interaction effect between sunlight and water.If the sample prevalence do not reflect the real prevalence of the disease, The p-value for the interaction between sunlight and water is.05, this means watering frequency also has a statistically significant effect on plant growth. 05, this means sunlight exposure has a statistically significant effect on plant growth. The p-value associated with sunlight is.Here’s how to interpret the output of the ANOVA: However, we can also perform a two-way ANOVA to formally test whether or not the independent variables have a statistically significant relationship with the dependent variable.įor example, the following code shows how to perform a two-way ANOVA for our hypothetical plant scenario in R: #make this example reproducible Plotting the means is a visualize way to inspect the effects that the independent variables have on the dependent variable. Rather, there is an interaction effect between the two independent variables. ![]() In other words, sunlight and watering frequency do not affect plant growth independently. The two lines are not parallel at all (in fact, they cross!), which indicates that there is likely an interaction effect between them.įor example, this means the effect that sunlight has on plant growth depends on the watering frequency. In the previous plot, the two lines were roughly parallel so there is likely no interaction effect between watering frequency and sunlight exposure.
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