A Cohen’s D is a standardized effect size which is defined as the difference between your two groups measured in standard deviations. Because the Cohen’s D unit is standard deviations, it can be used when you have no pilot data. As a general guide a Cohen’s D of 0.3, 0.5 and 0.8 corresponds to mild, […]
The calculator is for Sidak corrected multiple t-tests. It’s useful for when you have pilot data and have estimates for the mean of the control group and the standard deviations. It allows you to estimate the number of animals required to detect a range of percentage changes from the control group. Example paragraph: A power analysis […]
A Cook’s distance greater than 1 is a sign that this data point (or random factor) is having a disproportionate influence on your model and should be looked into. Note: I’m not normally a fan of removing data without a valid reason, for me, you need both a statistical and experimental reason for removal. #Loading […]
When the data is unbalanced or there are missing values, repeated measures ANOVAs fail to report unbiased results. Furthermore, all the data of one individual will be ignored if data from one time point is missing. Multi-level linear models gets around this by comparing models of the data, not the data itself, using log-likelihood/Chi squared distribution […]
A principal component analysis (PCA), is a way to take a large amount of data and plot it on two or three axes. It does this without knowing which groups the data belongs to, so if you perform a PCA, plot it, and the data clusters nicely into the experiment groups, you know there are […]
Here is your basic one-way and two-way ANOVAs. The assumption check is the same for the one-way or two-way ANOVA, you are looking for an even scattering of dots in all three residual plots and your looking for a straight 45 degree line in the q-q plot. A log transformation often helps with biological data. […]
#Power analysis t-test ## Computing the required n power.t.test(delta=100, sd=50, power=0.8, sig.level=0.05, strict=FALSE) ## Computing the power power.t.test(delta=100, sd=50, n=4, sig.level=0.05, strict=FALSE) #Power analysis Two-way ANOVA install.packages(“pwr2”) library(pwr2) ss.2way(a=2, b=2, alpha=0.05, beta=0.2, f.A=0.36, f.B=0.64, B=100) #a and b are the number of factors in each group, B is bootstrap number # F.A and F.B are […]
#Load data example<- read.table(url(“http://jackrrivers.com/wp-content/uploads/2018/03/Example.txt”), header=T) #Welch corrected ttest<-t.test(Dependent~Factor1, data=example) ttest #non corrected noncorrect.ttest<-t.test(Data~Group, data=example, var.equal=T) noncorrect.ttest #Easy way to check the assumptions of equal variance and normality par(mfrow=c(2,2)) plot(aov(Dependent~Factor1, data=example))