You can indicate the levels of the factor and the size of the effects of each level with respect to the global mean (that is the mean without any effect).
Here, we simulate a number of samples from the selected conditions. Fro each sample, a linear modell is fitted and the corresponding p-value is stored. When the effects are different from 0, the % of samples with p<0.05 approximate the statistical power for discovering the difference. If all the effects are 0, we expect that a 5% of the sample will produce a p<0.05 (significance level). The histogram reflects the results obtained. If the power is low, you need to increase the sample size.
First compute a linear model for the means of Biomarker by group
res <- lm(Biomarker~Group)
The anova table can be obtained as:
A summary will produce the coefficients of the linear model:
The estimation of the CI for mean differences is obtrained as:
And the plot of the CI as: