We are receiving a lot of questions about relevant analyses in the Analyse-it Method Validation edition to help in evaluating new diagnostic tests in the fight against COVID-19. Below are some quick links that will help, but contact us if you have questions - we are working as normal.
Also see our latest blog post: Sensitivity/Specificity and The Importance of Predictive Values for a COVID-19 test
Once the plot is constructed, there are many additional options that can be used to
tweak its appearance.
The quality of the representation of the axis (known as the
predictivity and shown in brackets at the end of each axis label) indicates how much of
the variance in the original variable is explained in the plot. Some of the variables –
such as green space, schools and diversity – are poorly represented in a two-dimensional
PCA biplot, which means projections of points onto those axes aren’t very accurate.
Likewise, some neighborhoods may not be well represented by the two-dimensional
Another issue is that the axes cross over the data points, which
makes them difficult to see.
The plot will be rotated so the axis that is best represented will be
Points that are poorly approximated will be more
Axes which are poorly represented will be hidden.
The plot is rotated so that the axis that is best represented (affordability)
is horizontal. Other axes are offset to the edge of the plot, which makes the points
easier to see.
Points are filled with a lighter transparency the poorer the
representation. Axes that represent less than 60% of the variation in the variable
are hidden, which eliminates the risk of interpreting the projections onto