library(blandr) knitr::opts_chunk$set( echo = TRUE , cache=FALSE ) params$method1 params$method2 results <- blandr.statistics( method1 , method2 )
This is an autogenerated report from the blandr R library. This report relies on the data being supplied to be accurate. Whilst it tries to provide some information on the data provided, it IS NOT a replacement for proper review (and sense checks) of the data. It is also NOT a replacement for formal statistical advice.
The blandr report automated tool is intended to be a tool for the start of exploration of the data generated. Statistical agreement (or lack of) does not mean that there is no clinical (or "practical") agreement: the level of acceptable agreement should be defined before this tool is used. All use of the statistics provided is at the user's own risk.
You can find the citation information through the usual R citation commands:
citation("blandr")
The DOI will refer to all versions of blandr. If you need to cite specific releases DOIs, the full versioning information can be found at Zenodo (https://zenodo.org/record/824514), with the full source code at the blandr GitHub page (https://github.com/deepankardatta/blandr/).
The two methods used in the comparison will be labelled "Method 1" and "Method 2". It is up to the user to understand which data they have supplied as these methods. The package always calculates the differences as "Method 1" minus "Method 2".
Questions to think about when interepreting the data include:
A suggestion by Chhapola et al. (doi:10.1177/0004563214553438) on reporting the data includes:
The dashed lines represent the summary Bland-Altman statistical data, namely: (1) bias, (2) upper limit of agreement, (3) lower limit of agreement. The dotted line (when present) represent confidence intervals for the summary statistics.
method1 summary(method1) hist(method1)
method2 summary(method2) hist(method2)
Bland & Altman suggest to create what is thought of as a standard Bland-Altman plot, the differences need to be normally distributed. We can assess this with histograms, QQ plots, or with the Shapiro-Wilk normality test. Do seek formal statistical advice for assumptions behind all these tests.
We can describe graphically the distribution of the differences. Further, a QQ plot can be generated - in general the scatter points should be as close to the line as possible to suggest the data is normally distributed.
blandr.plot.normality( results ) blandr.plot.qq( results )
In the Shapiro-Wilk test the null hypothesis is that the data is normally distributed. If p>0.05, normality can be assumed.
shapiro.test( results$differences )
blandr.draw ( method1 , method2 , ciDisplay = FALSE , plotProportionalBias = TRUE ) print(results$regression.equation)
# Uses one of the Jamovi functions as it gives you a nice table as well # Although you can call the blandr.draw function for a ggplot as well ba_data <- data.frame( method1 , method2 ) blandr::jamoviBAanalysis( data=ba_data , method1="method1" , method2="method2" )
blandr.method.comparison ( method1 , method2 , sig.level=0.95 )
print(results)
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