Contrast variable
In statistics, a contrast variable is a linear combination of random variables in which the sum of the coefficients is zero[1]. Each variable may represent random values in one of multiple groups involved in a comparison. Associated with a contrast variable are two terms: the standardized mean of contrast variable (SMCV) and c+-probability. The SMCV is the ratio of mean to standard deviation of a contrast variable and the c+-probability is the probability that a contrast variable obtains a positive value.
Traditional contrast is a statistical parameter defined only on the group means. A contrast variable allows us to consider not only group means but also group variances in a comparison. In addition, the concept of contrast variable can help to derive the effect sizes across any number of groups readily and smoothly.[2] The concepts of contrast variable, SMCV and c+-probability were recently proposed for one-way ANOVA cases [1] and were then extended to multi-factor ANOVA cases [3] [2] . When there are only two groups involved in a comparison, SMCV becomes SSMD. The contrast variable, SMCV and c+-probability are critical for deriving statistical methods for assessing the size of siRNA effects in genome-scale RNAi screens.[4]
See also
References
- ^ a b Zhang XHD (2009). "A method for effectively comparing gene effects in multiple conditions in RNAi and expression-profiling research". Pharmacogenomics. 10: 345–58. doi:10.2217/14622416.10.3.345.
{{cite journal}}: Cite has empty unknown parameter:|month=(help) - ^ a b Zhang XHD (2010). "Contrast variable potentially providing a consistent interpretation to effect sizes". Journal of Biometrics & Biostatistics. 1: 108. doi:doi:10.4172/2155-6180.1000108.
{{cite journal}}: Check|doi=value (help); Cite has empty unknown parameter:|month=(help) - ^ Zhang XHD (2010). "Assessing the size of gene or RNAi effects in multifactor high-throughput experiments". Pharmacogenomics. 11: 199–213. doi:10.2217/PGS.09.136.
{{cite journal}}: Cite has empty unknown parameter:|month=(help) - ^ Zhang XHD (2011). Optimal High-Throughput Screening: Practical Experimental Design and Data Analysis for Genome-scale RNAi Research. Cambridge University Press. ISBN 978-0-521-73444-8.