Bottom-Up Step-Wise VCA-Analysis of the Complete Dataset
Function performs step-wise VCA-analysis on a fitted VCA-object by leaving out N-1 to 0 top-level variance components (VC).
stepwiseVCA(obj, VarVC.method = c("scm", "gb"))obj |
(VCA) object representing the complete analysis |
VarVC.method |
(character) string specifying the algorithm to be used for estimating variance-covariance matrix
of VCs (see |
This function uses the complete data to quantify sub-sets of variance components.
In each step the current total variance is estimated by subtracting the sum of all left-out VCs
from the total variance of the initial VCA object. Doing this guarantees that the contribution to the total
variance which is due to left-out VCs is accounted for, i.e. it is estimated but not included/reported.
The degrees of freedom (DFs) of the emerging total variances of sub-sets are determined using the Satterthwaite
approximation. This is achieved by extracting the corresponding sub-matrix from the coefficient matrix C of
the 'VCA' object, the sub-vector of ANOVA mean squares, and the sub-vector of degrees of freedom and calling
function SattDF method="total".
This step-wise procedure starts one-level above error (repeatability) and ends at the level of the upper-most VC.
It can only be used on models fitted by ANOVA Type-1, i.e. by function anovaVCA.
(list) of (simplified) 'VCA' objects representing analysis-result of sub-models
Andre Schuetzenmeister andre.schuetzenmeister@roche.com
## Not run: data(VCAdata1) datS7L1 <- VCAdata1[VCAdata1$sample == 7 & VCAdata1$lot == 1, ] fit0 <- anovaVCA(y~device/day/run, datS7L1, MME=TRUE) # complete VCA-analysis result fit0 # perform step-wise (bottom-up) VCA-analyses sw.res <- stepwiseVCA(fit0) sw.res # get CIs on intermediate precision VCAinference(sw.res[["device:day"]]) ## End(Not run)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.