checkCCV(data, useRank = FALSE, f = 1/2)
checkModel(data, fit, model = c("gamma", "lognormal", "lnnmv"), number = 9, nb = 10, cluster = 1, groupid = NULL)
checkVarsQQ(data, groupid, ...)
checkVarsMar(data, groupid, xlab, ylab, ...)
plotMarginal(fit, data, kernel = "rect", n = 100, bw = "nrd0", adjust = 1, xlab, ylab,...)
plotCluster(fit, data, cond = NULL, ncolors = 123, sep=TRUE, transform=NULL)
"plot"(x, data, plottype="cluster", ...)
TRUE
, ranks of means and c.v.-s are
used in the scatterplot
lowess
emfit
density
qqmath
,
histogram
and xyplot
call used to produce the final result
checkModel
, checkVarsQQ
and checkVarsMar
return an object of class
``trellis'', using function in the Lattice package. Note that in
certain situations, these may need to be explicitly `print'-ed to have
any effect.
checkCCV
checks the constant coefficient of variation assumption
made in the GG and LNN models.
checkModel
generates QQ plots for subsets of (log) intensities
in a small window. They are used to check the Log-Normal assumption on
observation component of the LNN and LNNMV models and the Gamma
assumption on observation component of the GG model.
checkVarsQQ
generates QQ plot for gene specific sample
variances. It is used to check the assumption of a scaled inverse
chi-square prior on gene specific variances, made in the LNNMV model.
checkVarsMar
is another diagnostic tool to check this
assumption. The density histogram of gene specific sample variances
and the density of the scaled inverse chi-square distribution with
parameters estimated from data will be plotted.
checkMarginal
generates predictive marginal distribution from
fitted model and compares with estimated marginal (kernel) density of
data. Available for the GG and LNN models only.
plotCluster
generate heatmap for gene expression data with clusters
Kendziorski, C.M., Newton, M.A., Lan, H., Gould, M.N. (2003). On parametric empirical Bayes methods for comparing multiple groups using replicated gene expression profiles. Statistics in Medicine 22:3899-3914.
Newton, M.A. and Kendziorski, C.M. Parametric Empirical Bayes Methods for Microarrays in The analysis of gene expression data: methods and software. Eds. G. Parmigiani, E.S. Garrett, R. Irizarry and S.L. Zeger, New York: Springer Verlag, 2003.
Newton, M.A., Noueiry, A., Sarkar, D., and Ahlquist, P. (2004). Detecting differential gene expression with a semiparametric hierarchical mixture model. Biostatistics 5: 155-176.
Yuan, M. and Kendziorski, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.
emfit
, lowess