MSE calculation function

msecalc {LMGene}

R Documentation

MSE calculation function

Description

Computes the mean square error and gradient for the global anova

Usage

msecalc(eS, lam, alpha, lowessnorm, R)

Arguments

eS	Array data. must be exprSet type.
lam	A parameter for glog transformation
alpha	A parameter for glog transformation
lowessnorm	TRUE, if lowess method is going to be used
R	The residual matrix, i.e., identity minus the hat matrix

Details

The input argument, eS, must be exprSet type from Biobase package. If you have a matrix data and information about the considered factors, then you can use neweS to conver the data into exprSet. Please see neweS in more detail.

Value

msev	A vector which contains MSE and gradient of two parameters

Author(s)

David Rocke and Geun-Cheol Lee

References

B. Durbin and D.M. Rocke, (2003) Estimation of Transformation Parameters for Microarray Data, Bioinformatics, 19, 1360-1367.
http://www.idav.ucdavis.edu/~dmrocke/

See Also

jggrad2, tranest2

Examples

#library
library(Biobase)
library(LMGene)

#data
data(sample.eS)

lmod <- GetLMObj(sample.eS)
X <- lmod$x
U <- svd(X)$u
H = crossprod(t(U), t(U))
n = dim(H)[1]
R = diag(rep(1,n)) - H

msecalc(sample.eS,500,50, FALSE, R)

---

[Package LMGene version 1.6.0 Index]

weibull fitdistr problem

This works:
x <- c(23,19,37,38,40,36,172,48,113,90,54,104,90,54,157,51,77,78,144,34,29,45,16,15,37,218,170,44,121)
rate <- c(.01,.02,.04,.05,.1,.2,.3,.4,.5,.8,.9)
year <- c(100,50,25,20,10,5,3.3,2.5,2,1.2,1.1)
library(MASS)
x <- sort(x)
tryCatch(
  f<-fitdistr(x, 'weibull'),
  error = function(e) cat(as.character(e))
)
shape = f$estimate["shape"]
scale = f$estimate["scale"]
cat(shape,scale,"\n")

for (i in 1:length(rate)){
  cat(scale*(-log(rate[i]))^(1/shape))
  cat(",")
}

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