View source: R/mainFunctions.R
parameters | R Documentation |
The function estimates the parameters of two normal distributions. Both maximum likelihood estimates and shrinkage estimate of covariance matrices are supplied.
parameters( data, classes, shrink = TRUE, shrink.function = shrinkTEGS, shrink.param = list(probs = 0.05, type = "min") )
data |
an expression matrix with colnames for variables and row names for samples |
classes |
a vector of length equal to the number of rows of data. It indicates the class (condition) of each statistical unit. Only two classes, labeled as 1 and 2, are allowed |
shrink |
boolean. if |
shrink.function |
function that implements the shrinkage method. It must return a list object with all the elements required as input arguments in |
shrink.param |
additional parameters to pass as input arguments of the shrink function specified in the |
The function returns a list containing: three matrices with maximum likelihood or the shrinkage estimates (pooled, condition1 and condition2), and a list with the used parameters.
shrinkTEGS
, testMeanVariance
if(require(mvtnorm)){ ## Generate two random samples of size 50 from two multivariate normal distributions # sample size n<-50 # true parameters of class 1 and class 2 param.class1<-simulation$condition1 param.class2<-simulation$condition2$`5`$`2` # simulated dataset data.class1<-rmvnorm(n = n,mean =param.class1$mu ,sigma =param.class1$S) data.class2<-rmvnorm(n = n,mean =param.class2$mu ,sigma=param.class2$S) data<-rbind(data.class1,data.class2) classes<-c(rep(1,nrow(data.class1)),rep(2,nrow(data.class2))) ## estimated parameters: maximum likelihood estimate est.param<-parameters(data = data,classes =classes ,shrink = FALSE) ## estimated parameters: regularized estimate est.param.shrink<-parameters(data = data,classes =classes ,shrink = TRUE) # tuning values and other info on shrinkage estimate str(est.param.shrink$shrink.info) }
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