SSI methods
Useful methods for retrieving, summarizing and visualizing important results from an object of the class 'SSI'
## S3 method for class 'SSI'
coef(object, ..., df=NULL, tst=NULL)
## S3 method for class 'SSI'
fitted(object, ...)
## S3 method for class 'SSI'
summary(object, ...)
## S3 method for class 'SSI'
plot(..., title=NULL, py=c("accuracy","MSE"))object |
An object of the class 'SSI'. One or more objects must be passed as |
df |
(numeric) Average (across testing individuals) number of non-zero regression coefficients |
tst |
(integer vector) Which individuals are in testing set. Default |
py |
(character) Either 'accuracy' or 'MSE' to plot the correlation between observed and predicted values or the mean squared error, respectively, in the y-axis |
title |
(character or expression) Title of the plot |
... |
Arguments to be passed:
|
Method fitted returns a matrix with the predicted values for each individual in the testing set (in rows) for each value of lambda (in columns).
Method coef (list of matrices) returns the regression coefficients for each testing set individual (elements of the list). Each matrix contains the coefficients for each value of lambda (in rows) associated to each training set individual (in columns). If tst is specified, the elements of the list will correspond only to the testing individuals given in tst. If df is specified, only the coefficients for the lambda associated to df are returned as a 'matrix' with testing individuals in rows.
Method summary returns a list object containing:
lambda: (vector) sequence of (across testing individuals) values of lambda used in the coefficients' estimation.
df: (vector) degrees of freedom (across testing individuals) at each solution associated to each value of lambda.
accuracy: (vector) correlation between observed and predicted values associated to each value of lambda.
MSE: (vector) mean squared error associated to each value of lambda.
optCOR: (vector) summary of the SSI with maximum accuracy.
optMSE: (vector) summary of the SSI with minimum MSE.
Method plot creates a plot of either accuracy or MSE versus the (average across testing individuals) number of predictors (with non-zero regression coefficient) and versus lambda.
Marco Lopez-Cruz (maraloc@gmail.com) and Gustavo de los Campos
require(SFSI)
data(wheatHTP)
X = scale(X[1:200,])/sqrt(ncol(X)) # Subset and scale markers
G = tcrossprod(X) # Genomic relationship matrix
y = as.vector(scale(Y[1:200,"YLD"])) # Subset ans scale response variable
fm1 = SSI(y,K=G,tst=1:50,trn=51:length(y))
yHat = fitted(fm1) # Predicted values for each SSI
out = summary(fm1) # Useful function to get results
corTST = out$accuracy # Testing set accuracy (correlation cor(y,yHat))
out$optCOR # SSI with maximum accuracy
out$optMSE # SSI with minimum MSE
B = coef(fm1) # Regression coefficients
B = coef(fm1,df=out$optCOR$df) # Regression coefficients associated with one 'df'
plot(fm1,title=expression('corr('*y[obs]*','*y[pred]*') vs sparsity'))
plot(fm1,py="MSE",title='Mean Square Error vs sparsity')Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.