ci.spls {spls} | R Documentation |
Calculate the bootstrapped confidence intervals of the coefficients of the selected predictors and draw the confidence interval plots of coefficients.
ci.spls( object, coverage=0.95, B=1000, plot.it=FALSE, plot.fix="y", plot.var=NA, K=object$K, fit=object$fit )
object |
A fitted SPLS object. |
coverage |
Coverage of the confidence intervals.
coverage should have a number between 0 and 1.
Default is 0.95 (95% confidence interval). |
B |
Number of bootstrap iterations. Default is 1000. |
plot.it |
Plot the confidence intervals of the coefficients? |
plot.fix |
If plot.fix="y" , then it plots the confidence intervals
of the predictors for a given response.
If plot.fix="x" , then it plots the confidence intervals
of a given predictor across all the responses.
Relevant only when plot.it=TRUE . |
plot.var |
Index vector of the responses (if plot.fix="y" )
or predictors (if plot.fix="x" ) to be fixed in plot.fix .
The indices of predictors are defined
among the set of the selected predictors.
Relevant only when plot.it=TRUE . |
K |
Number of hidden components.
Default is to use the same K as in the original SPLS fit. |
fit |
PLS algorithm for model fitting. Alternatives are
"kernelpls" , "widekernelpls" ,
"simpls" , or "oscorespls" .
Default is to use the same PLS algorithm
as in the original SPLS fit. |
Invisibly returns a list with components:
cibeta |
A list with as many matrix elements as the number of responses. Each matrix element is p by 2, where ith row of the matrix lists the upper and lower bounds of the bootstrapped confidence interval of the ith predictor. |
betahat |
Matrix of the original coefficients of the SPLS fit. |
lbmat |
Matrix of the lower bounds of confidence intervals (for internal use). |
ubmat |
Matrix of the upper bounds of confidence intervals (for internal use). |
Dongjun Chung, Hyonho Chun, and Sunduz Keles.
Chun, H. and Keles, S. (2007). "Sparse partial least squares for simultaneous dimension reduction and variable selection", (http://www.stat.wisc.edu/~keles/Papers/SPLS_Nov07.pdf).
correct.spls
and spls
.
data(mice) # SPLS with eta=0.6 & 1 hidden components f <- spls( mice$x, mice$y, K=1, eta=0.6 ) # Calculate the confidence intervals of coefficients ci.f <- ci.spls( f, plot.it=TRUE, plot.fix="x", plot.var=20 ) # Bootstrapped confidence intervals cis <- ci.f$cibeta cis[[20]] # equivalent, 'cis$1422478_a_at'