ci.spls {spls} | R Documentation |
Calculate bootstrapped confidence intervals of coefficients of the selected predictors and generate confidence interval plots.
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 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 confidence intervals of coefficients? |
plot.fix |
If plot.fix="y" , then plot confidence intervals
of the predictors for a given response.
If plot.fix="x" , then plot confidence intervals
of a given predictor across all the responses.
Relevant only when plot.it=TRUE . |
plot.var |
Index vector of 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 i-th row of the matrix lists the upper and lower bounds of the bootstrapped confidence interval of the i-th predictor. |
betahat |
Matrix of original coefficients of the SPLS fit. |
lbmat |
Matrix of lower bounds of confidence intervals (for internal use). |
ubmat |
Matrix of upper bounds of confidence intervals (for internal use). |
Dongjun Chung, Hyonho Chun, and Sunduz Keles.
Chun, H. and Keles, S. (2009). "Sparse partial least squares for simultaneous dimension reduction and variable selection", To appear in Journal of the Royal Statistical Society - Series B (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 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'