psVCSignal  R Documentation 
psVCSignal
is used to regress a (glm) response onto a
signal such that the signal coefficients can vary over another covariate t
.
Anisotripic penalization of tensor product Bsplines produces a 2D coefficient surface that
can be sliced at t
.
@details Support functions needed: pspline_fitter
, pspline_2dchecker
,
and bbase
.
@import stats
psVCSignal(
y,
X,
x_index,
t_var,
Pars = rbind(c(min(x_index), max(x_index), 10, 3, 1, 2), c(min(t_var), max(t_var), 10,
3, 1, 2)),
family = "gaussian",
link = "default",
m_binomial = 1 + 0 * y,
wts = 1 + 0 * y,
r_gamma = 1 + 0 * y,
X_pred = X,
t_pred = t_var,
y_predicted = NULL,
ridge_adj = 1e08,
int = TRUE
)
y 
a glm response vector of length 
X 
a 
x_index 

t_var 

Pars 
a matrix with 2 rows, each with Pspline parameters:

family 
the response distribution, e.g.

link 
the link function, one of 
m_binomial 
a vector of binomial trials having 
wts 
a 
r_gamma 
a vector of gamma shape parameters. Default is 1 vector for 
X_pred 
a matrix of signals with 
t_pred 
a vector for the VC indexing variable with length 
y_predicted 
a vector for the responses associated with 
ridge_adj 
a small ridge penalty tuning parameter to regularize estimation (default 
int 
intercept set to TRUE or FALSE for intercept term. 
pcoef 
a vector of length 
summary_predicted 
inverse link prediction vectors, and twice se bands. 
dev 
the deviance of fit. 
eff_dim 
the approximate effective dimension of fit. 
family 
the family of the response. 
link 
the link function. 
aic 
AIC. 
df_resid 
approximate df residual. 
cv 
leaveoneout standard error prediction when 
cv_predicted 
standard error prediction for 
Pars 
design and tuning parameters; see arguments above. 
dispersion_parm 
estimate of dispersion, 
summary_predicted 
inverse link prediction vectors, and twice se bands. 
eta_predicted 
estimated linear predictor of 
press_mu 
leaveoneout prediction of mean when 
bin_percent_correct 
percent correct classification based on 0.5 cutoff when 
Bx 
Bspline basis matrix of dimension 
By 
Bspline basis matrix of dimension 
Q 
Modified tensor basis ( 
yint 
the estimated yintercept (when 
int 
a logical variable related to use of yintercept in model. 
Paul Eilers and Brian Marx
Eilers, P.H.C. and Marx, B.D. (2003). Multivariate calibration with temperature interaction using twodimensional penalized signal regression. Chemometrics and Intellegent Laboratory Systems, 66, 159–174.
Eilers, P.H.C. and Marx, B.D. (2021). Practical Smoothing, The Joys of Psplines. Cambridge University Press.
library(fds)
data(nirc)
iindex < nirc$x
X < nirc$y
sel < 50:650 # 1200 <= x & x<= 2400
X < X[sel, ]
iindex < iindex[sel]
dX < diff(X)
diindex < iindex[1]
y < as.vector(labc[1, 1:40]) # percent fat
t_var < as.vector(labc[4, 1:40]) # percent flour
oout < 23
dX < t(dX[, oout])
y < y[oout]
t_var = t_var[oout]
Pars = rbind(c(min(diindex), max(diindex), 25, 3, 1e7, 2),
c(min(t_var), max(t_var), 20, 3, 0.0001, 2))
fit1 < psVCSignal(y, dX, diindex, t_var, Pars = Pars,
family = "gaussian", link = "identity", int = TRUE)
plot(fit1, xlab = "Coefficient Index", ylab = "VC: % Flour")
names(fit1)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.