pfr_old: Penalized Functional Regression (old version)

In refund: Regression with Functional Data

Penalized Functional Regression (old version)

Description

This code implements the function pfr() available in refund 0.1-11. It is included to maintain backwards compatibility.

Functional predictors are entered as a matrix or, in the case of multiple functional predictors, as a list of matrices using the funcs argument. Missing values are allowed in the functional predictors, but it is assumed that they are observed over the same grid. Functional coefficients and confidence bounds are returned as lists in the same order as provided in the funcs argument, as are principal component and spline bases. Increasing values of nbasis will increase computational time and the values of nbasis, kz, and kb in relation to each other may need to be adjusted in application-specific ways.

Usage

pfr_old(
  Y,
  subj = NULL,
  covariates = NULL,
  funcs,
  kz = 10,
  kb = 30,
  nbasis = 10,
  family = "gaussian",
  method = "REML",
  smooth.option = "fpca.sc",
  pve = 0.99,
  ...
)

Arguments

Y	vector of all outcomes over all visits
subj	vector containing the subject number for each observation
covariates	matrix of scalar covariates
funcs	matrix, or list of matrices, containing observed functional predictors as rows. NA values are allowed.
kz	can be NULL; can be a scalar, in which case this will be the dimension of principal components basis for each and every observed functional predictors; can be a vector of length equal to the number of functional predictors, in which case each element will correspond to the dimension of principal components basis for the corresponding observed functional predictors
kb	dimension of the B-spline basis for the coefficient function (note: this is a change from versions 0.1-7 and previous)
nbasis	passed to refund::fpca.sc (note: using fpca.sc is a change from versions 0.1-7 and previous)
family	generalized linear model family
method	method for estimating the smoothing parameters; defaults to REML
smooth.option	method to do FPC decomposition on the predictors. Two options available – "fpca.sc" or "fpca.face". If using "fpca.sc", a number less than 35 for nbasis should be used while if using "fpca.face",35 or more is recommended.
pve	proportion of variance explained used to choose the number of principal components to be included in the expansion.
...	additional arguments passed to gam to fit the regression model.

Value

fit	result of the call to gam
fitted.vals	predicted outcomes
fitted.vals.level.0	predicted outcomes at population level
fitted.vals.level.1	predicted outcomes at subject-specific level (if applicable)
betaHat	list of estimated coefficient functions
beta.covariates	parameter estimates for scalar covariates
varBetaHat	list containing covariance matrices for the estimated coefficient functions
Bounds	list of bounds of a pointwise 95% confidence interval for the estimated coefficient functions
X	design matrix used in the model fit
D	penalty matrix used in the model fit
phi	list of B-spline bases for the coefficient functions
psi	list of principal components basis for the functional predictors
C	stacked row-specific principal component scores
J	transpose of psi matrix multiplied by phi
CJ	C matrix multiplied J
Z1	design matrix of random intercepts
subj	subject identifiers as specified by user
fixed.mat	the fixed effects design matrix of the pfr as a mixed model
rand.mat	the fixed effects design matrix of the pfr as a mixed model
N_subj	the number of unique subjects, if subj is specified
p	number of scalar covariates
N.pred	number of functional covariates
kz	as specified
kz.adj	For smooth.option="fpca.sc", will be same as kz (or a vector of repeated values of the specified scalar kz). For smooth.option="fpca.face", will be the corresponding number of principal components for each functional predictor as determined by fpca.face; will be less than or equal to kz on an elemental-wise level.
kb	as specified
nbasis	as specified
totD	number of penalty matrices created for mgcv::gam
funcs	as specified
covariates	as specified
smooth.option	as specified

Warning

Binomial responses should be specified as a numeric vector rather than as a matrix or a factor.

Author(s)

Bruce Swihart [e-mail] and Jeff Goldsmith [e-mail]

References

Goldsmith, J., Bobb, J., Crainiceanu, C., Caffo, B., and Reich, D. (2011). Penalized functional regression. Journal of Computational and Graphical Statistics, 20(4), 830-851.

Goldsmith, J., Crainiceanu, C., Caffo, B., and Reich, D. (2012). Longitudinal penalized functional regression for cognitive outcomes on neuronal tract measurements. Journal of the Royal Statistical Society: Series C, 61(3), 453-469.

Swihart, Bruce J., Goldsmith, Jeff; and Crainiceanu, Ciprian M. (July 2012). Testing for functional effects. Johns Hopkins University Dept. of Biostatistics Working Paper 247, available at https://biostats.bepress.com/jhubiostat/paper247/ American Statistical Association, 109(508): 1425-1439.

See Also

rlrt.pfr, predict.pfr.

Examples

## Not run: 
##################################################################
#########               DTI Data Example                 #########
##################################################################

##################################################################
# For more about this example, see Swihart et al. 2013
##################################################################

## load and reassign the data;
data(DTI2)
Y  <- DTI2$pasat ## PASAT outcome
id <- DTI2$id    ## subject id
W1 <- DTI2$cca   ## Corpus Callosum
W2 <- DTI2$rcst  ## Right corticospinal
V  <- DTI2$visit ## visit

## prep scalar covariate
visit.1.rest <- matrix(as.numeric(V > 1), ncol=1)
covar.in <- visit.1.rest 


## note there is missingness in the functional predictors
apply(is.na(W1), 2, mean)
apply(is.na(W2), 2, mean)

## fit two univariate models
pfr.obj.t1 <- pfr(Y = Y, covariates=covar.in, funcs = list(W1),     subj = id, kz = 10, kb = 50)
pfr.obj.t2 <- pfr(Y = Y, covariates=covar.in, funcs = list(W2),     subj = id, kz = 10, kb = 50)

### one model with two functional predictors using "smooth.face"
###  for smoothing predictors
pfr.obj.t3 <- pfr(Y = Y, covariates=covar.in, funcs = list(W1, W2), 
                  subj = id, kz = 10, kb = 50, nbasis=35,smooth.option="fpca.face")

## plot the coefficient function and bounds
dev.new()
par(mfrow=c(2,2))
ran <- c(-2,.5)
matplot(cbind(pfr.obj.t1$BetaHat[[1]], pfr.obj.t1$Bounds[[1]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "CCA", xlab="Location", ylim=ran)
abline(h=0, col="blue")
matplot(cbind(pfr.obj.t2$BetaHat[[1]], pfr.obj.t2$Bounds[[1]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "RCST", xlab="Location", ylim=ran)
abline(h=0, col="blue")
matplot(cbind(pfr.obj.t3$BetaHat[[1]], pfr.obj.t3$Bounds[[1]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "CCA  - mult.", xlab="Location", ylim=ran)
abline(h=0, col="blue")
matplot(cbind(pfr.obj.t3$BetaHat[[2]], pfr.obj.t3$Bounds[[2]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "RCST - mult.", xlab="Location", ylim=ran)
abline(h=0, col="blue")


##################################################################
# use baseline data to regress continuous outcomes on functional 
# predictors (continuous outcomes only recorded for case == 1)
##################################################################

data(DTI)

# subset data as needed for this example
cca = DTI$cca[which(DTI$visit ==1 & DTI$case == 1),]
rcst = DTI$rcst[which(DTI$visit ==1 & DTI$case == 1),]
DTI = DTI[which(DTI$visit ==1 & DTI$case == 1),]
# note there is missingness in the functional predictors
apply(is.na(cca), 2, mean)
apply(is.na(rcst), 2, mean)

# fit two models with single functional predictors and plot the results
fit.cca = pfr(Y=DTI$pasat, funcs = cca, kz=10, kb=50, nbasis=20)
fit.rcst = pfr(Y=DTI$pasat, funcs = rcst, kz=10, kb=50, nbasis=20)

par(mfrow = c(1,2))
matplot(cbind(fit.cca$BetaHat[[1]], fit.cca$Bounds[[1]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "CCA")
matplot(cbind(fit.rcst$BetaHat[[1]], fit.rcst$Bounds[[1]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "RCST")

# fit a model with two functional predictors and plot the results
fit.cca.rcst = pfr(Y=DTI$pasat, funcs = list(cca, rcst), kz=10, kb=30, nbasis=20)

par(mfrow = c(1,2))
matplot(cbind(fit.cca.rcst$BetaHat[[1]], fit.cca.rcst$Bounds[[1]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "CCA")
matplot(cbind(fit.cca.rcst$BetaHat[[2]], fit.cca.rcst$Bounds[[2]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "RCST")

##################################################################
# use baseline data to regress binary case-status outcomes on 
# functional predictors
##################################################################

data(DTI)

# subset data as needed for this example
cca = DTI$cca[which(DTI$visit == 1),]
rcst = DTI$rcst[which(DTI$visit == 1),]
DTI = DTI[which(DTI$visit == 1),]

# fit two models with single functional predictors and plot the results
fit.cca = pfr(Y=DTI$case, funcs = cca, family = "binomial")
fit.rcst = pfr(Y=DTI$case, funcs = rcst, family = "binomial")

par(mfrow = c(1,2))
matplot(cbind(fit.cca$BetaHat[[1]], fit.cca$Bounds[[1]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "CCA")
matplot(cbind(fit.rcst$BetaHat[[1]], fit.rcst$Bounds[[1]]),
        type = 'l', lty = c(1,2,2), col = c(1,2,2), ylab = "BetaHat", 
        main = "RCST")

##################################################################
#########              Octane Data Example               #########
##################################################################

data(gasoline)
Y = gasoline$octane
funcs = gasoline$NIR
wavelengths = as.matrix(2*450:850)

# fit the model using pfr and the smoothing option "fpca.face"
fit = pfr(Y=Y, funcs=funcs, kz=15, kb=50,nbasis=35,smooth.option="fpca.face")

matplot(wavelengths, cbind(fit$BetaHat[[1]], fit$Bounds[[1]]), 
        type='l', lwd=c(2,1,1), lty=c(1,2,2), xlab = "Wavelengths", 
        ylab = "Coefficient Function", col=1)

## End(Not run)

refund documentation built on Sept. 21, 2024, 1:07 a.m.