| Title: | A Suite of R Functions Implementing Spline Smoothing Techniques |
|---|---|
| Description: | Fit various smoothing spline models. Includes an ssr() function for smoothing spline regression, an nnr() function for nonparametric nonlinear regression, an snr() function for semiparametric nonlinear regression, an slm() function for semiparametric linear mixed-effects models, and an snm() function for semiparametric nonlinear mixed-effects models. See Wang (2011) <doi:10.1201/b10954> for an overview. |
| Authors: | Yuedong Wang [aut, cre], Chunlei Ke [aut], Cleve Moler [cph] |
| Maintainer: | Yuedong Wang <[email protected]> |
| License: | GPL-2 |
| Version: | 3.1.9 |
| Built: | 2024-12-14 06:25:58 UTC |
| Source: | CRAN |
To calculate a spline estimate with a single smoothing parameter
dsidr(y, q, s=NULL, weight=NULL, vmu="v", varht=NULL, limnla=c(-10, 3), job=-1, tol=0)dsidr(y, q, s=NULL, weight=NULL, vmu="v", varht=NULL, limnla=c(-10, 3), job=-1, tol=0)
y |
a numerical vector representing the response. |
q |
a square matrix of the same order as the length of y, with elements equal to the reproducing kernel evaluated at the design points. |
s |
the design matrix of the null space |
weight |
A weight matrix for penalized weighted least-square: |
vmu |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively.
"u |
varht |
needed only when vmu="u", which gives the fixed variance in calculation of the UBR function. Default is NULL. |
limnla |
a vector of length 2, specifying a search range for the n times smoothing parameter on |
job |
an integer representing the optimization method used to find the smoothing parameter.
The options are job=-1: golden-section search on (limnla(1), limnla(2));
job=0: golden-section search with interval specified automatically;
job >0: regular grid search on |
tol |
tolerance for truncation used in ‘dsidr’. Default is 0.0, which sets to square of machine precision. |
info |
an integer that provides error message. info=0 indicates normal termination, info=-1 indicates dimension error, info=-2 indicates
|
fit |
fitted values. |
c |
estimates of c. |
d |
estimates of d. |
resi |
vector of residuals. |
varht |
estimate of variance. |
nlaht |
the estimate of log10(nobs*lambda). |
limnla |
searching range for nlaht. |
score |
the minimum GCV/GML/UBR score at the estimated smoothing parameter. When job>0, it gives a vector of GCV/GML/UBR functions evaluated at regular grid points. |
df |
equavilent degree of freedom. |
nobs |
length(y), number of observations. |
nnull |
dim( |
s, qraux, jpvt
|
QR decomposition of S=FR, as from Linpack ‘dqrdc’. |
q |
first dim( |
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Gu, C. (1989). RKPACK and its applications: Fitting smoothing spline models. Proceedings of the Statistical Computing Section, ASA, 42-51.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Calculate and output p-values for tests available.
## S3 method for class 'anova.ssr' print(x, ...)## S3 method for class 'anova.ssr' print(x, ...)
x |
an object inheriting from class anova.ssr, generally obtained by applying the anova.ssr method to an ssr object. |
... |
other available arguments, currently unused. |
Chunlei Ke [email protected] and Yuedong Wang [email protected]
The acid data frame has 112 rows and 4 columns of data derived based on the Eastern Lakes Survey of 1984 implemented by the Environmental Protection Agency of the USA.
data(acid)data(acid)
The data frame contains the following columns:
ph a numeric vector of surface pH values.
t1 a numeric vector of calcium concentrations in log10 milligrams per liter.
x1, x2 numeric vectors of the lakes' geographic locations.
112 lakes are extracted in the southern Blue Ridge mountains area. The surface pH values were recorded together with the calcium concentration and geographic locations.
Douglas, A. and Delampady, M. (1990), Eastern Lake Survey Phase I: Documentation for the Data Base and the Derived Data sets. Tech Report 160 (SIMS), Dept. Statistics, University of British Columbia.
Gu, C. and Wahba, G. (1993) Smoothing Spline ANOVA with component-wise Bayesian confidence intervals. Journal of Computational and Graphic Statistics 55, 353-368.
Perform an inverse logit calculation
alogit(x)alogit(x)
x |
a numeric value |
Returned is .
Chunlei ke [email protected] and Yuedong Wang [email protected]
For smoothing spline models with a single smoothing parameter, test the hypothesis that the unknown funciton lies in the null space using the local most powerful (LMP) test and a GCV or GML test.
## S3 method for class 'ssr' anova(object, simu.size=100, ...)## S3 method for class 'ssr' anova(object, simu.size=100, ...)
object |
an object of class "ssr" fitted with a single smooting parameter. |
simu.size |
an optional integer giving the number of simulations to calcualte p-values based on simulation. Default is 100. |
... |
other available arguments, currently unused. |
For Gaussian data with one smoothing parameter, test the hypothesis that the function is in the
null space , i.e. the parametric part of the fitted model is sufficient.
Available are the LMP and GCV or GML methods. However, the p-values cannot be calculated analytically
since the null distributions for these testing statistics under are unknown.
Monte Carlo simulation is used to approximate the p-values
for the LMP, and GCV (if spar="v") or GML (if spar="m") methods. Due to computation burden,
the smoothing parameters are fixed at their estimate in the currect calculation.
When spar="m", an approximate p-value based on a mixture of two Chi-square distributions is also provided for the GML test, which tends to be conservationve (Pinherio and Bates, 2002).
Methods further developed in Liu and Wang (2004) and Liu, Meiring and Wang (2004) will be implemented in the future.
a list including test values.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Cox, D. and Koh, E. (1989). A smoothing spline based test of model adequency in polynomial regression. Ann. Ins. Stat. Math. 41, 383-400.
Cox, D., Koh, E., Wahba, G. and Yandell, B.S. (1988). Testing the parameteric null model hypothesis in semi-parametric partial and generalized spline models. Ann. Statist. 16, 113-119.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Pinherio, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-Plus. Springer.
Liu, A. and Wang, Y. (2004) Hypothesis Testing in Smoothing Spline Models. Journal of Statistical Computation and Simulation, to appear.
Liu, A., Meiring, W. and Wang, Y. (2004), Testing Generalized Linear Models Using Smoothing Spline Methods. Statistica Sinica, to appear,
## Not run: data(acid) # fit a partial thin-plate spline temp <- ssr(ph~t1+x1+x2, rk=tp(t1), data=acid, spar="m") anova(temp, 500) ## End(Not run)## Not run: data(acid) # fit a partial thin-plate spline temp <- ssr(ph~t1+x1+x2, rk=tp(t1), data=acid, spar="m") anova(temp, 500) ## End(Not run)
The Arosa data frame has 518 rows and 3 columns of data for monthly mean ozone thickness.
data(Arosa)data(Arosa)
The data frame contains the following columns:
year a vector of integers from 1 to 46 indicating the years when the measures were taken from 1926.
month a vector of integers from 1 to 12 represeting the months in a year.
thick a numeric vetor of mean ozone thickness (Dobson units).
Monthly mean ozone thickness in Arosa, Switzerland was recorded from 1926-1971.
Andrew,D. F. and Herzberg, A. M. (1985). Data: a collection of problems from many fields for the students and research workers. Springer: Berlin: New York.
Return a block diagonal matrix formed from the input list of matrices
bdiag(x)bdiag(x)
x |
a list of matrices |
Returned is a matrix of the form diag(x1, ..., xn) where n is the length of the list.
The bond data frame has 1234 rows and 5 columns of data derived from 144 General Electronic Company bonds and 78 Treasury bonds.
data(bond)data(bond)
The data frame contains the following columns:
name a vector of index for individual bond
price a numeric vector of current price
time a numeric vector of future time points at which the payments are made
payment a numeric vector of future payments
type a vector of character strings identifying the groups, "govt" or "ge", which the individual bonds belong to.
Bloomberg
Chunlei Ke and Yuedong Wang (2004), Nonlinear Nonparametric Regression Models. Journal of the American Statistical Association 99, 1166-1175.
The canadaTemp data frame has 420 rows and 3 columns of data for monthly mean temperatures in Canada
data(canadaTemp)data(canadaTemp)
The data frame contains the following columns:
temp a numeric vector of mean temperatures at some stations in Canada.
month a vector of integers from 1 to 12 represeting the months in a year.
station a vetor of integers from 1 to 35 indicating the sations where the temperatures were recorded.
The data set was downloaded from https://www.psych.mcgill.ca/misc/fda/downloads/FDAfuns/R/data/.
Ramsay, J. O and Silverman, B. W. (1997). Functional Data Analysis. New York:Springer.
Ke, C. and Wang, Y. (2001). Semi-parametric Nonlinear Mixed Effects Models and Their Applications. JASA 96:1272-1298.
The chickenpox data frame has 498 rows and 3 columns of data
recording the number of Chickenpox occurrences in New York City.
data(chickenpox)data(chickenpox)
The data frame contains the following columns:
count the number of monthly reported Chickenpox cases.
month a vector of integers from 1 to 12 representing the month for the reported cases. year a numeric vector representing the year when the cases were reported.
This data frame contains monthly number of reported cases of chickenpox in New York City from 1931 to the first six months of 1972.
The data were downloaded from https://robjhyndman.com/tsdl/.
Returned a matrix forming Cholesky Decomposition
chol.new(Q)chol.new(Q)
Q |
a symmetric matrix, maybe non-positive. |
This is used internally as an extension of chol that works on a positive matrix.
A mtrix M suth that .
The data frame climate, obrained from the Carbon Dioxide Information and Analysis Center, has 690 rows and 5 columns of data representing station winter temperature measurements.
data(climate)data(climate)
The data frame contains the following columns:
temp a numeric vector of temperatures in celsius.
lat, long numeric vectors identifying the lattitudes and longitudes of the stations in.
lat.degree, long.degree numeric vectors identifying the lattitudes and longitudes of the stations in degree.
The station winter average temperatures were the averages of the December, January and Febuary monthly average temperatures obtained from the Jones/Wigley data files obtainable from the CDIAC at Oak Ridge National Laboratory in the files ndp020r1/jonesnh.data.Z and ndp020r1/jonessh.dat.Z in the pbu directory at 128.219.24.36.
Jones, P., Wigley, T. and Briffa, K.. lobal and hemisphere temperature anaomalies-land and marine instrumental records. In T. Boden, D. Kaiser, R. Sepanski, and F. Stoss, editors, Trends '93: A Compendium of Data on Global Change, ORNL/CDIAC-65, pages 603-608, Oak Ridge, TN 1994. CDIAC, Oak Ridge National Laboratory.
Calculate some matrix operations needed to construct Bayesian confidence intervals
dcrdr(rkpk.obj, r)dcrdr(rkpk.obj, r)
rkpk.obj |
an object returned from calling dsidr |
r |
a matrix to evaluate reproducing kernels on grid points |
See the document for the corresponding Fortran subroutine.
Extract deviance from a fitted ssr object
## S3 method for class 'ssr' deviance(object,residuals=FALSE, ...)## S3 method for class 'ssr' deviance(object,residuals=FALSE, ...)
object |
a fitted |
.
residuals |
a logical value. If 'TRUE', deviance residuals are returned. If 'FALSE', the sum of deviance residuals squares is returned. Default is FALSE. |
... |
other arguments, currently unused. |
This is a method for the function deviance for objects
inheriting from class ssr.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
To calculate a spline estimate with multiple smoothing parameters
dmudr(y, q, s, weight = NULL, vmu = "v", theta = NULL, varht = NULL, tol = 0, init = 0, prec = 1e-06, maxit = 30)dmudr(y, q, s, weight = NULL, vmu = "v", theta = NULL, varht = NULL, tol = 0, init = 0, prec = 1e-06, maxit = 30)
y |
a numerical vector representing the response. |
q |
a list, or an array, of square matrices of the same order as the length of y, which are the reproducing kernels evaluated at the design points. |
s |
the design matrix of the null space |
weight |
a weight matrix for penalized weighted least-square: |
vmu |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. "u |
theta |
If ‘init=1’, theta includes intial values for smoothing parameters. Default is NULL. |
varht |
needed only when vmu="u", which gives the fixed variance in calculation of the UBR function. Default is NULL. |
tol |
the tolerance for truncation in the tridiagonalization. Default is 0.0. |
init |
an integer of 0 or 1 indicating if initial values are provided for theta. If init=1, initial values are provided using theta. Default is 0. |
prec |
precision requested for the minimum score value, where precision is the weaker of the absolute and relative precisions. Default is |
maxit |
maximum number of iterations allowed. Default is 30. |
info |
an integer that provides error message. info=-1 indicates dimension error,
info=-2 indicates |
fit |
fitted values. |
c |
estimates of c. |
d |
estimates of d. |
resi |
vector of residuals. |
varht |
estimate of variance. |
theta |
estimates of parameters |
nlaht |
the estimate of |
score |
the minimum GCV/GML/UBR score at the estimated smoothing parameters. |
df |
equavilent degree of freedom. |
nobs |
length(y), number of observations. |
nnull |
dim( |
nq |
length(rk), number of reproducing kernels. |
s, q, y
|
changed from the inputs. |
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Gu, C. (1989). RKPACK and its applications: Fitting smoothing spline models. Proceedings of the Statistical Computing Section, ASA, 42-51.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59
The dog data frame has 252 rows and 4 columns of data considered by Grizzle and Alen (1969)
data(dog)data(dog)
The data frame contains the following columns:
y a numeric vector of meansurements of coronary sinus postassium concentrations.
group a vector of group index for the four groups of dogs.
dog a vector of integers identifying dogs.
time a numeric vector of time points measurements were made.
The data are coronary sinus potassium concentrations measured on each of 36 dogs. These 36 dogs were divided into 4 treatment groups, and the measurements on each dog were taken every two minutes from 1 to 13 minutes after occlusion.
Grizzle, J. E. and Allen, D. M. (1969). Analysis of growth and dose response curves, Biometrics 25: 357-381.
Calculate a matrix operation needed to construct Bayesian confidence intervals
dsms(rkpk.obj)dsms(rkpk.obj)
rkpk.obj |
an object returned from calling dsidr |
a matrix. See the corresponding Fortran subroutine.
To calculate a spline estimate with multiple smoothing parameters for non-Gaussian data
gdmudr(y, q, s, family, vmu = "v", varht = NULL, init = 0, theta = NULL, tol1 = 0, tol2 = 0, prec1 = 1e-06, maxit1 = 30, prec2 = 1e-06, maxit2 = 30)gdmudr(y, q, s, family, vmu = "v", varht = NULL, init = 0, theta = NULL, tol1 = 0, tol2 = 0, prec1 = 1e-06, maxit1 = 30, prec2 = 1e-06, maxit2 = 30)
y |
a numerical vector representing the response, or a matrix of two columns for binomial data with the first column as the largest possible counts and the second column as the counts actually obsered. |
q |
a list, or an array, of square matrices of the same order as the length of y, which are the reproducing kernels evaluated at the design points. |
s |
the design matrix of the null space |
family |
a string specifying the family of distribution. Families supported are "binary", "binomial", "poisson" and "gamma" for Bernoulli, binomial, poisson, and gamma distributions respectively. Canonical links are used except for Gamma family where log link is used. |
vmu |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively.
"u |
varht |
needed only when vmu="u", which gives the fixed variance in calculation of the UBR function. Default is 1.0. |
init |
an integer of 0 or 1 indicating if initial values are provided for theta. If init=1, initial values are provided using theta. Default is 0. |
theta |
If ‘init=1’, theta includes intial values for smoothing parameters. Default is NULL. |
tol1 |
the tolerance for elements of w's. Default is 0.0 which sets to square of machine precision. |
tol2 |
tolerance for truncation used in ‘dsidr’. Default is 0.0 which sets to square of machine precision. |
prec1 |
precision requested for the minimum score value, where precision is the weaker of the absolute and relative precisions. Default is 1e-06. |
maxit1 |
maximum number of iterations allowed for DMUDR subroutine. Default is 30. |
prec2 |
precision requested for stopping the iteration. Default is |
maxit2 |
maximum number of iterations allowed for the iteration in GRKPACK. Default is 30. |
info |
an integer that provides error message. info=-1 indicates dimension error,
info=-2 idicates |
fit |
estimate of the function at design points. |
c |
estimates of c. |
d |
estimates of d. |
resi |
vector of working residuals. |
varht |
estimate of dispersion parameter. |
theta |
estimates of parameters |
nlaht |
the estimate of |
score |
the minimum GCV/GML/UBR score at the estimated smoothing parameters. |
df |
equavilent degree of freedom. |
nobs |
length-of-y, number of observations. |
nnull |
|
nq |
length(rk), number of reproducing kernels. |
s, q, y, init, maxit2
|
changed from the inputs. |
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Wang, Y. (1997). GRKPACK: Fitting Smoothing Spline ANOVA Models for Exponential Families. Communications in Statistics: Simulation and Computation, 24: 1037-1059.
To calculate a spline estimate with single smoothing parameter for non-Gaussian data.
gdsidr(y, q, s, family, vmu="v", varht=NULL, limnla=c(-10, 3), maxit=30, job=-1, tol1=0, tol2=0, prec=1e-06)gdsidr(y, q, s, family, vmu="v", varht=NULL, limnla=c(-10, 3), maxit=30, job=-1, tol1=0, tol2=0, prec=1e-06)
y |
a numerical vector representing the response, or a matrix of two columns for binomial data with the first column as the largest possible counts and the second column as the counts actually obsered. |
q |
a square matrix of the same order as the length of y, with elements equal to the reproducing kernel evaluated at the design points. |
s |
the design matrix of the null space |
family |
a string specifying the family of distribution. Families supported are "binary", "binomial", "poisson" and "gamma" for Bernoulli, binomial, poisson, and gamma distributions respectively. Canonical links are used except for Gamma family where a log link is used. |
vmu |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. "u |
varht |
needed only when vmu="u", which gives the fixed variance in calculation of the UBR function. Default is 1.0. |
limnla |
a vector of length 2, specifying a search range for the n times smoothing parameter on log10 scale. Default is (-10, 3). |
maxit |
maximum number of iterations allowed for the iteration in GRKPACK. |
job |
an integer representing the optimization method used to find the smoothing parameter. The options are job=-1: golden-section search on (limnla(1), limnla(2)); job=0: golden-section search with interval specified automatically; job >0: regular grid search on [limnla(1), limnla(2)] with the number of grids = job + 1. Default is -1. |
tol1 |
the tolerance for elements of w's. Default is 0.0 which sets to square of machine precision. |
tol2 |
tolerance for truncation used in ‘dsidr’. Default is 0.0 which sets to square of machine precision. |
prec |
precision requested for stopping the iteration. Default is |
info |
an integer that provides error message. info=0 indicates normal termination, info=-1 indicates dimension error,
info=-2 indicates |
fit |
estimate of the function at design points. |
c |
estimates of c. |
d |
estimates of d. |
resi |
vector of working residuals. |
varht |
estimate of dispersion parameter. |
nlaht |
the estimate of |
limnla |
searching range for nlaht. |
score |
the minimum GCV/GML/UBR score at the estimated smoothing parameter. When job>0, it gives a vector of GCV/GML/UBR functions evaluated at regular grid points. |
df |
equavilent degree of freedom. |
nobs |
length-of-y, number of observations. |
nnull |
|
s, qraux, jpvt
|
QR decomposition of S=FR, as from Linpack ‘dqrdc’. |
q |
first |
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Wang, Y. (1997). GRKPACK: Fitting Smoothing Spline ANOVA Models for Exponential Families. Communications in Statistics: Simulation and Computation, 24: 1037-1059.
Calculate the hat matrix for a ssr object.
hat.ssr(ssr.obj)hat.ssr(ssr.obj)
ssr.obj |
a fitted ssr object. |
The hat matrix may be used for diagnosis. Note that the full name hat.ssr shoud be used since the function hat already exist.
returned is the hat (influence, smoother) matrix.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Eubank, R. L. (1984). The Hat Matrix for Smoothing Splines. Statistics and Probability Letters, 2:9-14.
Eubank, R. L. (1985). Diagnostics for Smoothing Splines. Journal of the Royal Statistical Society B. 47: 332-341.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
## Not run: library(MASS) ## Not run: fit1<- ssr(accel~times, data=mcycle, scale=T, rk=cubic(times)) ## Not run: h <- hat.ssr(fit1)## Not run: library(MASS) ## Not run: fit1<- ssr(accel~times, data=mcycle, scale=T, rk=cubic(times)) ## Not run: h <- hat.ssr(fit1)
The horm.cort data frame has 425 rows and 4 columns of data representing measurement of cortisol on 36 individuals.
data(horm.cort)data(horm.cort)
The data frame contains the following columns:
ID a vector of index indicating individuals on whom measures were made.
time a numeric vector of time points of every 2 hours in 24 hours. The time is scaled into [0, 1].
type a vector of character strings identifying the groups, "normal", "depressed", or "cushing", which the individuals belong to.
conc cortisol concentration measurements in scale.
Blood samples were collected every 2 hours for 24 hours from three group of healthy normal volunteers and volunteers with depresession and suchsing syndrome. They were analyzed for parameters that measure hormones of the hypothalamic-pituitary axix. Human circadian thythm is one of the research objective. In this data set, only measurements of cortisol concetration were included.
This data set was extracted from a stress study conducted in the medical center of the University of Michigan.
Wang, Y. and Brown, M. B. (1996). A Flexible Model for Human Circadian Rhythms. Biometrics 52, 588-596.
Yuedong Wang, Chunlei Ke and Morton B. Brown (2003), Shape Invariant Modelling of Circadian Rhythms with Random Effects and Smoothing Spline ANOVA Decompositions. Biometrics, 59:804-812.
Perform standarization of vector relative to another.
ident(x, y = x)ident(x, y = x)
x |
a numeric vector, matrix or data frame |
y |
an optional numeric vector, matrix or data frame. Default is x. |
Scale y based on x component by component. For example, if both are a matrix,
.
a scaled y.
Return a spline fit of a increasing curve.
inc(y, x, spar = "v", limnla = c(-6, 0), grid = x, prec = 1e-06, maxit = 50, verbose = F)inc(y, x, spar = "v", limnla = c(-6, 0), grid = x, prec = 1e-06, maxit = 50, verbose = F)
y |
a vecetor, used as the response data |
x |
a vector, used as the covariate. Assume an increasing relationshop of |
spar |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. Default is "v" for GCV |
limnla |
a vector of length one or two, specifying a search range for log10(n*lambda), where lambda is the smoothing parameter and n is the sample size. If it is a single value, the smoothing parameter will be fixed at this value. |
grid |
a vector of |
prec |
a numeric value used to assess convergence. Default is 1e-6 |
maxit |
an integer represeenting the maximum iterations. Default is 50. |
verbose |
an optional logical value. If ‘TRUE’, detailed iteration results are displayed. Default is "FALSE" |
This function is to fit a increasing fucntion to the data. The monotone function is expressed as integral of an unknown function that a cubic spline is used to estimate.
a split fit together with the convergence information
Yuedong Wang [email protected] and Chunlei Ke [email protected]
ssr
Approximate posterior standard deviations are calculated for the spline estimate of
nonparametric functions from a nnr object, based on which approximate Bayesian
confidence intervals may be constructed.
## S3 method for class 'nnr' intervals(object,level=0.95, newdata=NULL, terms, pstd=TRUE, ...)## S3 method for class 'nnr' intervals(object,level=0.95, newdata=NULL, terms, pstd=TRUE, ...)
object |
an object inheriting from class |
newdata |
a data frame on which the fitted spline estimates are to be evaluated.
Only those predictors, referred in |
terms |
an optional named list of vectors or matrices containing 0's and 1's collecting one or several combinations of the components of spline estimates in the fitted snr object. The length and names of the list shall match those of the unknown functions appearing in the 'snr' fit object. For the case of a single function, a vector of 0's and 1's can also be accepted. A value "1" at a particular position means that the component at that position is collected. Default is a vector of 1's, representing the overall fits of all unknown functions. |
pstd |
an optional logic value. If TRUE (the default), the posterior standard deviations are calculated. Orelse, only the predictions are calculated. Computation required for posterior standard deviations could be intensive. |
level |
a numeric value set as 0.95. |
... |
other arguments, currently unused. |
The standard deviation returned is based on approximate Bayesian confidence intervals as formulated in Ke and Wang (2002).
an object of class bCI is returned, which is a list of length 2.
Its first element is a matrix which contains predictions for
combinations specified by terms, and second element is a matrix which contains
corresponding posterior standard deviations.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Ke, C. and Wang, Y. (2002). Nonlinear Nonparametric Regression Models. Submitted.
## Not run: ## fit a generalized varying coefficient models data(Arosa) Arosa$csmonth <- (Arosa$month-0.5)/12 Arosa$csyear <- (Arosa$year-1)/45 ozone.fit <- nnr(thick~f1(csyear)+exp(f2(csyear))*f3(csmonth), func=list(f1(x)~list(~I(x-.5),cubic(x)), f2(x)~list(~I(x-.5)-1,cubic(x)), f3(x)~list(~sin(2*pi*x)+cos(2*pi*x)-1,lspline(x,type="sine0"))), data=Arosa[Arosa$year%%2==1,], spar="m", start=list(f1=mean(thick),f2=0,f3=sin(csmonth)), control=list(backfit=1)) x <- seq(0,1,len=50) u <- seq(0,1,len=50) ## calculate Bayesian confidence limits for all components of all functions p.ozone.fit <- intervals(ozone.fit, newdata=list(csyear=x,csmonth=u), terms=list(f1=matrix(c(1,1,1,1,1,0,0,0,1),nrow=3,byrow=TRUE), f2=matrix(c(1,1,1,0,0,1),nrow=3,byrow=TRUE), f3=matrix(c(1,1,1,1,1,0,0,0,1),nrow=3,byrow=TRUE))) plot(p.ozone.fit, x.val=x) ## End(Not run)## Not run: ## fit a generalized varying coefficient models data(Arosa) Arosa$csmonth <- (Arosa$month-0.5)/12 Arosa$csyear <- (Arosa$year-1)/45 ozone.fit <- nnr(thick~f1(csyear)+exp(f2(csyear))*f3(csmonth), func=list(f1(x)~list(~I(x-.5),cubic(x)), f2(x)~list(~I(x-.5)-1,cubic(x)), f3(x)~list(~sin(2*pi*x)+cos(2*pi*x)-1,lspline(x,type="sine0"))), data=Arosa[Arosa$year%%2==1,], spar="m", start=list(f1=mean(thick),f2=0,f3=sin(csmonth)), control=list(backfit=1)) x <- seq(0,1,len=50) u <- seq(0,1,len=50) ## calculate Bayesian confidence limits for all components of all functions p.ozone.fit <- intervals(ozone.fit, newdata=list(csyear=x,csmonth=u), terms=list(f1=matrix(c(1,1,1,1,1,0,0,0,1),nrow=3,byrow=TRUE), f2=matrix(c(1,1,1,0,0,1),nrow=3,byrow=TRUE), f3=matrix(c(1,1,1,1,1,0,0,0,1),nrow=3,byrow=TRUE))) plot(p.ozone.fit, x.val=x) ## End(Not run)
Provide a way to calculate approximate posterior standard deviations and fitted
values at any specified values for any combinations of elements of the spline
estimate of nonparametric functions from a slm object, based on which
approximate Bayesian confidence intervals may be constructed.
## S3 method for class 'slm' intervals(object, level=0.95, newdata=NULL, terms, pstd=TRUE, ...)## S3 method for class 'slm' intervals(object, level=0.95, newdata=NULL, terms, pstd=TRUE, ...)
object |
an object inheriting from class "slm", representing a semi-parametric nonlinear regression model fit. |
level |
set as 0.95, unused currently |
newdata |
an optional data frame on which the fitted spline estimate is to be evaluated. |
terms |
an optional vector of 0's and 1's collecting a combination of components, or a matrix of 0's and 1's
collecting several combinations of components, in a fitted ssr object. All components include bases on
the right side of |
pstd |
an optional logic value. If TRUE (the default), the posterior standard deviations are calculated. Orelse, only the predictions are calculated. Computation required for posterior standard deviations could be intensive. |
... |
other arguments, currently unused. |
The standard deviation returned is based on approximate Bayesian confidence intervals as formulated in Wang (1998).
an object of class bCI is returned, which is a list of length 2. Its first element is a matrix which contains predictions for
combinations specified by terms, and second element is a matrix which contains
corresponding posterior standard deviations.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wang, Y. (1998). Mixed-effects smoothing spline ANOVA. Journal of the Royal Statistical Society, Series B 60, 159-174.
## Not run: data(dog) # fit a SLM model with random effects for dogs dog.fit<-slm(y~group*time, rk=list(cubic(time), shrink1(group), rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time), shrink1(group))), random=list(dog=~1), data=dog) intervals(dog.fit) ## End(Not run)## Not run: data(dog) # fit a SLM model with random effects for dogs dog.fit<-slm(y~group*time, rk=list(cubic(time), shrink1(group), rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time), shrink1(group))), random=list(dog=~1), data=dog) intervals(dog.fit) ## End(Not run)
Provide a way to calculate approximate posterior standard deviations and fitted values at any specified values for any combinations of elements of the spline estimate of nonparametric functions from a snm object, based on which approximate Bayesian confidence intervals may be constructed.
## S3 method for class 'snm' intervals(object,level=0.95,newdata=NULL, terms, pstd=TRUE, ...)## S3 method for class 'snm' intervals(object,level=0.95,newdata=NULL, terms, pstd=TRUE, ...)
object |
an object inheriting from class snm, representing a semi-parametric nonlinear mixed effects model fit. |
newdata |
a data frame on which the fitted spline estimates are to be evaluated. Only those predictors, referred in 'func' of 'snm' fitting, have to be present. The variable names of the data frame should correspond to the function(s)' arguments appearing in the opion func= of snm. Default is NULL, where predictions are made at the same values used to fit the object. |
terms |
an optional vector of 0's and 1's collecting a combination of components, or a matrix of 0's and 1's collecting several combinations of components of spline estimates in a fitted snm object. Note that in the cases of multiple functions, the order of all componets is collection of base functions for all functions followed by RK's. A value "1" at a particular position means that the component at that position is collected. Default is a vector of 1's, representing the overall fit. |
pstd |
an optional logic value. If TRUE (the default), approximate posterior standard deviations are calculated. Orelse, only the predictions are calculated. Computation required for posterior standard deviations could be intensive. |
level |
a numeric value set as 0.95. |
... |
other arguments, currently unused. |
The standard deviation returned is based on approximate Bayesian confidence intervals as formulated in Ke and Wang (2001).
an object of class bCI is returned, which is a list of length 2.
Its first element is a matrix which contains predictions for
combinations specified by "terms", and second element is a matrix
which contains corresponding posterior standard deviations.
Chunlei Ke [email protected] and Yuedong Wang [email protected].
Ke, C. and Wang, Y. (2001). Semi-parametric Nonlinear Mixed Effects Models and Their Applications. JASA 96:1272-1298.
## Not run: data(horm.cort) ## extract normal dubjects cort.nor<- horm.cort[horm.cort$type=="normal",] ## fit a self-modelling model with random effects cort.fit<- snm(conc~b1+exp(b2)*f(time-alogit(b3)), func=f(u)~list(periodic(u)), fixed=list(b1~1), random=pdDiag(b1+b2+b3~1), data=cort.nor, groups= ~ID,start=mean(cort.nor$conc)) ## note the variable name of newdata intervals(cort.fit, newdata=data.frame(u=seq(0,1,len=50))) ## End(Not run)## Not run: data(horm.cort) ## extract normal dubjects cort.nor<- horm.cort[horm.cort$type=="normal",] ## fit a self-modelling model with random effects cort.fit<- snm(conc~b1+exp(b2)*f(time-alogit(b3)), func=f(u)~list(periodic(u)), fixed=list(b1~1), random=pdDiag(b1+b2+b3~1), data=cort.nor, groups= ~ID,start=mean(cort.nor$conc)) ## note the variable name of newdata intervals(cort.fit, newdata=data.frame(u=seq(0,1,len=50))) ## End(Not run)
Approximate posterior standard deviations are calculated for the spline estimate of nonparametric functions from a snr object, based on which approximate Bayesian confidence intervals may be constructed.
## S3 method for class 'snr' intervals(object, level=0.95,newdata=NULL, terms=list(), pstd=TRUE, ...)## S3 method for class 'snr' intervals(object, level=0.95,newdata=NULL, terms=list(), pstd=TRUE, ...)
object |
an object inheriting from class 'snr', representing a semi-parametric nonlinear regression model fit. |
level |
set as 0.95, unused currently |
newdata |
a data frame on which the fitted spline estimates are to be evaluated. Only those predictors, referred in 'func' of 'snr' fitting, have to be present. The variable names of the data frame should correspond to the function(s)' arguments appearing in the opion func= of snr. Default is NULL, where predictions are made at the same values used to fit the object. |
terms |
an optional named list of vectors or matrices containing 0's and 1's collecting one or several combinations of the components of spline estimates in the fitted snr object. The length and names of the list shall match those of the unknown functions appearing in the 'snr' fit object. For the case of a single function, a vector of 0's and 1's can also be accepted. A value "1" at a particular position means that the component at that position is collected. Default is a vector of 1's, representing the overall fits of all unknown functions. |
pstd |
an optional logic value. If TRUE (the default), the posterior standard deviations are calculated. Orelse, only the predictions are calculated. Computation required for posterior standard deviations could be intensive. |
... |
other arguments, currently unused. |
The standard deviation returned is based on approximate Bayesian confidence intervals as formulated in Ke (2000).
a named list of objects of class "bCI" is returned, each component of which is a list of length 2. Within each component, the first element is a matrix which contains predictions for combinations specified by "terms", and the second element is a matrix which contains corresponding posterior standard deviations.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Ke, C. (2000). Semi-parametric Nonlinear Regression and Mixed Effects Models. PhD thesis, University of California, Santa Barbara.
## Not run: data(CO2) options(contrasts=rep("contr.treatment", 2)) ## get start values co2.fit1 <- nlme(uptake~exp(a1)*(1-exp(-exp(a2)*(conc-a3))), fixed=list(a1+a2~Type*Treatment,a3~1), random=a1~1, groups=~Plant, start=c(log(30),0,0,0,log(0.01),0,0,0,50), data=CO2) M <- model.matrix(~Type*Treatment, data=CO2)[,-1] ## fit a SNR model co2.fit2 <- snr(uptake~exp(a1)*f(exp(a2)*(conc-a3)), func=f(u)~list(~I(1-exp(-u))-1,lspline(u, type="exp")), params=list(a1~M-1, a3~1, a2~Type*Treatment), start=list(params=co2.fit1$coe$fixed[c(2:4,9,5:8)]), data=CO2) p.co2.fit2<- intervals(co2.fit2, newdata=data.frame(u=seq(0,10,len=50))) ## End(Not run)## Not run: data(CO2) options(contrasts=rep("contr.treatment", 2)) ## get start values co2.fit1 <- nlme(uptake~exp(a1)*(1-exp(-exp(a2)*(conc-a3))), fixed=list(a1+a2~Type*Treatment,a3~1), random=a1~1, groups=~Plant, start=c(log(30),0,0,0,log(0.01),0,0,0,50), data=CO2) M <- model.matrix(~Type*Treatment, data=CO2)[,-1] ## fit a SNR model co2.fit2 <- snr(uptake~exp(a1)*f(exp(a2)*(conc-a3)), func=f(u)~list(~I(1-exp(-u))-1,lspline(u, type="exp")), params=list(a1~M-1, a3~1, a2~Type*Treatment), start=list(params=co2.fit1$coe$fixed[c(2:4,9,5:8)]), data=CO2) p.co2.fit2<- intervals(co2.fit2, newdata=data.frame(u=seq(0,10,len=50))) ## End(Not run)
Return a matrix evaluating reproducing kernels for the one-dimensional space usually spanned by a vector
kron(x,y=x)kron(x,y=x)
x |
a vector or a list of numerical values which spans the one-dimensional space. |
y |
a vector or a list of numerical values. Default is x. |
a matrix with the numbers of row and column equal to the length of x and y respectively. The [i, j] element is the reproducing kernel evaluated at the ith element of x and jth element of y.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
## Not run: x<-runif(10) kron(x) ## End(Not run)## Not run: x<-runif(10) kron(x) ## End(Not run)
Return a matrix evaluating reproducing kernels for some L-splines at observed points.
lspline(x,y=x, type="exp", ...)lspline(x,y=x, type="exp", ...)
x |
a numeric vector on which reproducing kerenls are evaluated. |
y |
an optional vector, specifying the second argument of reproducing kernels. Default is |
type |
a string indicating the type of L-splines. Available options are "exp", "logit","sine", "sine1", and "linSinCos". Default is "exp". |
... |
other arguments needed. |
Denote L as the differential oprator, as the null (kernel) space. The available kernels
correspond to the following L:
exp: , . , default to be 1;
logit: , ;
sine0: , ;
sine1: , ;
linSinCos: , .
a matrix with the numbers of row and column equal to the lengths of x and y respectively. The [i, j] element is the reproducing kernel evaluated at (x[i], y[j]).
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Heckman, N and Ramsay, J. O. (2000). Penalised regression with model-based penalties. To appear in Canadian Journal of Statisitcs.
## Not run: x<- seq(0,1, len=20) lspline(x, type="exp", r=1.5) ## End(Not run)## Not run: x<- seq(0,1, len=20) lspline(x, type="exp", r=1.5) ## End(Not run)
Fit a nonlinear nonparametric regression models with spline smoothing based on extended Gauss-Newton/Newton-Raphson and backfitting.
nnr(formula, func, spar="v", data=list(), start=list(),verbose=FALSE, control=list())nnr(formula, func, spar="v", data=list(), start=list(),verbose=FALSE, control=list())
formula |
a model formula, with the response on the left of a |
func |
a required formula specifying the spline components necessary to estimate the non-parametric function.
On the left of a |
spar |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. Default is "v" for GCV. |
data |
an optional data frame. |
start |
a list of vectors or expressions which input inital values for the unknown functions. If expressions,
the argument(s) inside should be the same as in |
verbose |
an optional logical numerical value. If |
control |
an optional list of control values to be used. See nnr.control for details. |
A nonlinear nonparametric model is fitted using the algorithms developed in Ke and Wang (2002).
an object of class nnr is returned, containing fitted values, fitted function values as well as
other information used to assess the estimate.
Chunlei Ke [email protected] and Yuedong Wang [email protected].
Ke, C. and Wang, Y. (2002). Nonlinear Nonparametric Regression Models. Submitted.
nnr.control, ssr, print.nnr, summary.nnr, intervals.nnr
## Not run: x<- 1:100/100 y<- exp(sin(2*pi*x))+0.3*rnorm(x) fit<- nnr(y~exp(f(x)), func=list(f(u)~list(~u, cubic(u))), start=list(0)) ## fit a generalized varying coefficient models data(Arosa) Arosa$csmonth <- (Arosa$month-0.5)/12 Arosa$csyear <- (Arosa$year-1)/45 ozone.vc.fit <- nnr(thick~f1(csyear)+exp(f2(csyear))*f3(csmonth), func=list(f1(x)~list(~I(x-.5),cubic(x)), f2(x)~list(~I(x-.5)-1,cubic(x)), f3(x)~list(~sin(2*pi*x)+cos(2*pi*x)-1,lspline(x,type="sine0"))), data=Arosa[Arosa$year%%2==1,], spar="m", start=list(f1=mean(thick),f2=0,f3=sin(csmonth)), control=list(backfit=1)) ## End(Not run)## Not run: x<- 1:100/100 y<- exp(sin(2*pi*x))+0.3*rnorm(x) fit<- nnr(y~exp(f(x)), func=list(f(u)~list(~u, cubic(u))), start=list(0)) ## fit a generalized varying coefficient models data(Arosa) Arosa$csmonth <- (Arosa$month-0.5)/12 Arosa$csyear <- (Arosa$year-1)/45 ozone.vc.fit <- nnr(thick~f1(csyear)+exp(f2(csyear))*f3(csmonth), func=list(f1(x)~list(~I(x-.5),cubic(x)), f2(x)~list(~I(x-.5)-1,cubic(x)), f3(x)~list(~sin(2*pi*x)+cos(2*pi*x)-1,lspline(x,type="sine0"))), data=Arosa[Arosa$year%%2==1,], spar="m", start=list(f1=mean(thick),f2=0,f3=sin(csmonth)), control=list(backfit=1)) ## End(Not run)
Control parameters supplied in the function call replace
the defaults to be used in calling nnr.
nnr.control(job = -1, tol = 0, max.iter = 50, init = 0, limnla = c(-10, 0), varht = NULL, theta = NULL, prec = 1e-06, maxit = 30, method = "NR", increment = 1e-04, backfit = 5, converg = "coef", toler = 0.001)nnr.control(job = -1, tol = 0, max.iter = 50, init = 0, limnla = c(-10, 0), varht = NULL, theta = NULL, prec = 1e-06, maxit = 30, method = "NR", increment = 1e-04, backfit = 5, converg = "coef", toler = 0.001)
job |
an integer representing the optimization method used to find the smoothing parameter. The options are job=-1: golden-section search on (limnla(1), limnla(2)); job=0: golden-section search with interval specified automatically; job >0: regular grid search on [limnla(1), limnla(2)] with the number of grids = job + 1. Default is -1. |
tol |
tolerance for truncation used in ‘dsidr’. Default is 0.0, which sets to square of machine precision. |
max.iter |
maximum number of iterations allowed for the Gauss-Newton/Newton-Raphson iteration. |
init |
an integer of 0 or 1 indicating if initial values are provided for theta. If init=1, initial values are provided using theta. Default is 0. |
limnla |
a vector of length 2, specifying a search range for the n times smoothing parameter on log10 scale. Default is (-10, 0). |
varht |
needed only when vmu="u", which gives the fixed variance in calculation of the UBR function. Default is NULL. |
theta |
If ‘init=1’, theta includes intial values for smoothing parameters. Default is NULL. |
prec |
precision requested for the minimum score value, where precision is the weaker of the absolute and relative precisions. Default is 1e-06. |
maxit |
maximum number of iterations allowed. Default is 30. |
method |
a character string specifying a method for iterations, "GN" for Gauss-Newton and "NR" for Newton-Raphson. Default is "GN". |
increment |
specifies a small value as increment to calcuate derivatives. Default is 1e-04. |
backfit |
an integer representing the number of backfitting iterations for multiple functions. Default is 5. |
converg |
an optional character, with possible values "coef" and "ortho", specifying the convergence criterion to be used. "coef" uses the change of estimate of parameters and functions to assess convergence, and "ortho" uses a criterion similar to the relative offset used in nls. Default is "coef". |
toler |
tolerance for convergence of the algorithm. Default is 0.001. |
returned is a list includes all re-seted control parameters.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
## Not run: ## use Newton-Raphson nnr.control(method="NR") ## End(Not run)## Not run: ## use Newton-Raphson nnr.control(method="NR") ## End(Not run)
The 'paramecium' data frame has 25 rows and 2 columns of data from an experiment that grow paramecium caudatum
data(paramecium)data(paramecium)
The data frame contains the following columns:
day a numeric vector of days since the start of the experiment
density a numeric vector of mean number of individuals in 0.5 ml of medium of four different cultures started simultaneously
Gause, G.F. (1934). The Struggle for Existence. Baltimore, MD: Williams & Wilkins.
Neal, D. (2004). Introduction to Population Biology. Cambridge University Press.
Return a matrix evaluating reproducing kernels for periodic polynomial splines at observed points.
periodic(s, t=s, order=2)periodic(s, t=s, order=2)
s |
a numeric vector. |
t |
an optional vector. Default is the same as s. |
order |
an optional integer sepcifying the order of the polynomial spline. Default is 2 for the periodic cubic spline. |
The general formula of the reproducing kernel is sum of an infinite series, which is approximated by taking the first 50 terms. For the case of order=2, the close form is available and used.
a matrix with the numbers of row and column equal to the lengths of s and t respectively. The [i, j] element is the reproducing kernel evaluated at (s[i], t[j]).
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Gu, C. (2001). Smoothing Spline ANOVA Modes. Chapman and Hall.
## Not run: x<- seq(0, 1, len=100) periodic(x, order=3) ## End(Not run)## Not run: x<- seq(0, 1, len=100) periodic(x, order=3) ## End(Not run)
Create trellis plots of a nonparametric function fit together with its (approximate) 95% Bayesian confidence intervals from a ssr/slm/snr/snm object.
## S3 method for class 'bCI' plot(x, x.val=NULL, type.name=NULL, ...)## S3 method for class 'bCI' plot(x, x.val=NULL, type.name=NULL, ...)
x |
an object of class "bCI" containing point evaluation of the unknown function and/or corresponding posterior standard devaitions. |
x.val |
an optional vector representing values of argument based on which the function is to evaluate. |
type.name |
an optional character vector specifying the names of fits. |
... |
options suitable for xyplot. |
This function is to visualize a spline fit by use of trellis graphic facility with Bayesian confidence intervals superposed. Multi-panel plots, based on xyplot, are suitable for SS ANOVA decomposition of a spline estimate.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
predict.ssr, intervals.slm,
intervals.snr, intervals.snm
## Not run: x<- seq(0, 1, len=100) y<- 2*sin(2*pi*x)+rnorm(x)*0.5 fit<- ssr(y~x, cubic(x)) p.fit<- predict(fit) plot(p.fit) plot(p.fit,type.name="fit") ## End(Not run)## Not run: x<- seq(0, 1, len=100) y<- 2*sin(2*pi*x)+rnorm(x)*0.5 fit<- ssr(y~x, cubic(x)) p.fit<- predict(fit) plot(p.fit) plot(p.fit,type.name="fit") ## End(Not run)
Creates a set of plots suitable for assessing a fitted smoothing spline model of class ssr.
## S3 method for class 'ssr' plot(x, ask=FALSE, ...)## S3 method for class 'ssr' plot(x, ask=FALSE, ...)
x |
a |
ask |
if TRUE, plot.ssr operates in interactive mode. |
... |
Other options used for plot, currently inactive. |
This function is a method for the generic function plot for class ssr.
It can be invoked by calling plot for an object of the appropriate class,
or directly by calling plot.ssr regardless of the class of the object.
An appropriate x-y plot is produced to display diagnostic plots. These can be one or all of the following choices:
Estimate of function with CIs
Residuals against Fitted values
Response against Fitted values
Normal QQplot of Residuals
The first plot of estimate of function with CIs is only useful for univariate smoothing spline fits.
When ask=TRUE, rather than produce each plot sequentially, plot.ssr displays a menu listing all the plots that can be produced. If the menu is not desired but a pause between plots is still wanted one must set par(ask=TRUE) before invoking this command with argument ask=FALSE.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
plot, ssr, predict.ssr
## Not run: library(MASS) ## Not run: fit1<- ssr(accel~times, data=mcycle, scale=TRUE, rk=cubic(times)) ## Not run: plot(fit1,ask=TRUE)## Not run: library(MASS) ## Not run: fit1<- ssr(accel~times, data=mcycle, scale=TRUE, rk=cubic(times)) ## Not run: plot(fit1,ask=TRUE)
Return a matrix evaluating reproducing kernels for polynomial splines at observed points.
linear(s, t=s) cubic(s, t=s) quintic(s, t=s) septic(s, t=s)linear(s, t=s) cubic(s, t=s) quintic(s, t=s) septic(s, t=s)
s |
a vector of values in [0, 1], at which the kernels are evaluated. |
t |
an optional vector in [0, 1]. Default is the same as s. |
The reproducing kernels implemented in these functions are based on Bernoulli functions with domain [0, 1].
a matrix with the numbers of row and column equal to the lengths of s and t respectively. The [i, j] element is the reproducing kernel of linear, cubic, quintic, or septic spline evaluated at (s[i], t[j]).
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
ssr, linear2, cubic2,
quintic2, septic2
## Not run: x<-seq(0, 1, len=10) cubic(x) ## End(Not run)## Not run: x<-seq(0, 1, len=10) cubic(x) ## End(Not run)
Return a matrix evaluating reproducing kernels for polynomial splines at observed points.
linear2(s, t=s) cubic2(s, t=s) quintic2(s, t=s) septic2(s, t=s)linear2(s, t=s) cubic2(s, t=s) quintic2(s, t=s) septic2(s, t=s)
s |
a vector of non-negative values, at which the kernels are evaluated. |
t |
an optional non-negative vector. Default is the same as s. |
The reproducing kernels implemented in these functions are based on Green functions. The domain is [0, T], where T is a given positive number.
a matrix with the numbers of row and column equal to the length of s and t respectively. The [i, j] element is the reproducing kernel of linear, cubic, quintic, or septic spline evaluated at (s[i], t[j]).
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
ssr, linear, cubic,
quintic, septic
## Not run: x<- seq(0, 5, len=10) linear2(x) ## End(Not run)## Not run: x<- seq(0, 5, len=10) linear2(x) ## End(Not run)
Predicted Values on different levels of random effects with the spline fit as part of fixed effects
## S3 method for class 'slm' predict(object, newdata=NULL, ...)## S3 method for class 'slm' predict(object, newdata=NULL, ...)
object |
an object inheriting from class |
newdata |
a data frame containing the values at which predictions are required. Only those predictors, referred to in the right side of the formula in the object, need to be present by name in newdata. Default is NULL, where predictions are made at the same values used to compute the object. |
... |
other arguments, but currently unused. |
returned is a data.frame with columns given by the predictions at different levels and the grouping factors. Note that the smooth part of the spline fit is regarded as fixed.
Chunlei Ke [email protected] and Yuedong Wang [email protected].
Wang, Y. (1998) Mixed Effects Smoothing Spline ANOVA. JRSS, Series B, 60:159–174.
Pinherio, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-Plus. Springer.
## Not run: data(dog) dog.fit<-slm(y~group*time, rk=list(cubic(time), shrink1(group), rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time), shrink1(group))), random=list(dog=~1), data=dog) predict(dog.fit) ## End(Not run)## Not run: data(dog) dog.fit<-slm(y~group*time, rk=list(cubic(time), shrink1(group), rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time), shrink1(group))), random=list(dog=~1), data=dog) predict(dog.fit) ## End(Not run)
The predictions are obtained on a semiparametric nonlinear mixed effects model object by replacing the unknown functuons and the unknown parameters with their estimates. Of note, only a population level of predictions is available.
## S3 method for class 'snm' predict(object, newdata, ...)## S3 method for class 'snm' predict(object, newdata, ...)
object |
a fitted |
newdata |
a data frame containing the values at which predictions are required. Default are data used to fit the object. |
... |
other arguments, but currently unused. |
This function is a method for the generic function predict for class snm.
a vector of prediction values, obtained by evaluating the model in the frame newdata
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Ke, C. and Wang, Y. (2001). Semi-parametric Nonlinear Mixed Effects Models and Their Applications. JASA.
The predictions on a semiparametric nonlinear regression model object are obtained by substituting the unknwon functions together with unknown parameters with their estimates and evaluating the regression functional based on provided or default covariate values.
## S3 method for class 'snr' predict(object, newdata, ...)## S3 method for class 'snr' predict(object, newdata, ...)
object |
a fitted |
newdata |
a data frame containing the values at which predictions are required. Default are NULL, where data used to produce the fit are to be taken. |
... |
other arguments, but currently unused. |
This function is a method for the generic function predict for class snr
a vector of prediction values, obtained by evaluating the model in the frame newdata.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Ke, C. (2000). Semi-parametric Nonlinear Regression and Mixed Effects Models. PhD thesis, University of California, Santa Barbara.
Provide a way to calculate predictions at any specified values for any combinations of elements in the fitted model. Posterior standard deviations may be used to construct Bayesian confidence intervals.
## S3 method for class 'ssr' predict(object, newdata=NULL, terms, pstd=TRUE, ...)## S3 method for class 'ssr' predict(object, newdata=NULL, terms, pstd=TRUE, ...)
object |
a fitted |
newdata |
an optional data frame containing the values at which predictions are required. Default is NULL, where predictions are made at the same values used to compute the object. Note that if scale=T, the newdata is on the original scale before transformation. |
terms |
an optional vector of 0's and 1's collecting a combination of components, or a matrix of 0's and 1's collecting several combinations of components, in a fitted ssr object. All components include bases on the right side of |
pstd |
an optional logic value. If TRUE (the default), the posterior standard deviations are calculated. Otherwise, only the predictions are calculated. Computation required for posterior standard deviations could be intensive. |
... |
other arguments, but currently unused. |
This function is a method for the generic function predict for class ssr.
It can be used to construct Bayesian confidence intervals for any combinations
of components in the fitted model.
an object of class bCI is returned, which is a list of length 2. Its first element is a matrix which contains predictions for combinations specified by terms, and second element is a matrix which contains corresponding posterior standard deviations.
Chunlei Ke [email protected] and Yuedong Wang [email protected].
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
## Not run: data(acid) # tp.pseudo calculates the pseudo kernel acid.fit<- ssr( ph ~ t1 + x1 + x2, rk = list(tp.pseudo(t1), tp.pseudo(list(x1, x2))), spar = "m", data=acid) # extract the main effect of t1 grid <- seq(min(acid$t1),max(acid$t1),length=100) p <- predict(acid.fit,data.frame(t1=grid,x1=0,x2=0), terms=c(0,1,0,0,1,0)) # extract the main effect of (x1,x2) grid <- expand.grid(x1=seq(min(acid$x1),max(acid$x1),length=20), x2=seq(min(acid$x2),max(acid$x2),length=20)) p <- predict(acid.fit,data.frame(t1=0,x1=grid$x1,x2=grid$x2), terms=c(0,0,1,1,0,1),pstd=FALSE) ## End(Not run)## Not run: data(acid) # tp.pseudo calculates the pseudo kernel acid.fit<- ssr( ph ~ t1 + x1 + x2, rk = list(tp.pseudo(t1), tp.pseudo(list(x1, x2))), spar = "m", data=acid) # extract the main effect of t1 grid <- seq(min(acid$t1),max(acid$t1),length=100) p <- predict(acid.fit,data.frame(t1=grid,x1=0,x2=0), terms=c(0,1,0,0,1,0)) # extract the main effect of (x1,x2) grid <- expand.grid(x1=seq(min(acid$x1),max(acid$x1),length=20), x2=seq(min(acid$x2),max(acid$x2),length=20)) p <- predict(acid.fit,data.frame(t1=0,x1=grid$x1,x2=grid$x2), terms=c(0,0,1,1,0,1),pstd=FALSE) ## End(Not run)
Print the arguments of a 'nnr' object.
## S3 method for class 'nnr' print(x, ...)## S3 method for class 'nnr' print(x, ...)
x |
a |
... |
unused argument |
This is a method for the function print for objects
inheriting from class nnr.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a slm object.
## S3 method for class 'slm' print(x, ...)## S3 method for class 'slm' print(x, ...)
x |
a |
... |
unused argument |
This is a method for the function print for objects
inheriting from class slm.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a 'snm' object.
## S3 method for class 'snm' print(x, ...)## S3 method for class 'snm' print(x, ...)
x |
a |
... |
unused argument |
This is a method for the function print for objects
inheriting from class ‘snm’.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a snr object.
## S3 method for class 'snr' print(x, ...)## S3 method for class 'snr' print(x, ...)
x |
a |
... |
unused argument |
This is a method for the function print for objects
inheriting from class snr.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a ssr object.
## S3 method for class 'ssr' print(x, ...)## S3 method for class 'ssr' print(x, ...)
x |
a |
... |
unused argument |
This is a method for the function print for objects
inheriting from class ssr.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a summary.nnr object
## S3 method for class 'summary.nnr' print(x, ...)## S3 method for class 'summary.nnr' print(x, ...)
x |
an object of class |
... |
unused argument |
This is a method for the function print for objects
inheriting from class summary.nnr.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a summary.slm object
## S3 method for class 'summary.slm' print(x, ...)## S3 method for class 'summary.slm' print(x, ...)
x |
an object of class |
... |
unused argument |
This is a method for the function print for objects
inheriting from class summary.slm.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a summary.snm object
## S3 method for class 'summary.snm' print(x, ...)## S3 method for class 'summary.snm' print(x, ...)
x |
an object of class |
... |
unused argument |
This is a method for the function print for objects
inheriting from class summary.snm.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a summary.snr object
## S3 method for class 'summary.snr' print(x, ...)## S3 method for class 'summary.snr' print(x, ...)
x |
an object of class |
... |
unused argument |
This is a method for the function print for objects
inheriting from class summary.snr.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Print the arguments of a summary.ssr object
## S3 method for class 'summary.ssr' print(x, ...)## S3 method for class 'summary.ssr' print(x, ...)
x |
an object of class |
... |
unused argument. |
This is a method for the function print for objects
inheriting from class summary.ssr.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Return a matix as product of reproducing kernels
rk.prod(x, ...)rk.prod(x, ...)
x |
a matrix evaluating a reproducing kernel, or a vector. |
... |
optional lists of matrices evaluating reproducing kernels or vectors. All matrics must have the same dimensions. All vectors must have the same length. The length of each vector must equal to the column and row numbers of each matrix. |
The product of reproducing kernels is agian a reproducing kernel. In SS ANOVA, product of reproduing kernels is often used to model interaction spline terms.
a matrix as the product of reproducing kernels. If one argument is a vector, a kron
kernel is constructed first.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Gu, C. and Wahba, G. (1993a). Smoothing Spline ANOVA with component-wise Bayesian confidence intervals. Journal of Computational and Graphical Statistics 55, 353–368.
Gu, C. and Wahba, G. (1993b). Semiparametric analysis of variance with tensor product thin plate splines. JRSS B 55, 353–368.
## Not run: x1<- 1:10/10 x2<- runif(10) rk.prod(cubic(x1), periodic(x2)) ## End(Not run)## Not run: x1<- 1:10/10 x2<- runif(10) rk.prod(cubic(x1), periodic(x2)) ## End(Not run)
The 'seizure' data frame has 60,000 rows and 3 columns of data from an IEEG time series
data(seizure)data(seizure)
The baseline segment contains 5-minute IEEG signal extracted at least four hours before the seizure's onset. The preseizure segment contains 5-minute IEEG signal right before a seizure's clinical onset. The sampling rate of the IEEG signal is 200 observations per second. Therefore there are 60,000 time points in each segment.
The data frame contains the following columns:
t a numeric vector of the observation number
base a numeric vector of the baseline segment
preseizure a numeric vector of the segment right before a seizure
D'Alessandro, M., Vachtsevanos, G., Esteller, R., Echauz, J. and Litt, B. (2001). A Generic Approach to Selecting the Optimal Feature for Epileptic Seizure Prediction. IEEE International Meeting of the Engineering in Medicine and Biology Society.
Qin, L. and Wang, Y. (2008), Nonparametric Spectral Analysis With Applications to Seizure Characterization Using EEG Time Series. Annals of Applied Statistics 2, 1432-1451.
Return a matrix evaluating reproducing kernels for the discrete shrinkage towards zero or the mean estimate
shrink0(x, y=x) shrink1(x, y=x)shrink0(x, y=x) shrink1(x, y=x)
x |
a vector of numerical values or factor indicating different levels. |
y |
a vector of numerical values or factor indicating different levels. Default is x. |
a matrix with the numbers of row and column equal to the length of x and y respectively.
The element is the reproducing kernel evaluated at the ith element of x and jth element of y.
shink0 shrinks towards zero, and shrink1 shinks towards the mean.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
## Not run: x<-rep(1:10,2) shrink1(x) ## End(Not run)## Not run: x<-rep(1:10,2) shrink1(x) ## End(Not run)
Return a matrix evaluating reproducing kernels for periodic L-splines at observed points.
sine4p(s, t=s)sine4p(s, t=s)
s |
a numeric vector. |
t |
an optional vector. Default is the same as s. |
The general formula of the reproducing kernel is provided in Gu (2001). The close form is not available, so an approximate based on the first 50 terms of the series is used.
a matrix with the numbers of row and column equal to the lengths of s and t respectively. The [i, j] element is the reproducing kernel evaluated at (s[i], t[j]).
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Gu, C. (2001). Smoothing Spline ANOVA Modes. Chapman and Hall.
## Not run: x<- seq(0, 1, len=100) sine4p(x) ## End(Not run)## Not run: x<- seq(0, 1, len=100) sine4p(x) ## End(Not run)
Returns an object of class slm that represents a
semi-parametric linear mixed effects model fit.
slm(formula, rk, data=list(), random, weights=NULL, correlation=NULL, control=list(apVar=FALSE))slm(formula, rk, data=list(), random, weights=NULL, correlation=NULL, control=list(apVar=FALSE))
formula |
a formula object, with the response on the left of a |
rk |
a list of expressions that specify the reproducing kernels of the spline function(s), |
data |
An optional data frame containing the variables appearing in |
random |
A named list of formulae, lists of formulae, or pdMat objects, which defines nested random effects structures. See help file of lme for more details. |
weights |
An optional |
correlation |
An optional |
control |
an optional list of any applicable control parameters from |
This generic function fits a semi-parametric linear mixed effects model (or non-parametric mixed effects models) as described in Wang (1998), but allowing for general random and correlation structures. Because the connection to a linear mixed effects model is adopted, only GML is available to choose smoothing parameters.
An object of class slm is returned. Generic functions such as print, summary, predict and intervals have
methods to show the results of the fit.
Note: output from earlier versions of slm shows incorrect smoothing spline parameters for SSANOVA, which is corrected in this version.
Chunlei Ke [email protected] and Yuedong Wang [email protected].
Wang, Y. (1998) Mixed Effects Smoothing Spline ANOVA. JRSS, Series B, 60:159–174.
Pinherio, J. C. and Bates, D. M. (2000) Mixed-effects Models in S and S-Plus. Springer.
ssr, predict.slm, intervals.slm,
print.slm,summary.slm
## Not run: ## SS ANOVA is used to model "time" and "group" ## with random intercept for "dog". data(dog) dog.fit<- slm(y~group*time, rk=list(cubic(time), shrink1(group), rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time), shrink1(group))), random=list(dog=~1), data=dog) ## End(Not run)## Not run: ## SS ANOVA is used to model "time" and "group" ## with random intercept for "dog". data(dog) dog.fit<- slm(y~group*time, rk=list(cubic(time), shrink1(group), rk.prod(kron(time-0.5),shrink1(group)),rk.prod(cubic(time), shrink1(group))), random=list(dog=~1), data=dog) ## End(Not run)
This generic function fits a semi-paramteric nonlinear mixed-effects model in the formulation described in Ke and Wang (2001). Current version only allows linear dependence on non-parametric functions.
snm(formula, func, data=list(), fixed, random=fixed, groups, start, spar="v", verbose=FALSE, method="REML", control=NULL, correlation=NULL, weights=NULL)snm(formula, func, data=list(), fixed, random=fixed, groups, start, spar="v", verbose=FALSE, method="REML", control=NULL, correlation=NULL, weights=NULL)
formula |
a formula object, with the response on the left of a ~ operator, and an expression of variables, parameters and non-parametric functions on the right. |
func |
a list of spline formulae each specifying the spline components necessary to
estimate each non-parametric function. On the left of a |
fixed |
a two-sided formula specifying models for the fixed effects.
The syntax of |
start |
a numeric vector, the same length as the number of fixed effects, supplying starting values for the fixed effects. |
spar |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. Default is "v" for GCV. |
data |
An optional data frame containing the variables appearing in |
random |
an optional random effects structure specifying models for the random effects.
The same syntax of |
groups |
an optional one-sided formula of the form ~g1 (single level) or ~g1/.../gQ (multiple levels of nesting), specifying the partitions of the data over which the random effects vary. g1,...,gQ must evaluate to factors in data. See nlme for details. |
verbose |
an optional logical numerical value. If |
method |
a character string. If 'REML' the model is fit by maximizing the restricted log-likelihood. If 'ML' the log-likelihood is maximized. Default is 'REML. |
control |
a list of parameters to control the performance of the algorithm. |
correlation |
an optional |
weights |
an optional |
an object of class snm is returned, representing a semi-parametric nonlinear
mixed effects model fit. Generic functions such as print, summary, predict and
intervals have methods to show the results of the fit.
Chunlei Ke [email protected] and Yuedong Wang [email protected].
Ke, C. and Wang, Y. (2001). Semi-parametric Nonlinear Mixed Effects Models and Their Applications. JASA 96:1272-1298.
Pinheiro, J.C. and Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. Springer.
predict.snm, intervals.snm, snm.control,
print.snm,summary.snm
## Not run: data(CO2) options(contrasts=rep("contr.treatment", 2)) co2.fit1 <- nlme(uptake~exp(a1)*(1-exp(-exp(a2)*(conc-a3))), fixed=list(a1+a2~Type*Treatment,a3~1), random=a1~1, groups=~Plant, start=c(log(30),0,0,0,log(0.01),0,0,0,50), data=CO2) M <- model.matrix(~Type*Treatment, data=CO2)[,-1] co2.fit2 <- snm(uptake~exp(a1)*f(exp(a2)*(conc-a3)), func=f(u)~list(~I(1-exp(-u))-1,lspline(u, type="exp")), fixed=list(a1~M-1,a3~1,a2~Type*Treatment), random=list(a1~1), group=~Plant, verbose=TRUE, start=co2.fit1$coe$fixed[c(2:4,9,5:8)], data=CO2) ## End(Not run)## Not run: data(CO2) options(contrasts=rep("contr.treatment", 2)) co2.fit1 <- nlme(uptake~exp(a1)*(1-exp(-exp(a2)*(conc-a3))), fixed=list(a1+a2~Type*Treatment,a3~1), random=a1~1, groups=~Plant, start=c(log(30),0,0,0,log(0.01),0,0,0,50), data=CO2) M <- model.matrix(~Type*Treatment, data=CO2)[,-1] co2.fit2 <- snm(uptake~exp(a1)*f(exp(a2)*(conc-a3)), func=f(u)~list(~I(1-exp(-u))-1,lspline(u, type="exp")), fixed=list(a1~M-1,a3~1,a2~Type*Treatment), random=list(a1~1), group=~Plant, verbose=TRUE, start=co2.fit1$coe$fixed[c(2:4,9,5:8)], data=CO2) ## End(Not run)
Control parameters supplied in the function call replace
the defaults to be used in calling snm.
snm.control(rkpk.control, nlme.control, prec.out=0.0005, maxit.out=30, converg="COEF", incDelta)snm.control(rkpk.control, nlme.control, prec.out=0.0005, maxit.out=30, converg="COEF", incDelta)
rkpk.control |
a optional list of control parameters for dsidr or dmudr to estimate the unknown functions. |
nlme.control |
a list of control parameters for the nonlinear regression step,
the same as nlmeControl. Default is |
prec.out |
tolerance for convergence criterion. Default is 0.0005. |
maxit.out |
maximum number of iterations for the algorithm. Default is 30. |
converg |
an optional character, with possible values "COEF" and "PRSS", specifying the convergence criterion to be used. "COEF" uses the change of estimate of parameters and functions to assess convergence, and "PRSS" uses penalized residual sums of squares. Default is "COEF". |
incDelta |
specifies a small value as increment to calcuate derivatives. Default is 0.001. |
Returned is a list includes all re-seted control parameters.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
## Not run: ## set maximum iteration to be 50 snm.control(maxit.out=50) ## End(Not run)## Not run: ## set maximum iteration to be 50 snm.control(maxit.out=50) ## End(Not run)
This generic function fits a Semi-parametric Nonlinear Regression Model as formulated in Ke (2000).
snr(formula, func, params, data, start, spar = "v", verbose = FALSE, control = list(), correlation = NULL, weights = NULL)snr(formula, func, params, data, start, spar = "v", verbose = FALSE, control = list(), correlation = NULL, weights = NULL)
formula |
a model formula, with the response on the left of a |
func |
a list of spline formulae each specifying the spline components
necessary to estimate each non-parametric function. On the left
of a |
params |
a two-sided formula specifying models for the parameters.
The syntax of |
data |
an optional data frame containing the variables named in model, params, correlation and weights. By default the variables are taken from the environment from which snr is called. |
start |
a numeric list with two components: "params=", a vector of the size of the length of the unknown parameters, providing inital values for the paramters, and "f=" a list of vectors or expressions which input inital values for the unknown functions. If the unknown functions appear linear in the model, the intial values then are not necessary. |
spar |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. Default is "v" for GCV. |
verbose |
an optional logical numerical value. If |
control |
an optional list of control parameters. See |
correlation |
an optional |
weights |
an optional |
A semi-parametric regression model is generalization of self-modeling regression, nonlinear regression and smoothing spline models, including as special cases (nonlinear) partial spline models, varying coefficients models, PP regression and some other popular models. 'snr' is implemented with an alternate iterative procedures with smoothing splines to estimate the unknown functions and general nonlinear regression to estimate parameters.
An object of class snr is returned, representing a semi-parametric
nonlinear regression fit. Generic functions such as print, summary,
intervals and predict have methods to show the results of the fit.
Chunlei Ke [email protected] and Yuedong Wang [email protected].
Ke, C. (2000). Semi-parametric Nonlinear Regression and Mixed Effects Models. PhD thesis, University of California, Santa Barbara.
Pinheiro, J.C. and Bates, D. M. (2000). Mixed-Effects Models in S and S-PLUS. Springer.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
intervals.snr, predict.snr, snr.control
## Not run: data(CO2) options(contrasts=rep("contr.treatment", 2)) co2.fit1 <- nlme(uptake~exp(a1)*(1-exp(-exp(a2)*(conc-a3))), fixed=list(a1+a2~Type*Treatment,a3~1), random=a1~1, groups=~Plant, start=c(log(30),0,0,0,log(0.01),0,0,0,50), data=CO2) M <- model.matrix(~Type*Treatment, data=CO2)[,-1] ## fit a SNR model co2.fit2 <- snr(uptake~exp(a1)*f(exp(a2)*(conc-a3)), func=f(u)~list(~I(1-exp(-u))-1,lspline(u, type="exp")), params=list(a1~M-1, a3~1, a2~Type*Treatment), start=list(params=co2.fit1$coe$fixed[c(2:4,9,5:8)]), data=CO2) ## End(Not run)## Not run: data(CO2) options(contrasts=rep("contr.treatment", 2)) co2.fit1 <- nlme(uptake~exp(a1)*(1-exp(-exp(a2)*(conc-a3))), fixed=list(a1+a2~Type*Treatment,a3~1), random=a1~1, groups=~Plant, start=c(log(30),0,0,0,log(0.01),0,0,0,50), data=CO2) M <- model.matrix(~Type*Treatment, data=CO2)[,-1] ## fit a SNR model co2.fit2 <- snr(uptake~exp(a1)*f(exp(a2)*(conc-a3)), func=f(u)~list(~I(1-exp(-u))-1,lspline(u, type="exp")), params=list(a1~M-1, a3~1, a2~Type*Treatment), start=list(params=co2.fit1$coe$fixed[c(2:4,9,5:8)]), data=CO2) ## End(Not run)
Control parameters supplied in the function call replace
the defaults to be used in calling snr.
snr.control(rkpk.control = list(job = -1, tol = 0, init = 0, limnla = c(-10, 0), varht = NULL, theta = NULL, prec = 1e-06, maxit = 30), nls.control = list(returnObject = TRUE, maxIter = 5), incDelta = 0.001, prec.out = 0.001, maxit.out = 30, converg = "COEF", method = "GN", backfit = 5)snr.control(rkpk.control = list(job = -1, tol = 0, init = 0, limnla = c(-10, 0), varht = NULL, theta = NULL, prec = 1e-06, maxit = 30), nls.control = list(returnObject = TRUE, maxIter = 5), incDelta = 0.001, prec.out = 0.001, maxit.out = 30, converg = "COEF", method = "GN", backfit = 5)
rkpk.control |
a optional list of control parameters for dsidr or dmudr to estimate the unknown functions. Default is "list(job = -1, tol = 0, init = 0, limnla = c(-10, 0), varht = NULL, theta = NULL, prec = 1e-06, maxit = 30)". |
nls.control |
a list of control parameters for the nonlinear regression step, the same as gnlsControl. Default is "list(returnObject = TRUE, maxIter = 5). |
incDelta |
the incremental value to be used to calculate derivatives for the unknown functions. Default is 0.001 |
prec.out |
tolerance for convergence criterion. Default is 0.0001. |
maxit.out |
maximum number of iterations for the algorithm. Default is 30. |
converg |
an optional character, with possible values |
method |
an optional string of value either |
backfit |
an integer to set the number of backfitting iterations inside the loop. Default is 5 |
.
returned is a list includes all re-seted control parameters.
Chunlei Ke [email protected] and Yuedong Wang [email protected].
## use Newton-Raphson iteration and only a single backfitting ## Not run: snr.control(method="NR", backfit=1) ## End(Not run)## use Newton-Raphson iteration and only a single backfitting ## Not run: snr.control(method="NR", backfit=1) ## End(Not run)
Return a matrix evaluating reproducing kernels for splines on a sphere.
sphere(x, y=x, order=2)sphere(x, y=x, order=2)
x |
a matrix of two columns or a list of two components, representing observed latitude and longitude respectively. |
y |
a matrix of two columns or a list of two components, representing latitude and longitude respectively. Default is the same as x. |
order |
an optional integer sepcifying the order of the spherical spline. Available are 2, 3, 4, 5 and 6, with a default 2. |
The kernel for sperical splines is a series inconvenient to compute. This pseudo kernel is based on a topological equivalence as described in Wahba (1981), for which cases the closed form can be derived.
a matrix with the numbers of row and column equal to the lengths of x and y respectively.
The [i, j] element is the reproducing kernel evaluated at
(or for lists).
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1981). Spline Interprolation and Smoothing on the Sphere. SIAM J. Sci. Stat.Comput., Vol. 2, No. 1, March 1981.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
## Not run: x<- seq(0, 2*pi, len=10) y<- seq(-pi/2, pi/2, len=10) s.ker<- sphere(cbind(x, y), order=3) ## End(Not run)## Not run: x<- seq(0, 2*pi, len=10) y<- seq(-pi/2, pi/2, len=10) s.ker<- sphere(cbind(x, y), order=3) ## End(Not run)
Returns an object of class ssr which is a general/generalized/correlated smoothing spline fit.
ssr(formula, rk, data = list(), subset, weights = NULL, correlation = NULL, family = "gaussian", scale = FALSE, spar = "v", varht = NULL, limnla = c(-10, 3), control = list())ssr(formula, rk, data = list(), subset, weights = NULL, correlation = NULL, family = "gaussian", scale = FALSE, spar = "v", varht = NULL, limnla = c(-10, 3), control = list())
formula |
a |
rk |
a list of expressions specifying reproducing kernels |
data |
a data frame containing the variables occurring in the formula and the |
subset |
an optional expression indicating which subset of the rows of the data should be used in the fit. This can be a logical vector (which is replicated to have length equal to the number of observations), a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default. |
weights |
a vector or a matrix specifying known weights for weighted smoothing, or a varFunc structure specifying a variance function structure. Its length, if a vector, or its number of columns and rows, if a matrix, must be equal to the length of responses. See documentations of nlme for availabe varFunc structures. The default is that all weights are equal. |
correlation |
a corStruct object describing the correlation structure for random errors. See documentations of corClasses for availabe correlation structures. Default is NULL for no correlation. |
family |
an optional string specifying the distribution family. Families supported are "binary", "binomial", "poisson", "gamma" and "gaussian" for Bernoulli, binomial, poisson, gamma and Gaussian distributions respectively. Default is "gaussian". |
scale |
an optional logical value. If ‘TRUE’, all covariates appearing in "rk" will be scaled into interval [0, 1]. This transformation will affect predict.ssr. Default is FALSE. |
spar |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent
GCV, GML and UBR respectively. "u |
varht |
needed only when 'u' is chosen for 'method', which gives the fixed variance in calculation of the UBR function. Default is NULL for ‘family="gaussian"’ and 1 for all other families. |
limnla |
a vector of length one or two, specifying a search range for log10(n*lambda), where lambda is the smoothing parameter and n is the sample size. If it is a single value, the smoothing parameter will be fixed at this value. This option is only applicable to spline smoothing with a single smoothing parameter. |
control |
a list of iteration and algorithmic constants. See ssr.control for details and default values. |
We adopt notations in Wahba (1990) for the general spline and smoothing spline ANOVA models.
Specifically, the functional relationship between the predictor and independent variable is unknown
and is assumed to be in a reproducing kernel Hilbert space H. H is decomposed into and
, where the null space is a finite dimensional space spanned by
bases specified at the right side of in formula, and
,... , are reproducing kernel Hilbert spaces with reproducing kernel specified in the list rk.
The function is estimated from weighted penalized least square. ssr can be used to fit the general spline and smoothing spline ANOVA models (Wahba, 1990), generalized spline models (Wang, 1997) and correlated spline models (Wang, 1998). ssr can also fit partial spline model with additional parametric terms specified in the formula (Wahba, 1990).
ssr could be slow and memory intensive, especially for large sample size and/or when p is large.
For fitting a cubic spline with CV or GCV estimate of the smoothing parameter,
the S-Plus function smooth.spline is more efficient.
Components can be extracted using extractor functions predict, deviance, residuals, and summary. The output can be modified using update.
an object of class ssr is returned. See ssr.object for details.
Note: output from earlier versions of ssr shows incorrect smoothing spline parameters for SSANOVA, which is corrected in this version.
Yuedong Wang [email protected] and Chunlei Ke [email protected]
Gu, C. (1989). RKPACK and its applications: Fitting smoothing spline models. Proceedings of the Statistical Computing Section, ASA, 42-51.
Gu, C. (2002). Smoothing Spline ANOVA. Spinger, New York.
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Wang, Y. (1995). GRKPACK: Fitting Smoothing Spline ANOVA Models for Exponential Families. Communications in Statistics: Simulation and Computation, 24: 1037-1059.
Wang, Y. (1998) Smoothing Spline Models with Correlated Random Errors. JASA, 93:341-348.
Ke, C. and Wang, Y. (2002) ASSIST: A Suite of S-plus functions Implementing Spline smoothing Techniques. Available at: https://yuedong.faculty.pstat.ucsb.edu/
deviance.ssr, hat.ssr, plot.ssr, ssr.control,
predict.ssr, print.ssr,
ssr.object, summary.ssr, smooth.spline.
## Not run: library(MASS) # fitting a cubic spline fit1<- ssr(accel~times, data=mcycle, scale=T, rk=cubic(times)) summary(fit1) # using GML to choose the smoothing parameter fit2<- update(fit1, spar="m") data(acid) ## fit an additive thin plate spline acid.fit<- ssr( ph ~ t1 + x1 + x2, rk = list(tp(t1), tp(list(x1, x2))), data = acid, spar = "m", scale = FALSE) acid.fit ## End(Not run)## Not run: library(MASS) # fitting a cubic spline fit1<- ssr(accel~times, data=mcycle, scale=T, rk=cubic(times)) summary(fit1) # using GML to choose the smoothing parameter fit2<- update(fit1, spar="m") data(acid) ## fit an additive thin plate spline acid.fit<- ssr( ph ~ t1 + x1 + x2, rk = list(tp(t1), tp(list(x1, x2))), data = acid, spar = "m", scale = FALSE) acid.fit ## End(Not run)
The values supplied in the function call replace the defaults and a list with all possible arguments is returned. The returned list is used as the ‘control’ argument to the ‘ssr’ function.
ssr.control(job=-1, tol=0.0, init=0.0, theta, prec=1e-06, maxit=30, tol.g=0.0, prec.g=1e-06, maxit.g=30)ssr.control(job=-1, tol=0.0, init=0.0, theta, prec=1e-06, maxit=30, tol.g=0.0, prec.g=1e-06, maxit.g=30)
job |
an integer representing the optimization method used to find the smoothing parameter.
The options are job=-1: golden-section search on (limnla(1), limnla(2));
job=0: golden-section search with interval specified automatically;
job >0: regular grid search on |
tol |
tolerance for truncation used in ‘dsidr’ or ‘dmudr’. Default is 0.0 which sets to square of machine precision. |
init |
init=0 means no initial values are provided for smoothing parameters theta; init=1 means initial values are provided for the theta. Default is 0. This option is only applicable to smoothing spline models with multiple smoothing parameters. |
theta |
If init=1, theta includes intial values for smoothing parameters. Default is NULL. This is only applicable to smoothing spline models with multiple smoothing parameters. |
prec |
precision requested for the minimum score value in ‘dmudr’, where precision is the weaker of the absolute and relative precisions. Default is 1e-06. This is only applicable to smoothing spline models with multiple smoothing parameters. |
maxit |
maximum number of iterations allowed in ‘dmudr’. Default is 30. This is only applicable to smoothing spline model with multiple smoothing parameters. |
tol.g |
the tolerance for elements of w's in GRKPK. Default is 0.0 which means using the machine precision. This is only applicable to generalized spline smoothing. |
prec.g |
precision for stopping the iteration in GRKPK. Default is 1e-06. This is only applicale to generalized spline smoothing. |
maxit.g |
maximum number of iterations allowed for the iteration in GRKPACK. Default is 30. This is only applicale to generalized spline smoothing. |
a list with components for each of the possible arguments.
## Not run: # use regular grid seach method with 100 grid points ssr.control(job=99) ## End(Not run)## Not run: # use regular grid seach method with 100 grid points ssr.control(job=99) ## End(Not run)
An object returned by the ssr function, inheriting from class ssr,
and representing a fitted smoothing spline model. Objects of this
class have methods for the generic functions predict, print and
summary.
The following components must be included in a legitimate ssr object:
call |
a list containing an image of the |
coef |
estimated coefficients for the spline estimate |
lambda |
a vector representing the estimate smoothing parameters |
fitted |
fitted values of the unknown mean function |
family |
the distribution family used |
cor.est |
estiamted parameters, if any, in corMatrix |
var.est |
estiamted parameters, if any, in varFunc |
s |
design matrix extracted from |
q |
a list of matrices representing reproducing kernels evaluated at design points. |
residuals |
working residuals from the fit. |
df |
equivalent degrees of freedom. It is calculated as the trace of the hat matrix. |
weight |
a matrix representing the covariance matrix. It is NULL for iid data. |
rkpk.obj |
an object representing fits from dsidr/dmudr/gdsidr/gdmudr. See help files for dsidr/dmudr/gdsidr/gdmudr for more details. |
scale |
a logical value, specifying if scaling is used. |
Chunlei Ke [email protected] and Yuedong Wang [email protected]
ssr, predict.ssr, summary.ssr,
plot.ssr, dsidr, dmudr, gdsidr,
gdmudr
The star data frame has 1086 rows and 2 columns of data from the Mira Variable R Hydrae
data(star)data(star)
This dataset contains magnitude (brightness) of the Mira variable R Hydrae during 1900-1950.
The data frame contains the following columns:
time a numeric vector of the observation time in days
magnitude a numeric vector of brightness of the Mira variable R Hydrae
Genton, M. G. and Hall, P. (2007). Statistical Inference for Envolving Periodic Functions, Journal of the Royal Statistical Society B 69, 643-657.
Yuedong Wang and Chunlei Ke (2009), Smoothing Spline Semi-parametric Nonlinear Regression Models, Journal of Computational and Graphical Statistics 18, 165-183.
The Stratford data frame has 73 rows and 2 columns of data containing daily maximum temperatures in Stratford every five days in 1990
data(Stratford)data(Stratford)
Daily maximum temperatures from the station in Stratford, Texas, in the year 1990 were extracted. The year was divided into 73 five-day periods and measurements on the third day in each period were selected as observations.
The data frame contains the following columns:
x a numeric vector representing time in a year scaled into [0,1]
y a numeric vector of the observed maximum temperature in Fahrenheit
This is part of a climate dataset downloaded from the Carbon Dioxide Information Analysis Center at http://cdiac.ornl.gov/ftp/ndp070.
Summarize a nnr object
## S3 method for class 'nnr' summary(object, ...)## S3 method for class 'nnr' summary(object, ...)
object |
a fitted |
... |
unused argument |
This is a method for the function summary for objects inheriting from class
nnr. See summary for the general behavior of this function.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Summarize a slm object
## S3 method for class 'slm' summary(object, ...)## S3 method for class 'slm' summary(object, ...)
object |
a fitted |
... |
unused argument |
This is a method for the function summary for objects inheriting
from class slm.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Summarize a snm object
## S3 method for class 'snm' summary(object, ...)## S3 method for class 'snm' summary(object, ...)
object |
a fitted 'snm' object. |
... |
unused argument |
This is a method for the function summary for objects inheriting from class
snm.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Summarize a snr object
## S3 method for class 'snr' summary(object, ...)## S3 method for class 'snr' summary(object, ...)
object |
a fitted |
... |
unused argument |
This is a method for the function summary for objects inheriting from class
snr. See summary for the general behavior of this function.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Provides a synopsis of a ssr object and perform tests.
## S3 method for class 'ssr' summary(object, ...)## S3 method for class 'ssr' summary(object, ...)
object |
a fitted |
... |
unused option. |
This is a method for the function summary for objects inheriting
from class ssr.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Return a matrix evaluating reproducing kernels for thin plate splines at observed points.
tp.pseudo(s, u=s, order=2) tp(s, u=s, order=2) tp.linear(s, u=s)tp.pseudo(s, u=s, order=2) tp(s, u=s, order=2) tp.linear(s, u=s)
s |
a list or matrix of observations. One component, if a list, and one column, if a matrix, contains observations on one variable. If a list, all components must be of the same length. |
u |
a list or matrix of observations. If a list, all components must be of the same length. The number of componets of the list, or the number of column of the matrix must be the same as that for s. Default is s. |
order |
an optional integer specifying the order of the thin plate spline. Default is 2. Let d be the
dimension of s (and u). Then order must satisfy |
The pseudo kernel, which is conditional definite positive instead of definite positive, is easy to calculate, while the true reproducing kernel is complicated. Pseudo Kernels are enough to compute spline estimates, but to calcualte Bayesian confidnece intervals, the true kernel is required. For the special case of d=2 and order=2, the function tp.linear computes evaluations of the reproducing kernel of the space spanned by linear basis.
a matrix with the numbers of row and column equal to the common length of componets or the number of row of s and t respectively. The [i, j] element is the pseudo, true, or linear reproducing kernel evaluated at the ith element of s and jth element of u.
Chunlei Ke [email protected] and Yuedong Wang [email protected]
Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59.
Gu, C. and Wahba, G (1993). Smoothing Spline ANOVA with component-wise Bayesian confidence intervals. Journal of Computational and Graphical Statistics 55, 353–368.
data(acid) ## Not run: tp.pseudo(list(acid$x1, acid$x2)) ## Not run: tp.pseud0(list(acid$x1, acid$x2), order=3)data(acid) ## Not run: tp.pseudo(list(acid$x1, acid$x2)) ## Not run: tp.pseud0(list(acid$x1, acid$x2), order=3)
The data frame TXtemp, obtained from the Carbon Dioxide Information and Analysis Center at Oak Ridge National Laboratory, has 17280 rows and 6 columns of data representing monthly temperature records for stations in Texas.
data(TXtemp)data(TXtemp)
The data frame contains the following columns:
stacode a numeric vector of the unique station code formed by combining the two-digit state number [state numbers range from 1 to 48] and the four-digit station number (values range from 0008 to 9933);
lat, long numeric vectors identifying the lattitudes and longitudes of the stations in decimal degree.
year a numeric vector comprising the year for the records
month a numeric vector of values 1 to 12, represeting the month for the data
mmtemp a numeric vector of monthly average temperature in Fahrenheit scale.
The data set was extracted from a large national historical climate data, containing data for 48 stations in Texas from 1961 to 1990. Monthly temperature records as well as the latitude and longitude for each station were available.
Of note, the missing values were coded as -99.99.
Data are downloadable from https://ess-dive.lbl.gov/
The 'ultrasound' data frame has 1,215 rows and 4 columns of data from an ultrasound experiment
data(ultrasound)data(ultrasound)
A Russian speaker produced the consonant sequence, /gd/, in three different linguistic environments: '2words', 'cluster' and 'Schwa', with three replications for each environment. 15 points from each of 9 slices of toungue curves separated by 30 ms (milliseconds) are extracted. Therefore, in total there are 15*9*3*3=1,215 observations.
The data frame contains the following columns:
height a numeric vector of toungue height in mm
length a numeric vector of toungue length in mm
time a numeric vector of time in ms
env a factor with three levels: 1 2 and 3 for environment '2words', 'cluster' and 'Schwa' respectively
Phonetics-Phonology Lab of New York University.
Davidson, L. (2006). Comparing Tongue Shapes from Ultrasound Imaging Using Smoothing Spline Analysis of Variance. Journal of the Acoustical Society of America 120, 407-415.
The USAtemp data frame has 1214 rows and 3 columns of data containing average Winter temperatures in 1981 from 1205 stations in USA.
data(USAtemp)data(USAtemp)
The data frame contains the following columns:
temp a numeric vector of average temperatures (Fahrenheit)
lat a numeric vector of the latitude of a station
long a numeric vector of the longitude of a station
The average Winter temperatures are calculated as the averages of temperatures in December, January and February. The geological locations of 1214 stations are given in terms of longitude and latitude.
The wesdr data frame has 669 rows and 5 columns of data
from an ongoing epidemiological study of a cohort of patients receiving their medical care in an 11-country area in southern Wisconsin.
data(wesdr)data(wesdr)
The progression of diabetic retinopathy was assessed together with a number of medical, demographic, ocular and other covariates and the retinopathy scores.
This data frame contains the following columns:
num a numeric vector giving IDs for individuals.
dur a numeric vector of duration of at baseline in year.
gly a numeric vector of glycosylated hemoglobin, a measuer of hyperglycemia.
bmi a numeric vecttor of body mass index, weight in .
prg a vector of 0 or 1's representing disease progression for each individual.
Klein, R., Klein, B. E. K., Moss, S. E., Davis, M. D. and Demets, D. L. (1989a). The Wisconsin epidemiologic study of diabetic retinopathy. IX. Four year incidence and progression of diabetic retinopathy when age at diagnosis is less than 30 years. Arch. Ophthalmal. 107, 237-243.
Klein, R., Klein, B. E. K., Moss, S. E., Davis, M. D. and Demets, D. L. (1989b). The Wisconsin epidemiologic study of diabetic retinopathy. X. Four year incidence and progression of diabetic retinopathy when age at diagnosis is less than 30 years. Arch. Ophthalmal. 107, 244-249.
Extend xyplot to superpose one or more symbols to each panel.
xyplot2(formula, data, type = "l", ...)xyplot2(formula, data, type = "l", ...)
formula |
a two-sided formula as accepted in |
data |
a list of data frames. Each component shall be able to evaluate the vatiables appearing in |
type |
a vector of characters to indicate what type of plots are to draw. Default is line. |
... |
any options as accepted in |
On each panel, several plot types, the length of data, are superposed.