flm.test: Goodness-of-fit test for the Functional Linear Model with...
In fda.usc: Functional Data Analysis and Utilities for Statistical Computing
flm.test R Documentation
Goodness-of-fit test for the Functional Linear Model with scalar response
Description
The function flm.test tests the composite null hypothesis of
a Functional Linear Model with scalar response (FLM),
H_0:\,Y=\big<X,\beta\big>+\epsilon,
versus
a general alternative. If \beta=\beta_0 is provided, then the
simple hypothesis H_0:\,Y=\big<X,\beta_0\big>+\epsilon is tested.
The testing of the null hypothesis is done by a Projected Cramer-von Mises statistic (see Details).
Usage
flm.test(
X.fdata,
Y,
beta0.fdata = NULL,
B = 5000,
est.method = "pls",
p = NULL,
type.basis = "bspline",
verbose = TRUE,
plot.it = TRUE,
B.plot = 100,
G = 200,
...
)
Arguments
X.fdata
Functional covariate for the FLM. The object must be in the class
fdata.
Y
Scalar response for the FLM. Must be a vector with the same number of elements
as functions are in X.fdata.
beta0.fdata
Functional parameter for the simple null hypothesis, in the fdata class.
Recall that the argvals and rangeval arguments of beta0.fdata must be the same
of X.fdata. A possibility to do this is to consider, for example for \beta_0=0
(the simple null hypothesis of no interaction),
beta0.fdata=fdata(mdata=rep(0,length(X.fdata$argvals)),argvals=X.fdata$argvals,rangeval=X.fdata$rangeval).
If beta0.fdata=NULL (default), the function will test for the composite null hypothesis.
B
Number of bootstrap replicates to calibrate the distribution of the test statistic.
B=5000 replicates are the recommended for carry out the test, although for exploratory analysis
(not inferential), an acceptable less time-consuming option is B=500.
est.method
Estimation method for the unknown parameter \beta,
only used in the composite case. Mainly, there are two options: specify the number of basis
elements for the estimated \beta by p or optimally select p by a
data-driven criteria (see Details section for discussion). Then, it must be one of the following
methods:
-
"pc": If p, the number of basis elements, is given, then \beta is estimated by fregre.pc. Otherwise, an optimum p is chosen using fregre.pc.cv and the "SICc" criteria.
-
"pls": If p is given, \beta is estimated by fregre.pls. Otherwise, an optimum p is chosen using fregre.pls.cv and the "SICc" criteria.
This is the default argument as it has been checked empirically that provides a good balance between the performance of the test and the estimation of \beta.
-
"basis": If p is given, \beta is estimated by fregre.basis. Otherwise, an optimum p is chosen using fregre.basis.cv and the "GCV.S" criteria. In these functions, the same basis for the arguments basis.x and basis.b is considered.
The type of basis used will be the given by the argument type.basis and must be one of the class of create.basis. Further arguments passed to create.basis (not rangeval that is taken as the rangeval of X.fdata), can be passed throughout ....
p
Number of elements of the basis considered. If it is not given, an optimal p will be chosen using a specific criteria (see est.method and type.basis arguments).
type.basis
Type of basis used to represent the functional process. Depending on the hypothesis, it will have a different interpretation:
Simple hypothesis. One of these options:
-
"bspline": If p is given, the functional process is expressed in a basis of p B-splines. If not, an optimal p will be chosen by optim.basis, using the "GCV.S" criteria.
-
"fourier": If p is given, the functional process is expressed in a basis of p Fourier functions. If not, an optimal p will be chosen by optim.basis, using the "GCV.S" criteria.
-
"pc": p must be given. Expresses the functional process in a basis of p principal components.
-
"pls": p must be given. Expresses the functional process in a basis of p partial least squares.
Although other basis types supported by create.basis are possible, "bspline" and "fourier" are recommended. Other basis types may cause incompatibilities.
Composite hypothesis. This argument is only used when est.method="basis" and, in this case, it specifies the type of basis used in the basis estimation method of the functional parameter. Again, basis
"bspline" and "fourier" are recommended, as other basis types may cause incompatibilities.
verbose
Either to show or not information about computing progress.
plot.it
Either to show or not a graph of the observed trajectory,
and the bootstrap trajectories under the null composite hypothesis, of the
process R_n(\cdot) (see Details). Note that if plot.it=TRUE,
the function takes more time to run.
B.plot
Number of bootstrap trajectories to show in the resulting plot of the test.
As the trajectories shown are the first B.plot of B, B.plot must be
lower or equal to B.
G
Number of projections used to compute the trajectories of the process
R_n(\cdot) by Monte Carlo.
...
Further arguments passed to create.basis.
Details
The Functional Linear Model with scalar response (FLM), is defined as
Y=\big<X,\beta\big>+\epsilon, for a functional process
X such that E[X(t)]=0, E[X(t)\epsilon]=0
for all t and for a scalar variable Y such that E[Y]=0.
Then, the test assumes that Y and X.fdata are centred and will automatically
center them. So, bear in mind that when you apply the test for Y and X.fdata,
actually, you are applying it to Y-mean(Y) and fdata.cen(X.fdata)$Xcen.
The test statistic corresponds to the Cramer-von Mises norm of the Residual Marked
empirical Process based on Projections R_n(u,\gamma) defined in
Garcia-Portugues et al. (2014).
The expression of this process in a p-truncated basis of the space L^2[0,T]
leads to the p-multivariate process R_{n,p}\big(u,\gamma^{(p)}\big),
whose Cramer-von Mises norm is computed.
The choice of an appropriate p to represent the functional process X,
in case that is not provided, is done via the estimation of \beta for the composite
hypothesis. For the simple hypothesis, as no estimation of \beta is done, the choice
of p depends only on the functional process X. As the result of the test may
change for different p's, we recommend to use an automatic criterion to select p
instead of provide a fixed one.
The distribution of the test statistic is approximated by a wild bootstrap resampling on the
residuals, using the golden section bootstrap.
Finally, the graph shown if plot.it=TRUE represents the observed trajectory, and the
bootstrap trajectories under the null, of the process RMPP integrated on the projections:
R_n(u)\approx\frac{1}{G}\sum_{g=1}^G R_n(u,\gamma_g),
where \gamma_g are simulated as Gaussians processes. This gives a graphical idea of
how distant is the observed trajectory from the null hypothesis.
Value
An object with class "htest" whose underlying structure is a list containing
the following components:
-
statistic: The value of the test statistic.
-
boot.statistics: A vector of length B with the values of the bootstrap test statistics.
-
p.value: The p-value of the test.
-
method: The method used.
-
B: The number of bootstrap replicates used.
-
type.basis: The type of basis used.
-
beta.est: The estimated functional parameter \beta in the composite
hypothesis. For the simple hypothesis, the given beta0.fdata.
-
p: The number of basis elements passed or automatically chosen.
-
ord: The optimal order for PC and PLS given by fregre.pc.cv and fregre.pls.cv. For other methods, it is set to 1:p.
-
data.name: The character string "Y=<X,b>+e".
Note
No NA's are allowed neither in the functional covariate nor in the scalar response.
Author(s)
Eduardo Garcia-Portugues. Please, report bugs and suggestions to
edgarcia@est-econ.uc3m.es
References
Escanciano, J. C. (2006). A consistent diagnostic test for regression models using projections. Econometric Theory, 22, 1030-1051. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1017/S0266466606060506")}
Garcia-Portugues, E., Gonzalez-Manteiga, W. and Febrero-Bande, M. (2014). A goodness–of–fit test for the functional linear model with scalar response. Journal of Computational and Graphical Statistics, 23(3), 761-778. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2013.812519")}
See Also
Adot, PCvM.statistic, rwild,
flm.Ftest, dfv.test,
fregre.pc, fregre.pls, fregre.basis,
fregre.pc.cv, fregre.pls.cv,
fregre.basis.cv, optim.basis,
create.basis
Examples
# Simulated example #
X=rproc2fdata(n=100,t=seq(0,1,l=101),sigma="OU")
beta0=fdata(mdata=cos(2*pi*seq(0,1,l=101))-(seq(0,1,l=101)-0.5)^2+
rnorm(101,sd=0.05),argvals=seq(0,1,l=101),rangeval=c(0,1))
Y=inprod.fdata(X,beta0)+rnorm(100,sd=0.1)
dev.new(width=21,height=7)
par(mfrow=c(1,3))
plot(X,main="X")
plot(beta0,main="beta0")
plot(density(Y),main="Density of Y",xlab="Y",ylab="Density")
rug(Y)
## Not run:
# Composite hypothesis: do not reject FLM
pcvm.sim=flm.test(X,Y,B=50,B.plot=50,G=100,plot.it=TRUE)
pcvm.sim
flm.test(X,Y,B=5000)
# Estimated beta
dev.new()
plot(pcvm.sim$beta.est)
# Simple hypothesis: do not reject beta=beta0
flm.test(X,Y,beta0.fdata=beta0,B=50,B.plot=50,G=100)
flm.test(X,Y,beta0.fdata=beta0,B=5000)
# AEMET dataset #
data(aemet)
# Remove the 5\
dev.new()
res.FM=depth.FM(aemet$temp,draw=TRUE)
qu=quantile(res.FM$dep,prob=0.05)
l=which(res.FM$dep<=qu)
lines(aemet$temp[l],col=3)
aemet$df$name[l]
# Data without outliers
wind.speed=apply(aemet$wind.speed$data,1,mean)[-l]
temp=aemet$temp[-l]
# Exploratory analysis: accept the FLM
pcvm.aemet=flm.test(temp,wind.speed,est.method="pls",B=100,B.plot=50,G=100)
pcvm.aemet
# Estimated beta
dev.new()
plot(pcvm.aemet$beta.est,lwd=2,col=2)
# B=5000 for more precision on calibration of the test: also accept the FLM
flm.test(temp,wind.speed,est.method="pls",B=5000)
# Simple hypothesis: rejection of beta0=0? Limiting p-value...
dat=rep(0,length(temp$argvals))
flm.test(temp,wind.speed, beta0.fdata=fdata(mdata=dat,argvals=temp$argvals,
rangeval=temp$rangeval),B=100)
flm.test(temp,wind.speed, beta0.fdata=fdata(mdata=dat,argvals=temp$argvals,
rangeval=temp$rangeval),B=5000)
# Tecator dataset #
data(tecator)
names(tecator)
absorp=tecator$absorp.fdata
ind=1:129 # or ind=1:215
x=absorp[ind,]
y=tecator$y$Fat[ind]
tt=absorp[["argvals"]]
# Exploratory analysis for composite hypothesis with automatic choose of p
pcvm.tecat=flm.test(x,y,B=100,B.plot=50,G=100)
pcvm.tecat
# B=5000 for more precision on calibration of the test: also reject the FLM
flm.test(x,y,B=5000)
# Distribution of the PCvM statistic
plot(density(pcvm.tecat$boot.statistics),lwd=2,xlim=c(0,10),
main="PCvM distribution", xlab="PCvM*",ylab="Density")
rug(pcvm.tecat$boot.statistics)
abline(v=pcvm.tecat$statistic,col=2,lwd=2)
legend("top",legend=c("PCvM observed"),lwd=2,col=2)
# Simple hypothesis: fixed p
dat=rep(0,length(x$argvals))
flm.test(x,y,beta0.fdata=fdata(mdata=dat,argvals=x$argvals,
rangeval=x$rangeval),B=100,p=11)
# Simple hypothesis, automatic choose of p
flm.test(x,y,beta0.fdata=fdata(mdata=dat,argvals=x$argvals,
rangeval=x$rangeval),B=100)
flm.test(x,y,beta0.fdata=fdata(mdata=dat,argvals=x$argvals,
rangeval=x$rangeval),B=5000)
## End(Not run)
fda.usc documentation built on April 4, 2025, 4:35 a.m.
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| flm.test | R Documentation |
Goodness-of-fit test for the Functional Linear Model with scalar response
Description
The function flm.test tests the composite null hypothesis of
a Functional Linear Model with scalar response (FLM),
H_0:\,Y=\big<X,\beta\big>+\epsilon,
versus
a general alternative. If \beta=\beta_0 is provided, then the
simple hypothesis H_0:\,Y=\big<X,\beta_0\big>+\epsilon is tested.
The testing of the null hypothesis is done by a Projected Cramer-von Mises statistic (see Details).
Usage
flm.test(
X.fdata,
Y,
beta0.fdata = NULL,
B = 5000,
est.method = "pls",
p = NULL,
type.basis = "bspline",
verbose = TRUE,
plot.it = TRUE,
B.plot = 100,
G = 200,
...
)
Arguments
X.fdata |
Functional covariate for the FLM. The object must be in the class
|
Y |
Scalar response for the FLM. Must be a vector with the same number of elements
as functions are in |
beta0.fdata |
Functional parameter for the simple null hypothesis, in the |
B |
Number of bootstrap replicates to calibrate the distribution of the test statistic.
|
est.method |
Estimation method for the unknown parameter
|
p |
Number of elements of the basis considered. If it is not given, an optimal |
type.basis |
Type of basis used to represent the functional process. Depending on the hypothesis, it will have a different interpretation:
|
verbose |
Either to show or not information about computing progress. |
plot.it |
Either to show or not a graph of the observed trajectory,
and the bootstrap trajectories under the null composite hypothesis, of the
process |
B.plot |
Number of bootstrap trajectories to show in the resulting plot of the test.
As the trajectories shown are the first |
G |
Number of projections used to compute the trajectories of the process
|
... |
Further arguments passed to create.basis. |
Details
The Functional Linear Model with scalar response (FLM), is defined as
Y=\big<X,\beta\big>+\epsilon, for a functional process
X such that E[X(t)]=0, E[X(t)\epsilon]=0
for all t and for a scalar variable Y such that E[Y]=0.
Then, the test assumes that Y and X.fdata are centred and will automatically
center them. So, bear in mind that when you apply the test for Y and X.fdata,
actually, you are applying it to Y-mean(Y) and fdata.cen(X.fdata)$Xcen.
The test statistic corresponds to the Cramer-von Mises norm of the Residual Marked
empirical Process based on Projections R_n(u,\gamma) defined in
Garcia-Portugues et al. (2014).
The expression of this process in a p-truncated basis of the space L^2[0,T]
leads to the p-multivariate process R_{n,p}\big(u,\gamma^{(p)}\big),
whose Cramer-von Mises norm is computed.
The choice of an appropriate p to represent the functional process X,
in case that is not provided, is done via the estimation of \beta for the composite
hypothesis. For the simple hypothesis, as no estimation of \beta is done, the choice
of p depends only on the functional process X. As the result of the test may
change for different p's, we recommend to use an automatic criterion to select p
instead of provide a fixed one.
The distribution of the test statistic is approximated by a wild bootstrap resampling on the
residuals, using the golden section bootstrap.
Finally, the graph shown if plot.it=TRUE represents the observed trajectory, and the
bootstrap trajectories under the null, of the process RMPP integrated on the projections:
R_n(u)\approx\frac{1}{G}\sum_{g=1}^G R_n(u,\gamma_g),
where \gamma_g are simulated as Gaussians processes. This gives a graphical idea of
how distant is the observed trajectory from the null hypothesis.
Value
An object with class "htest" whose underlying structure is a list containing
the following components:
-
statistic: The value of the test statistic. -
boot.statistics: A vector of lengthBwith the values of the bootstrap test statistics. -
p.value: The p-value of the test. -
method: The method used. -
B: The number of bootstrap replicates used. -
type.basis: The type of basis used. -
beta.est: The estimated functional parameter\betain the composite hypothesis. For the simple hypothesis, the givenbeta0.fdata. -
p: The number of basis elements passed or automatically chosen. -
ord: The optimal order for PC and PLS given byfregre.pc.cvandfregre.pls.cv. For other methods, it is set to1:p. -
data.name: The character string "Y=<X,b>+e".
Note
No NA's are allowed neither in the functional covariate nor in the scalar response.
Author(s)
Eduardo Garcia-Portugues. Please, report bugs and suggestions to edgarcia@est-econ.uc3m.es
References
Escanciano, J. C. (2006). A consistent diagnostic test for regression models using projections. Econometric Theory, 22, 1030-1051. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1017/S0266466606060506")}
Garcia-Portugues, E., Gonzalez-Manteiga, W. and Febrero-Bande, M. (2014). A goodness–of–fit test for the functional linear model with scalar response. Journal of Computational and Graphical Statistics, 23(3), 761-778. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2013.812519")}
See Also
Adot, PCvM.statistic, rwild,
flm.Ftest, dfv.test,
fregre.pc, fregre.pls, fregre.basis,
fregre.pc.cv, fregre.pls.cv,
fregre.basis.cv, optim.basis,
create.basis
Examples
# Simulated example #
X=rproc2fdata(n=100,t=seq(0,1,l=101),sigma="OU")
beta0=fdata(mdata=cos(2*pi*seq(0,1,l=101))-(seq(0,1,l=101)-0.5)^2+
rnorm(101,sd=0.05),argvals=seq(0,1,l=101),rangeval=c(0,1))
Y=inprod.fdata(X,beta0)+rnorm(100,sd=0.1)
dev.new(width=21,height=7)
par(mfrow=c(1,3))
plot(X,main="X")
plot(beta0,main="beta0")
plot(density(Y),main="Density of Y",xlab="Y",ylab="Density")
rug(Y)
## Not run:
# Composite hypothesis: do not reject FLM
pcvm.sim=flm.test(X,Y,B=50,B.plot=50,G=100,plot.it=TRUE)
pcvm.sim
flm.test(X,Y,B=5000)
# Estimated beta
dev.new()
plot(pcvm.sim$beta.est)
# Simple hypothesis: do not reject beta=beta0
flm.test(X,Y,beta0.fdata=beta0,B=50,B.plot=50,G=100)
flm.test(X,Y,beta0.fdata=beta0,B=5000)
# AEMET dataset #
data(aemet)
# Remove the 5\
dev.new()
res.FM=depth.FM(aemet$temp,draw=TRUE)
qu=quantile(res.FM$dep,prob=0.05)
l=which(res.FM$dep<=qu)
lines(aemet$temp[l],col=3)
aemet$df$name[l]
# Data without outliers
wind.speed=apply(aemet$wind.speed$data,1,mean)[-l]
temp=aemet$temp[-l]
# Exploratory analysis: accept the FLM
pcvm.aemet=flm.test(temp,wind.speed,est.method="pls",B=100,B.plot=50,G=100)
pcvm.aemet
# Estimated beta
dev.new()
plot(pcvm.aemet$beta.est,lwd=2,col=2)
# B=5000 for more precision on calibration of the test: also accept the FLM
flm.test(temp,wind.speed,est.method="pls",B=5000)
# Simple hypothesis: rejection of beta0=0? Limiting p-value...
dat=rep(0,length(temp$argvals))
flm.test(temp,wind.speed, beta0.fdata=fdata(mdata=dat,argvals=temp$argvals,
rangeval=temp$rangeval),B=100)
flm.test(temp,wind.speed, beta0.fdata=fdata(mdata=dat,argvals=temp$argvals,
rangeval=temp$rangeval),B=5000)
# Tecator dataset #
data(tecator)
names(tecator)
absorp=tecator$absorp.fdata
ind=1:129 # or ind=1:215
x=absorp[ind,]
y=tecator$y$Fat[ind]
tt=absorp[["argvals"]]
# Exploratory analysis for composite hypothesis with automatic choose of p
pcvm.tecat=flm.test(x,y,B=100,B.plot=50,G=100)
pcvm.tecat
# B=5000 for more precision on calibration of the test: also reject the FLM
flm.test(x,y,B=5000)
# Distribution of the PCvM statistic
plot(density(pcvm.tecat$boot.statistics),lwd=2,xlim=c(0,10),
main="PCvM distribution", xlab="PCvM*",ylab="Density")
rug(pcvm.tecat$boot.statistics)
abline(v=pcvm.tecat$statistic,col=2,lwd=2)
legend("top",legend=c("PCvM observed"),lwd=2,col=2)
# Simple hypothesis: fixed p
dat=rep(0,length(x$argvals))
flm.test(x,y,beta0.fdata=fdata(mdata=dat,argvals=x$argvals,
rangeval=x$rangeval),B=100,p=11)
# Simple hypothesis, automatic choose of p
flm.test(x,y,beta0.fdata=fdata(mdata=dat,argvals=x$argvals,
rangeval=x$rangeval),B=100)
flm.test(x,y,beta0.fdata=fdata(mdata=dat,argvals=x$argvals,
rangeval=x$rangeval),B=5000)
## End(Not run)
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