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Any continuous density function \(f\) on a known closed interval \([a,b]\) can be approximated by Bernstein polynomial \(f_m(x; p)=(b-a)^{-1}\sum_{i=0}^m p_i\beta_{mi}[(x-a)/(b-a)]\), where \(p_i\ge 0\), \(\sum_{i=0}^m p_i=1\) and \(\beta_{mi}(u)=(m+1){m\choose i}u^i(1-u)^{m-i}\), \(i=0,1,\ldots,m\), is the beta density with shapes \((i+1, m-i+1)\). This provides a way to approximately model the unknwon underlying density with a mixture beta model with an appropriate \emph{model degree} \(m\) and solve a nonparametric or semiparametric statistical problem “parametrically'' using the maximum likelihood method. For instance, based on one-sample data, \(x_1,\ldots,x_n\), one can estimate a nonparametric density \(f\) parametrically by maximizing the approximate likelihood \(\ell(p)=\sum_{j=0}^n\log f_m(x_j; p)\) with respect to \(p\) [@Guan-jns-2015].
Since the Bernstein polynomial model of degree \(m\) is nested in the model of degree \(m+1\), the maximum likelihood is increasing in \(m\). The increase is negligible when the model becomes overfitting. Therefore an optimal degree can be chosen as the change-point of the log likelihood ratios over a set of consecutive candidate model degrees.
This approach works surprisingly well for even more complicated models and data. With an estimate \(\hat p=(\hat p_0,\ldots,\hat p_m)\) of \(p\) one can estimate the cumulative distribution function \(F\) by \(\hat F(x)=F_m(x;\hat p)=\sum_{i=0}^m \hat p_i B_{mi}[(x-a)/(b-a)]\), where \(B_{mi}(u)=\int_0^u\beta_{mj}(t)dt\), \(i=0,\ldots,m\), is the beta distribution function with shapes \((i+1,m-i+1)\). This manual will illustrate the use of the R package \texttt{mable} for obtaining not only smooth estimates of density, cumulative distribution, and survival functions but also estimates of parameters such as regression coefficients.
Let \(x_1,\ldots,x_n\) be a sample from a population with cumulative distribution function \(F\) and density function \(f\). on \([a,b]\). If \([a,b]\) is unknown we choose \([a,b]\supset [x_{(1)},x_{(n)}]\), where \(x_{(1)}\) and \(x_{(n)}\) are the minimum and maximum statistics, respectively.
For the annual flow data of Vaal River at Standerton as given by Table 1.1 of @Linhart-and-Zucchini-model-selection-book give the flow in millions of cubic metres,
data(Vaal.Flow)
head(Vaal.Flow, 3)
## Year Flow
## 1 1905 222
## 2 1906 1094
## 3 1907 452
we want to estimate the density and the distribution functions of annual flow
vaal<-mable(Vaal.Flow$Flow, M=c(2,100), interval=c(0, 3000), IC="all",
controls = mable.ctrl(sig.level=1e-8, maxit=2000, eps=1.0e-9))
Here we truncate the density by \texttt{interval=c(0, 3000)} and choose an optimal degree \(m\) among the candidate degrees M[1]:M[2] using the method of change-point. The maximum number of iterations is \texttt{maxit} and the convergence criterion is \texttt{eps} for each \(m\) of M[1]:M[2]. The search of an optimal degree stops when the \(p\)-value \texttt{pval} of change-point test falls below the specified significance level \texttt{sig.level} or the largest degree, M[2], has been reached. If the latter occurs a warning message shows up. In this case we should check the last value of \texttt{pval}. In the above example, we got warning message and the last \texttt{pval}, 1.3396916 × 10-8, which is small enough. The selected optimal degree is \(m=\) 19. One can also look at the Bayesian information criteria, \texttt{BIC}, and other information criteria, Akaike information criterion \texttt{AIC} and Hannan–Quinn information criterion \texttt{QHC}, at each candidate degree. These information criteria are not reliable due to the difficulty of determining the model dimension. The \texttt{plot} method for \texttt{mable} class object can visualize some of the results.
op<-par(mfrow=c(1,2))
layout(rbind(c(1, 2), c(3, 3)))
plot(vaal, which="likelihood", cex=.5)
plot(vaal, which="change-point", lgd.x="topright")
hist(Vaal.Flow$Flow, prob=TRUE, xlim=c(0,3000), ylim=c(0,.0022), breaks=100*(0:30),
main="Histogram and Densities of the Annual Flow of Vaal River",
border="dark grey",lwd=1,xlab="Flow", ylab="Density", col ="light grey")
lines(density(x<-Vaal.Flow$Flow, bw = "nrd0", adjust = 1), lty=2, col = 2,lwd=2)
lines(y<-seq(0, 3000, length=100), dlnorm(y, mean(log(x)),
sqrt(var(log(x)))), lty=4, col=4, lwd=2)
plot(vaal, which="density", add=TRUE, lwd=2)
legend("topright", lty=c(1, 4, 2), col=c(1, 4, 2), bty="n",lwd=2,
c(expression(paste("MABLE: ",hat(f)[B])),
expression(paste("Log-Normal: ",hat(f)[P])),
expression(paste("KDE: ",hat(f)[K]))))
par(op)
In Figure \ref{fig:vaal-river-data-plot}, the unknown density \(f\) is estimated by \texttt{MABLE} \(\hat f_B\) using optimal degrees \(m=\) 19 selected using the exponential change-point method, the parametric estimate using \texttt{Log-Normal} model and the kernel density estimate \texttt{KDE}: \(\hat f_K\).
We can also look at the plots of AIC, BIC, and QHC, and likelihood ration(LR) of gamma change-point.
M<-vaal$M[1]:vaal$M[2]
aic<-vaal$ic$AIC
bic<-vaal$ic$BIC
qhc<-vaal$ic$QHC
vaal.gcp<-optim.gcp(vaal) # choose m by gamma change-point model
lr<-vaal.gcp$lr
plot(M, aic, cex=0.7, col=1, xlab="m", ylab="", ylim=c(ymin<-min(aic,bic,qhc,lr),
ymax<-max(aic,bic,qhc,lr)), main="AIC, BIC, QHC, and LR")
points(M, bic, pch=2, cex=0.7, col=2)
points(M, qhc, pch=3, cex=0.7, col=3)
points(M[-1], lr, pch=4, cex=0.7, col=4)
segments(which.max(aic)+M[1]-1->m1, ymin, m1, max(aic), lty=2)
segments(which.max(bic)+M[1]-1->m2, ymin, m2, max(bic), lty=2, col=2)
segments(which.max(qhc)+M[1]-1->m3, ymin, m3, max(qhc), lty=2, col=3)
segments(which.max(lr)+M[1]->m4, ymin, m4, max(lr), lty=2, col=4)
axis(1, c(m1,m2, m3, m4), as.character(c(m1,m2,m3,m4)), col.axis=4)
legend("topright", pch=c(1,2,3,4), c("AIC", "BIC", "QHC", "LR"), bty="n", col=c(1,2,3,4))
From Figure \ref{fig:Vaal-River-data-AIC-BIC-plot} we see that the gamma change-point method gives the same optimal degree \(m=\) 19 as the exponential method; BIC and QHC give the same degree \(m=\) 11; the degree \(m=\) 16 selected by AIC method is closer to the one selected by change-point methods. From this Figure we also see that unlike the LR plot the information criteria do not have clear peak points.
For any given degree \(m\), one can fit the data by specifying \texttt{M=m} in \texttt{mable()} to obtain an estimate of \(f\).
The \texttt{summary} method \texttt{summary.mable} prints and returns invisibly the summarized results.
summary(vaal)
## Call: mable() for raw data
## Obj Class: mable
## Input Data: Vaal.Flow$Flow
## Dimsion of data: 1
## Optimal degree m: 19
## P-value of Change-point: 1.339692e-08
## Maximum loglikelihood: 56.81939
## MABLE of p: can be retrieved using name 'p'
## Note: the optimal model degree is selected by the method of change-point.
The mixing proportions, the coefficients of the Bernstein polynomials, \(p\) can be obtained as either \texttt{p<-vaal$p}
p<-vaal$p
p
## [1] 6.663285e-142 1.049278e-01 7.594313e-01 2.309036e-08 2.508190e-16
## [6] 7.193781e-21 1.714946e-20 1.049294e-07 1.202354e-01 1.097612e-22
## [11] 3.689377e-96 2.261275e-228 0.000000e+00 0.000000e+00 0.000000e+00
## [16] 5.038406e-234 1.226874e-39 1.540542e-02 4.734984e-197 0.000000e+00
or \texttt{summary(res)$p}. However, the latter only returns \texttt{p.cp} with an optimal degree \(m\) selected by the method of change-point while \texttt{p.bic} is the one with a degree \(m\) selected by BIC method.
With a grouped dataset, a frequency table of data from a continuous population, one can estimate the density from histogram using \texttt{mable.group()} with an optimal degree \(m\) chosen from \texttt{M[1]:M[2]} or with a given degree \(m\) using \texttt{M=m} [@Guan-2017-jns].
Consider the chicken embryo data contain the number of hatched eggs on each day during the 21 days of incubation period. The times of hatching (\(n=43\)) are treated as grouped by intervals with equal width of one day. The data were studied first by @Jassim-et-al-1996. @Kuurman-et-al-2003 and @Lin-and-He-2006-bka also analyzed the data using the minimum Hellinger distance estimator, in addition to other methods assuming some parametric mixture models including Weibull model.
data(chicken.embryo)
head(chicken.embryo, 2)
## $day
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
##
## $nT
## [1] 6 5 11 2 2 3 0 0 0 0 1 0 0 0 1 0 1 5 1 4 1
a<-0
b<-21
day<-chicken.embryo$day
nT<-chicken.embryo$nT
embryo<-mable.group(x=nT, breaks=a:b, M=c(2,100), interval=c(a, b), IC="aic",
controls=mable.ctrl(sig.level=1e-6, maxit=2000, eps=1.0e-7))
Day<-rep(day,nT)
op<-par(mfrow=c(1,2), lwd=2)
layout(rbind(c(1, 2), c(3, 3)))
plot(embryo, which="likelihood")
plot(embryo, which="change-point")
fk<-density(x=rep((0:20)+.5, nT), bw="sj", n=101, from=a, to=b)
hist(Day, breaks=seq(a,b, length=12), freq=FALSE, col="grey",
border="white", main="Histogram and Density Estimates")
plot(embryo, which="density", cex=0.7, add=TRUE)
lines(fk, lty=2, col=2)
legend("top", lty=c(1:2), c("MABLE",
"Kernel"), bty="n", col=c(1:2))
par(op)
We see in Figure \ref{fig:chicken-embryo-data-AIC-BIC-plot} that AIC and gamma change-point method give the same optimal degree as the one, \(m=\) 13, given by the exponential change-point method. However, BIC fails in choosing a useful model degree.
M<-embryo$M[1]:embryo$M[2]
aic<-embryo$ic$AIC
bic<-embryo$ic$BIC
res.gcp<-optim.gcp(embryo) # choose m by gamma change-point model
lr<-res.gcp$lr
plot(M, aic, cex=0.7, col=1, xlab="m", ylab="", ylim=c(ymin<-min(aic,bic,lr),
ymax<-max(aic,bic,lr)), main="AIC, BIC, and LR")
points(M, bic, pch=2, cex=0.7, col=2)
points(M[-1], lr, pch=3, cex=0.7, col=4)
segments(which.max(aic)+M[1]-1->m1, ymin, m1, max(aic), lty=2)
segments(which.max(bic)+M[1]-1->m2, ymin, m2, max(bic), lty=2, col=2)
segments(which.max(lr)+M[1]->m3, ymin, m3, max(lr), lty=2, col=4)
axis(1, c(m1,m2, m3), as.character(c(m1,m2,m3)), col.axis=4)
legend("right", pch=c(1,2,3), c("AIC", "BIC", "LR"), bty="n", col=c(1,2,4))
The results are summarized as follows.
summary(embryo)
## Call: mable.group() for grouped data
## Obj Class: mable
## Input Data: nT
## Dimsion of data: 1
## Optimal degree m: 13
## P-value of Change-point: 9.925959e-07
## Maximum loglikelihood: -107.318
## MABLE of p: can be retrieved using name 'p'
## Note: the optimal model degree is selected by the method of change-point.
Consider the additive measurement error model \(Y = X + \epsilon\), where \(X\) has an unknown distribution \(F\), \(\epsilon\) has a known distribution \(G\), and \(X\) and \(\epsilon\) are independent. We want to estimate density \(f=F'\) based on independent observations, \(y_i = x_i + \epsilon_i\), \(i=1,\ldots,n\), of \(Y\), where \(x_i\)'s and \(\epsilon_i\)'s are unobservable. \texttt{mable.decon()} implements the method of @Guan-2019-mable-deconvolution and gives an estimate of the density \(f\) using the approximate Bernstein polynomial model.
set.seed(123)
mu<-1; sig<-2; a<-mu-sig*5; b<-mu+sig*5;
gn<-function(x) dnorm(x, 0, 1)
n<-50;
x<-rnorm(n, mu, sig); e<-rnorm(n); y<-x+e;
decn<-mable.decon(y, gn, interval=c(a,b), M=c(5, 50))
op<-par(mfrow=c(2,2),lwd=2)
plot(decn, which="likelihood")
plot(decn, which="change-point", lgd.x="right")
plot(xx<-seq(a, b, length=100), yy<-dnorm(xx, mu, sig), type="l", xlab="x",
ylab="Density", ylim=c(0, max(yy)*1.1))
plot(decn, which="density", add=TRUE, lty=2, col=2)
# kernel density based on pure data
lines(density(x), lty=5, col=4)
legend("topright", bty="n", lty=c(1,2,5), col=c(1,2,4), c(expression(f),
expression(hat(f)), expression(tilde(f)[K])))
plot(xx, yy<-pnorm(xx, mu, sig), type="l", xlab="x", ylab="Distribution Function")
plot(decn, which="cumulative", add=TRUE, lty=2, col=2)
legend("bottomright", bty="n", lty=c(1,2), col=c(1,2), c(expression(F),
expression(hat(F))))
par(op)
When data are interval censored, the ”\texttt{interval2}'' type observations are of the form \((l,u)\) which is the interval containing the event time. Data is uncensored if \(l = u\), right censored if \(u =\) \texttt{Inf} or \(u =\) \texttt{NA}, and left censored data if \(l =0\).
Let \(f(t)\) and \(F(t)=1-S(t)\) be the density and cumulative distribution functions of the event time, respectively, on \((0, \tau)\), where \(\tau\le \infty\). If \(\tau\) is unknown or \(\tau=\infty\), then \(f(t)\) on \([0,\tau_n]\) can be approximated by \(fm(t; p)=\tau_n^{-1}\sum_{i=0}^m p_i\beta_{mi}(t/\tau_n)\), where \(p_i\ge 0\), \(i=0,\ldots,m\), \(\sum_{i=0}^mp_i=1-p_{m+1}\), and \(\tau_n\) is the largest observation, either uncensored time, or right endpoint of interval/left censored, or left endpoint of right censored time. So we can approximate \(S(t)\) on \([0,\tau]\) by \(Sm(t; p)=\sum_{i=0}^{m+1} p_i \bar B_{mi}(t/\tau)\), where \(\bar B_{mi}(u)=1-\int_0^u\beta_{mj}(t)dt\), \(i=0,\ldots,m\), is the beta survival function with shapes \((i+1,m-i+1)\), \(\bar B_{m,m+1}(t)=1\), \(p_{m+1}=1-\pi\), and \(\pi=F(\tau_n)\). For data without right-censored time, \(p_{m+1}=1-\pi=0\). The search for optimal degree \(m\) among \texttt{M=c(m0,m1)} using the method of change-point is stopped if either \texttt{m1} is reached or the test for change-point results in a p-value \texttt{pval} smaller than \texttt{sig.level}. @Guan-arXiv-2019 proposed a method, as a special case of a semiparametric regression model, for estimating \(p\) with an optimal degree \(m\). The method is implemented in R function \texttt{mable.ic()}.
Consider the breast cosmesis data as described in @Finkelstein-and-Wolfe-1985-biom is used to study the cosmetic effects of cancer therapy. The time-to-breast-retractions in months (\(T\)) were subject to interval censoring and were measured for 94 women among them 46 received radiation only (\(X=0\)) (25 right-censored, 3 left-censored and 18 interval censored) and 48 received radiation plus chemotherapy (\(X=1\)) (13 right-censored, 2 left-censored and 33 interval censored). The right-censored event times were for those women who did not experienced cosmetic deterioration.
Load package \texttt{interval} to access Breast Cosmesis Data. We fit the two-sample data separately.
library(interval)
## Loading required package: survival
## Loading required package: perm
## Loading required package: Icens
## Loading required package: MLEcens
data(bcos)
head(bcos, 3)
## left right treatment
## 1 45 Inf Rad
## 2 6 10 Rad
## 3 0 7 Rad
bc.res0<-mable.ic(bcos[bcos$treatment=="Rad",1:2], M=c(1,50), IC="none")
bc.res1<-mable.ic(bcos[bcos$treatment=="RadChem",1:2], M=c(1,50), IC="none")
As the warning message suggested, we check the \texttt{pval}. The \texttt{pval} when the search stopped is 0.0452.
op<-par(mfrow=c(2,2),lwd=2)
plot(bc.res0, which="change-point", lgd.x="right")
plot(bc.res1, which="change-point", lgd.x="right")
plot(bc.res0, which="survival", xlab="Months", ylim=c(0,1), main="Radiation Only")
plot(bc.res1, which="survival", xlab="Months", main="Radiation and Chemotherapy")
par(op)
A \(d\)-variate density \(f\) on a hyperrectangle \([a,b]=[a_1,b_1]\times \cdots\times[a_d,b_d]\) can be approximated by a mixture of \(d\)-variate beta densities on \([a,b]\), \(\beta_{mj}(x; a, b)=\prod_{i=1}^d\beta_{m_i,j_i}[(x_i-a_i)/(b_i-a_i)]/(b_i-a_i)\), with proportions \(p(j_1,\ldots,j_d)\), \(0\le j_i\le m_i, i=1,\ldots,d\). Because all the marginal densities can be approximated by Bernstein polynomials, we can choose optimal degree \(m_i\) based on observations of the \(i\)-th component of \(x\). For the \(i\)-th marginal density, an optimal degree is selected using \texttt{mable()}. Then fit the data using EM algorithm with the selected optimal degrees \(m=(m_1,\ldots,m_d)\) to obtain a vector \(p\) of the mixture proportions \(p(j_1,\ldots,j_d)\), arranged in the lexicographical order of \(j = (j_1,\ldots,j_d)\), \((p_0,\ldots,p_{K-1})\), where \(K=\prod_{i=1}^d (m_i+1)\). The proposed method of @Wang-and-Guan-2019 is implemented by function \texttt{mable.mvar()}.
data(faithful)
head(faithful, 3)
## eruptions waiting
## 1 3.600 79
## 2 1.800 54
## 3 3.333 74
a<-c(0, 40); b<-c(7, 110)
faith2<-mable.mvar(faithful, M=c(100,100), interval=cbind(a,b))
plot(faith2, which="density")
The density surface for two-dimensional data can be plot using the \texttt{plot} method (see Figure\ref{fig:Old-Faithful-Data-plot}). The summarized results are given below.
summary(faith2)
## Call: mable.mvar() for multivariate data
## Obj Class: mable
## Input Data: eruptions waiting
## Dimsion of data: 2
## Optimal degrees:
## m P-value
## eruptions 42 1e-04
## waiting 26 1e-04
## Maximum loglikelihood: 510.9957
## MABLE of p: can be retrieved using name 'p'
## Note: the optimal model degree is selected by the method of change-point.
Let \(T\) be an event time and \(X\) be an associated \(d\)-dimensional covariate with distribution \(H(x)\) on \(\cal{X}\). We denote the marginal and the conditional survival functions of \(T\), respectively, by \(S(t) = \bar F(t)= 1- F(t)=\Pr(T > t)\) and \(S(t | x) = \bar F(t | x)= 1- F(t | x)=\Pr(T > t | X=x).\) Let \(f(t | x)\) denote the conditional density of a continuous \(T\) given \(X=x\). The conditional cumulative hazard function and odds ratio, respectively, \(\Lambda(t | x)=-\log S(t | x)\) and \(O(t | x)={S(t | x)}/[{1-S(t | x)}]\). We will consider the general situation where the event time is subject to interval censoring. The observed data are \((X, Y)\), where \(Y=(Y_1,Y_2]\), \(0\le Y_1\le Y_2\le\infty\). The reader is referred to @Huang-and-Wellner-1997 for a review and more references about interval censoring. Special cases are right-censoring \(Y_2=\infty\), left-censoring \(Y_1=0\), and current status data.
Consider the Cox proportional hazard regression model [@Cox1972] \begin{equation}\label{eq: Cox PH model-conditional survival function} S(t | x) =S(t | x; \gamma, f0)=S_0(t){\exp(\gammaT\tilde{x})}, \end{equation} where \(\gamma\in \mathbb{G} \subset \mathbb{R}^d\), \(\tilde{x} =x -x_0\), \(x_0\) is any fixed covariate value, \(f_0(\cdot)=f(\cdot | x_0)\) is the unknown baseline density and \(S_0(t)=\int_t^\infty f_0(s)ds\). Define $\tau=\inf{t: F(t | x{0})=1}$. It is true that \(\tau\) is independent of \(x_0\), \(0<\tau\le\infty\), and \(f(t | x)\) have the same support \([0,\tau]\) for all \(x\in \cal{X}\). Let \((x_i, y_i)=(x_i, (y_{i1}, y_{j2}])\), \(i=1,\dots,n\), be independent observations of \((X, Y)\) and \(\tau_n\ge y_{(n)}=\max\{y_{i1}, y_{j2}: y_{j2}<\infty;\, i,j=1,\dots,n\}\). Given any \(x_0\), denote \(\pi=\pi(x_0)=1-S_0(\tau_n)\). For integer \(m\ge 1\) we define \(\mathbb{S}_m\equiv \{(u_0,\ldots,u_m)^T\in \mathbb{R}^{m+1}: u_i\ge 0, \sum_{i=0}^m u_i=1.\}.\) @Guan-arXiv-2019 propose to approximate \(f_0(t)\) on \([0,\tau_n]\) by \(f_m(t; p) = \tau_n^{-1}\sum_{i=0}^{m} p_i\beta_{mi}(t/\tau_n)\), where \(p=p(x_0)=(p_0,\ldots,p_{m+1})\) are subject to constraints \(p\in \mathbb{S}_{m+1}\) and \(p_{m+1}=1-\pi\). Here the dependence of \(\pi\) and \(p\) on \(x_0\) will be suppressed. If \(\pi< 1\), although we cannot estimate the values of \(f_0(t)\) on \((\tau_n,\infty)\), we can put an arbitrary guess on them such as \(f_m(t; p)= p_{m+1} \alpha(t-\tau_n)\), \(t\in (\tau_n,\infty)\), where \(\alpha(\cdot)\) is a density on \([0,\infty)\) such that \((1-\pi)\alpha(0)=(m+1)p_m/\tau_n\) so that \(f_m(t; p)\) is continuous at \(t=\tau_n\), e.g., \(\alpha(t)=\alpha(0)\exp[-\alpha(0)t]\). If \(\tau\) is finite and known we choose \(\tau_n=\tau\) and specify \(p_{m+1}=0\). Otherwise, we choose \(\tau_n=y_{(n)}\). For data without right-censoring or covariate we also have to specify \(p_{m+1}=0\) due to its unidentifiability.
The above method is implemented in function \texttt{mable.ph()} which returns maximum approximate Bernstein likelihood estimates of \((\gamma, p)\) with an optimal model degree \(m\) and a prespecified \(m\), respectively. With a efficient estimate of \(\gamma\) obtained using other method, \texttt{maple.ph()} can be used to get an optimal degree \(m\) and a mable of \(p\).
The \texttt{plot} method \texttt{plot.mable_reg()} for class \texttt{mable_reg} object returned by all the above functions produces graphs of the loglikelihoods at \(m\) in a set \texttt{M[1]:M[2]} of consecutive candidate model degrees, the likelihood ratios of change-point at \(m\) in \texttt{(M[1]+1):M[2]}, estimated density and survival function on the truncated \texttt{support}=\([0,\tau_n]\).
The Ovarian Cancer Survival Data is included in package \texttt{interval}.
library(interval)
futime2=ovarian$futime
futime2[ovarian$fustat==0]=Inf
ovarian2<-data.frame(age=ovarian$age, futime1=ovarian$futime, futime2=futime2)
head(ovarian2, 3)
## age futime1 futime2
## 1 72.3315 59 59
## 2 74.4932 115 115
## 3 66.4658 156 156
ova<-mable.ph(cbind(futime1, futime2) ~ age, data = ovarian2, M=c(2,35), g=.16)
summary(ova)
## Call: mable.ph(cbind(futime1, futime2) ~ age)
## Data: ovarian2
## Obj Class: mable_reg
## Dimsion of response: 1
## Dimsion of covariate: 1
## Optimal degree m: 23
## P-value: 0.008884947
## Maximum loglikelihood: -0.3875965
## MABLE of p: can be retrieved using name 'p'
##
## Estimate Std.Err Z value Baseline x0
## age 0.17665 0.01218 14.50256 38.893
##
## Note: the optimal model degree is selected by the method of change-point.
op<-par(mfrow=c(2,2))
plot(ova, which="likelihood")
plot(ova, which="change-point")
plot(ova, y=data.frame(c(60)), which="survival", type="l", xlab="Days",
main="Age = 60")
plot(ova, y=data.frame(c(65)), which="survival", type="l", xlab="Days",
main="Age = 65")
par(op)
Alternatively we can use \texttt{mable.reg()}.
ova1<-mable.reg(cbind(futime1, futime2) ~ age, data = ovarian2, M=c(2,35))
The AFT model can be specified as \begin{equation}\label{eq: AFT model in terms of density} f(t\mid x)=f(t\mid x;\gamma)=e{-\gammaT x}f(te{-\gammaT x}\mid 0),\quad t\in[0,\infty), \end{equation} where \(\gamma\in \mathbb{G}\subset \mathbb{R}^d\). Let \(\gamma_0\in \mathbb{G}\) be the true value of \(\gamma\). The AFT model (\ref{eq: AFT model in terms of density}) is equivalent to \[S(t\mid x;\gamma)=S(te^{-\gamma^T x}\mid 0),\quad t\in[0,\infty).\] Thus this is actually a scale regression model. The AFT model can also be written as linear regression \(\log(T)=\gamma^T x +\varepsilon\). It is clear that one can choose any \(x_0\) in \(\mathcal{X}\) as baseline by transform \(\tilde{x}=x-x_0\). If \(f(t\mid 0)\) has support \([0,\tau_0)\), \(\tau_0\le\infty\), then \(f(t\mid x)\) has support \([0,\tau_0 e^{\gamma_0^T x})\). We define \(\tau=\max\{\tau_0 e^{\gamma_0^T x}: x\in\mathcal{X}\}\) if \(\tau_0<\infty\) and \(\tau=\infty\) otherwise. The above AFT model can also be written as \[f(t\mid x;\gamma)=e^{-\gamma^T {x}} f_0(te^{-\gamma^T {x}}), \quad S(t\mid x;\gamma)=S_0(te^{-\gamma^T {x}}),\] where \(f_0(t)=f(t\mid 0)\) and \(S_0(t)=S(t\mid 0)=\int_t^\infty f_0(u)du\).
As in Cox PH model, we choose \(\tau_n>y_{(n)}= \max\{y_{i1}, y_{j2}: y_{j2}<\infty;\, i,j=1,\dots,n\}\) so that \(S(\tau_n)\) and \(\max_{x\in\mathcal{X}} S(\tau_n\mid x)\) are believed very small. Then we approximate \(f_0(t)\) and \(S_0(t)\) on \([0,\tau_n]\), respectively, by \begin{align} f0(t)&\approx f_m(t; p)=\frac{1}{\tau_n}\sum{j=0}m pj\beta{mj}\Big(\frac{t}{\taun}\Big),\quad t\in[0,\tau_n];\ S_0(t)&\approx S_m(t; p)= \sum{j=0}{m} pj \bar B{mj}\Big(\frac{t}{\tau_n}\Big),\quad t\in[0,\tau_n], \end{align} where \(S_m(\infty; p)=0\), and \(p= (p_0,\ldots,p_{m})^T\in \mathbb{S}_{m}\). Then \(f(t\mid x; \gamma)\) and \(S(t\mid x; \gamma)\) can be approximated, respectively, by \begin{align}\nonumber fm(t\mid x; \gamma, p)&=e{-\gammaT {x}}f_m\Big(te{-\gammaT {x}}; p\Big)\ &= \frac{e{-\gammaT {x}}}{\tau_n}\sum{j=0}m pj\beta{mj}\Big(e{-\gammaT {x}}\frac{t}{\taun}\Big),\quad t\in[0,\tau_ne{\gammaT {x}}];\nonumber S_m(t\mid x;\gamma,p)&=S_m\Big(te{-\gammaT {x}}; p\Big)\ &= \sum{j=0}{m} pj\bar B{mj}\Big(e{-\gammaT { x}}\frac{t}{\tau_n}\Big),\quad t\in[0,\tau_ne{\gammaT {x}}]. \end{align} @Guan-2019-mable-aft's proposal is implemented by functions \texttt{mable.aft()} and \texttt{maple.aft()}.
library(interval)
data(bcos)
bcos2<-data.frame(bcos[,1:2], x=1*(bcos$treatment=="RadChem"))
g<--0.41 # Hanson and Johnson 2004, JCGS
aft.res<-mable.aft(cbind(left, right)~x, data=bcos2, M=c(1, 30), g, tau=100, x0=1)
## Warning in mable.aft(cbind(left, right) ~ x, data = bcos2, M = c(1, 30), :
## The maximum candidate degree has been reached
## with a p-value of the change-point 0.000380.
summary(aft.res)
## Call: mable.aft(cbind(left, right) ~ x)
## Data: bcos2
## Obj Class: mable_reg
## Dimsion of response: 1
## Dimsion of covariate: 1
## Optimal degree m: 5
## P-value: 0.0003802489
## Maximum loglikelihood: -144.4767
## MABLE of p: can be retrieved using name 'p'
##
## Estimate Std.Err Z value Baseline x0
## x -0.381874 0.094206 -4.053588 1
##
## Note: the optimal model degree is selected by the method of change-point.
op<-par(mfrow=c(1,2), lwd=1.5)
plot(x=aft.res, which="likelihood")
plot(x=aft.res, y=data.frame(x=0), which="survival", type="l", col=1,
main="Survival Function")
plot(x=aft.res, y=data.frame(x=1), which="survival", lty=2, col=1, add=TRUE)
legend("bottomleft", bty="n", lty=1:2, col=1, c("Radiation Only",
"Radiation and Chemotherapy"), cex=.7)
par(op)
Alternatively we can use \texttt{mable.reg()}.
aft.res1<-mable.reg(cbind(left, right)~x, data=bcos2, 'aft', M=c(1, 30), tau=100, x0=1)
## Warning in mable.aft(formula, data, M, g, tau, x0, controls, progress):
## The maximum candidate degree has been reached
## with a p-value of the change-point 0.000380.