R/dfnsmult.R

In smoothHR: Smooth Hazard Ratio Curves Taking a Reference Value

dfnsmult <- function(time, time2=NULL, status, nl.predictors, other.predictors, method, mindf=NULL, maxdf=NULL, ntimes=NULL, data) {
	options(warn=-1);
	ctype <- "FALSE";
	if ( missing(data) ) {stop("The argument data is missing");}
	if ( missing(time) ) {stop("The argument time is missing");}
	if ( missing(status) ) {stop("The argument status is missing");}
	if ( missing(nl.predictors) ) {stop("The argument 'nl.predictors' is missing");}
	if ( !missing(time2) ) {ctype <- "TRUE";}
	if ( missing(method) ) {method <- "AIC";}
	if ( missing(ntimes) ) {ntimes <- 5;}
	if ( method != "AIC" & method != "AICc" & method != "BIC") {stop("The argument 'method' is not valid");}
	if (ntimes < 2 | ntimes > 10) {stop("The argument 'ntimes' must be between 2 and 10");}
	p0 <- match(names(data), time, nomatch=0);
	p1 <- which(p0 == 1);
	ntime <- data[,p1];
	p2 <- match(names(data), status, nomatch=0);
	p3 <- which(p2 == 1);
	nstatus <- data[,p3];
	if (ctype == TRUE) {
		p2 <- match(names(data), time2, nomatch=0);
		p3 <- which(p2 == 1);
		ntime2 <- data[,p3];
	}
	if ( !missing(other.predictors) ) {opred <- paste(other.predictors, collapse="+");}
	#if ( !missing(other.predictors) ) {opred <- paste(other.predictors, collapse="+");}
	#npred0 <- paste("ns(", nl.predictors, ",df=0, caic=TRUE)", collapse="+");
	#if ( !missing(other.predictors) ) {pred <- paste(npred0, opred, sep="+");}
	#else {pred <- npred0;}
	nnl <- length(nl.predictors);
	if ( missing(mindf) ) {mindf <- rep(1,nnl);}
	if ( missing(maxdf) ) {maxdf <- rep(15,nnl);}
	if ( nnl == 1 & is.null(time2) ) {ndf1 <- dfnsuniv(time=time, status=status, nl.predictor=nl.predictors, other.predictors=other.predictors, method=method, maxdf=maxdf, data=data);}
	if ( nnl == 1 & !is.null(time2) ) {ndf1 <- dfnsuniv(time=time, time2=time2, status=status, nl.predictor=nl.predictors, other.predictors=other.predictors, method=method, maxdf=maxdf, data=data);}
	if (nnl > 1) {
		if (nnl > 10) {stop("The maximum number of nonlinear predictors is 10");}
		if (dim( table(nl.predictors) ) < nnl) {stop("Check variables in argument nl.predictors");}
		if ( !missing(other.predictors) ) {
			nop <- length(other.predictors);
			if (dim( table(other.predictors) ) < nop) {stop("Check variables in argument other.predictors");}
			all.predictors <- c(other.predictors, nl.predictors);
			if (dim( table(all.predictors) ) < nop+nnl) {stop("Check variables in argument's nl.predictors and other.predictors");}
		}
		if (nnl == 5) {cat("This may take a few seconds...\n");}
		nnl1 <- seq(1:nnl);
		ndf <- paste("df[", nnl1, "]", sep="");
		df0 <- c( rep(1, nnl) );
		df1 <- c( rep(15, nnl) );
		if ( !missing(mindf) ) {df0 <- mindf;}
		if ( !missing(maxdf) ) {df1 <- maxdf;}
		df2 <- cbind(df0, round( (df0+df1)/2, 0 ), df1);
		for (i in 1:ntimes) {
			aaa <- lapply( apply( df2, 1, function(z) {list( c(z[1],z[2],z[3]) );} ), function(y) {unlist(y);} );
			out <- do.call(expand.grid, aaa);
			myaic <- rep(100000, dim(out)[1]);
			for (k in 1:dim(out)[1]) {
				df <- round(out[k,], 0);
				npred1 <- paste("ns(", nl.predictors, ",df=", df, ")", collapse="+");
				if ( !missing(other.predictors) ) {pred1 <- paste(npred1, opred, sep="+");}
				else {pred1 <- npred1;}
				if (ctype == "TRUE") {
					covar <- as.formula( paste(" Surv(ntime,ntime2,nstatus)~ ", pred1) );
					try(fit1 <- coxph(covar, data=data, x=TRUE), TRUE);
				} else {
					covar <- as.formula( paste(" Surv(ntime,nstatus)~ ", pred1) );
					try(fit1 <- coxph(covar, data=data, x=TRUE), TRUE);
				}
				#try(myaic[k] <- -2*fit1$loglik[2]+2*sum(out[k,]), TRUE);
				if (method == "AIC") {try(myaic[k] <- -2*fit1$loglik[2]+2*sum(out[k,]), TRUE);}
				else if (method == "AICc") {try(myaic[k] <- -2*fit1$loglik[2]+2*fit1$nevent*(sum(out[k,])+1)/(fit1$nevent-sum(out[k,])-2), TRUE);}
				else if (method == "BIC") {try(myaic[k] <- -2*fit1$loglik[2]+2*log(fit1$nevent)*sum(out[k,]), TRUE);}
			}
			p <- which.min(myaic);
			ndf1 <- vector(length=nnl);
			for (s in 1: nnl) {ndf1[s] <- out[p,s];}
			aic <- myaic[p];
			ndf2 <- df2;
			for (j in 1:nnl) {
				if (ndf1[j] == df2[j,1]) {
					a1 <- df2[j,1];
					a2 <- df2[j,2];
					a3 <- (df2[j,1]+df2[j,2])/2;
					ndf2[j,] <- sort( c(a1, a2, a3) );
				}
				if (ndf1[j] == df2[j,3]) {
					a1 <- df2[j,3];
					a2 <- df2[j,2];
					a3 <- (df2[j,3]+df2[j,2])/2;
					ndf2[j,] <- sort( c(a1, a2, a3) );
				}
				if (ndf1[j] == df2[j,2]) {
					p <- c();
					for ( t in 1:(3^nnl) ) {
						if ( out[t,j] != ndf1[j] & all(out[t,-j]-ndf1[-j] == 0) ) {p <- c(p, t);}
					}
					p1 <- which.min(myaic[p]);
					if (p1 == 1) {a2 <- df2[j,1];}
					else {a2 <- df2[j,3];}
					a1 <- ndf1[j];
					a2 <- a2;
					a3 <- (ndf1[j]+a2)/2;
					a4 <- sort( c(a1, a2, a3) );
					ndf2[j,] <- a4;
				}
			}
			df2 <- ndf2;
		}
  }
	options(warn=0);
	return( round(ndf1) );
} # dfnsmult