version 1.13

DESCRIPTION

@@ -1,16 +1,16 @@
 Package: bhm
 Type: Package
 Title: Biomarker Threshold Models
-Version: 1.11
-Date: 2017-05-06
+Version: 1.13
+Date: 2017-12-19
 Author: Bingshu E. Chen
 Maintainer: Bingshu E. Chen <bingshu.chen@queensu.ca>
 Depends: R (>= 2.10.0), coda, survival
 Imports: methods
-Description: Biomarker threshold models are tools to fit both predictive and prognostic biomarker effects. 
+Description: Contains tools to fit both predictive and prognostic biomarker effects using biomarker threshold models. Evaluate the treatment effect, biomarker effect and treatment-biomarker interaction using probability index measurement. Test for treatment-biomarker interaction using residual bootstrap method.
 License: GPL (>= 2)
 LazyLoad: yes
 NeedsCompilation: no
-Packaged: 2017-05-08 19:04:01 UTC; chenbe
+Packaged: 2017-12-20 21:02:13 UTC; b3chen
 Repository: CRAN
-Date/Publication: 2017-05-11 16:28:14 UTC
+Date/Publication: 2017-12-20 21:15:00 UTC

MD5

@@ -1,16 +1,19 @@
-0b2e4f31671ff8402302ba9b6b7c9023 *DESCRIPTION
-1337f751fbbb0a94bc713cca64fcbd6d *NAMESPACE
+3e3445f8e4c2ec919ce391a4c1b1fe9c *DESCRIPTION
+164e567e1c3f543c2d33e97a64cdefe6 *NAMESPACE
 d3046ce94c557ca4e021de5f990948eb *R/bhm.R
 154c78c7a4b19a4cdc4828ada6157070 *R/bhm_fit.R
-bbbabbd6283f8a861619e707e2cb8b55 *R/bhm_lib.R
-cbeff78ff76e1408f164a15b4f71d227 *R/pIndex.R
-a4263b1e6b22eb511ffd60fe1222f4e5 *R/prolik.R
+1a4c222d1ad7a728f6c8573626d7589b *R/bhm_lib.R
+74fbe0cc51e80e494aca48a8a4df6fb7 *R/pIndex.R
+38d9937f78588fbafa667ae135d77056 *R/prolik.R
+55a7ff1833b9b38e910263d9f76d0cf4 *R/resboot.R
 c84d68567329ac77192b098af2c2c943 *data/data.rda
 d273eb7c975522763535e8e7fb5149ae *inst/CITATION
-20eb14d3a5885ea9c06354996423ea84 *man/bhm-package.Rd
-e0cbdda870e449b4b095863ffc540faa *man/bhm.Rd
-bed635491c04690a15ee10610dd15156 *man/control.Rd
+cb263f3b36c659c86ecb6ef66211baf2 *man/bhm-package.Rd
+09f155377871452c53345fddd4db1324 *man/bhm.Rd
+1b8ecaf4c48c2f22d0a3a062fe83eb8c *man/control.Rd
 b0cc661060af77834ef1c1a0ba09d0ba *man/data.Rd
-bdde2f311ebfac278ad5fb79561ea0a9 *man/pIndex.Rd
-29de3cf98f378f2d94aa59254eac93e7 *man/pIndexControl.Rd
-5ee5937f65d07e4435547acb3f9fba1a *man/print.Rd
+7c0a1804e8a4e4e204228c8864a71632 *man/pIndex.Rd
+9c0eb1cc58a57d2f6c5c16ae514e6264 *man/pIndexControl.Rd
+7e2f3a4866d28dd129755e2ec82ccc6f *man/plot.Rd
+7596c5caf1eb3a609464f11b09b4ed04 *man/print.Rd
+386efc1029c1ea6de3bbd4d0fe8f9f2f *man/resboot.Rd

NAMESPACE

@@ -3,17 +3,30 @@ exportPattern("^[^\\.]")
 
 import(coda)
 import(survival)
+importFrom("graphics", "abline", "hist", "lines", "plot")
 importFrom("methods", "is")
-importFrom("stats", "cov", "dbeta", "glm", "logLik", "model.frame",
-           "model.matrix", "model.response", "nlm", "printCoefmat",
-           "quantile", "rbinom", "rexp", "rgamma", "rnorm", "runif", "vcov")
+importFrom("stats", "aggregate", "cov", "dbeta", "dnorm", "glm", "logLik", 
+           "model.frame",
+           "model.matrix", "model.response", "nlm", "optim", "printCoefmat",
+           "quantile", "qnorm", "rbinom", "rexp", "rgamma", "rnorm", "runif", 
+           "sd", "vcov")
 
 S3method(bhm, default)
 S3method(bhm, formula)
-S3method(summary, bhm)
+
 S3method(print, bhm)
+S3method(print, pIndex)
+S3method(print, resboot)
 S3method(print, summary.bhm)
 
 S3method(pIndex, default)
 S3method(pIndex, formula)
-S3method(print, pIndex)
+
+S3method(plot, pIndex)
+S3method(plot, resboot)
+
+S3method(resboot, default)
+S3method(resboot, formula)
+
+S3method(summary, bhm)
+

R/bhm_lib.R

@@ -84,13 +84,14 @@ surv.gendat =  function(n, c0, beta){
   zx = z*x1
   x0 = rep(1, n)
   X = cbind(z, x1, zx)
-  h0 = 1
+  h0 = 0.5
   h = h0*exp(X%*%beta)
   stime = rexp(n, h)               #Failure time.
-  endstudy = runif(n, 2, 4)
+  endstudy = runif(n, 0, 5)
   cstatus = ifelse(stime>endstudy, 0, 1) ##Censored at end of study time.
+  cat('\nCensoring: ', 1-mean(cstatus), '\n')
   stime = ifelse(stime>endstudy, endstudy, stime)
-  dat = cbind(stime, cstatus, z, x)
+  dat = cbind(time=stime, status=cstatus, z=z, x=x)
   return(dat)
 }
 
@@ -119,8 +120,27 @@ x.cdf = function(x){
   n = length(x)
   p = rep(0, n)
   for (i in 1:n) {
-    p[i] = sum(x<x[i])
+    p[i] = sum(x<=x[i])
   }
   p = (p-0.5)/n
   return(p)
 }
+
+## Kernel function
+.K_func = function(w, u, h, kernel = c("gaussian", "epanechnikov", "rectangular", 
+		     "triangular", "biweight", "cosine", "optcosine")) {
+  kernel = match.arg(kernel)
+  x = w-u
+  ax = abs(x)
+  esp = 1e-40
+
+  kh = switch(kernel, gaussian = dnorm(x, sd = h/2),
+    rectangular = ifelse(ax < h, 0.5/h, esp), 
+    triangular = ifelse(ax < h, (1 - ax/h)/h, esp),
+    epanechnikov = ifelse(ax < h, 3/4 * (1 - (ax/h)^2)/h, esp),
+    biweight = ifelse(ax < h, 15/16 * (1 - (ax/h)^2)^2/h, esp),
+    cosine = ifelse(ax < h, (1 + cos(pi * x/h))/(2*h), esp),
+    optcosine = ifelse(ax < h, pi/4 * cos(pi * x/(2*h))/h, esp))
+  return(kh)
+}
+