version 0.5.2

cran · Jan 13, 2017 · 363fa46 · 363fa46
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diff --git a/ChangeLog b/ChangeLog
@@ -1,3 +1,10 @@
+2017-01-13 Arthur Allignol <arthur.allignol@uni-ulm.de> 
+
+	* version number 0.5.2
+	* NAMESPACE and DESCRIPTION: import stats and survival
+	* Examples and tests: require(survival) statements
+	* Rerun of the test file
+
 2013-09-16 Arthur Allignol <arthur.allignol@uni-ulm.de>
 
 	* DESCRIPTION: update version number;

diff --git a/DESCRIPTION b/DESCRIPTION
@@ -1,15 +1,15 @@
 Package: kmi
-Version: 0.5.1
-Date: 2014/9/4
-Title: Kaplan-Meier multiple imputation for the analysis of cumulative
-        incidence functions in the competing risks setting
+Version: 0.5.2
+Title: Kaplan-Meier Multiple Imputation for the Analysis of Cumulative
+        Incidence Functions in the Competing Risks Setting
 Author: Arthur Allignol <arthur.allignol@uni-ulm.de>
 Maintainer: Arthur Allignol <arthur.allignol@uni-ulm.de>
-Depends: survival
-Imports: mitools
-Description: The package performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions.
+Imports: mitools,survival,stats
+Description: The kmi package performs a Kaplan-Meier multiple imputation to recover the missing potential censoring information from competing risks events, so that standard right-censored methods could be applied to the imputed data sets to perform analyses of the cumulative incidence functions.
 License: GPL (>= 2)
-Packaged: 2014-09-04 21:06:46 UTC; arthur
+URL: https://github.com/aallignol/kmi
+BugReports: https://github.com/aallignol/kmi/issues
 NeedsCompilation: no
+Packaged: 2017-01-13 12:27:14 UTC; arthur
 Repository: CRAN
-Date/Publication: 2014-09-04 23:36:49
+Date/Publication: 2017-01-13 15:34:36
diff --git a/MD5 b/MD5
@@ -1,6 +1,6 @@
-f5aa689562839759d76a34079eb17207 *ChangeLog
-8cde2d75282d1c896ff3e2c220961573 *DESCRIPTION
-cfda81db12c5f646907bacc2edefad16 *NAMESPACE
+024dd0b9a31dbf45c3f0029a47c974bd *ChangeLog
+da5f68f0abcdd5367a0035af27c59536 *DESCRIPTION
+ddfcf4daaf5b71cbdbbfdc8cdbc797ed *NAMESPACE
 946e6564399cf69089aaf2bdd2ec14cd *R/cox.kmi.R
 14b8caf85c17ac55ea17a1b42e3d0e0d *R/kmi.R
 433e23db8b80c6b899fe5235db056d9f *R/kmi.classic.R
@@ -9,12 +9,12 @@ d420a5c943333e5e919d86b39d4269a8 *R/kmi.tdc.R
 052b3bd2d78d458f491145cffb2d0667 *R/print.summary.cox.kmi.R
 f41407106aa9c0c1557deda7b3ee0731 *R/summary.cox.kmi.R
 66b2fa917ef690eb2788f500ca7483eb *data/icu.pneu.rda
-b92a7ed912b41aa66bca1b7108c556df *man/cox.kmi.Rd
+4caee9672bf1ccb3c62f6ae069eeb239 *man/cox.kmi.Rd
 c5c3dac6e0a7357e2a9ee6c8b3253016 *man/icu.pneu.Rd
 5694dd1654e1b6677ecf2ad4bbcf4988 *man/kmi-package.Rd
-2b102835e9a5435c1c86a628a13bee28 *man/kmi.Rd
+3d5013331c9edbc2cf054dbaaeab10df *man/kmi.Rd
 76a3f1442e8117c86f881386bc20d7fc *man/print.cox.kmi.Rd
 756b9ebd16972c029b054ba2001cd623 *man/print.summary.cox.kmi.Rd
 e6b96bbd9684834226a67095d94191d8 *man/summary.cox.kmi.Rd
-4cd9aec85d4d5e8c954880aef62319aa *tests/test.kmi.R
-198567dea2a2fff85c515ad5c4923971 *tests/test.kmi.Rout.save
+17a86796978e079d6203d6c5886714e6 *tests/test.kmi.R
+54c1e806666d332d88273472c68dbc7e *tests/test.kmi.Rout.save
diff --git a/NAMESPACE b/NAMESPACE
@@ -1,5 +1,7 @@
 import(survival)
 importFrom(mitools, MIcombine)
+importFrom("stats", "formula", "model.extract", "model.matrix",
+           "printCoefmat", "pt", "qt", "update.formula")
 export(kmi, cox.kmi, summary.cox.kmi)
 S3method(print, cox.kmi)
 S3method(summary, cox.kmi)

diff --git a/man/cox.kmi.Rd b/man/cox.kmi.Rd
@@ -43,28 +43,32 @@ cox.kmi(formula, imp.data, df.complete = Inf, ...)
 \examples{
 data(icu.pneu)
 
-set.seed(1313)
-imp.dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
-               etype = event, id = id, failcode = 2, nimp = 5)
-
-fit.kmi <- cox.kmi(Surv(start, stop, event == 2) ~ pneu, imp.dat)
-
-summary(fit.kmi)
 
+if (require(survival)) {
+
+    set.seed(1313)
+    imp.dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
+                   etype = event, id = id, failcode = 2, nimp = 5)
+
+    fit.kmi <- cox.kmi(Surv(start, stop, event == 2) ~ pneu, imp.dat)
+
+    summary(fit.kmi)
+
 ### Now using the censoring-complete data
-fit <- coxph(Surv(start, adm.cens.exit, event == 2) ~ pneu, icu.pneu)
-
-summary(fit)
-
-## estimation of the censoring distribution adjusted on covariates
-dat.cova <- kmi(Surv(start, stop, status) ~ age + sex,
-                data = icu.pneu, etype = event, id = id,
-                failcode = 2, nimp = 5)
-
-fit.kmi2 <- cox.kmi(Surv(start, adm.cens.exit, event == 2) ~ pneu + age,
-                    dat.cova)
-
-summary(fit.kmi2)
+    fit <- coxph(Surv(start, adm.cens.exit, event == 2) ~ pneu, icu.pneu)
+
+    summary(fit)
+
+    ## estimation of the censoring distribution adjusted on covariates
+    dat.cova <- kmi(Surv(start, stop, status) ~ age + sex,
+                    data = icu.pneu, etype = event, id = id,
+                    failcode = 2, nimp = 5)
+
+    fit.kmi2 <- cox.kmi(Surv(start, adm.cens.exit, event == 2) ~ pneu + age,
+                        dat.cova)
+
+    summary(fit.kmi2)
+}
 }
 \keyword{survival}
 \keyword{models}

diff --git a/man/kmi.Rd b/man/kmi.Rd
@@ -91,20 +91,24 @@ kmi(formula, data, id = NULL, etype, failcode = 1, nimp = 10,
 \examples{
 data(icu.pneu)
 
-dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
-           etype = event, id= id, failcode = 2, nimp = 5)
-
-## another way to specify the formula if there is no status
-## variable
-icu.pneu$ev <- icu.pneu$event
-icu.pneu$ev[icu.pneu$status == 0] <- 0
-
-dat <- kmi(Surv(start, stop, ev != 0) ~ 1, data = icu.pneu,
-           etype = ev, id= id, failcode = 2, nimp = 5)
-
-## with covariates to model the censoring distribution
-dat.cova <- kmi(Surv(start, stop, status) ~ age + sex,
-                data = icu.pneu, etype = event, id = id,
-                failcode = 2, nimp = 5)
+if (require(survival)) {
+
+    dat <- kmi(Surv(start, stop, status) ~ 1, data = icu.pneu,
+               etype = event, id= id, failcode = 2, nimp = 5)
+
+    ## another way to specify the formula if there is no status
+    ## variable
+    icu.pneu$ev <- icu.pneu$event
+    icu.pneu$ev[icu.pneu$status == 0] <- 0
+
+    dat <- kmi(Surv(start, stop, ev != 0) ~ 1, data = icu.pneu,
+               etype = ev, id= id, failcode = 2, nimp = 5)
+
+    ## with covariates to model the censoring distribution
+    dat.cova <- kmi(Surv(start, stop, status) ~ age + sex,
+                    data = icu.pneu, etype = event, id = id,
+                    failcode = 2, nimp = 5)
+
+}
 }
 \keyword{survival}
diff --git a/tests/test.kmi.R b/tests/test.kmi.R
@@ -1,3 +1,4 @@
+require(survival)
 require(kmi)
 
 ### test 1 

diff --git a/tests/test.kmi.Rout.save b/tests/test.kmi.Rout.save
@@ -1,6 +1,6 @@
 
-R version 3.1.1 (2014-07-10) -- "Sock it to Me"
-Copyright (C) 2014 The R Foundation for Statistical Computing
+R version 3.3.2 (2016-10-31) -- "Sincere Pumpkin Patch"
+Copyright (C) 2016 The R Foundation for Statistical Computing
 Platform: x86_64-suse-linux-gnu (64-bit)
 
 R is free software and comes with ABSOLUTELY NO WARRANTY.
@@ -17,10 +17,10 @@ Type 'demo()' for some demos, 'help()' for on-line help, or
 'help.start()' for an HTML browser interface to help.
 Type 'q()' to quit R.
 
+> require(survival)
+Loading required package: survival
 > require(kmi)
 Loading required package: kmi
-Loading required package: survival
-Loading required package: splines
 > 
 > ### test 1 
 > 
@@ -146,7 +146,7 @@ cox.kmi(formula = Surv(time, ev == 1) ~ cov, imp.data = dat.kmib)
 Pooled estimates:
 *****************
           coef exp(coef)  se(coef)          t         p
-cov -0.2076261 0.8125108 0.3726519 -0.5571583 0.5774271
+cov -0.2216016 0.8012345 0.3743375 -0.5919834 0.5538821
 
 > fit.kmib.fact
 Call:
@@ -156,7 +156,7 @@ cox.kmi(formula = Surv(time, status == "rel") ~ cov, imp.data = dat.kmib.fact)
 Pooled estimates:
 *****************
           coef exp(coef)  se(coef)          t         p
-cov -0.2076261 0.8125108 0.3726519 -0.5571583 0.5774271
+cov -0.2216016 0.8012345 0.3743375 -0.5919834 0.5538821
 
 > 
 > summary(fit.kmib)
@@ -168,10 +168,10 @@ cox.kmi(formula = Surv(time, ev == 1) ~ cov, imp.data = dat.kmib)
 Pooled estimates:
 *****************
        coef exp(coef) se(coef)      t Pr(>|t|)
-cov -0.2076    0.8125   0.3727 -0.557    0.577
+cov -0.2216    0.8012   0.3743 -0.592    0.554
 
     exp(coef) exp(-coef) lower .95 upper .95
-cov    0.8125      1.231    0.3914     1.687
+cov    0.8012      1.248    0.3846     1.669
 
 > 
 > 
@@ -409,4 +409,4 @@ pneu1     3.034     0.3297     1.896     4.854
 > 
 > proc.time()
    user  system elapsed 
-  4.409   0.035   4.494 
+  5.104   0.024   5.119