km.ci/0000755000175100001440000000000011246471673011304 5ustar hornikuserskm.ci/NAMESPACE0000755000175100001440000000001710305572634012516 0ustar hornikusersexport(km.ci) km.ci/data/0000755000175100001440000000000011246471673012215 5ustar hornikuserskm.ci/data/critical.value.hall.90.rda0000755000175100001440000003014310351557474016764 0ustar hornikusersRDX2 X  critical.value.hall.90?&x?,?JL_?B?4֡a??&x?$ xF?Vu?rGE???sg?sP?ffffff?|?PH?@NU?~"?~($ ??"h ԕ?P{?{m]?wkQ???,BZc?qu!?JL_?u!.?-V??e+?$tS?_o?0 (?D*0?Vl!-?e+a?qu!S?{J#9?nP?p:??\(?$tT?N;6?:)y?:)y?:)y?:)y?:)y?:)y?zG?5Xy>?'/V?Ov_ح?S?=p =?nO?a@O?Dg8}?O;d?%1?+ I^?lC? ?ᰉ'R?sh?E84֡?q ?@?<64?iB?lC?Ov_?-w1?As?S&?bM?nO;?xl"h ?4m9?oiDg? ??qi?\(?\(?o?o?o?o?o?ߤ??ꎊqi? qu?E?- ?>BZ?S?0U2a|?oiDg?E?}H?a@N?쿱[W?D*? ?=p =?iB?:)y?Xe?D*?+jg?)^?(?=K]?NT?]cA ?jOv`?tSMj?|Q??oiDg?p:?qiC?C%?C%?q ?q ?q ?q ?q ?R<6??|hs?셇ݗ?kP|??fA?L_?nO;?n? ҈?P{?񐖻?)^ ?ѷX?2a|Q?_o?p:?zG?*0U?c ? k~(?!-w1?64K?H?W>6z?cA [?n.2?u!.?|Q?4m9?nP???󅇓ݗ?󅇓ݗ?oiDg?oiDg?oiDg?oiDg?oiDg?;5Xy?b}?_o?'RT`e?nO?%1?PH?L/{J?#?<64?}H˒?Y}?A [?$ xF?RT`d?|??wkQ?oi?%1?$tS??-V??|hs?NT?[6?eڹ?n.2?tSMj?xl"h ?{J#9?|hr?}H˒?~($ x?~($ x?~($ x?~($ x?~($ x?~($ x?*0U?ffffff?U=?zxl"h?7KƧ?)y ??? =p?!.H?ffffff?TɆ?ѷY?n?A7Kƨ?l!-w?ݗ+k?Xe? =p?䎊q? ҈?!R<6?4J?C\N?Q_p?[6?cA [?jOv`?nO;?qu!S?sPH?sg?tSMj?tSMj?tSMj?tSMj?tSMj?tSMj?S?Z?Ov_?Z?s?Zc ?ᰊ?n.?IQ ?qiC?8YK?lC?-V?Y|??L/{J?Ƨ-?Z?!.H?@N?&IQ?6z?C\N?NT?W>6z?]cA ?bM?e+a?fA?g k?h ԕ+?h ԕ+?h ԕ+?h ԕ+?h ԕ+?hr Ĝ?n?S?~"?w1?&x?ݗ?>BZ?%1?j~"?TɅ?Gz??BZc?l"h ԕ?N;6?g l?`A7?C,z? I^5?sh?&IQ?4J??|hs?H?NT?S&?Vl!-?XbM?YJE?YJE?Y|?Y|?Y|?Y|?Y|?2W?sg?ڹY?5Xy>?NT?lD?~$?Dg8}?oiDg?TɅo?!.I?&x?RT`d?y=b?ߤ@?/V?8YJ?hr!? I^5?@N?!R<6?-V?64K?=K]?As?D*0?F]cf?GzH?H?H˒:*?H˒:*?H˒:*?H˒:*?H˒:*?쎊qi? xF]?*0U2a|? '/?p:~?ȴ9Xb?Xe,?]ce?ߤ@?,?ohی?ae?v?8YJ?C,z?!.H? ҈??!-w1?(?-w1?0 (?2W?3ߤ?4J?4m8?4m8?4m8?4m8?4m8?/w?T`d?m\?)y ?L_?ᰉ'R?-V?p:~?q ?e+?u?<쿱[?bM?%2?ae?/V?`A7?Z?䎊q?$tS? k~(?)^?Ov_?_o?Q?1&?[?}H?}H?}H?}H?}H? [6?$tT???B?3?D?=p =?~($ x?Xe?ᰉ'?Xe,???bM?ohی?ߤ@?g l?Ƨ-? =p?%1?c ?+jg?lC?$tS?n.?ѷ?F]c?F]c?????W>6z?74?|?Vt?ᰊ?u%F ?H˒:*?oiDg?j~#?쿱[W??Xe,?<쿱[?\>?y=b?N;6?L/{J?Xe?oi?*0U?D*?iB?e+?䎊qj??1.?,BZc?VϪ?t?-V????????x?|?B?L_?ȴ9Xb?~$?%1?IQ ?ffffff?}H˒?񐖻?쿱[W? ?{?sg?rG?#w?~($ ?~($ ?~($ ?~($ ?~($ ?{J#9?74???)y ?p:~?lD?>BZ?n.?!.H?<64?P{?a@N?lC?u%F L?z?}Vl!?~$t?~"?~"?~"?~"?~"?W>6z?$tT?m\? '/?NT?ݗ?ᰊ? =p?#? ҈?}H?+ I^?4m8?:S&?=b??|hs???@NU?@NU?@NU?@NU? [6?T`d?*0U2a|?5Xy>?&x?Zc ???L/{J?n?E?%1?t?\(?"`?~$?PH?PH?PH?PH?PH dim . dimnames. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 al00 al02 al04 al06 al08 al10 al12 al14 al16 al18 al20 al22 al24 al26 al28 al30 al32 al34 al36 al38 al40 al42 al44 al46 al48 al50 al52 al54 al56 al58 al60km.ci/data/critical.value.nair.95.rda0000755000175100001440000002675510351557474017020 0ustar hornikusersRDX2 X  critical.value.nair.95F@@E8YK@}H˒@<쿲@@Fs@$/@1&x@JL_@a'R@weں@q @Q@ (@@T`d@e+@A [@4m9@qi@1&@*d7@7rG@E84֢@Rp:@_حV@l!-w@z@_o@O M@3@Vϫ@ (@̘_@ۋq @P{@lC@ O;dZ@ [6@ 5 '0@ L/{J#@ fA@ Fs@ (@ A [@ӎMj@4m8@ѷX@H˒:@!-w2@!.H@J#9@mC\@qi@<쿲@ǮzH@@N@ k~@@$/@9#w@L_@_o@qu!R@nP@@TɆ@ '0@84֢@E@7@#@tj@*0U2@#n.2@3g l@C$@T,<@de@vFs@u%F@Q@Vϫ@š@ce@kP@ ᰉ@ :)y@ irGF@ (@1.@^҈p;@<64@ @JL_@@-V@/v@%2@',@Gy @e`A7L@:S@S@m8@Ƨ@M@+@ @ ěT@49XbN@G)^@Y|@kC]@}E@@-w1@W@g8}@t@ k@ (@ L_@Q@1-@F L/@["`B@qA [@ѷ@`A@|hr@u!@ ݗ+@ :)y@ Fs@R<6@n.2@nO@lC@rG@l"h @0'RT@be+@qi@C-@6@E@}H@;/V@W =p@ql@ڹY@`A@Y}@H@ڹ@s@@#wk@74m@JD@^5?|@qu!R@?@+j@6z@ߤ?@ҽ<64@@lC@u@($ xG@?@Yb~@u%F L@ݗ+k@}Vl@u!@ ᰉ@ fA@F@v+jg@ܑN;@2-V@{J#9@)^@䎊q@$/@R䎊@{~%@R<6@*0@@?@!.H@=v@XD*@ql@ @n@*@ M:@e@xl"h @@$tSN@9#w@M@b@5@weں@I^5?@R<6@4m@@7@hr@R<6@1[W>6@NT@ohۋ@ݗ+k@|hr@kP@ L/{J#@Z1@UfA@3@oiDg@L_@ڹY@Zc@#@"3@Ks@r䎊q@@4֡b@e+@!.I@ݗ+k@/{J#:@J#9@cZ@|?@O M@<쿲@a@N@1'@oiDg8@&J@u"@2W@I@_o@u!.@qu"@TɆ@<64@֡af@A [@ @,zxl"@NT@u%F L@`A@ce@ 5 '0@fA@@G{@%F L/@L_@qu!R@@֡a@@iB@Vu@)lC@DS@^iB@wkP@bM@@n@bM@-V@Fs@%@64J@N_o @ffffff@b}V@PH@|@;dZ@hr!@ @1[W>6@Yb~@ѷ@š@ [6@@) @}Vl!@y @ 'RTa@E84֢@{J#9@>B[@ڹY@Q@,>B[@R䎊@u!R@@C-@՛=K@䎊q@\(@+ I^@E8YK@` qu@y"`@@N@<쿲@oi@cA @Fs@@(TɅ@BZc@\]ce@weں@@N@ (@;dZ@A [@R<6@?@qA [@Vϫ@ O;dZ@C@kP|@iB@{@ @+6z@`d8@N;6@[W>@JL@:)y@8}H@[$@}E@Q@ (@]ce@ (@Ov_@1-@M5Xy>@gfA@u%F @wkP@}Vl@а{@fB@S&@ 'RT`@;dZ@W&@tj~@@N@|@֡af@hr@($ xG@["`B@Q@lC@D@ (@Y|@Ov_خ@ܑN;@sh@JW&@{J#9@\)@ӎMj@lC@"3@Fs@iDg8~@qiB@0 @ɅoiE@fA@Q@!.H@> (@Y>BZ@u!R@)^@<쿲@ǮzH@S@.H@Q@9#w@W&@weں@PH@<64@7@u@F L/@u%F@P{@|@%2@LcA \@ߤ?@˒:)z@@73@h>BZc@sh@n@rGF@o@4m8@W@y (@vȴ@^5?|@JE@kP@L_@2ᰊ@Ov_ح@l"h ԕ@p:~@zxl"h@Zc@o@Vl!@Q@;dZ@\]ce@b}V@TɆ@@lC@1-@vFs@ۋq @v@J#9@ATɅo@n.@K]@SMj@'/V@XbM@ݗ@'RT@JE@4m9@%zxl"h@IoiE@kC]@M:@zG@_o @(@ ҈@*0U2a|@)^@eڹ@tj@3@n@o@.H@ 'RT`@BZc@ffffff@qu"@4m@@Q@de@̘_@ I^@SMj@8}H@vȴ9X@{@lC@kP|@JL_@xF]c@S&@/{J#@@e+a@=b@a@N@F]c@TɆ@m\@唯O @Q@$TɆ@C@c @u%F @3@Zc@S@S&@(TɅ@N_o @u!.@R<6@ҽ<64@ L_@T,<@ (@/V@c @0 ě@mhr!@ '@ܑN;@'/W@?@lC@+j@TɅo@rGF@\(@4m8@Xy=b@{~%@-V@H˒:@u!@:S@!.H@BZc@c @tj@zxl"h@ǮzH@fB@@64J@_o@I^5?@ߤ?@ (@C$@Vϫ@/@x@) @e@Ov_خ@ӎMj@oiDg@5Xy=@cS@ߤ?@*@҈p@K]d@-w1@R䎊@u!.@0 )@5Xz@ѷY@@!.H@C@eڹ@p:~@<쿲@а{@Fs@%@I@weں@6z@ k@3g l@3@h ԕ@`A7L@$ xF@_;dZ@$tS@s@($ x@.zG@[6@ݗ+@2W@ڹY@ I^6@(@N_o @r䎊q@@S&@ѷY@:S@$TɆ@H@l"h ԕ@)^@}Vl@cA @Fs@2W@b@5@+j@t@#n.2@O M@- @e@ @Y>BZ@4J@8YK@F]c@'-@UϪ͞@@4n@>B[@8YJ@\N<@%1@Ks@ql@@5Xz@u!@Q@*0U2a|@Ov_ح@u!R@wkP@oi@-V@u"@M@?@g8}@*0U2@_o@쿱[W@Fs@S@UfA@~($ x@C@PH@#9@Q @~\N<@ @ӎMj@?@$TɆ@Ks@r䎊q@0 )@H˒:@唯O @ ҈@2ᰊ@Y>BZ@u%F @<쿲@bM@&J@9#w@qu!R@W@tj@z@}H@ܑN;@@@QR@p:~@<64@-V@䎊r@N+@|Q@-@ҽ<64@?@%1@N_o @u!.@-V@m\@(@L_@> (@gfA@@N@n@oiDg8@$tSN@^5?|@-w1@#@l!-w@wkP@ڹY@R<6@Ov_ح@$/@S&@(@u@MjOv@{J#9@-@ӎMj@\N<@(@R䎊@{~%@TɆ@_o @kP@!.H@M5Xy>@y"`@@1'@@JD@@7@_حV@S@JE@O M@M%@M:@#w@"h ԕ@}H@MjOv@|Q@ @8YJ@ I^6@-w1@Xy=b@F]c@zG@JE@Q@1-@` qu@bM@a@N@xl"h @74m@}E@E@Rp:@u&@D*@tj~@LcA \@{J#9@YJ@"h ԕ@u@N+@~\N<@>B[@ڹY@K]d@4m8@a@N@M:@^5?|@fA@Ov_@E8YK@wkP@<쿲@e@#wk@kC]@84֢@E84֢@vȴ@@sP@Ks@{J#9@#w@(@䎊r@Q @@4n@2W@҈p@\(@=b@kC]@vȴ@ɅoiE@ (@+ I^@^iB@O M@ M:@@Y|@ '0@7rG@kP|@ק&@sP@LcA \@M:@S&@-V@#9@UϪ͞@ݗ+@*@rGF@e+a@IoiE@y (@0 @]ce@\(@DS@|?@*@s@G)^@TɆ@*d7@kP|@@tj~@M%@$/@<64@PH@'-@[6@ߤ?@TɅo@@%zxl"h@W@qiB@ (@䎊q@)lC@cZ@n@ڹ@49XbN@@1&@vȴ@D*@O M@Ov_ح@p:~@C@F]c@.zG@cS@+j@/{J#@4m9@4m8@iDg8~@Q@՛=K@Vu@J#9@ @H@ ěT@nP@qi@u&@JE@R<6@QR@~($ x@8YK@($ x@5Xy=@lC@S&@JE@o@Fs@}E@C-@iB@/{J#:@ql@Y}@ @qu!R@4m9@S@ڹY@@@UfA@4J@s@oiDg@?@xF]c@'RT@rGF@"3@[$@@@ݗ+k@XD*@`A@+@_o@A [@wkP@ܑN;@S@Y>BZ@$tS@ӎMj@'/W@JL_@ݗ@n@lC@8}H@u!R@֡a@!.I@=v@ڹY@M@L_@e+@}H@Fs@ @_;dZ@Ov_خ@ܑN;@kP|@XbM@sh@ӎMj@:)y@R䎊@@e+@!.H@ql@Ƨ@9#w@T`d dim - dimnames- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 al02 al04 al06 al08 al10 al12 al14 al16 al18 al20 al22 al24 al26 al28 al30 al32 al34 al36 al38 al40 al42 al44 al46 al48 al50 al52 al54 al56 al58 al60km.ci/data/rectum.dat.rda0000755000175100001440000001300710351557474014757 0ustar hornikusersRDX2 X  rectum.dat@0]mA@"@#wwvY"?""/{@5,Y@&ܿ@wwr$^@K@-K@.K?H@*K?@7ffffff@<,Y@A7ww/F@2]mA?ٙ@2wx@T8@&@*ܿ@k@Mww/F@)wwvY"@5*9@ UUYΨo?""4n@DDF@N@?;,Y@)@ۢ@N?E@Cn6!@N@'DDC%~@.O@GY@6_Č@-@wwr$^@@b""iT@0;t@T8?ۻ"?wwj@""x@1W@ffffff?@#wwvY"@!U@ @(@!ffffff@+ffffff@'@HUU@2n_Č@""&u<@0333333@1DDn@,@N@>UUT*@#UUVs?""/{@""x?ݖHA@ffffff@ UUYΨo@>wwx?򪪳P@@UU@% C@=@8L@)DDC%~@333333@wwy!???UU@2L@M@ @ @N@T8?UU@@"O@@ffffff?wwe+@:]mA?4۩?UU1@/@*UUVs@;^%@1^%@#DDC%~@)O@D @hB @2^%@9?Є@""&u<@0@! C@&wwvY"?U@Fn6!@&@Kfffff@N@8UUT*@hB @/333333?""4n@<]mA@ wwr$^@Eb@6,Y?陙@+DDC%~@)ܿ@@#ffffff@$@A""iT@1W@+ܿ@Afffff@+ܿ?򪪳P@DDF@@-ffffff@:333333@3UUT*@?Є@3DDDn@ ""#@v@ @+333333@333333@?^5?|??u!R?D?bM?n?*0 ?nP?a@O?}H?`d8?-V?ԕ*1?n/?1o?Xy=c?zxl"h ?+j?{???sPH??A [?"`??oh?_o? ѷ? ԕ*? L/{? L/{? L/{? L/{? L/{? L/{? L/{? L/{? L/{?)y ?)y ?s?2W?U2a|?x?q ?hr!?SMj?ȴ9Xb?)^??m?[?`A7K?IQ?,BZ?Ϫ͞?󖻘?C,zxl???h ԕ+?,6z?y=b?? ě?m\? =p?e?oiDg8? k~?.H???u%F? k~(? ԕ*? ԕ*? =p ? =p ? =p ? =p ? =p ? =p ? =p ?xl"h ?e+a?#9?Ƨ-?XbM?ڹY?Q ?v? [6?zxl"h ???\(\??`A7?u%F ?.2X?Vt?xF]d?$/?1?2W?Ϫ͞?%1?4֡a?+?\N6z?$/?5Xy>?.H?+a@?S&?u!R? ҉?>B[?&IR?a@O?e+?hr!?+jg?PH?ѷX?tj?n/??Fs?_o?_o?_o?_o?_o?_o?_o?_o?#?eO ?zG?TɅ?+ I^?U=?1&x?ffffff?n.? k~(?Ov_ح?qi?y ?"`?&x?NT?qu!S?\(?*0U2a?vȴ9X?|?҈p:?JL?䎊q?rGE8?E?ѷX?ѷ??tj?oh?oh?oh?oh?oh?oh?oh?oh?~$?҈p:?zG{?u%F?M:?{m?TɅoi?>B[?#x?Dg8}???vȴ9X?s?䎊r?H?kP{?'RTa?n.3?~($ ?˒:)z?>BZ??c ?D?rGE8?PH?\N6z?bM?ohی?S?-V?2W?Z?҈p;?fA?c ?䎊q?+jg?+? k~?rGE8?"`?"`?"`?"`?"`?"`?"`?"`?H˒:?ڹY?&x?TɅ?PH?{J#9?䎊? ?dZ1?o??K]d?1-?VϪ͟?v_ح?hr ?-?)_??Ϫ͞?҈p;??JL?hr!?4֡a?oiDg8?D?A [?A [?A [?A [?A [?A [?A [?A [?)^? =p?m8Y?JL?U2a|?-V??L/{J#?L_?ěS?+jg?!R<6?GE84?g k?{J#9?,BZ?҈p;?e+?%1?e??????????Xy=c?e+a?͞&?"h ԕ?#? qu?,zxl"?p:~?U=?;dZ? ?3ߤ?U=K?rGE85? ?u?zG?)_?2W?˒:)z?|?a@O?Ϫ͞? =p?sPH?sPH????????ᰉ?QR?҈p;?Q ?-V?S&?MjOv?~($ x?74?{m?qu!?>6z?\N;?u!R? ?,B[?1? ě?'RT?{?{?{?{?{?{?{?{?Y|?Xy=?+a@?*0?쿱[W??6C-?u!.?1&y?Q_?n.?"`A?>6z?U=K?g k?v_ح?ohی?'RTa?\(? ҉?$/??O;d?+j?+j?+j?+j?+j?+j?+j?+j????E84֡?lD?!.H?C,zxl?~($ x? ě?Q_?E?qu!?3ߤ?GE84?VϪ͟?bM?kP{?qu!S?u!R?xF]d?y=b?z?zxl"h ?zxl"h ?zxl"h ?zxl"h ?zxl"h ?zxl"h ?zxl"h ?zxl"h ?1&x?ۋq ?Vt?}Vl?S&?GE84?~($ x?1&y?҈p:?{m? ?!R<6?1-?>6z?H?NT?S&?Vt?W>6z?XbM?Xy=c?Xy=c?Xy=c?Xy=c?Xy=c?Xy=c?Xy=c?Xy=c?BZc?fA?^5?|?)_?S&?C,zxl?u!.?'RT`?74?;dZ?+jg?K]d?sh?䎊r?&x?+a@?.2X?0 (?1o?1o?1-?1-?1-?1-?1-?1-?1-?JL_?VϪ?^5?|?}Vl?!.H?6C-?eڹ?~($ x?U=?ěS???s?"`?.H?u%F ?oh?S&?n/?$/?$/?$/?$/?$/?$/?$/?JL_?fA?Vt?lD?쿱[W??!.H?MjOv?p:~?L_?o?-V?vȴ9X?y ?5Xy>?`A7?m?,6z?\(\?^?`A7K?`d8?a@N?a@N?a@N?a@N?a@N?a@N?a@N??Xy=? ԕ*?Q ?#?-V?䎊?g ?#x? k~(?nO??u%?[?}H?,=?,=?,=?,=?,=?,=?,=?Y|??҈p;?"h ԕ?U2a|?{J#9?+j?>B[?n.????`A7?m?a@O?,6z@zxl"h @@iDg8@poiDg8@_p@쿱[W@_?@@]cf@u!S@@:S&@ZQ_@xF]d@tj~@B@š@?@䎊q@1&x@1&@0U2a|@Ca@N@V8YJ@hr Ĝ@zC\@҈@IQ@2X@[W>@`A7@e+@@JM@@(\)@;dZ@NT@b}Vm@w+@(@S@@>@($ x@&@Y|@@ߤ?@b}V@Zݘ@IQ@lC@䎊r@QR@~$t@RT`e@cA \@O;@qi@,q @H˒:*@c @|?@O M@1&y@\(@D*@hr!@@4n@R<6@*0U2a|@=b@QN;5@dqj@w+@'/@Ov_خ@2W@m\@ٳ|@.2@{J#9@e+a@0'RT@H@c @$tS@!-w1@a@N@c @&@zxl"h @@R<64@3@ ~($ x@UfA@Z@:@b}V@-V@VϪ͟@}!.H@ k@&IR@ᰉ'R@b}V@qu!@6z@PH@iDg8~@:S@tj@{J#:@š@ۋq @ @YJ@qu!@0U2a|@E84֡@ZQ_@oiDg8@JM@vȴ@{@@@ߤ?@l"h @sP@.2X@M5Xy>@ohۋ@$/@a@N@($ x@W>6z@@*0 @$/@2a|@vȴ@XbM@\(@7Kƨ@4֡a@e+a@@n@e+a@ݗ+@-@]cf@Z1@hۋq@qu!@5Xy=@O M;@gfA@@O;d@2X@š@]ce@䎊q@ k~(@ 'RT`@6}Vl@M%@de@|#x@L_@2X@@7@ @"`A@Fs@ohۋ@!-w1@>@9D@ qv@ (@ae@1&y@C\@) @_?@$tT@6z@rGE@@5Xy=@XU@z@_o@}H@fA@u!S@ @'RT`e@A [7@ZQ_@sMjO@q @ xF@n.@,<@"h ԕ@M:@1&@5?|h@Np:~@hr Ĝ@nP@쿱[W@j~#@ۋq @Vl!@"`A@M5Xy>@$tS@@ [6@ae@- @Fs@L_@)^ @?@9~($ @jJL@O;d@@x@'/W@2ᰊ@T*1@tD@Fs@-V@As@Q@+ I@!.H@BZc@0{@r䎊q@{J#:@@vȴ@JL_@weں@R<6@ɅoiD@4֡a@@N@5Xy=@VϪ͟@vFs@$/@g l@|@!-w@p:@$tSM@?|hs@ZQ_@u%F L@rGE9@0 @š@@N@hr@e+a@6}Vl@UfA@uY|@O;d@j~#@7@sP@H@(@j~@@W>6z@vȴ9@^@@@+@:S@0U2a|@\?@Fs@2X@Ʌoi@ k~@ߤ@@=v@^5?|@}H˒@wkP@^5?|@@j~@4J@-w1@IoiD@eO @n.@,=@S&@ק&@Y|@@4J@UfA@xF@쿱[W@@l"h @0'RT@w+@|Q@L/{@а{@u@N_o @$/@)_@"h ԕ@vȴ@F-@poiDg8@_F@6z@S@@(TɅ@I@jJL@'RTa@@]cf@7@@4n@vȴ9@=K]@ZQ_@xF]d@sh@J@&@䎊q@@6}Vl@[q @nP@2X@ߤ?@e+a@b}Vm@As@oiDg@K]c@$/@@6@vFs@lD@Q_@eO@4J@]cA @n/@<쿱@_p@D@O;d@9XbM@ZQ_@z>B@6C@~($ @y=c@1@sh@4m8@S&@rGE85@N;6@-@&@Y|@e+a@?|hs@hr Ĝ@L_@@@{J#9@NT@ (@@ [7@?@43@iB@Q@˒:)z@ k~@$tSM@N_o @v+jg@1&@&IR@fffff@ 'RTa@,1&y@M%@nt@\(@{J#:@v_ح@iDg8@\(@/O M@Ov_ح@p:~@N;6@J@ק&@hr@$?@Np:~@|#x@{@.2@;dZ@w1@~$t@/V@#@+ I^@^҈p;@rGE9@@"h ԕ@Xe,@A [7@iDg8~@rGE9@?@څݘ@\N<@!ae@C@e@_o@@ɺ^5?}@fB@ L_@-w1@Ov_ح@rGE85@sh@S&@@N@ 'RTa@5?|h@de@vȴ@ٳ|@(\)@}H@{lC@E@C\@#S&@U=K@$/@3@|Q@ ~($ x@6E@^@n/@a@@_p@!R@e+a@<쿱[@_?@ I^5@tSM@]cf@Dg8~@ L_@/O M@S&@xF]d@,=@š@-V@1&@M%@JM@m\@@(@y (@-V@>BZc@ߤ@@N_o @}E@1&y@y=c@@-w1@Vt@}ڹZ@?@#9@4֡a@ݗ+k@7rG@[6@$tS@9@]cf@fB@\(@4m8@ZQ_@n.@0 @t@M:@6}Vl@oiDg8@2W@JM@~m\@w+@2X@eO@+ J@GzH@v!.I@/@N;5@?@&IQ@Ov_ح@weں@Q@84֡@C,z@@5Xy=@Zݘ@$tS@tSM@ɺ^5?}@iDg8@sh@=K]@eO @rGE9@j~#@"h ԕ@ 'RT`@ZQ_@Ov_خ@@o@v+jg@a@@ߤ?@N;6@BZc@qu!S@!-w1@˒:)z@!.I@!-w1@JL_@sPH@u%@&IR@rGE@\(@5Xy=@[6@ I^5@@v_ح@1@vȴ9@IoiD@u%F L@S&@,<@ k~(@E84֡@'/@e+@aGz@tj~@^@]ce@ (@>BZc @m(@,<@E84@䎊q@}H@GE84@poiDg8@0 )@ [7@rGE@@7rG@_?@_o@{J#:@y=c@@4n@-w1@ZQ_@'RTa@n.@䎊q@0U2a|@w+@`A7@R<64@sE@&@>BZ@ '/@:~@iB@O;d@2W@:~@=K^@E8YK@o-V@0 )@&IR@C,z@ݗ+k@<쿱[@e@\(@~($ @7@4J@?|hs@p:~@ xF@]ce@qu!@dqj@[W>@D2W@r䎊q@S@@p:@8YJ@glC@L_@\(@t@vȴ@E8YK@poiDg8@u%@84֡@4֡a@e+a@C@nt@6C@]cf@j~@$tSM@Vl!-@q @š@YJ@QN;5@2X@5Xy=@rs@TɆ@l!-@+ I@6C-@eڹ@Fs@s@t@=K^@GE84@sPH@Q@#9@!R@!ae@M%@z>B@@@p:@BZc @qu!S@/@y=c@ ~($ x@A [7@v+jg@<쿱@S@ߤ@@VϪ͟@Fs@fA@qu!@iDg8~@\(@0U2a|@̘_@rs@S@>BZ@ (@BZc@v!.I@1&y@|Q@Xe,@N_o @n/@6z@ k~@5Xy=@tD@}H@hۋq@PH@1&y@1&@j~#@r䎊q@&@]ce@N;6@GzH@}E@3@"h ԕ@$tSM@]cA @_F@Ʌoi@@N@T*1@_o@Z1@6z@O M@1&x@U=@sE@^@ߤ?@+ J@N_o @$/@@ k~@4J@poiDg8@2X@4֡a@2ᰊ@z@]cf@qu!@|?@䎊q@6C dim - dimnames- 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 al02 al04 al06 al08 al10 al12 al14 al16 al18 al20 al22 al24 al26 al28 al30 al32 al34 al36 al38 al40 al42 al44 al46 al48 al50 al52 al54 al56 al58 al60km.ci/data/critical.value.nair.99.rda0000755000175100001440000002675510351557474017024 0ustar hornikusersRDX2 X  critical.value.nair.99F@ Q@ ٳ|@ L/{@ 2m\@ UfA@ s@ (@ 1@ dZ@ v_ح@ e@ PH@ {J#9@ sP@ !ae@ /O M@ BZ@JE@?[W>@zG@(\@ 6C@ P|@ $/@ >B[@ ԕ*1@ =b@ =K^@ :)_@ Xy=c@ t#@ '/W@ @ C@ y=c@ 4֡a@ S&@ ,<@ /Vϫ@ DS@ Y+ @ m\@ ohی@ sh@ lD@ vȴ9X@ Z@ @ $t@ L_@ -B@ F-@ ae@ 4m9@ 3@ ʌL_@ E@ Jn@K)^ @\(@$?@vFs@K]@xl"h @ 0{@ a'R@ (@ z@ 6@ \N<@ 쿱[W@ =p =@ ZQ_@ u!.@ - q@ @ s@ ٳ|@ =q@ + I@ IQ@ 2m\@ H9Xb@ ^ѷY@ s@ p:@ %@ m8@ D@ e@ "`@ sP@ -B@ I^5?}@ irGE@ ~($ x@ z@ Q@ :)_@?|hs@ ě@ (@]cA @S&@ qu@ R<6@ GE84@ sg@ u"@ @ 1.@ _o@ & '@ Dg8}@ `҈p@ {lC@ Ov_@ {J#:@ @ qu@ kP@ qi@ %zxl"h@ <n.@ R<64@ iDg8~@ @ $tS@ }Vl@ Ƨ-@ ߤ?@ "`@ L_@ 2m\@ SMj@ xl"h @ wk@ Q_@ +a@@64K@ k@J#9@I@O;dZ@ɺ^5?}@˒:)@ 0{@ ]cA @ + I@ U=@ ѷX@ @ *0U2@ 1-@ OAs@ kC,z@ $/@ [6@ Y}@ ҽ<64@ P{@ {J#9@ =K^@ 2m\@ Jn@ b}Vm@ zG{@ tj~@ zxl"@ Ƨ-@ e@ $t@ }H@ ?@ e+a@ N;6@ #9@ ,=@.}Vl@zG@빌~($@7rG@zG{@}Vl@쿱[W?@ ,=@ JD@ tD@ Q@ NU@ C,@ {J#9@ "`A@ @4m@ ]ce@ y=b@ Fs@ 2X@ ȴ9Xb@ e+@ J#9@ Fs@ -B@ F?@ _o@ y=b@ tj~@ }Vl@ D@ @ u%F@ +jf@ R<64@ ѷX@ )_@ @($ xG@ @҈p;@)*0U2a@kP{@ '@]ce@ @ ;/V@ e`A7L@ @ 2W@ E@ _ح@ Ov_@ 4֡a@ Rp:@ o4֡a@ '/@ L/{J@ [7@ 6@ !.@ rGE9@ *0U2a|@ DS@ _o@ zG{@ $tS@ m8@ Z@ #@ tj@ @C@ poiDg8@ 1&y@ %2@"`A@:S@a@O@1&@^ѷY@PH@ΤT@ @ -U=@ XD*@ @ 1@ ɅoiD@ Q@ L_@ +jf@ I@ glC@ M:@ - @ 64@ @ MjO@ ߤ?@ *0U2a|@ F?@ b}Vm@ @ %@ vȴ9X@ d8@ oiDg@ /r@ `u!@ -V@ eں@%@y"`@W'@:)y@R<64@O;dZ@74@SMj@ "h ԕ@ M5Xy>@ u!R@ Q@ 6@ C,@ M:@ $tSM@ C$@ ae@ ˒:)@ [@ Y}@ ֡af@ MjO@ rGE9@ -B@ Jn@ iDg8~@ p:@ lD@ jOv@ E@ Q@ P`A7@ @ Q@vȴ@s@\(@ 7KƧ@I@{J#9@#w@g @ PH@ D2W@ m(@ ݗ+k@ 4֡b@ ]ce@ \N<@ U=@ ?|hs@ ^҈p;@ }ڹZ@ Q@ Y}@ @ !.@ Fs@ 2m\@ R<64@ s@ sh@ )y@ e@ !R<@ A7Kƨ@ @ حV@O;d@nc @5Xy@n.@ATɅo@{J#9@{@C,@ hr @ =<64@ f,<@ (@ ߤ@ ק&@ >B@ wkP@ =p =@ ]cA @ }ڹZ@ [@ 64@ 6@ J#9@ =K^@ <n.@ ^ѷY@ ohی@ RT`e@ *0U@ ڹZ@ 2ᰊ@ tSMj@ `d@Z@iᰉ'@E@~$@:S&@tj~@d7@ܑN;@ xF]@ 73@ a'R@ ԕ*@ ě@ E@ - @ qu!@ =p =@ ^҈p;@ ˒:)@ - @ [7@ e+@ {J#9@ %zxl"h@ H9Xb@ m\@ Fs@ vȴ9X@ (@ #n.3@ g@ @S&@f1.@2W@_ح@5?|h@oiDg8@F L0@ק&@ + I@ 4J@ ^@ + I@ H@ ,<@ - @ wkP@ ?|hs@ ae@ M:@ L/{J@ ȴ9Xb@ P{@ qi@ 2m\@ Y+ @ :S@ zxl"@ ?@ zG@ Y|@ b@$tT@cS@zG@GE85@1&x@kP{@R<6@`d@ M:@ 12W@ \>@ YJ@ H@ E@ >B@ U=@ C$@ glC@ '/@ 2X@ ҽ<64@ kP@ IQ@ DS@ n.2@ ,<@ cA \@ ݗ@ LcA \@ W@@`҈p@6z@4֡a@.zG@hr Ĝ@!-w1@T`d@ ѷ@ 0{@ \>@ + I@ ě@ ק&@ \N<@ $tSM@ I@ o4֡a@ Fs@ Y}@ qu@ + I@ /Vϫ@ ZY@ p:@ 5Xy@ Fs@ ?@ &@Vu@_Ft@\)@(@,1&y@fA@,=@ѷX@ ѷ@ 12W@ ^@ ԕ*@ ߤ@ ]ce@ M:@ +jf@ Rp:@ y=b@ [6@ @ =q@ ,<@ G)^@ vFs@ 6z@ lC@ 12W@ C,zx@ M:@]cA @/V@빌~($@+6z@f1.@,=@T`d@ M:@ 4J@ a'R@ (@ 4֡b@ C,@ L_@ 4֡a@ ]ce@ $/@ {J#:@ ٳ|@ S&@ 2m\@ d%1@ 6C@ bM@ $?@ 4J@ @]/w@x@P{@+6z@fA@!-w1@`d@ + I@ 73@ f,<@ ݗ+k@ 6@ Q@ Ov_@ @4m@ kC,z@ Ov_@ s@ 4֡a@ %@ QN;5@ p:@ ˒:*@ + J@ n/@ @\?@x@빌~($@,1&y@hr Ĝ@R<6@ק&@ xF]@ =<64@ m(@ Q@ ɅoiD@ _ح@ "`A@ OAs@ {lC@ @ y=c@ 'RTa@ =b@ w1@ YJ@ u%F@ zC\@ @]/w@/V@(@.zG@kP{@F L0@ܑN;@ hr @ D2W@ u!R@ 1@ E@ {J#9@ 1-@ `҈p@ - q@ C@ g@ )lD@ de@ Xy=@ xl"h @ nO;@ @]cA @\)@4֡a@1&x@oiDg8@d7@C,@ PH@ M5Xy>@ @ 2W@ C,@ *0U2@ Dg8}@ u!.@ @ ce@ L_@ RT`d@ O;d@ 빌~($@ b@4@ M:@_Ft@6z@GE85@5?|h@tj~@{@g @ "h ԕ@ XD*@ @ NU@ @ & '@ ZQ_@ '/W@ L/@ @ ?@ YJ@ ?@ Vl!-@Vu@`҈p@zG@_ح@:S&@{J#9@#w@SMj@ -U=@ e`A7L@ Q@ ѷX@ _o@ =p =@ t#@ w1@ B@ + I^@ t#@ jOv@ I^5?}@@cS@2W@~$@ATɅo@{J#9@74@ @ ;/V@ tD@ U=@ 1.@ 쿱[W@ Xy=c@ ݗ+k@ T`d@ ᰉ@ ae@ p =@ 6z?lC?~$t??u%F ? =p?U=?[W>6?sg?rG?~($ ?^5?|?)_?)_?)_?)_?)_?)_?)_? =p?Z?ohۋ?73?_Ft?Xy=?D*?A [?zxl"h ?MjO?ceO?s?-V?ݗ+?"`B?)^?qu!S?9XbN?l?*0 ?^?!R6z?lC?~$t??u%F ? =p?U=?[W>6?sg?rG?~($ ?^5?|?)_?)_?)_?)_?)_?)_?)_?ԕ*1?n.?ohۋ?73?_Ft?Xy=?D*?A [?zxl"h ?MjO?ceO?s?-V?ݗ+?"`B?)^?qu!S?9XbN?l?*0 ?^?!R6z?lC?~$t??u%F ? =p?U=?[W>6?sg?rG?~($ ?^5?|?)_?)_?)_?)_?)_?)_?)_?Z?-V?jfB?Fs?^5?|?Xy=?D*?A [?zxl"h ?MjO?ceO?s?-V?ݗ+?"`B?)^?qu!S?9XbN?l?*0 ?^?!R6z?lC?~$t??u%F ? =p?U=?[W>6?sg?rG?~($ ?^5?|?)_?)_?)_?)_?)_?)_?)_?TɅoi?oiDg?TɅoi?C,z?W&???Ϫ͞?-V?z?䎊q?ceO?s?-V?+ I?"`B?(\)?qu!S?9XbN?l?*0 ?^?!R6z?lC?~$t??u%F ? =p?U=?[W>6?sg?rG?~($ ?^5?|?)_?)_?)_?)_?)_?)_?)_?ѷ?Z?@?C,zxl?$tS?͞%?ᰉ'?xl"h ?l?bM?)^ ?,zxl"?+ I?"`B?(\)?q ?|?l?*0U2a|?^?!R6z?lC?~$t??u%F ? =p?U=?[W>6?sg?rG?~($ ?^5?|?)_?)_?)_?)_?)_?)_?)_?{m]?vȴ9X?n?xF]d?Q?"`B?sPH?C\?_حV??+ I^?򅇓ݗ?>BZ?($ xG?p =q?g l?A [?*0U2a|?^5?|?􍸺?#w? qu?F]c?"`A?>6z?W>6z?l"h ԕ?~($ x?O;dZ?u%F ? =p?U=?[W>6? '0?rG?~($ ?^5?|?^5?|?)_?)_?)_?)_?)_?)_?n?-V?:S&?qi?･v?h ԕ+??YJE?2W?'RT`e?򂩓 ?sPH?&?ohۋ?-V??(\)?]ce??Q?ߤ??ѷ?"h ԕ?>BZc ?VϪ͟?k~($?}H˒???n.3?6z?{? '0?4m?#w?Y}?^5?|?^5?|?^5?|?^5?|?^5?|?^5?|?^5?|?\(?͞%?E? ?U2a|?sPH?N;5X?A?䎊r?|hr?҈p:?!.H?kP{?1?hr!?&x?[W>6z?C%?}Vl?ѷY?n.?!-w1?=K]?U=K?jfB?|hr?L_?@?wkQ?d7? ě?9XbN?}Vl?Q?#w?~($ ?~($ ?~($ ?~($ ?~($ ?~($ ?~($ ?u%F ?˒:)z?6z?6C-?n?<64?>B[??sPH?n?u%?eO ?lD?rGE?"`A?W&?#?|?ۋq ?.H?!-w1?:~?SMj?hۋq ?{J#9?qiC?$tS? k? ?}Vl?ᰊ? '0?}Vl?4m?rG?rG?rG?rG?rG?rG?rG?D?2W?K]d?IQ?!-w1?$tS?u%F ?dZ1? (?bM?\(\?o?ᰉ'R?1&?QR?nP?O M? =p?s?u%?73?PH?eڹ?xF]d?ݗ+?Fs?u?S?U=?r?ᰊ?9XbN? '0? '0?sg?sg?sg?sg?sg?sg?+?4m9?iB?"`?xF]d?rGE?Ov_ح?q ?:S?O M;?R<6?sPH?@N?I^5?}?{J#9?Xy=?`A7?Y|?sh?2a|Q?K]cA?a@N?s?nP?o?u%F ?o?&?U=?}Vl? ě?{?[W>6?[W>6?[W>6?[W>6?[W>6?[W>6?[W>6?S&?[??M:???2W?Fs?VϪ?=K]?YJ?ɅoiD??>BZc ?p =q?%?@?쿱[W?? M:?*0 ?C\N?Z1'?l!-w?|??'RTa?ݗ+k?ߤ@?o?S?TɅ?d7?6z?U=?U=?U=?U=?U=?U=?U=?B?p =q?)^?$tS? I^5??sg?`A7?%F L0?qu!S?}Vl?\(?/v?b}Vm?N;6?n.?ᰉ'R?F]c? ěT?:S&?Q_p?cA [?sg?ohی?q ?ݗ+k?u%F ?u? k?wkQ?n.3? =p? =p? =p? =p? =p? =p? =p?҈p?/V?W>6z?+ ?I^5?}?B??Vl!-?쿱[W? qu?qu!?Q_p?$/?q ?Z?\(??.H?E84֡?Xe+?iDg8~?v_ح?ohی?'RTa?o?Fs?$tS?@??u%F ?u%F ?u%F ?u%F ?u%F ?u%F ?u%F ?6C-?ڹZ?*0?@N?˒:)?ߤ??4m8?IQ?m\?F]c?:S&?lC?0 )?TɅo?䎊qj??!-w1?6z?JL_?[W>6z?iDg8~?sg?|??nP?ݗ+?qiC?L_??O;dZ???????u%F L?73?˒:)z?E?}Vl? =p ?[6???`A7L? ?SMj??U=?*0U?oiDg8? I^5??$/?9XbM?JL_?Xe+?cA [?l!-w?s?xF]d?{J#9?|hr?}H˒?~($ x?~$t?~$t?~$t?~$t?~$t?~$t?U=?j~"?>B?q ?8YJ?.H?|Q?TɅo?m\?5?|h?eڹ?hr ?4m?+ ?+jg?'/W?$/?6z?E84֡?Q_p?Z1'?a@N?eڹ?hۋq ?jfB?k~($?l"h ԕ?lC?lC?lC?lC?lC?lC?Q_?ᰉ?"h ԕ?$/?!.I?KƧ?ᰉ?sPH?u?D*0?rGE85?kP|?v?$?+jg? I^5??!-w1?.H?:S&?C\N?K]cA?PH?SMj?U=K?VϪ͟?W>6z?W>6z?W>6z?W>6z?W>6z?W>6z?W>6z?u%F ?^5?|?C,zxl?ᰊ?)^?bM?*0U2a?lC?Ov_خ?N;5X?xl"h ?IQ?v?+ ?oiDg8??? ěT?*0 ?2a|Q?73?:~?=K]?>BZc ?>6z?????????????#9??]ce?ɅoiD?$tSM?rGE85???D?$tSM?Q ?xl"h ?kP|?4m?*0U?䎊qj?\(?F]c? M:?sh?u%?!-w1?!-w1?"h ԕ?"`A?#9?#9?#9?#9?#9?#9?=p =?t?q ?b}?1&x?{m]?j~#?!-w2?$tSM?N;5X?rGE85?hr ?U=?TɅo?Z?ᰉ'R?쿱[W??Y|?s?.H?n.?ѷ?F]c???????P{?m\?}H˒?S?73?~"?j~#?D?Ov_خ?D*0?eڹ??0 )?q ?n.?@?`A7? =p?ۋq ?ѷY?ߤ?? qu?u!?u!?u!?u!?u!?u!?]cA ?u%F?M:?,BZc ?I^5?}?QR?W&?[W>6z?]ce?^5?|?^?^?^?^?^?^?]cA ?m\?q ?ɅoiD?)^?KƧ?|Q???m\? qu?\(??@N?1&?"`A?&x?(\)?*0U2a|?*0U2a|?*0 ?*0 ?*0 ?*0 ?*0 ?P{?t?]ce?ᰊ?!.I?.H?[6?IQ?쿱[W?}Vl?ɅoiD?sPH?ᰉ'R?rGE?hr!??A [?l?l?l?l?l?l?=p =??C,zxl?$/?8YJ? =p ?4m8?Vl!-?qu!S?YJ?R<6?o?lD?1?-V?g l?|?9XbN?9XbN?9XbN?9XbN?9XbN?#9?^5?|?"h ԕ?q ?}Vl?ߤ???%F L0?=K]?O M;?\(\?eO ?kP{?ohۋ?p =q?q ?qu!S?qu!S?qu!S?qu!S?qu!S?u%F ?ᰉ?>B?E?˒:)?B?`A7?VϪ?:S?bM?u%?!.H?&?($ xG?(\)?(\)?)^?)^?)^?)^ dim . dimnames. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 al00 al02 al04 al06 al08 al10 al12 al14 al16 al18 al20 al22 al24 al26 al28 al30 al32 al34 al36 al38 al40 al42 al44 al46 al48 al50 al52 al54 al56 al58 al60km.ci/R/0000755000175100001440000000000011246471673011505 5ustar hornikuserskm.ci/R/lrt.confints.R0000755000175100001440000000303310351557474014255 0ustar hornikusers"lrt.confints" <- function(time,status,t0,alpha=0.05) { ftimes<-sort(unique(time[status==1])) k<-length(ftimes) dvec<-rep(1,k) nvec<-dvec jmax<-0 for(i in 1:k) { dvec[i]<-sum(time==ftimes[i] & status==1) nvec[i]<-sum(time>=ftimes[i]) if(t0 >= ftimes[i]) jmax<-i } nsub<-nvec[1:jmax] dsub<-dvec[1:jmax] theta<-qchisq(1-alpha,1) #handle no deaths specially if( jmax == 0 || sum(dsub) == 0 ) return(list(lower=exp(-theta/(2*max(nsub))),upper=1)) #else lrt<-function(lambda,opar) { nsub<-opar$nsub dsub<-opar$dsub opar$theta -2*sum(nsub*log(1+lambda/nsub)-(nsub-dsub)*log(1 + lambda/(nsub-dsub))) } plambda<-function(lambda,opar) { nsub<-opar$nsub dsub<-opar$dsub prod(1-dsub/(nsub+lambda)) } t1<-sum(1/(nsub-dsub)-1/nsub) l1<- -sqrt(theta/t1) #the likelihood is undefined if l1 < lbd lbd<- dvec[jmax]-nvec[jmax] tol2 <- 1/100 #if( nvec[jmax]-dvec[jmax]+l1 < 0 ) { # l1<-(dvec[jmax]-nvec[jmax]-0.001)/10 #} parlist<-list(nsub=nsub,dsub=dsub,theta=theta) for(i in 1:10) { if( l1 < lbd ) { l1<-lbd + tol2 tol2<- tol2/2 } v1 <- lrt(l1,parlist) if(v1 < 0 ) break else { slope<-(theta - v1)/l1 l1<-(theta + 0.5 )/slope } } lower<-bisect(lrt,parlist,l1,0) l1<- -l1 for(i in 1:10) { v1<-lrt(l1,parlist) if(v1 < 0 ) break else { slope<-(theta - v1)/l1 l1<-(theta + 0.5 )/slope } } upper<-bisect(lrt,parlist,0,l1) lp<-plambda(lower,parlist) up<-plambda(upper,parlist) return(list(lower=lp,upper=up)) } km.ci/R/confi.nair.fun.R0000755000175100001440000000076010351557474014453 0ustar hornikusers"confi.nair.fun" <- function(abw,kap.mei,method) { # Using the already calculated derivation this function calculates # the upper and lower boundary of a confidence band by substracting # resp. adding the derivation "abw". if(method=="linear") { lower <- kap.mei-abw upper <- kap.mei+abw } if(method=="log") { lower <- kap.mei^(1/abw) upper <- kap.mei^abw } return(list(lower=lower,upper=upper)) } km.ci/R/km.ci.R0000755000175100001440000000503410351557474012636 0ustar hornikusers"km.ci" <- function(survi,conf.level=0.95, tl=NA, tu=NA, method="rothman") { # This function can compute the most desirable confidence bands. # The method "log" is implemented as "log" in R survfit. # The method "loglog" is implemented as "log-log" in R survfit. # The method "linear" is called "plain" in R survfit. if(conf.level < 0 || conf.level > 1) stop("confidence level must be between 0 and 1") if (data.class(survi)!="survfit") stop("Survi must be a survival object") method <- match.arg(method,c( "peto", "linear", "log" ,"loglog", "rothman","grunkemeier", "epband", "logep", "hall-wellner","loghall")) if(method=="grunkemeier") { result <- grunk.all.fun(survi,1-conf.level) result$conf.type <- "Grunkemeier" } if(method=="linear") { result <- survi cf <- comp.npci(survi,conf.level) result$lower <- cf$linear$lower result$upper <- cf$linear$upper result$conf.type <- "Linear" } if(method=="rothman") { result <- rothman.fun(survi,conf.level)$surv.object result$conf.type <- "Rothman" } if(method=="peto") { result <- survi cf <- comp.npci(survi,conf.level) result$lower <- cf$peto$lower result$upper <- cf$peto$upper result$conf.type <- "Peto" } if(method=="log") { result <- survi cf <- comp.npci(survi,conf.level) result$lower <- cf$greenwood$lower result$upper <- cf$greenwood$upper result$conf.type <- "Log" } if(method=="loglog") { result <- survi cf <- comp.npci(survi,conf.level) result$lower <- cf$log$lower result$upper <- cf$log$upper result$conf.type <- "Log-Log" } if(method=="hall-wellner") { result <- hall.wellner.fun(survi, tl=tl, tu=tu, conf.lev=conf.level) result$conf.type <- "Hall-Wellner" } if(method=="loghall") { result <- hall.wellner.fun(survi,tl=tl, tu=tu, method="log", conf.lev=conf.level) result$conf.type <- "Log(Hall-Wellner)" } if(method=="epband") { result <- epband.fun(survi, tl=tl, tu=tu, conf.lev=conf.level) result$conf.type <- "Equal Precision" } if(method=="logep") { result <- epband.fun(survi, tl=tl, tu=tu, method="log",conf.lev=conf.level) result$conf.type <- "Log(Equal Precision)" } return(result) } km.ci/R/hall.wellner.fun.R0000755000175100001440000000702610351557474015016 0ustar hornikusers"hall.wellner.fun" <- function(survi,tl=NA,tu=NA, method="linear", conf.lev=0.95) { # This function takes a survfit object and modifies it, such that # its lower and upper boundaries are now computed using the # method by Hall-Wellner. # Essentially required are table of critical values, # named "critical.value.hall.90", "critical.value.hall.95" # "critical.value.hall.99" (see also Appendix C # in Klein & Moeschberger p. 451). data(critical.value.hall.90, critical.value.hall.95, critical.value.hall.99) survi <- survi tl <- tl tu <- tu if(max(conf.lev==c(0.90, 0.95, 0.99))!=1) { stop("confidence level for simultaneous bands must be either 0.90, 0.95 or 0.99") } # if no tl,tu is given the band covers the whole curve if(is.na(tl)&is.na(tu)) { tl <- min(survi$time[survi$n.event>0]) tu <- max(survi$time[survi$n.event>0 &survi$n.risk>survi$n.event]) } n <- survi$n aa <- a.up.low.fun(survi,tl,tu) au <- aa$a.up #determines row in table of critical values al <- aa$a.low #determines column ... dat.mat <- aa$sigma.mat # columns used to interpolate index.al.left <- floor(al/2*100+1) index.al.right <- ceiling(al/2*100+1) # rows ... index.au.top <- floor((au-0.1)/2*100+1) index.au.bottom <- ceiling((au-0.1)/2*100+1) #critical values: readingwise 1. topleft,...,3.bottomleft,... if(conf.lev==0.90) { crit1 <- critical.value.hall.90[index.au.top,index.al.left] crit2 <- critical.value.hall.90[index.au.top,index.al.right] crit3 <- critical.value.hall.90[index.au.bottom,index.al.left] crit4 <- critical.value.hall.90[index.au.bottom,index.al.right] } if(conf.lev==0.95) { crit1 <- critical.value.hall.95[index.au.top,index.al.left] crit2 <- critical.value.hall.95[index.au.top,index.al.right] crit3 <- critical.value.hall.95[index.au.bottom,index.al.left] crit4 <- critical.value.hall.95[index.au.bottom,index.al.right] } if(conf.lev==0.99) { crit1 <- critical.value.hall.99[index.au.top,index.al.left] crit2 <- critical.value.hall.99[index.au.top,index.al.right] crit3 <- critical.value.hall.99[index.au.bottom,index.al.left] crit4 <- critical.value.hall.99[index.au.bottom,index.al.right] } if(is.na(crit2))# just in case { crit2 <- (crit1+crit4)/2 } #percentages of interpolation vert.perc <- 1-(ceiling(au/2*100)-au/2*100) hori.perc <- 1-(ceiling(al/2*100)-al/2*100) #interpolations: numbering clockwise inter1 <- crit1-(abs(crit1-crit2)*hori.perc) inter2 <- crit4-(abs(crit4-crit2)*vert.perc) inter3 <- crit3-(abs(crit3-crit4)*hori.perc) inter4 <- crit3-(abs(crit3-crit1)*vert.perc) interpol <- inter1*(1-vert.perc)+inter4*(1-hori.perc)+inter2*hori.perc+inter3*vert.perc crit <- interpol/2 # First: compute a vector with the deviations devia <- abweich.fun(dat.mat,crit,n) # Now, produce a list with the lower and upper boundary # dependent of the method. if(method=="linear") { up.low.list <- confi.fun(devia$lin.dev,dat.mat[,2],method) } if(method=="log") { up.low.list <- confi.fun(devia$log.dev,dat.mat[,2],method) } # Finally, modify the survfit object with the new boundaries survi$lower <- up.low.list$lower survi$upper <- up.low.list$upper survi <- modify.surv.fun(survi,aa$start,aa$end,method) return(survi) } km.ci/R/bisect.R0000755000175100001440000000063010351557474013103 0ustar hornikusers"bisect" <- function(fun,opar,lval,uval,tol=1e-7) { t1<-fun(lval,opar) t2<-fun(uval,opar) if(t1*t2 > 0 ) stop("in bisect both function values have the same sign") if(t1>0) { t2<-uval uval<-lval lval<-t2 } converged<-F while( !converged ) { t1<-(lval+uval)/2 nf<-fun(t1,opar) if(abs(nf) < tol ) return(t1) else if( nf<0) lval<-t1 else uval<-t1 } } km.ci/R/abweich.nair.fun.R0000755000175100001440000000064310351557474014757 0ustar hornikusers"abweich.nair.fun" <- function(matrix,c) { # Calculates the upper and lower derivation to the Kaplan-Meier estimator # for determining the boundaries of a Hall-Wellner band (which is # symmetric). kap.mei <- matrix[,2] sigma <- matrix[,3] result1 <- c*sqrt(sigma)*kap.mei result2 <- exp((c*sqrt(sigma))/log(kap.mei)) return(list(lin.dev=result1,log.dev=result2)) } km.ci/R/confi.fun.R0000755000175100001440000000075310351557474013525 0ustar hornikusers"confi.fun" <- function(abw,kap.mei,method) { # Using the already calculated derivation this function calculates # the upper and lower boundary of a confidence band by substracting # resp. adding the derivation "abw". if(method=="linear") { lower <- kap.mei-abw upper <- kap.mei+abw } if(method=="log") { lower <- kap.mei^(1/abw) upper <- kap.mei^abw } return(list(lower=lower,upper=upper)) } km.ci/R/Expllike.R0000755000175100001440000000015510351557474013411 0ustar hornikusers"Expllike" <- function(lambda,times,cens) { d<-sum(cens) return(d*log(lambda)-lambda*sum(times)) } km.ci/R/comp.npci.R0000755000175100001440000000300710351557474013521 0ustar hornikusers"comp.npci" <- function(sfit,conf.level=0.95, restrict=F) { if(!inherits(sfit,"survfit")) stop("need the output of survfit") if(conf.level < 0 || conf.level > 1) stop("confidence level must be between 0 and 1") else zalpha<-qnorm( 1 - (1-conf.level)/2) tvec<-sfit$n.event/(sfit$n.risk*(sfit$n.risk - sfit$n.event)) tv2<-cumsum(tvec) sqrt.tv2<-sqrt(tv2) peto.se<-sqrt.tv2/log(sfit$surv) gw.se<-sfit$surv * sqrt.tv2 gw.lower<-exp(log(sfit$surv)-zalpha*sqrt.tv2) gw.upper<-exp(log(sfit$surv)+zalpha*sqrt.tv2) #gw.upper<-ifelse(gw.upper>1,1,gw.upper) llog.lower<-sfit$surv^(exp(-zalpha*peto.se)) llog.upper<-sfit$surv^(exp(zalpha*peto.se)) linear.lower<-sfit$surv-zalpha*gw.se linear.upper<-sfit$surv+zalpha*gw.se binom.se<-sfit$surv * sqrt((1-sfit$surv)/(sfit$n.risk)) peto.lower<-sfit$surv - zalpha * binom.se peto.upper<-sfit$surv + zalpha * binom.se if( restrict) { gw.lower<-ifelse(gw.lower<0,0,gw.lower) gw.upper<-ifelse(gw.upper>1,1,gw.upper) llog.lower<-ifelse(llog.lower<0,0,llog.lower) llog.upper<-ifelse(llog.upper>1,1,llog.upper) peto.lower<-ifelse(peto.lower<0,0,peto.lower) peto.upper<-ifelse(peto.upper>1,1,peto.upper) linear.lower<-ifelse(linear.lower<0,0,linear.lower) linear.upper<-ifelse(linear.upper>1,1,linear.upper) } return(list(greenwood=list(lower=gw.lower,upper=gw.upper), loglog=list(lower=llog.lower,upper=llog.upper), peto=list(lower=peto.lower,upper=peto.upper), linear=list(lower=linear.lower,upper=linear.upper))) } km.ci/R/epband.fun.R0000755000175100001440000000725010351557474013657 0ustar hornikusers"epband.fun" <- function(survi, tl=NA,tu=NA, method="linear",conf.lev=0.95) { # This function takes a survfit object and modifies it, such that # its lower and upper boundaries are now computed using the # method by Hall-Wellner. # Essentially required are table of critical values, # named "critical.value.nair.90", "critical.value.nair.95" # "critical.value.nair.99" (see also Appendix C # in Klein & Moeschberger p. 451). data(critical.value.nair.90, critical.value.nair.95, critical.value.nair.99) survi <- survi tl <- tl tu <- tu if(max(conf.lev==c(0.90, 0.95, 0.99))!=1) { stop("confidence level for simultaneous bands must be either 0.90, 0.95 or 0.99") } # if no tl,tu is given the band covers the whole curve if(is.na(tl)&is.na(tu)) { tl <- min(survi$time[survi$n.event>0]) tu <- max(survi$time[survi$n.event>0 &survi$n.risk>survi$n.event]) } n <- survi$n aa <- a.up.low.fun(survi,tl,tu) au <- aa$a.up #determines row in table of critical values al <- aa$a.low #determines column ... dat.mat <- aa$sigma.mat # columns used to interpolate index.al.left <- floor(al/2*100+1)-1 index.al.right <- ceiling(al/2*100+1)-1 if(index.al.left==0) { index.al.left <- 1 } # rows ... index.au.top <- floor((au-0.1)/2*100+1) index.au.bottom <- ceiling((au-0.1)/2*100+1) if(index.au.bottom==46) { index.au.bottom <- 45 } #critical values: readingwise 1. topleft,...,3.bottomleft,... if(conf.lev==0.90) { crit1 <- critical.value.nair.90[index.au.top,index.al.left] crit2 <- critical.value.nair.90[index.au.top,index.al.right] crit3 <- critical.value.nair.90[index.au.bottom,index.al.left] crit4 <- critical.value.nair.90[index.au.bottom,index.al.right] } if(conf.lev==0.95) { crit1 <- critical.value.nair.95[index.au.top,index.al.left] crit2 <- critical.value.nair.95[index.au.top,index.al.right] crit3 <- critical.value.nair.95[index.au.bottom,index.al.left] crit4 <- critical.value.nair.95[index.au.bottom,index.al.right] } if(conf.lev==0.99) { crit1 <- critical.value.nair.99[index.au.top,index.al.left] crit2 <- critical.value.nair.99[index.au.top,index.al.right] crit3 <- critical.value.nair.99[index.au.bottom,index.al.left] crit4 <- critical.value.nair.99[index.au.bottom,index.al.right] } if(is.na(crit2))# just in case { crit2 <- (crit1+crit4)/2 } #percentages of interpolation vert.perc <- 1-(ceiling(au/2*100)-au/2*100) hori.perc <- 1-(ceiling(al/2*100)-al/2*100) #interpolations: numbering clockwise inter1 <- crit1-(abs(crit1-crit2)*hori.perc) inter2 <- crit4-(abs(crit4-crit2)*vert.perc) inter3 <- crit3-(abs(crit3-crit4)*hori.perc) inter4 <- crit3-(abs(crit3-crit1)*vert.perc) interpol <- inter1*(1-vert.perc)+inter4*(1-hori.perc)+inter2*hori.perc+inter3*vert.perc crit <- interpol/2 # First: compute a vector with the deviations devia <- abweich.nair.fun(dat.mat,crit) # Now, produce a list with the lower and upper boundary # dependent of the method. if(method=="linear") { up.low.list <- confi.nair.fun(devia$lin.dev,dat.mat[,2],method) } if(method=="log") { up.low.list <- confi.nair.fun(devia$log.dev,dat.mat[,2],method) } # Finally, modify the survfit object with the new boundaries survi$lower <- up.low.list$lower survi$upper <- up.low.list$upper survi <- modify.surv.fun(survi,aa$start,aa$end,method) return(survi) } km.ci/R/modify.surv.fun.R0000755000175100001440000000122010351557474014702 0ustar hornikusers"modify.surv.fun" <- function(survi,start,end,method) { # This function simply modifys an survival object # and cuts off the ends where no upper and lower # boundary is calculated survi <- survi survi$time <- survi$time[start:end] survi$n.risk <- survi$n.risk[start:end] survi$n.event <- survi$n.event[start:end] survi$surv <- survi$surv[start:end] survi$std.err <- survi$std.err[start:end] survi$upper <- survi$upper[start:end] #survi$upper[survi$upper>1] <- 1 survi$lower <- survi$lower[start:end] #survi$lower[survi$lower<0] <- 0 survi$conf.type <- method return(survi) } km.ci/R/abweich.fun.R0000755000175100001440000000066010351557474014026 0ustar hornikusers"abweich.fun" <- function(matrix,k,n) { # Calculates the upper and lower derivation to the Kaplan-Meier estimator # for determining the boundaries of a Hall-Wellner band (which is # symmetric). kap.mei <- matrix[,2] sigma <- matrix[,3] result1 <- k*(1+n*sigma)*kap.mei/sqrt(n) result2 <- exp(k*(1+n*sigma)/(sqrt(n)*log(kap.mei))) return(list(lin.dev=result1,log.dev=result2)) } km.ci/R/grunk.all.fun.R0000755000175100001440000000147110351557474014322 0ustar hornikusers"grunk.all.fun" <- function(survi,alpha=0.05) { survi <- survi n.event<- survi$n.event n.lost <- survi$n.risk-c(survi$n.risk[-1],0) n.cens <- n.lost - n.event time <- rep(survi$time,n.lost) len <- length(survi$time) upper <- numeric(len) lower <- numeric(len) status <- numeric() for(i in 1:length(n.cens)) { status <- c(status, rep(0,n.cens[i]),rep(1,n.event[i])) } for (i in 1:len) { if(survi$n.event[i]!=survi$n.risk[i]) { grunk.estimate <- lrt.confints(time,status,survi$time[i],alpha) upper[i] <-grunk.estimate$upper lower[i] <-grunk.estimate$lower } if(survi$n.event[i]==survi$n.risk[i]) { upper[i] <-NA lower[i] <-NA } } survi$upper <- upper survi$lower <- lower return(survi) } km.ci/R/Expllike2.R0000755000175100001440000000016110351557474013470 0ustar hornikusers"Expllike2" <- function(lambda,opar) { d<-sum(opar[[2]]) return(d*log(lambda)-lambda*sum(opar[[1]])) } km.ci/R/a.up.low.fun.R0000755000175100001440000000227310351557474014071 0ustar hornikusers"a.up.low.fun" <- function(survi, tl, tu) { # Calculates the indices used to derive the critical value # determining a Hall-Wellner band. It takes the a survfit object # and returns the values belonging to the two timepoints # and a matrix with time, the Kaplan-Meier estimator, the sigmas # (the sum in Greenwoods formula) and the std. error. # t1 should be at least the smallest time, tu the smaller or equal # than the highest time. survi <- survi n <- survi$n time <- survi$time kap.mei <- survi$surv indices <- (1:length(time))[survi$n.event>0] n.risk <- survi$n.risk n.event <- survi$n.event a <- n.event/(n.risk*(n.risk-n.event)) a <- cumsum(a) var.st <- kap.mei^2*a std.err <- sqrt(var.st) sigma <- var.st/kap.mei^2 index.low <- max((1:length(time))[(time-tl)<=0]) index.up <- max((1:length(time))[(time-tu)<=0]) sigma.low <- sigma[index.low] sigma.up <- sigma[index.up] al <- n*sigma.low/(1+n*sigma.low) au <- n*sigma.up/(1+n*sigma.up) return(list(a.low=al,a.up=au,sigma.mat=cbind(time,kap.mei,sigma,std.err) ,start=index.low,end=index.up)) } km.ci/R/rothman.fun.R0000755000175100001440000000151710351557474014076 0ustar hornikusers"rothman.fun" <- function(sfit,conf.level=0.95) { if(conf.level < 0 || conf.level > 1) stop("confidence level must be between 0 and 1") else s.t <- sfit$surv zalpha<-qnorm( 1 - (1-conf.level)/2) tvec<-sfit$n.event/(sfit$n.risk*(sfit$n.risk - sfit$n.event)) tv2<-cumsum(tvec) var.st <- tv2*s.t^2 n.null <- s.t*(1-s.t)/var.st roth.upper <- n.null/(n.null+zalpha^2)*(s.t+zalpha^2/(2*n.null)+zalpha*sqrt(var.st+zalpha^2/(4*n.null^2))) roth.lower <- n.null/(n.null+zalpha^2)*(s.t+zalpha^2/(2*n.null)-zalpha*sqrt(var.st+zalpha^2/(4*n.null^2))) sfit <- sfit roth.upper[is.na(roth.upper)] <- 1 roth.lower[is.na(roth.lower)] <- 1 sfit$upper <- roth.upper sfit$lower <- roth.lower return(list(rothman.upper=roth.upper,rothman.lower=roth.lower,surv.object=sfit)) } km.ci/R/Expllike.deriv.R0000755000175100001440000000014710351557474014522 0ustar hornikusers"Expllike.deriv" <- function(lambda,times,cens) { d<-sum(cens) return(d/lambda-sum(times)) } km.ci/DESCRIPTION0000755000175100001440000000122111246507331013000 0ustar hornikusersPackage: km.ci Type: Package Title: Confidence intervals for the Kaplan-Meier estimator Version: 0.5-2 Date: 2009-08-30 Author: Ralf Strobl Maintainer: Tobias Verbeke Depends: R (>= 1.8.0), survival, stats Description: Computes various confidence intervals for the Kaplan-Meier estimator, namely: Petos CI, Rothman CI, CI's based on Greenwoods variance, Thomas and Grunkemeier CI and the simultaneous confidence bands by Nair and Hall and Wellner. License: GPL (>= 2) Packaged: 2009-08-30 12:42:35 UTC; tobias Repository: CRAN Date/Publication: 2009-08-30 14:38:17 km.ci/man/0000755000175100001440000000000011246471673012057 5ustar hornikuserskm.ci/man/critical.value.hall.99.Rd0000755000175100001440000000057410303631077016430 0ustar hornikusers\name{critical.value.hall.99} \alias{critical.value.hall.99} \docType{data} \title{Critical Values} \description{ Critical values for the 99 \% Hall-Wellner band. } \usage{data(critical.value.hall.99)} \details{ These values are taken from the book by Klein & Moeschberger. } \source{ Klein, Moeschberger (2002): Survival Analysis, Springer. } \keyword{datasets} km.ci/man/critical.value.hall.90.Rd0000755000175100001440000000057410303631077016417 0ustar hornikusers\name{critical.value.hall.90} \alias{critical.value.hall.90} \docType{data} \title{Critical Values} \description{ Critical values for the 90 \% Hall-Wellner band. } \usage{data(critical.value.hall.90)} \details{ These values are taken from the book by Klein & Moeschberger. } \source{ Klein, Moeschberger (2002): Survival Analysis, Springer. } \keyword{datasets} km.ci/man/critical.value.nair.90.Rd0000755000175100001440000000060710350043614016421 0ustar hornikusers\name{critical.value.nair.90} \alias{critical.value.nair.90} \docType{data} \title{Critical Values} \description{ Critical values for the 90 \% equal precision band by Nair. } \usage{data(critical.value.hall.90)} \details{ These values are taken from the book by Klein & Moeschberger. } \source{ Klein, Moeschberger (2002): Survival Analysis, Springer. } \keyword{datasets} km.ci/man/rectum.dat.Rd0000755000175100001440000000147310351551543014413 0ustar hornikusers\name{rectum.dat} \alias{rectum.dat} \docType{data} \title{Rectum carcinoma data set.} \description{ The rectum data contains 205 persons from a study about the survival of patients with rectum carcinoma. Due to the severe course of disease the follow-up was almost perfect in these data and involves hardly any censoring and survivors. The data was used to analyze the behavior of the confidence intervals in data sets with low censoring rate. } \usage{data(rectum.dat)} \format{ A data frame with 205 observations on the following 2 variables. \describe{ \item{\code{time}}{Time in months} \item{\code{status}}{Status at dropout} } } \source{Merkel, Mansmann et al.(2001).The prognostic inhomogeneity in pT3 rectal carcinomas. Int J Colorectal Dis.16, 305--306. } \keyword{datasets} km.ci/man/km.ci.Rd0000755000175100001440000000621711246471361013352 0ustar hornikusers\name{km.ci} \alias{km.ci} \title{Confidence intervals for the Kaplan-Meier estimator.} \description{ Computes pointwise and simultaneous confidence intervals for the Kaplan-Meier estimator. } \usage{ km.ci(survi, conf.level=0.95, tl=NA, tu=NA, method="rothman") } \arguments{ \item{survi}{A survival object for which the new confidence limits should be computed. This can be built using the "Surv" and the "survfit" function in the R package "survival". "km.ci" modifies the confidence limits in this object.} \item{conf.level}{The level for a two-sided confidence interval on the survival curve. Default is 0.95. } \item{tl}{The lower time boundary for the simultaneous confidence limits. If it is missing the smallest event time is used.} \item{tu}{The upper time boundary for the simultaneous confidence limits. If it is missing the largest event time is used.} \item{method}{One of '"peto"', '"linear"', '"log"', "loglog"', '"rothman"', "grunkemeier"', '"hall-wellner"', '"loghall"', "epband"', "logep" } } \details{ A simulation study showed, that three confidence intervals produce satisfying confidence limits. One is the "loglog" confidence interval, an interval which is based on the log of the hazard. The other competitive confidence concept was introduced by Rothman (1978) and is using the assumption that the survival estimator follows a binomial distribution. Another good confidence concept was invented by Thomas and Grunkemeier (1975) and is derived by minimizing the likelihood function under certain constraints. Special thanks goes to Robert Gentleman for providing code for the confidence interval by Thomas and Grunkemeier. The confidence interval using Peto's variance can not be recommended since it yields confidence limits outside the admissible range [0;1] as well as the "linear" and the "log" (which is based on the logarithm of S(t)). The function can produce simultaneous confidence bands, too. The Hall-Wellner band (1980) and the Equal Precision band by Nair (1984) together with their log-transformed counterpart. From all simultaneous confidence intervals only the log-transformed Equal Precision "logep" band can be recommended. The limits are computed according to the statistical tables in Klein and Moeschberger (2002). } \value{ a 'survfit' object; see the help on 'survfit.object' for details. } \author{Strobl, R.} \references{Strobl, R., Dirschedl, P. and Mansmann, U.. Comparison of simultaneous and pointwise confidence intervals for survival functions. (2005, submitted to Biom. J.).} \seealso{\code{\link[survival]{survfit}}, \code{\link[survival]{print.survfit}}, \code{\link[survival]{plot.survfit}}, \code{\link[survival]{lines.survfit}}, \code{\link[survival]{summary.survfit}}, \code{\link[survival]{survfit.object}}, \code{\link[survival]{coxph}}, \code{\link[survival]{Surv}}, \code{\link[survival]{strata}}. } \examples{ require(survival) data(rectum.dat) # fit a Kaplan-Meier and plot it fit <- survfit(Surv(time, status) ~ 1, data=rectum.dat) plot(fit) fit2 <- km.ci(fit) plot(fit2) } \keyword{survival} km.ci/man/critical.value.nair.99.Rd0000755000175100001440000000060710350043652016434 0ustar hornikusers\name{critical.value.nair.99} \alias{critical.value.nair.99} \docType{data} \title{Critical Values} \description{ Critical values for the 99 \% equal precision band by Nair. } \usage{data(critical.value.hall.99)} \details{ These values are taken from the book by Klein & Moeschberger. } \source{ Klein, Moeschberger (2002): Survival Analysis, Springer. } \keyword{datasets} km.ci/man/critical.value.hall.95.Rd0000755000175100001440000000057310303630721016416 0ustar hornikusers\name{critical.value.hall.95} \alias{critical.value.hall.95} \docType{data} \title{Critical Values} \description{ Critical values for the 95 \% Hall-Wellner band. } \usage{data(critical.value.hall.95)} \details{ These values are taken from the book by Klein & Moeschberger. } \source{ Klein, Moeschberger (2002): Survival Analysis, Springer } \keyword{datasets} km.ci/man/critical.value.nair.95.Rd0000755000175100001440000000060710350043634016430 0ustar hornikusers\name{critical.value.nair.95} \alias{critical.value.nair.95} \docType{data} \title{Critical Values} \description{ Critical values for the 95 \% equal precision band by Nair. } \usage{data(critical.value.hall.95)} \details{ These values are taken from the book by Klein & Moeschberger. } \source{ Klein, Moeschberger (2002): Survival Analysis, Springer. } \keyword{datasets} km.ci/README0000755000175100001440000000055610305570567012172 0ustar hornikusers1. Put any C/C++/Fortran code in 'src' 2. If you have compiled code, add a .First.lib() function in 'R' to load the shared library 3. Edit the help file skeletons in 'man' 4. Run R CMD build to create the index files 5. Run R CMD check to check the package 6. Run R CMD build to make the package file Read "Writing R Extensions" for more information.