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mlr3proba and additional packages
 
 if (!require("ooplah")) install.packages("ooplah")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'ooplah'
 
 if (!require("dictionar6")) install.packages("dictionar6")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'dictionar6'
 
 if (!require("set6")) install.packages("set6",
                                          repos = "https://mlr-org.r-universe.dev")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'set6'
 
 if (!require("param6")) install.packages("param6",
                                          repos = "https://mlr-org.r-universe.dev")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'param6'
 
-if (!require("distr6")) install.packages("distr6",
+if (!require("distr6")) install.packages("distr6",
                                          repos = "https://mlr-org.r-universe.dev")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'distr6'
 
 if (!require("mlr3")) install.packages("mlr3")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'mlr3'
 
 if (!require("mlr3proba")) install.packages("mlr3proba",
                                             repos = "https://mlr-org.r-universe.dev")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'mlr3proba'
 if (!require("mlr3extralearners")) install.packages("mlr3extralearners",
                                                     repos = "https://mlr-org.r-universe.dev")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'mlr3extralearners'
 if (!require("mlr3pipelines")) install.packages("mlr3pipelines")
+#> Warning in library(package, lib.loc = lib.loc, character.only = TRUE,
+#> logical.return = TRUE, : there is no package called 'mlr3pipelines'
 
 library(mlr3proba)
 library(mlr3extralearners)
@@ -172,7 +190,7 @@ 

Creating an explainer for m #> -> predict function : predict_newdata()$crank will be used ( default ) #> -> predict survival function : predict_newdata()$distr$survival will be used ( default ) #> -> predict cumulative hazard function : predict_newdata()$distr$cumHazard will be used ( default ) -#> -> model_info : package mlr3proba , ver. 0.5.6 , task survival ( default ) +#> -> model_info : package mlr3proba , ver. 0.6.0 , task survival ( default ) #> A new explainer has been created!

Example explanation @@ -194,7 +212,7 @@

Cr

Not all models can predict the survival distribution (distr predict type), however they can be combined with baseline survival distribution estimator to create composite models. -Such models created using the distrcompositor() pipeline +Such models created using the distrcompositor() pipeline can also be explained easily. There is one caveat, as the pipeline strips the LearnerSurv() class, it has to be added again manually.

@@ -222,7 +240,7 @@

Cr #> -> predict function : predict_newdata()$crank will be used ( default ) #> -> predict survival function : predict_newdata()$distr$survival will be used ( default ) #> -> predict cumulative hazard function : predict_newdata()$distr$cumHazard will be used ( default ) -#> -> model_info : package mlr3proba , ver. 0.5.6 , task survival ( default ) +#> -> model_info : package mlr3proba , ver. 0.6.0 , task survival ( default ) #> A new explainer has been created! gbm_composite_learner_explainer |> model_parts(type = "ratio") |> plot(subtitle = NULL) diff --git a/articles/mlr3proba-usage_files/figure-html/unnamed-chunk-3-1.png b/articles/mlr3proba-usage_files/figure-html/unnamed-chunk-3-1.png index c708be45062c01bfbcf4ae8a967bbf5bfae883dc..a5f5d7ad0ee83a1f359e92c0c790acc6076b4fdd 100644 GIT binary patch delta 62625 zcmaHSWmJ`2*Y3S(k(8E@6r_=Elv0tB?h=p&>AW{6AxNooH`2N3mXPl5?(RI>=auLE zzH!bt_ZaYpF<5KOyyi9MOgTjwI7TWe5(jf~wru9-9D5U<(~F_KuQYIKB%xb;g>R8j 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#> -> predict function : predict.model_fit with type = 'linear_pred' will be used ( default ) #> -> predict survival function : predict.model_fit with type = 'survival' will be used ( default ) #> -> predict cumulative hazard function : -log(predict_survival_function) will be used ( default ) -#> -> model_info : package parsnip , ver. 1.1.1 , task survival ( default ) +#> -> model_info : package parsnip , ver. 1.2.0 , task survival ( default ) #> A new explainer has been created! ###### explain_survival() ###### diff --git a/reference/extract_predict_survshap-1.png b/reference/extract_predict_survshap-1.png index 9bad50bd16d8fe30eff333f97990bc7e1c27cbfb..d7956118876fed357b68762fcfb5e9b17c6479b6 100644 GIT binary patch delta 82887 zcmY&FCDJ)M|w!EE9`ty7A^Os#x*`d)x|^pu>1~3-Tv($!4`v_n4Bk{b6$Dom9Vhg&eNkGh`p{1Z4qYUsx|+Y# zZ{bad7rf|J-5y^y_Mp#)nFdWxKOZ}RrN9JOPiT{|7%8Xs)<0zPu3O=SMb!=FVo1=^Zrcu-U+eHh3u?J-V;IKb3Ya+k@?m0Y~lPX%Ex}w<>n*4K|Mba 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b/reference/model_parts.surv_explainer.html @@ -192,40 +192,40 @@

Examples

cph_model_parts_brier <- model_parts(cph_exp) print(head(cph_model_parts_brier$result)) -#> _times_ _full_model_ trt celltype karno diagtime -#> 1 1.5 0 2.778714e-05 -6.259026e-04 0.0001338949 2.757778e-07 -#> 12 4.0 0 -1.150694e-04 -6.950593e-04 0.0018542687 1.479591e-06 -#> 23 7.0 0 3.200550e-04 -8.387974e-05 0.0053082074 6.851046e-07 -#> 35 8.0 0 -2.230333e-04 1.316869e-03 0.0094318451 5.707535e-06 -#> 48 10.0 0 -8.349802e-04 1.806509e-04 0.0131695326 8.376118e-06 -#> 57 12.0 0 -1.926562e-03 -4.769109e-04 0.0160642567 6.841627e-06 -#> age prior _baseline_ _permutation_ label -#> 1 1.558568e-05 1.351912e-05 0.000102907 0 coxph -#> 12 5.173491e-04 4.387803e-05 0.002362006 0 coxph -#> 23 5.023033e-04 6.202155e-06 0.007344552 0 coxph -#> 35 5.866577e-04 2.082978e-04 0.014061676 0 coxph -#> 48 5.287559e-04 2.916372e-04 0.017815150 0 coxph -#> 57 3.929032e-04 5.883547e-04 0.020626294 0 coxph +#> _times_ _full_model_ trt celltype karno diagtime +#> 1 1.5 0 3.414508e-05 -0.0004391802 0.0001930351 -3.052674e-08 +#> 12 4.0 0 -9.083787e-05 -0.0002643615 0.0020820016 7.604845e-07 +#> 23 7.0 0 4.752907e-04 0.0013203051 0.0052460287 4.063020e-07 +#> 35 8.0 0 2.247208e-04 0.0031117431 0.0102433899 5.050466e-06 +#> 48 10.0 0 -3.788362e-04 0.0019621813 0.0137033300 7.451684e-06 +#> 57 12.0 0 -1.265335e-03 0.0017288758 0.0149247381 8.220809e-06 +#> age prior _baseline_ _permutation_ label +#> 1 1.540466e-05 9.416094e-06 0.0001320184 0 coxph +#> 12 5.021007e-04 4.424765e-05 0.0021482684 0 coxph +#> 23 5.190239e-04 -3.183107e-05 0.0066581972 0 coxph +#> 35 5.216642e-04 1.909002e-04 0.0129284258 0 coxph +#> 48 5.817038e-04 2.948293e-04 0.0158831815 0 coxph +#> 57 5.084629e-04 5.295008e-04 0.0187495665 0 coxph plot(cph_model_parts_brier) rsf_ranger_model_parts <- model_parts(rsf_ranger_exp) print(head(rsf_ranger_model_parts$result)) -#> _times_ _full_model_ trt celltype karno diagtime -#> 1 1.5 0 0.0003862719 0.001515602 0.002353320 0.001053983 -#> 12 4.0 0 0.0010308162 0.001912301 0.007735008 0.003629827 -#> 23 7.0 0 0.0023298791 0.002767161 0.014972515 0.005040090 -#> 35 8.0 0 0.0021990678 0.004848354 0.023939391 0.010272433 -#> 48 10.0 0 0.0025582883 0.005409363 0.033466211 0.012335734 -#> 57 12.0 0 0.0027748648 0.007302606 0.036913250 0.013090128 +#> _times_ _full_model_ trt celltype karno diagtime +#> 1 1.5 0 0.0001627702 0.001154185 0.002194825 0.0007448171 +#> 12 4.0 0 0.0010166134 0.002589573 0.009145478 0.0026006903 +#> 23 7.0 0 0.0014333630 0.003611515 0.016965397 0.0040812810 +#> 35 8.0 0 0.0019726986 0.006026738 0.025968757 0.0091819261 +#> 48 10.0 0 0.0022475016 0.005699407 0.038798780 0.0110994975 +#> 57 12.0 0 0.0025773798 0.007753140 0.046387337 0.0150343326 #> age prior _baseline_ _permutation_ label -#> 1 0.003297578 0.0003964083 0.005590278 0 ranger -#> 12 0.009782656 0.0008382632 0.017649442 0 ranger -#> 23 0.013978963 0.0010454687 0.025958645 0 ranger -#> 35 0.014025862 0.0021708900 0.039946511 0 ranger -#> 48 0.016020002 0.0025689911 0.053797355 0 ranger -#> 57 0.016538725 0.0037078347 0.057599532 0 ranger +#> 1 0.002751134 0.0002786377 0.005572104 0 ranger +#> 12 0.011073665 0.0006944964 0.018361833 0 ranger +#> 23 0.014504751 0.0003502657 0.025381473 0 ranger +#> 35 0.014323920 0.0009249015 0.039236130 0 ranger +#> 48 0.015248906 0.0012535640 0.051156514 0 ranger +#> 57 0.015342076 0.0023220928 0.060363730 0 ranger plot(cph_model_parts_brier, rsf_ranger_model_parts) # } diff --git a/reference/model_performance.surv_explainer-1.png b/reference/model_performance.surv_explainer-1.png index 077e4528ec0fc9908f88b74d8c568f9b3554d512..809110ed22d89e98c2e64d7de2fa6a24e26422b8 100644 GIT binary patch delta 45069 zcmb@tXH-*N7cQDtR8$mf2#SD80O?2-X)h{52)%cZDmC;L!nOh;f`r~t0!Z&2f{os! zh87~-(0hOYxf}HR#<}B;d&jxwob@X(_Fj9gwPtzdGw1p>Lq(aUDk_u%Dhh-sQ-nFC zic&ALD@I+q=PtmrnRx8D%GA#>-(`+rOc>(IIJjgU_^nKE)zbV76&2NaDi8BCj~b|# zX46x0ag_D5D@vn3U{o3GKOZJEl|(K(T$ZfJAnzZ3n7$vzFdW0%zFTJ|(BM|+xhxT0 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head(cph_model_profile$result) #> _vname_ _vtype_ _label_ _x_ _times_ _yhat_ _ids_ -#> 1 age numerical coxph 34.00 1.5 0.9818366 0 -#> 2 age numerical coxph 34.94 1.5 0.9819820 0 -#> 3 age numerical coxph 35.00 1.5 0.9819913 0 -#> 4 age numerical coxph 35.88 1.5 0.9821263 0 -#> 5 age numerical coxph 36.00 1.5 0.9821446 0 -#> 6 age numerical coxph 36.82 1.5 0.9822694 0 +#> 1 age numerical coxph 34.00 1.5 0.9840125 0 +#> 2 age numerical coxph 34.94 1.5 0.9841410 0 +#> 3 age numerical coxph 35.00 1.5 0.9841492 0 +#> 4 age numerical coxph 35.88 1.5 0.9842684 0 +#> 5 age numerical coxph 36.82 1.5 0.9843949 0 +#> 6 age numerical coxph 37.00 1.5 0.9844190 0 plot(cph_model_profile) @@ -230,12 +230,12 @@

Examples

head(rsf_model_profile$result) #> _vname_ _vtype_ _label_ _x_ _times_ _yhat_ _ids_ -#> 1 age numerical rfsrc 35 1.5 0.9750189 0 -#> 2 age numerical rfsrc 35 4.0 0.9453011 0 -#> 3 age numerical rfsrc 35 7.0 0.9258519 0 -#> 4 age numerical rfsrc 35 8.0 0.9000139 0 -#> 5 age numerical rfsrc 35 10.0 0.8847792 0 -#> 6 age numerical rfsrc 35 12.0 0.8641364 0 +#> 1 age numerical rfsrc 34 1.5 0.8541533 0 +#> 2 age numerical rfsrc 34 4.0 0.7929659 0 +#> 3 age numerical rfsrc 34 7.0 0.7756136 0 +#> 4 age numerical rfsrc 34 8.0 0.7516203 0 +#> 5 age numerical rfsrc 34 10.0 0.7406234 0 +#> 6 age numerical rfsrc 34 12.0 0.7260259 0 plot(rsf_model_profile, variables = c("age", "celltype"), numerical_plot_type = "contours") diff --git a/reference/model_profile_2d.surv_explainer-1.png b/reference/model_profile_2d.surv_explainer-1.png index a533020464a9668d549c7953790cc8379d9ea9f5..e36fefa50de27bb926eaeb75dcaf8eac21768107 100644 GIT binary patch literal 52299 zcmdSBWl&sA^geh84G`QtcyMl007YC-%6_k0HQYlz?;25g8j0P zoO=xGkjxb1q+va*-AFYD!a5Y^w|Z^>Kpyt|0mW{fSOUN+KwkQdrgz5v64;+it5y7X z)XS6KRuXM97jNETf&s)BTQE$I_)|*)^hQ-bgr1T0T|D!(`$e8n;9uWXF$0K74&fKV z+_|47s*NARy{EKuc+zo#&D3&!4$!k==Hn?WSnvyxlrY#+sUoy&zAI+3P4u59| zZvFn9#@)hmBG+L%#K+%f8^7Q(Ru{GyKu@%Ql1@0w8C0W6w!X9ZIIwSfz-S z^yk`J8?P_kf8SgIcKMX`i`_3Wn>0au)b}r)rR&i)+zcQ8E&w^X72vue5z2*vKYyl{ z@6RTy&V+EWv9T@JSiF#S_huRl{ODU1dUQiXufq|EfYy!Phck0}I*Xk3A&4wXnB>TyN=4}Olf_G- z(wj)^5DmHnm8#8@7+n@zX*&MD+4wTr0Z4)?1tNKwJ(vNv__hp$DA|D~ntj z)_SangPppj8%iVDg@2whq$6kf;o8&;rt(r#Q|HkPrrNG(bskA?x`_pzxee5bFMZ($ z-Fq?$QSP{5f{kDC_gsdYtO(W|3f=5Llbrr$RgCN$+AaC$IvzW%rSUs)g<3R$>?$l* zn1FJCeh!XLSSq-bWOv$Va;e_Tt-|(sow1|kzEpJzh6nH2mr`sWI^D?5aXmkoI|8#G zJS6f$M=HZ1r z4X&39K5%DH5&PJ^yU4P?Tv#)1gEzO&vwpV230t~6dgS2D%Ung)Dk{>LBu9CCtQjxi&`0u5YcN7(324`X8cm3p3&ly#JondQ&xF|A$?aQ7z313+$%iF5^)NTxw`rR}s81nOu!pRqK~ifO zv0oT83HRd7F~8bP4c#=22RQN3AGZ5K2DWB3e+#*qO?$vL4o_Cp-U&p+1IqSVqzXUT zqt3~sXPT$EWBpPLaV2BBy4&=(ui4gVo7z_A!2^2GP~H%#yXT$07}xx@x^2mh$Oso4 zL^?&rML{QU=t_iJ_`}MW9FURa7>MoPUI#fnn+xy}xAVPIn@ZgHpyiL!v2TB=)_KGP z-(5cU7$@(eSTmy%M(hj3g<9O{Q5E_Np_IPr zGD=<@(y*}Q)GB5ORd5NQz9DHeDiJ@ss5eTn3Tp`&ZN0$?x_$ZP=!KH-`9N0IRCjbO zR1Cj?W{>~oo^{JQ-AHO_o?gwIHK^Va`+huff((aVyhz>+5P~qz9Zq z7F8s&WUiFX)xHtzb@Ek$f_pd~R_oLZ>n6w~w^t^D)X)=ZQ^;YM+wet&LeID1)- zb5mkPzvNo6_Y=aUlt(S3{Q^4qMpNW4CdeTK{=^CXJ>VMP82Jvi9&X?2S5BKpddg_N zsLlNIQoRl2d8Uj)rtS5REAu9*54!@{v2K4>o!q9d+t+}_LE zG&%ng=GX4>dU5;PI`=YR7nab-^ioS+Rq$;8ob;H=Ca_~YA^8b^)Owr)1<(=5oC+Ms z5BL^j3HSO$&>&Jdtna`uaoxqT*kX%?;cUxGTTu*a_TwU{$RF1Zyk9e0jnxOtb1j`b zyIYBt-`XP;RLVy;vkdV;k5dG(g2napQx+A(*{1m;6A1pw$){-vqh2&XhfnR*@6>zk zl@JzEFVKoj_~Lr;!L?v&d!ko?(64z~lwMmoLyyZrOM9~~JcA$uqQ1N>}Z9%W6`*udqT zj7&03K-sCr6=Lk&d%hEuYp&q6Y^j{#<7RkFAbd}$SfWsOp2$^O`Qj!WJ-dCbh@>oo zI}$1=+ILxr zBq){*3rDkP{gn2c2HN67W=M6KhDu zb0mA=CKQVzzYPI)?KxXt_N#Z7h=}<N`6r!o=5W$9)d0hos&2l^3bNCN)=@YC?qpnrQKVyc7megtIkA>~&8?5W zT=x8hPnd#)pLg&TGP8j`)?EUG(Dy?t<;exjFtc~ii@KtGW0(UwrzM3y73=uLwvTxH zBB$NS*~@^vLxnoMlrG!}{`!|qjC^pRqV;A%7WvSl>GG{RI~2iLXP@YSbqh{HL&-YH z62Tsw;}$-iSMgd_C_Py3c322~1p|}@2gMFgywZok z{Urv8r<3LSC_PhkTCus3(4z%tOQ6Nn@c6kA;H+V;%?U*n(v=9NbS?wi)2 z?3)7g6l40x zKnzm5t$_F4<^*!B#U+Gvq=n#h3Nqlh{ z$=mLmymYTpJR4E7Y*qC6<_uf&9&iDz0&`0B)(gZIz#bQKFO;df#ohd9a8{M#3PkVO zfw^Lb&OHNV#xO^tUcZ5Bp!N}y0YApCE>U+EMBR^f#L*i&x#<+pL`KOxniV+Ty!Ip) zblDSnMn{ldOH61%Q|*@IiQ1iz!U^T(Am*!+=*B!czuK{nE&)yQ@)UlSQxt_lgj5w? 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2eb9582..96f0ae5 100644 --- a/reference/model_profile_2d.surv_explainer.html +++ b/reference/model_profile_2d.surv_explainer.html @@ -188,12 +188,12 @@

Examples

#> 5 age celltype numerical categorical 41.8333333333333 adeno 1.5 #> 6 age celltype numerical categorical 43.7916666666667 adeno 1.5 #> _label_ _yhat_ -#> 1 coxph 0.9727194 -#> 2 coxph 0.9731710 -#> 3 coxph 0.9736152 -#> 4 coxph 0.9740523 -#> 5 coxph 0.9744822 -#> 6 coxph 0.9749052 +#> 1 coxph 0.9707866 +#> 2 coxph 0.9712691 +#> 3 coxph 0.9717438 +#> 4 coxph 0.9722109 +#> 5 coxph 0.9726704 +#> 6 coxph 0.9731225 plot(cph_model_profile_2d) #> Warning: Plot will be prepared for the median survial time. For another time point, set the value of `times`. @@ -204,19 +204,19 @@

Examples

) head(cph_model_profile_2d_ale$result) #> _v1name_ _v2name_ _v1type_ _v2type_ _v1value_ _v2value_ _times_ _yhat_ -#> 1 age karno numerical numerical 34 10 1.5 0.9812883 -#> 2 age karno numerical numerical 34 10 4.0 0.9540177 -#> 3 age karno numerical numerical 34 10 7.0 0.9272976 -#> 4 age karno numerical numerical 34 10 8.0 0.8926547 -#> 5 age karno numerical numerical 34 10 10.0 0.8758836 -#> 6 age karno numerical numerical 34 10 12.0 0.8516812 +#> 1 age karno numerical numerical 35 20 1.5 0.9844186 +#> 2 age karno numerical numerical 35 20 4.0 0.9614581 +#> 3 age karno numerical numerical 35 20 7.0 0.9386648 +#> 4 age karno numerical numerical 35 20 8.0 0.9086675 +#> 5 age karno numerical numerical 35 20 10.0 0.8939623 +#> 6 age karno numerical numerical 35 20 12.0 0.8725289 #> _right_ _left_ _top_ _bottom_ _count_ _label_ -#> 1 36 34 10 15 0 coxph -#> 2 36 34 10 15 0 coxph -#> 3 36 34 10 15 0 coxph -#> 4 36 34 10 15 0 coxph -#> 5 36 34 10 15 0 coxph -#> 6 36 34 10 15 0 coxph +#> 1 36.5 35 20 25 0 coxph +#> 2 36.5 35 20 25 0 coxph +#> 3 36.5 35 20 25 0 coxph +#> 4 36.5 35 20 25 0 coxph +#> 5 36.5 35 20 25 0 coxph +#> 6 36.5 35 20 25 0 coxph plot(cph_model_profile_2d_ale) #> Warning: Plot will be prepared for the median survial time. 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    Arguments

    colors
    -

    character vector containing the colors to be used for plotting variables (containing either hex codes "#FF69B4", or names "blue")

    +

    character vector containing the colors to be used for plotting variables (containing either hex codes "#FF69B4", or names "blue"). If geom = "importance", the first color will be used for the barplot, the rest for the lines.

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    Examples

    ) head(cph_model_profile_2d$result) #> _v1name_ _v2name_ _v1type_ _v2type_ _v1value_ _v2value_ _times_ -#> 1 age celltype numerical categorical 35 adeno 1.5 -#> 2 age celltype numerical categorical 36.9166666666667 adeno 1.5 -#> 3 age celltype numerical categorical 38.8333333333333 adeno 1.5 -#> 4 age celltype numerical categorical 40.75 adeno 1.5 -#> 5 age celltype numerical categorical 42.6666666666667 adeno 1.5 -#> 6 age celltype numerical categorical 44.5833333333333 adeno 1.5 +#> 1 age celltype numerical categorical 34 adeno 1.5 +#> 2 age celltype numerical categorical 35.9583333333333 adeno 1.5 +#> 3 age celltype numerical categorical 37.9166666666667 adeno 1.5 +#> 4 age celltype numerical categorical 39.875 adeno 1.5 +#> 5 age celltype numerical categorical 41.8333333333333 adeno 1.5 +#> 6 age celltype numerical categorical 43.7916666666667 adeno 1.5 #> _label_ _yhat_ -#> 1 coxph 0.9731107 -#> 2 coxph 0.9735464 -#> 3 coxph 0.9739753 -#> 4 coxph 0.9743973 -#> 5 coxph 0.9748127 -#> 6 coxph 0.9752214 +#> 1 coxph 0.9711210 +#> 2 coxph 0.9715980 +#> 3 coxph 0.9720673 +#> 4 coxph 0.9725290 +#> 5 coxph 0.9729833 +#> 6 coxph 0.9734302 plot(cph_model_profile_2d, variables = list(c("age", "celltype")), times = cph_exp$times[20]) @@ -194,19 +194,19 @@

    Examples

    ) head(cph_model_profile_2d_ale$result) #> _v1name_ _v2name_ _v1type_ _v2type_ _v1value_ _v2value_ _times_ _yhat_ -#> 1 age karno numerical numerical 34 10 1.5 0.9816427 -#> 2 age karno numerical numerical 34 10 4.0 0.9550537 -#> 3 age karno numerical numerical 34 10 7.0 0.9291853 -#> 4 age karno numerical numerical 34 10 8.0 0.8959009 -#> 5 age karno numerical numerical 34 10 10.0 0.8798823 -#> 6 age karno numerical numerical 34 10 12.0 0.8568626 +#> 1 age karno numerical numerical 34 10 1.5 0.9821661 +#> 2 age karno numerical numerical 34 10 4.0 0.9562840 +#> 3 age karno numerical numerical 34 10 7.0 0.9310471 +#> 4 age karno numerical numerical 34 10 8.0 0.8984986 +#> 5 age karno numerical numerical 34 10 10.0 0.8828059 +#> 6 age karno numerical numerical 34 10 12.0 0.8602254 #> _right_ _left_ _top_ _bottom_ _count_ _label_ -#> 1 36.5 34 10 15 0 coxph -#> 2 36.5 34 10 15 0 coxph -#> 3 36.5 34 10 15 0 coxph -#> 4 36.5 34 10 15 0 coxph -#> 5 36.5 34 10 15 0 coxph -#> 6 36.5 34 10 15 0 coxph +#> 1 36 34 10 15 0 coxph +#> 2 36 34 10 15 0 coxph +#> 3 36 34 10 15 0 coxph +#> 4 36 34 10 15 0 coxph +#> 5 36 34 10 15 0 coxph +#> 6 36 34 10 15 0 coxph plot(cph_model_profile_2d_ale, times = cph_exp$times[c(10, 20)], marginalize_over_time = TRUE) # } diff --git a/reference/plot.model_profile_survival-1.png b/reference/plot.model_profile_survival-1.png index f4a7cb9731cabb4223b5761d64bfa6cd4d363204..032fb06cdf678cae3e301f2ddff4c92d3826ebeb 100644 GIT binary patch literal 127360 zcmeEuc{E$?_pgrL4tP6iDLNoErskUaR*M*Gs+rb2$2^nHT8fGxLQoadTw^MxwrVCS zK}-oXr4a-PDG_e;{r>JBciq44y6fKi$9=M#oRyrDbDq7Q{n>ke_CDS`Fw)^V$#;^2 zgM$mCd(V`E<5)Nc#}TK$jR)BgDrf6gBbWCSo6v%k$h=Qw@8E~zKcjm;B=DYjfdptb9{#JbDD~>!cmLKEmBZQR zO$!m!%?LPk_NyzH7O5eaV_*?ewq!P4x)WR6MymjW{@mgJ$<54m$A}6?+N2kd4-WAU zpQudeuxu?3U3W1U{akbZhP|ywy&lvctTQweVK({tfw$ecI|?H@_9mAHNzQrE7p2f> zG5UNRUL^<&^Lb;sdFN*BR8LH>Rx^tqTKBmks8WtW%6DBqE#F7=})LQxsg%^F>$f2c48(%_5s!^WI+FvFA~fs@U?r#CLH152VkGf;%lT($a4* 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z=Mn%$B#=vN5~FkPNJr{BdWG!#m^3?I{;W#cFgHy-`UW>cxsW*uoHX%a0#FQckSQt#bSWuZ>wZQ)a1q=?@-5qtn$bpqv+{uqV(3 YA8b6qvBnhq?_Uta%{w>BG;E*#56WOk1^@s6 diff --git a/reference/predict.surv_explainer.html b/reference/predict.surv_explainer.html index 1829409..97c08a7 100644 --- a/reference/predict.surv_explainer.html +++ b/reference/predict.surv_explainer.html @@ -170,8 +170,8 @@

    Examples

    #> 0.7354128 predict(rsf_ranger_exp, veteran[1, ], output_type = "chf")[, 1:10] -#> [1] 0.004136111 0.004136111 0.008346803 0.012096248 0.013620562 0.038050295 -#> [7] 0.039715701 0.044045645 0.044839763 0.044839763 +#> [1] 0.005945378 0.005945378 0.018315374 0.022121901 0.024215918 0.057908648 +#> [7] 0.059175652 0.063733170 0.069066503 0.070342694
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    Examples

    type = "survlime" ) head(cph_predict_parts_survlime$result) -#> trt karno diagtime age prior celltypelarge -#> 1 0.0117384 -0.001180857 0.0004831734 0.0009636637 0.001270599 -0.02852748 +#> trt karno diagtime age prior celltypelarge +#> 1 0.003629477 -0.0007383551 8.119279e-05 0.0004152425 0.000347701 -0.007316818 #> celltypesmallcell celltypesquamous -#> 1 -0.008329012 -0.03157334 +#> 1 0.005774009 -0.005378658 plot(cph_predict_parts_survlime, type = "local_importance") # } diff --git a/reference/theme_survex-1.png b/reference/theme_survex-1.png index b87084aaa04a5474734d6cfa9b77c6319429dcac..134cfde7c849984a2e89645cf5fa235c9de94f74 100644 GIT binary patch literal 79455 zcmeFZcT`hd@HUFCT~urcf`A23KtPZpT}4Xh9Z4wCdnc4oVgm#PBsA&0H|Y|Zih%S^ zfY21FA+!LYB;@Y!zQ4P^`^R1Derw%7zpND!PIAtfJ$vSvXJ*e%h=!WNg>y{jXlQ6I zKop;A(a@aqrlC1*cIFf~f{jgI2QR10log(XSMb=AtwVyBvo4AT?ld$k!PMumh`9|* z8k$=)kmpZzypvWYeLSw|j<;=?Ij8SU$rhCQ2 zatm=I#kmDex*Y0GlY%#bOwSw%MMHDROB*^iX~$_^b=)U-l!%mwWx#nZ+FNfz@RRZB6$W~8&jSlw 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