CoxModel[…]
CoxModelFitで得られる記号比例ハザードモデルを表す.
CoxModel
CoxModel[…]
CoxModelFitで得られる記号比例ハザードモデルを表す.
詳細とオプション
- コックス(Cox)モデルの特性はCoxModel[…]["property"]で得られる.
- CoxModel[…][{prop1,prop2,…}]は複数の特性を与える.
- CoxModel[…][x0][t]は特定の点 t における共変量レベル x0についての最適フィット関数を与える.
- NormalはCoxModelにおけるベースライン生存関数の式を与える.
- CoxModel[…][prop,ann]は特性 prop に関連する注釈 ann を与える.
- 指定タイプのフィットされたモデルで使用可能な特性が,そのモデルを生成するCoxModelFit等の関数ページにリストとして掲載されている.
- CoxModelは,次のオプションを取る.
-
ConfidenceLevel 
パラメータと予測に使う信頼水準 ConfidenceRange All 同時信頼区間の範囲 ConfidenceTransform "LogLog" 使用する信頼変換
例題
すべて開く すべて閉じる例 (1)
右側打切りデータからCoxModelを作成する:
data = EventData[{1, 2, 3, 4}, {0, 0, 1, 0}];mod = CoxModelFit[data]mod["EventTimes"]mod[3]スコープ (6)
CoxModelオブジェクトから特性を抽出する:
mod = CoxModelFit[{1, 2, 3, 4}]mod["StandardErrors"][]mod = CoxModelFit[{1, 2, 3, 4}]mod[{"MartingaleResiduals", "CoxSnellResiduals"}]mod = CoxModelFit[{1, 2, 3, 4}]mod["Properties"]mod = CoxModelFit[{1, 2, 3, 4}];mod[2]mod /@ {1, 2, 3, 4}ベースライン生存関数の式を得るために,Normalを使用する:
mod = CoxModelFit[{1, 2, 3, 4}];Normal[mod]mod = CoxModelFit[{1, 2, 3, 4}];Table[Row[{i, ":
", mod["BaselineList", i]}], {i, {"Description", "LongDescription"}}]//TableFormオプション (5)
ConfidenceLevel (3)
ξ = {{67, 1}, {11, 0}, {32, 0}, {50, 1}, {65, 1}, {44, 1}, {20, 1}, {26, 0}, {69, 1}, {50, 1}, {25, 1}, {18, 0}, {63, 1}, {41, 0}, {30, 0}, {32, 1}, {46, 1}, {35, 0}, {33, 0}, {22, 0}};
e = EventData[Automatic, {{8, 22, 6, 4, 12, 11, 2, 34, 25, 15, 8, 6, 34, 6, 32, 1, 15, 9, 9, 6},
{0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0}, None}];model = CoxModelFit[{ξ, e}, {Subscript[x, 1], Subscript[x, 2]}, {Subscript[x, 1], Subscript[x, 2]}]Table[Plot[Evaluate@{model["SF"][][t], model["PointwiseBands", "SF", ConfidenceLevel -> i][][t]}, {t, 0, 35}, PlotLabel -> i], {i, {.9, .95, .99}}]ξ = {{67, 1}, {11, 0}, {32, 0}, {50, 1}, {65, 1}, {44, 1}, {20, 1}, {26, 0}, {69, 1}, {50, 1}, {25, 1}, {18, 0}, {63, 1}, {41, 0}, {30, 0}, {32, 1}, {46, 1}, {35, 0}, {33, 0}, {22, 0}};
e = EventData[Automatic, {{8, 22, 6, 4, 12, 11, 2, 34, 25, 15, 8, 6, 34, 6, 32, 1, 15, 9, 9, 6},
{0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0}, None}];model = CoxModelFit[{ξ, e}, {Subscript[x, 1], Subscript[x, 2]}, {Subscript[x, 1], Subscript[x, 2]}];model["ParameterConfidenceIntervals"]Table[model["ParameterConfidenceIntervals", ConfidenceLevel -> i], {i, {.85, .9, .95}}]ξ = {{67, 1}, {11, 0}, {32, 0}, {50, 1}, {65, 1}, {44, 1}, {20, 1}, {26, 0}, {69, 1}, {50, 1}, {25, 1}, {18, 0}, {63, 1}, {41, 0}, {30, 0}, {32, 1}, {46, 1}, {35, 0}, {33, 0}, {22, 0}};model = CoxModelFit[{ξ, EventData[Automatic, {{8, 22, 6, 4, 12, 11, 2, 34, 25, 15, 8, 6, 34, 6, 32, 1, 15, 9, 9, 6},
{0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0}, None}]}, {Subscript[x, 1], Subscript[x, 2]}, {Subscript[x, 1], Subscript[x, 2]}]model["EventTable", "CHF", ConfidenceLevel -> .90][{50, 1}]ConfidenceRange (1)
model = CoxModelFit[EventData[Automatic, {{{2081, Infinity}, {1602, Infinity}, {1496, Infinity}, {1462, Infinity},
{1433, Infinity}, {1377, Infinity}, {1330, Infinity}, {996, Infinity}, {226, Infinity},
{1199, Infinity}, {1111, Infinity}, {530, Infinity}, {1182, Infinity}, {1167, Infinity}, 418,
383, 276, 104, 609, 172, 487, 662, 194, 230, 526, 122, 129, 74, 122, 86, 466, 192, 109, 55, 1,
107, 110, 332}, None, None}]]Plot[Evaluate@{model["SF"][][x], model["EqualPrecisionBands", ConfidenceRange -> {172, 383}][][x]}, {x, 100, 500}, PlotRange -> {0, 1}]デフォルトで,範囲はAllに設定される:
Plot[Evaluate@{model["SF"][][x], model["EqualPrecisionBands", ConfidenceRange -> All][][x]}, {x, 0, 2081}, PlotRange -> {0, 1}]範囲をFullに設定する:
Plot[Evaluate@{model["SF"][][x], model["EqualPrecisionBands", ConfidenceRange -> Full][][x]}, {x, 0, 2081}, PlotRange -> {0, 1}]ConfidenceTransform (1)
ξ = {1, 0, 0, 1, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0};model = CoxModelFit[{ξ, EventData[Automatic, {{8, 22, 6, 4, 12, 11, 2, 34, 25, 15, 8, 6, 34, 6, 32, 1, 15, 9, 9, 6},
{0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0}, None}]}, x, x]tr = {"Linear", "LogLog", "ArcSinSqrt", "Log", "Logit"};Table[{i, model["PointwiseBands", "SF", ConfidenceTransform -> i][][15]}, {i, tr}]//GridTable[Plot[Evaluate@{model["SF"][][t], model["PointwiseBands", "SF",
ConfidenceTransform -> i][][t]}, {t, 0, 35}, PlotLabel -> i], {i, tr}]model["PointwiseBands", ConfidenceTransform -> "LogLog"][{0}][15]model["PointwiseBands"][{0}][15]関連するガイド
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▪
- 生存率分析
テキスト
Wolfram Research (2012), CoxModel, Wolfram言語関数, https://reference.wolfram.com/language/ref/CoxModel.html.
CMS
Wolfram Language. 2012. "CoxModel." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/CoxModel.html.
APA
Wolfram Language. (2012). CoxModel. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/CoxModel.html
BibTeX
@misc{reference.wolfram_2026_coxmodel, author="Wolfram Research", title="{CoxModel}", year="2012", howpublished="\url{https://reference.wolfram.com/language/ref/CoxModel.html}", note=[Accessed: 08-September-2026]}
BibLaTeX
@online{reference.wolfram_2026_coxmodel, organization={Wolfram Research}, title={CoxModel}, year={2012}, url={https://reference.wolfram.com/language/ref/CoxModel.html}, note=[Accessed: 08-September-2026]}