model1=lrm(low~age+lwt+race1+race2+smoke+ptl+ht+ui+ftv,data = data1,x=T,y=T) model1 Logistic Regression Model lrm(formula = low ~ age + lwt + race1 + race2 + smoke + ptl + ht + ui + ftv, data = data1, x = T, y = T) Model Likelihood Discrimination Rank Discrim. Ra...
Logistic Regression Model lrm(formula = MPE ~ ADAPE + LDHS + Gender, data = train_data, x = TRUE, y = TRUE) Model Likelihood Discrimination Rank Discrim. Ratio Test Indexes Indexes Obs 366 LR chi2 45.01 R2 0.169 C 0.785 No 270 d.f. 3 R2(3,366)0.108 Dxy 0.570 Yes 96 Pr(> chi...
∂σ(z)∂z=σ(z)σ(−z)∂σ(z)∂z=σ(z)σ(−z) Logistic Function σ(x;θ)=11+e−θxσ(x;θ)=11+e−θx 首先我们考虑22分类问题, 所以f(x)f(x)的值域也是[−1,1][−1,1]。 P(y=1|x,θ)=σ(x)P(y=1|x,θ)=σ(x) ...
模型搭建 fit=lrm(n23~age+time+n4+n5+n7+n8+n9+n10new+n13+n14+n15+n16+n18+n19f1+n20+n21+n22+n32+n39new+n40new,data = mydata,x=T,y=T)Logistic Regression Modellrm(formula = n23 ~ age + time + n4 + n5 + n7 + n8 + n9 + n10new +n13 + n14 + n15 + n16 + n18 + n...
当分类变量为名义型时,通常需要创建虚拟变量。在R中,可以使用model.matrix()函数或因子变量结合lm()或glm()函数来自动创建虚拟变量。 1. 虚拟变量的创建 将分类变量转换为虚拟变量,可以使用R语言中的model.matrix()函数。举个例子,如果我们有一个名为“Brand”的分类变量,该变量有三个类别“A”、“B”和“C”...
Logistic Regression Model lrm(formula = n23 ~ age + time + n4 + n5 + n7 + n8 + n9 + n10new + n13 + n14 + n15 + n16 + n18 + n19f1 + n20 + n21 + n22 + n32 + n39new + n40new, data = mydata, x = T, y = T) ...
Logistic Regression (1)广义线性回归:glm(formula, family = gaussian, data, subset) family: binomial(link = "logit") 因变量服从伯努利分布,连接函数为logit; gaussian(link = "identity") 因变量服从高斯分布,连接函数为identity; poisson(link = "log") 因变量服从泊松分布,连接函数为log. subset :可选...
logistic regression testObs <- rnorm(nrow(iris)) > 0 testIris <- iris[testObs,] trainIris <- iris[!testObs,] multiLogit <- rxLogisticRegression( formula = Species~Sepal.Length + Sepal.Width + Petal.Length + Petal.Width, type = "multiClass", data = trainIris) # Score the model ...
rms包是回归模型策略(Regression Modeling Strategies)的函数集合,可以绘制列线图、校准曲线的绘制等。该包中各种模型有专用的构建函数,二分类logistic回归用lrm函数。不过rms包中未提供Hosmer-Lemeshow检验,多少有点遗憾。 #绘制列线图 library(rms);library(Hmis) ...
i.e. the car in our example. In the case of such a simple logistic regression, thelogistic functionhas a sigmoidal form. If there are several explanatory variables (Xi), then we manipulate with the multiple logistic regression technique. Formula(15)present the MultipleLogistic Regression model[...