R中的glm()和Excel中的逻辑回归的手动执行之间的结果不一致
您可以在Excel中find逻辑回归的手动实现: http : //blog.excelmasterseries.com/2014/06/logistic-regression-performed-in-excel.html 。
该实现使用下面的数据集并报告以下系数
b0 = 12.48285608
b1 = -0.117031374
b2 = -1.469140055
但是,当我用R中的 glm()
分析相同的数据集时,结果并不相同,即:
b0 = 1.687445
b1 = -0.012525
b2 = -0.116473
d <- structure(list(Y = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), X1 = c(78L, 73L, 73L, 71L, 68L, 59L, 57L, 49L, 35L, 27L, 59L, 57L, 44L, 38L, 36L, 36L, 22L, 22L, 15L, 10L), X2 = c(8L, 8L, 5L, 7L, 5L, 4L, 7L, 5L, 4L, 7L, 3L, 4L, 5L, 5L, 4L, 2L, 6L, 5L, 4L, 6L)), .Names = c("Y", "X1", "X2"), class = "data.frame", row.names = c(NA, -20L)) summary(glm(Y ~ X1+X2, data=d), family=binomial(link='logit')) # > summary(glm(Y ~ X1+X2, data=d), family=binomial(link='logit')) # # Call: # glm(formula = Y ~ X1 + X2, data = d) # # Deviance Residuals: # Min 1Q Median 3Q Max # -0.78318 -0.20641 0.07689 0.24375 0.49237 # # Coefficients: # Estimate Std. Error t value Pr(>|t|) # (Intercept) 1.687445 0.319872 5.275 6.18e-05 *** # X1 -0.012525 0.004376 -2.862 0.0108 * # X2 -0.116473 0.056959 -2.045 0.0567 . # --- # Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 # # (Dispersion parameter for gaussian family taken to be 0.146843) # # Null deviance: 5.0000 on 19 degrees of freedom # Residual deviance: 2.4963 on 17 degrees of freedom # AIC: 23.139 # # Number of Fisher Scoring iterations: 2
为什么结果不一样?
你有家庭参数在错误的地方。 它应该在glm()
调用中,而不是summary()
调用。
summary(glm(Y ~ X1+X2, data=d, family=binomial(link='logit')))
如果你不包含glm()
的家族,它将做一个高斯(线性)回归。