: 1116.9 on 982 degrees of freedom Residual deviance: 1116.9 on 982 degrees of freedom AIC: 3282.9...Call: Deviance Residuals: Min 1Q Median 3Q Max -3.0810 -0.8373 -0.1493 0.5676...: 2553.6 on 982 degrees of freedom Residual deviance: 1064.2 on 981 degrees of freedom AIC: 3762.7...Number of Fisher Scoring iterations: 5 如果我们保留偏移量并添加变量，我们可以看到它变得无用（对单位参数的测试） Call: Deviance...> summary(reg) Call: Deviance Residuals: Min 1Q Median 3Q Max -0.3988 -0.3388
GAMLSS-RS iteration 1: Global Deviance = 448.1315 GAMLSS-RS iteration 2: Global Deviance = 448.1315...GAMLSS-RS iteration 2: Global Deviance = 390.7803 GAMLSS-RS iteration 3: Global Deviance = 391.396...GAMLSS-RS iteration 4: Global Deviance = 391.3996 GAMLSS-RS iteration 5: Global Deviance = 391.3965...iteration 1: Global Deviance = 362.943 GAMLSS-RS iteration 2: Global Deviance = 359.1257 GAMLSS-RS...这个模型被拟合为 GAMLSS-RS iteration 1: Global Deviance = 492.7247 GAMLSS-RS iteration 2: Global Deviance =
GAMLSS-RS iteration 1: Global Deviance = 448.1315 GAMLSS-RS iteration 2: Global Deviance = 448.1315...iteration 2: Global Deviance = 390.7803 GAMLSS-RS iteration 3: Global Deviance = 391.396 GAMLSS-RS...iteration 4: Global Deviance = 391.3996 GAMLSS-RS iteration 5: Global Deviance = 391.3965GAMLSS-RS...1: Global Deviance = 362.943 GAMLSS-RS iteration 2: Global Deviance = 359.1257 GAMLSS-RS iteration...3: Global Deviance = 359.229 GAMLSS-RS iteration 4: Global Deviance = 359.2342 GAMLSS-RS iteration
Null); 30 Residual #> Null Deviance: 43.86 #> Residual Deviance: 25.53 AIC: 29.53 # More...Null); 30 Residual #> Null Deviance: 43.86 #> Residual Deviance: 42.95 AIC: 46.95 # More...Null); 29 Residual #> Null Deviance: 43.86 #> Residual Deviance: 20.65 AIC: 26.65 summary...(logr_vma) #> #> Call: #> glm(formula = vs ~ mpg + am, family = binomial, data = dat) #> #> Deviance...Null); 28 Residual #> Null Deviance: 43.86 #> Residual Deviance: 19.12 AIC: 27.12 summary
Call:Deviance Residuals: Min 1Q Median 3Q Max -3.0810 -0.8373 -0.1493 0.5676...of Fisher Scoring iterations: 5 如果我们保留偏移量并添加变量，我们可以看到它变得无用（对单位参数的测试） Call:Deviance Residuals: Min...> summary(reg)Call:Deviance Residuals: Min 1Q Median 3Q Max -0.3988 -0.3388...: 2567.31 on 982 degrees of freedomResidual deviance: 885.71 on 981 degrees of freedom 此处，系数（明显）...Deviance Residuals: Min 1Q Median 3Q Max -2.28402 -0.47763 -0.08215
= 99187.7, null deviance = 104432.7 (difference = 5245.0) ## overdispersion parameter = 83.1 ##...Dev Df Deviance F Pr(>F) ## 1 1196 104378 ## 2 1193 99188 3 5190 20.8 3.8e...Dev Df Deviance F Pr(>F) ## 1 1196 104378 ## 2 1191 8496 5 95882 2688 <2e-...= 1135.9, null deviance = 104432.7 (difference = 103296.8) ## overdispersion parameter = 1.0 ##...= 500.2, null deviance = 506.2 (difference = 5.9) ## overdispersion parameter = 10.9 ## residual
**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance...: 3.2913e+02 on 1 degrees of freedom Residual deviance: 1.3323e-13 on 0 degrees of freedom AIC: 18.371...(mod2) Call: glm(formula = y ~ x, family = "binomial", data = individualData, weights = freq) Deviance...**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance...: 2772.6 on 3 degrees of freedom Residual deviance: 2443.5 on 2 degrees of freedom AIC: 2447.5 Number
97.5 %(Intercept) 7.482379e-05 0.002822181Latitude 1.148221e+00 1.247077992 方差分析 Analysis of Deviance...Dev Df Deviance Pr(>Chi) 1 6 70.333 2 7 153.633 -1...Dev Df Deviance Pr(>Chi)1 23 29.370 2 24 29.648 -1 -0.27779...-66.4981 32.3787 -2.054 0.0400 *Continuous 0.9027 0.4389 2.056 0.0397 *Analysis of Deviance...Dev Df Deviance Pr(>Chi) 1 27 12.148 2 28 40.168 -1
0.05 '.' 0.1 ' ' 1 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance...: 74.786 on 53 degrees of freedom ## Residual deviance: 40.028 on 42 degrees of freedom ## AIC: 64.028...## ## Number of Fisher Scoring iterations: 6 结果详解： Deviance Residuals:表示偏差残差统计量。...Null deviance:无效偏差（零偏差），Residual deviance:残差偏差，无效偏差和残差偏差之间的差异越大越好，用来评价模型与实际数据的吻合情况。...Dev Df Deviance Pr(>Chi) ## 1 53 74.786 ## 2 42
runif（n）<prob） 首先，让我们拟合简单的未调整模型来估计边际治疗效果，但不使用基线协变量： Call: glm(formula = y ~ z, family = binomial) Deviance...**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance...: 1133.4 on 999 degrees of freedom Residual deviance: 1108.3 on 998 degrees of freedom AIC: 1112.3...z+x, data, family=binomial) > summary(adjusted) Call: glm(formula = y ~ z + x, family = binomial) Deviance...: 1133.37 on 999 degrees of freedom Residual deviance: 941.29 on 997 degrees of freedom AIC: 947.29
0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# # (Dispersion parameter for binomial family taken to be 1)# # Null deviance...● Null deviance和Residual devianve: 是指无效偏差(零偏差)和残差偏差，前者是指只有截距项（没有任何自变量）时模型的偏差，这个模型假设所有的观测值都预测为因变量的平均值（...通过比较 Null deviance 和 Residual deviance，可以评估引入自变量后模型的改进情况。...如果 Residual deviance 明显小于 Null deviance，说明自变量在解释因变量方面起到了重要作用，所以这两个值的差异越大越好。...N 1 287.55 291.55# + race 3 283.88 291.88# # Step: AIC=275.73# OS ~ T# # Df Deviance
Information Criterion (DIC) ...............: 948.12 ## Deviance Information Criterion (DIC, saturated... Information Criterion (DIC) ...............: 926.93 ## Deviance Information Criterion (DIC, saturated... Information Criterion (DIC) ...............: 904.12 ## Deviance Information Criterion (DIC, saturated... Information Criterion (DIC) ...............: 903.41 ## Deviance Information Criterion (DIC, saturated... Information Criterion (DIC) ...............: 903.14 ## Deviance Information Criterion (DIC, saturated
Information Criterion (DIC) ...............: 948.12 ## Deviance Information Criterion (DIC, saturated...Information Criterion (DIC) ...............: 926.93 ## Deviance Information Criterion (DIC, saturated...Information Criterion (DIC) ...............: 904.12 ## Deviance Information Criterion (DIC, saturated...Information Criterion (DIC) ...............: 903.41 ## Deviance Information Criterion (DIC, saturated...Information Criterion (DIC) ...............: 903.14 ## Deviance Information Criterion (DIC, saturated
., family = binomial(link = "logit"), data = iris) Deviance Residuals: Min 1Q Median...**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance...: 138.629 on 99 degrees of freedom Residual deviance: 11.899 on 95 degrees of freedom AIC: 21.899...**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance...: 138.629 on 99 degrees of freedom Residual deviance: 13.266 on 96 degrees of freedom AIC: 21.266
Null); 4 Residual Null Deviance: 14.13 Residual Deviance: 1.618 AIC: 34.54 或者： > glm(hyp.tbl...Call: glm(formula = prop.hyp ~ smoking + obesity + snoring, family = binomial, weights = n.tot) Deviance...‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance...: 14.1259 on 7 degrees of freedom Residual deviance: 1.6184 on 4 degrees of freedom AIC: 34.537...: 719.39 on 518 degrees of freedom Residual deviance: 200.66 on 517 degrees of freedom AIC: 204.66
Use deviance to answer this question.[2]Can the model in fit1 be simplified to the model in fit2?...Use change in deviance to answerthis question. [2]Can sex be removed from the model in fit2?...Use change in deviance to answer this question. [2]What are the maximum likelihood estimates of the parameters...但是他们的deviance resid 偏差残差值较大，即模型的预测值与实验结果有较大偏差，因此可以认为模型拟合度较差。...4从偏差残差值来看拟合模型1可以简化成模型2，因为他们的deviance residuals相差很大，模型2的偏差残差值要明显低于模型1.
Use the deviance as theconvergence criteria and initial guess of β as (0:5; 0:5; 0:5; 0:5)....Present your codeand along with your final estimate of β and final deviance....You may use the deviance function here. Includeyour code....x3 <- mydat$x3X=cbind(1,x1,x2,x3)ilogit <- function(u) 1/(1+exp(-u))D <- function(mu){#deviance函数 a...对比删去44号样本的模型和原来的模型 mod2=lm(y~x1+x2+x3, family = Gamma,data=mydat1)summary(mod2)可以看到修改后的模型deviance
‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance...: 163.490 on 5 degrees of freedom Residual deviance: 21.413 on 2 degrees of freedom AIC: 61.68...Number of Fisher Scoring iterations: 5 估计的回归系数都是非常显著的;Null deviance可以认为是模型的残差，它的值越小说明模型拟合效果越好;模型的AIC统计量为...61.68，它和deviance一起可以用来作为判断标准，选取合适的分布族和链接函数。...: 16.6831 on 5 degrees of freedom Residual deviance: 7.0412 on 2 degrees of freedom AIC: 60.45 Number
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