如果你也在 怎样代写广义线性模型Generalized linear model 这个学科遇到相关的难题，请随时右上角联系我们的24/7代写客服。广义线性模型Generalized linear model通过允许响应变量具有任意分布(而不是简单的正态分布)，以及响应变量的任意函数(链接函数)随预测器线性变化(而不是假设响应本身必须线性变化)，涵盖了所有这些情况。例如，上面预测海滩出席人数的情况通常会用泊松分布和日志链接来建模，而预测海滩出席率的情况通常会用伯努利分布(或二项分布，取决于问题的确切表达方式)和对数-几率(或logit)链接函数来建模。
广义线性模型Generalized linear model普通线性回归将给定未知量(响应变量，随机变量)的期望值预测为一组观测值(预测因子)的线性组合。这意味着预测器的恒定变化会导致响应变量的恒定变化(即线性响应模型)。当响应变量可以在任意一个方向上以良好的近似值无限变化时，或者更一般地适用于与预测变量(例如人类身高)的变化相比仅变化相对较小的任何数量时，这是适当的。
statistics-lab™ 为您的留学生涯保驾护航 在代写广义线性模型generalized linear model方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写广义线性模型generalized linear model代写方面经验极为丰富，各种代写广义线性模型generalized linear model相关的作业也就用不着说。
统计代写|广义线性模型代写generalized linear model代考|GOF statistics
Models can easily be constructed without thought to how well the model actually fits the data. All too often this is seen in publications-logistic regression results with parameter estimates, and standard errors with corresponding $p$-values and without their associated GOF statistics.
Models are simply that: models. They are not aimed at providing a one-toone fit, called overfitting. The problem with overfitting is that the model is difficult to adapt to related data situations outside the actual data modeled. If heart01. dta were overfit, it would have little value in generalizing to other heart patients.
One measure of fit is called the confusion matrix or classification table. The origins of the term may be more confusing than the information provided in the table. The confusion matrix simply classifies the number of instances where
- $y=1$, predicted $y=1$
- $y=1$, predicted $y=0$
- $y=0$, predicted $y=1$
- $y=0$, predicted $y=0$
Predicted values can be calculated by obtaining the linear predictor, $x \boldsymbol{\beta}$, or $\eta$, and converting it by means of the logistic transform, which is the inverse link function. Both of these statistics are available directly after modeling. In Stata, we can obtain the values after modeling by typing
. predict $\mathrm{xb}, \mathrm{xb} / *$ linear predictor */
or - predict $\mathrm{mu}, \mathrm{mu} / *$ fitted value */
We may also obtain the linear predictor and then compute the fit for them: - generate $m u=1 /(1+\exp (-x b))$
统计代写|广义线性模型代写generalized linear model代考|EGrouped data
Generally, the models presented throughout this chapter address dichotomous outcomes. In addition, binomial data can be modeled where the user communicates two pieces of information for each row of data. Those information pieces are the number of successes (numerator) and the number of trials (denominator).
In some research trials, an outcome might represent a proportion for which the denominator is unknown or missing from the data. There are a few approaches available to analyze associations with such an outcome measure. Some researchers analyze these outcomes using a linear regression model. The downside is that predicted values could be outside the range of $[0,1]$; certainly extrapolated values would be outside the range.
In GLMS, the approach is to transform the linear predictor (the so-called righthand side of the equation), but we could use the link function to transform the outcome (the left-hand side of the equation)
$$
\operatorname{logit}(y)=\ln \left(\frac{y}{1-y}\right)=\eta
$$
and then analyze the transformed value with linear regression. This approach works well except when there are values of zero or one in the outcome.
Strictly speaking, there is nothing wrong with fitting these outcomes in a GLM using Bernoulli variance and the logit link. In previous versions of the Stata software, the glm command would not allow noninteger outcomes, but current software will fit this model. As Papke and Wooldridge (1996) suggest, such models should use the sandwich variance estimator for inference on the coefficients.
广义线性模型代考
统计代写|广义线性模型代写generalized linear model代考|GOF statistics
可以很容易地构建模型，而无需考虑模型实际与数据的拟合程度。这种情况经常出现在出版物中——具有参数估计的逻辑回归结果和具有相应$p$值的标准误差，而没有相关的GOF统计。
模型就是模型。它们的目的不是提供一对一的匹配，称为过拟合。过拟合的问题是模型很难适应实际建模数据之外的相关数据情况。如果心脏。Dta是过拟合的，它在推广到其他心脏病患者方面几乎没有价值。
一种适合的度量称为混淆矩阵或分类表。这个术语的起源可能比表中提供的信息更令人困惑。混淆矩阵简单地对实例的数量进行分类
$y=1$预言的 $y=1$
$y=1$预言的 $y=0$
$y=0$预言的 $y=1$
$y=0$预测$y=0$
预测值可以通过获得线性预测器$x \boldsymbol{\beta}$或$\eta$，并通过逻辑变换(即逆链接函数)对其进行转换来计算。这两个统计数据都可以在建模后直接获得。在Stata中，我们可以通过输入来获得建模后的值
． 预测$\mathrm{xb}, \mathrm{xb} / $线性预测器/
或
预测$\mathrm{mu}, \mathrm{mu} / $拟合值/
我们也可以得到线性预测器，然后计算它们的拟合:
产生 $m u=1 /(1+\exp (-x b))$
统计代写|广义线性模型代写generalized linear model代考|EGrouped data
一般来说，本章提出的模型都是针对二分类结果的。此外，可以对二项数据进行建模，其中用户为每一行数据传递两条信息。这些信息片段是成功次数(分子)和试验次数(分母)。
在一些研究试验中，结果可能代表一个分母未知或数据缺失的比例。有几种方法可用于分析与这种结果测量的关联。一些研究人员使用线性回归模型分析这些结果。缺点是预测值可能超出$[0,1]$的范围;当然，外推值将超出范围。
在GLMS中，方法是转换线性预测器(所谓的方程的右侧)，但我们可以使用链接函数来转换结果(方程的左侧)
$$
\operatorname{logit}(y)=\ln \left(\frac{y}{1-y}\right)=\eta
$$
然后对变换后的值进行线性回归分析。这种方法非常有效，除非结果中的值为0或1。
严格来说，使用伯努利方差和logit链接在GLM中拟合这些结果并没有错。在Stata软件的早期版本中，glm命令不允许非整数结果，但当前的软件将适合此模型。正如Papke和Wooldridge(1996)所建议的那样，这种模型应该使用三明治方差估计器对系数进行推断。
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