r语言residuals vs fitted
时间: 2023-09-18 08:02:39 浏览: 139
在R语言中,residuals和fitted是线性回归模型中的两个重要概念。
Fitted表示回归模型对于观测数据的拟合程度。在使用lm()函数进行线性回归分析后,可以通过模型对象的$fitted.values属性来获取每个观测值对应的拟合值。拟合值可以用来衡量回归模型对于观测数据的拟合程度,可以与原始数据进行对比。较高的拟合值说明回归模型相对较好地描述了数据的趋势和规律,较低的拟合值则意味着模型拟合不理想。
Residuals(残差)则表示观测值与拟合值之间的差异。在R中,可以通过模型对象的$residuals属性来获取每个观测值对应的残差。残差是实际观测值与回归模型拟合值之间的差异,它衡量了模型无法解释的数据波动。通过分析残差的分布和模式,可以判断模型中的系统性误差或不符合模型假设的情况。若残差呈现随机分布、均值接近于零且符合正态分布,则表明模型的拟合程度较好。若残差呈现非随机分布、均值不为零或不符合正态分布,则可能需要重新考虑模型的形式或假设。
综上所述,fitted和residuals是在R语言中用来衡量线性回归模型拟合效果的两个重要指标。通过分析拟合值和残差,可以评估回归模型的准确性和可靠性,并作出相应的改进和调整。
相关问题
R语言 fit th Poisson GLM with a logarithmic link function ,using the systematic component Count~Species+factor(Quadrat),plot the deviance residuals against the fitted values and against thefitted values transformed to the constant information scale
Sure, I can help you with that. Here's how you can fit a Poisson GLM with a logarithmic link function in R:
```R
# Load the necessary packages
library(stats)
# Fit the Poisson GLM with a logarithmic link function
fit <- glm(Count ~ Species + factor(Quadrat), family = "poisson", link = "log")
# Plot the deviance residuals against the fitted values
plot(fit$fitted.values, residuals(fit, type = "deviance"), xlab = "Fitted values", ylab = "Deviance residuals")
# Plot the deviance residuals against the fitted values transformed to the constant information scale
plot(qnorm((fit$fitted.values + 1/2) / sum(fit$fitted.values + 1/2)), residuals(fit, type = "deviance"), xlab = "Fitted values transformed to constant information scale", ylab = "Deviance residuals")
```
In the first plot, we plot the deviance residuals against the fitted values, which gives us an idea of whether the model is fitting the data well or not. In the second plot, we plot the deviance residuals against the fitted values transformed to the constant information scale, which is a way of standardizing the fitted values and allows for easier comparison of the residuals across different parts of the fitted values.
R语言内学生化残差并绘制图
学生化残差是一种更稳定和更具有可比性的残差,它是将残差除以估计的标准误差得到的。在R语言中,可以通过以下步骤来计算学生化残差并绘制图:
1. 安装并加载“car”包:
```R
install.packages("car")
library(car)
```
2. 用线性模型拟合数据:
假设我们有以下数据:
```R
x <- c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10)
y <- c(2, 4, 6, 8, 10, 12, 14, 16, 18, 20)
model <- lm(y ~ x)
```
3. 计算学生化残差:
```R
studres <- studres(model)
```
4. 绘制学生化残差图:
```R
plot(studres ~ fitted(model), main = "Studentized Residuals vs Fitted Values", xlab = "Fitted Values", ylab = "Studentized Residuals")
abline(h = 0, lty = 2, col = "red")
```
这将绘制学生化残差图,其中横轴是拟合值,纵轴是学生化残差。在这个例子中,我们看到大多数残差都在-2到2之间,这是一个好的迹象。此外,我们还画了一条水平线,表示零残差线。