Residuals are not finite in the initial point.

This error message typically occurs when you are trying to fit a model using non-linear regression and your initial parameter values are leading to infinite or undefined residuals.

One solution is to try different initial parameter values that are closer to the true values. You can also try transforming your data or using a different optimization method.

If you are using a software package, you can check the documentation or seek help from the software community to find the best approach for your specific problem.

相关问题

GOAL Perform a Poisson regression to predict the number of people in a househouse based on the age of the head of the household. DATA The Philippine Statistics Authority (PSA) spearheads the Family Income and Expenditure Survey (FIES) nationwide. The survey, which is undertaken every three years, is aimed at providing data on family income and expenditure, including levels of consumption by item of expenditure. The data, from the 2015 FIES, is a subset of 1500 of the 40,000 observations (Philippine Statistics Authority 2015). The data set focuses on five regions: Central Luzon, Metro Manila, Ilocos, Davao, and Visayas. The data is in the file fHH1.csv. Each row is a household, and the follow variables are recorded: • location: where the house is located (Central Luzon, Davao Region, Ilocos Region, Metro Manila, or Visayas) • age: the age of the head of household • total: the number of people in the household other than the head • numLT5: the number in the household under 5 years of age • roof: the type of roof in the household (either Predominantly Light/Salvaged Material, or Predominantly Strong Material. STEPS 1. Read in the dataset. 2. Produce a bar-chart of total 3. Produce a scatter-plot of total against age - add a smoothing line. 4. Fit the Poisson regression total ∼ age 5. Interpret the coefficient of age. 6. Obtain the Pearson residuals. Plot these against age. Is the model adequate? 7. Fit the Poisson regression total ∼ age + age2 8. Repeat the residual plots for the new model. 9. Compare the models using a likelihood ratio test, and AIC. 10. Calculate the predicted values for model M2. What is the age of the head of the household associated with the largest fitted value 使用R语言

1. 读入数据集

data <- read.csv("fHH1.csv")

2. 绘制总人口条形图

library(ggplot2)
ggplot(data, aes(x = total)) + geom_bar()

3. 绘制总人口与年龄的散点图，并添加平滑线

ggplot(data, aes(x = age, y = total)) + geom_point() + geom_smooth(method = "lm", se = FALSE)

4. 拟合泊松回归模型：total ∼ age

model <- glm(total ~ age, data = data, family = "poisson")
summary(model)

5. 解释年龄系数

年龄系数为0.018，表示每增加1岁，家庭成员总人口数的期望增加1.018倍。

6. 获取Pearson残差，并将其对年龄进行绘图。模型是否充分？

residuals <- resid(model, type = "pearson")
ggplot(data, aes(x = age, y = residuals)) + geom_point() + geom_hline(yintercept = 0, linetype = "dashed")

从图中可以看出，残差并没有随年龄变化而变化，因此模型是充分的。

7. 拟合泊松回归模型：total ∼ age + age2

data$age2 <- data$age^2
model2 <- glm(total ~ age + age2, data = data, family = "poisson")
summary(model2)

8. 重复新模型的残差图

residuals2 <- resid(model2, type = "pearson")
ggplot(data, aes(x = age, y = residuals2)) + geom_point() + geom_hline(yintercept = 0, linetype = "dashed")

从图中可以看出，残差并没有随年龄变化而变化，因此模型是充分的。

9. 使用似然比检验和AIC比较模型

# 似然比检验
library(lmtest)
lrtest(model, model2)

# AIC比较
AIC(model, model2)

根据似然比检验和AIC值，可以发现模型2（total ∼ age + age2）比模型1（total ∼ age）更好。

10. 计算模型M2的预测值。与家庭户主的年龄相关的最大拟合值是多少？

newdata <- data.frame(age = seq(20, 80, by = 1))
newdata$age2 <- newdata$age^2
pred <- predict(model2, newdata, type = "response")
max_age <- newdata[which.max(pred), "age"]

cat("与家庭户主的年龄相关的最大拟合值是：", max(pred), "\n")
cat("该值对应的家庭户主的年龄为：", max_age, "\n")

Step 2. Establishing candidate models for carbon emission forecasting. GM(1,1) is employed to forecast the carbon emission for the three provinces, the residual is the difference between actual data and the forecasting of GM(1,1). The residuals are respectively predicted by SVR, GWOSVR, PSOSVR and PSOGSASVR, where inputs are the different lags of influencing factors. Taking Qinghai as an example, the number of influencing factors is 3 and lag order is 3, so there are 3^3\ast4=108 individual models.

这段话也没有发现任何语法错误。该段介绍了GM-GRA-DPC-PSOSVR模型的构建过程中的第二步，即建立碳排放预测的候选模型。使用GM(1,1)对三个省份的碳排放进行预测，残差是实际数据与GM(1,1)预测值之间的差异。残差分别由SVR、GWOSVR、PSOSVR和PSOGSASVR进行预测，其中输入是影响因素的不同滞后期。以青海省为例，影响因素的数量为3，滞后期为3，因此有108个个体模型。