在ggplot2中绘制bootstrap输出的中位数,置信区间

发布时间：2020-12-17 20:42:58 所属栏目：安全来源：网络整理

导读：我有一个数据帧df(见下文) dput(df) structure(list(x = c(49,50,51,52,53,54,55,56,1,2,3,4,5,14,15,16,17,6,10,11,30,64,66,67,68,69,34,35,37,39,18,99,100,102,103,70,72),y = c(2268.14043972082,2147.62290922552,2269.1387550775,2247.31983098201,19

我有一个数据帧df(见下文)

dput(df)
    structure(list(x = c(49,50,51,52,53,54,55,56,1,2,3,4,5,14,15,16,17,6,10,11,30,64,66,67,68,69,34,35,37,39,18,99,100,102,103,70,72),y = c(2268.14043972082,2147.62290922552,2269.1387550775,2247.31983098201,1903.39138268307,2174.78291538358,2359.51909126411,2488.39004804939,212.851575751527,461.398994384333,567.150629704352,781.775113821961,918.303706148872,1107.37695799186,1160.80594193377,1412.61328924168,1689.48879626486,260.737164468854,306.72700499362,283.410379620422,366.813913489692,387.570173754128,388.602676983443,477.858510450125,128.198042456082,535.519377609133,1028.8780498564,1098.54431357711,1265.26965941035,1129.58344809909,820.922447928053,749.343583476846,779.678206156474,646.575242339517,733.953282899613,461.156280127354,906.813018662913,798.186995701282,831.365377249207,764.519073183124,672.076289062505,669.879217186302,1341.47673353751,1401.44881976186,1640.27575962036)),.Names = c("x","y"),row.names = c(NA,-45L),class = "data.frame")

我基于我的数据集创建了非线性回归(nls).

nls1 <- nls(y~A*(x^B)*(exp(k*x)),data = df,start = list(A = 1000,B = 0.170,k = -0.00295),algorithm = "port")

然后,我为此函数计算了一个引导程序,以获取多组参数(A,B和k).

library(nlstools)
Boo <- nlsBoot(nls1,niter = 200)

我现在想要绘制中值曲线以及在一个ggplot2中从引导对象一起计算的上下置信区间曲线.每条曲线的参数(A,B和K)包含在Boo_Gamma $bootCI中.有人能帮帮我吗？提前致谢.

解决方法

AFAIK,包nlstools只返回bootstrapped参数估计值,而不是预测值……

因此,这是一个快速的解决方案,手动使用自举参数估计来计算预测,然后重新计算预测中的统计数据,因为这里的模型是非线性的.它不是最优雅的,但应该这样做:)

# Matrix with the bootstrapped parameter estimates
Theta_mat <- Boo$coefboot

# Model
fun <- function(x,theta) theta["A"] * (x ^ theta["B"]) * (exp(theta["k"] * x))

# Points where to evaluate the model
x_eval <- seq(min(df$x),max(df$x),length.out = 100)

# Matrix with the predictions
Pred_mat <- apply(Theta_mat,function(theta) fun(x_eval,theta))

# Pack the estimates for plotting
Estims_plot <- cbind(
    x = x_eval,as.data.frame(t(apply(Pred_mat,function(y_est) c(
        median_est = median(y_est),ci_lower_est = quantile(y_est,probs = 0.025,names = FALSE),ci_upper_est = quantile(y_est,probs = 0.975,names = FALSE)
    ))))
)

library(ggplot2)
ggplot(data = Estims_plot,aes(x = x,y = median_est,ymin = ci_lower_est,ymax = ci_upper_est)) + 
    geom_ribbon(alpha = 0.7,fill = "grey") + 
    geom_line(size = rel(1.5),colour = "black") + 
    geom_point(data = df,y = y),size = rel(4),colour = "red",inherit.aes = FALSE) + 
    theme_bw() + labs(title = "Bootstrap resultsn",x = "x",y = "y")
ggsave("bootpstrap_results.pdf",height = 5,width = 9)

Bootstrap results

（编辑：李大同）

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