数据集子集的回归

Question

我想做以下事情并需要一些帮助:

分别为“年龄”[lm(Height~Age)]计算“高度”的斜率和截距

(A) 每个个体

(二)性别

并创建一个包含结果(斜率和截距)的表格。我可以为此使用“申请”吗？

在下一步中，我想进行统计测试以确定性别之间的斜率和截距是否存在显着差异。我知道如何在 R 中进行测试，但也许有一种方法可以将斜率/截距计算和 T 测试结合起来。

示例数据:

example = data.frame(Age = c(1, 3, 6, 9, 12,
                             1, 3, 6, 9, 12,
                             1, 3, 6, 9, 12,
                             1, 3, 6, 9, 12), 
                Individual = c("Jack", "Jack", "Jack", "Jack", "Jack",
                               "Jill", "Jill", "Jill", "Jill", "Jill",
                               "Tony", "Tony", "Tony", "Tony", "Tony",
                               "Jen", "Jen", "Jen", "Jen","Jen"),
                    Gender = c("M", "M", "M", "M", "M",
                               "F", "F", "F", "F", "F",
                               "M", "M", "M", "M", "M",
                               "F", "F", "F", "F", "F"),
                    Height = c(38, 62, 92, 119, 165,
                               31, 59, 87, 118, 170,
                               45, 72, 93, 155, 171,
                               33, 61, 92, 115, 168))

Answer 1

对每个级别分别进行回归分析然后在数据框中组合斜率和截距的一种方法是使用函数 ddply()来自图书馆plyr .

library(plyr)

ddply(example,"Individual",function(x) coefficients(lm(Height~Age,x)))
  Individual (Intercept)      Age
1       Jack    26.29188 11.11421
2        Jen    22.10660 11.56345
3       Jill    18.33249 12.04315
4       Tony    33.02030 11.96447

ddply(example,"Gender",function(x) coefficients(lm(Height~Age,x)))
  Gender (Intercept)      Age
1      F    20.21954 11.80330
2      M    29.65609 11.53934