如何编写一个函数，将 运行 具有不同因变量的同一类型的多个回归模型，然后将它们存储为列表？
How to write a function that will run multiple regression models of the same type with different dependent variables and then store them as lists?
我正在尝试编写一个函数，该函数将 运行 进行多重回归，然后将输出存储在一个向量中。我想要的是函数从我将提供的列表中选择因变量，然后 运行 对相同右侧变量的回归。不知道该怎么做。任何提示将不胜感激。
my_data <- data.frame(x1=(1:10) + rnorm(10, 3, 1.5), x2=25/3 + rnorm(10, 0, 1),
dep.var1=seq(5, 28, 2.5), dep.var2=seq(100, -20, -12.5),
dep.var3=seq(1, 25, 2.5))
## The following is a list that tells the function
dep.var <- list(dep.var1=my_data$dep.var1, dep.var2=my_data$dep.var2)
## which dependent variables to use from my_data
all_models <- function(dep.var){lm(dep.var ~ x1 + x2, data=my_data)}
model <- sapply(dep.var, all_models) ## The "sapply" here tells the function to
## take the dependent variables from the list dep.var.
我希望“模型”列表有两个对象：带有 dep.var1 的 model1 和带有 dep.var2 的 model2。然后根据需要，我将使用 summary(model#) 来查看回归输出。
我知道这在理论上适用于使用矢量（即 p）的情况：
p <- seq(0.25, 0.95, 0.05)
s <- function(p) {1 - pnorm(35, p*1*44, sqrt(44)*sqrt(p*(1 - p)))}
f <- sapply(p, s)
但我无法让整个事情按照我的回归模型的要求工作。它有点工作，因为你可以 运行 并检查“模型”，它会向你显示两个回归输出 - 但它太可怕了。并且“模型”没有显示回归规范，即 dep.var1 ~ x1 + x2.
您可以 sapply 覆盖您可以用 grep 很好地识别的依赖变量的名称。在 lm 中使用 reformulate 构建公式。
sapply(grep('^dep', names(my_data), value=TRUE), \(x)
lm(reformulate(c('x1', 'x2'), x), my_data))
# dep.var1 dep.var2 dep.var3
# coefficients numeric,3 numeric,3 numeric,3
# residuals numeric,10 numeric,10 numeric,10
# effects numeric,10 numeric,10 numeric,10
# rank 3 3 3
# fitted.values numeric,10 numeric,10 numeric,10
# assign integer,3 integer,3 integer,3
# qr qr,5 qr,5 qr,5
# df.residual 7 7 7
# xlevels list,0 list,0 list,0
# call expression expression expression
# terms dep.var1 ~ x1 + x2 dep.var2 ~ x1 + x2 dep.var3 ~ x1 + x2
# model data.frame,3 data.frame,3 data.frame,3
dep.var* 很好地出现在结果中。
但是，您可能想使用 lapply 并将其通过管道传输到 setNames() 以获取命名的列表元素。您当然可以手动定义因变量，而不是 grep。为了存储干净的公式调用，我们使用 使用 do.call.
dv <- as.list(grep('^dep', names(my_data), value=TRUE)[1:2])
res <- lapply(dv, \(x) {
f <- reformulate(c('x1', 'x2'), x)
do.call('lm', list(f, quote(my_data)))
}) |>
setNames(dv)
res
# $dep.var1
#
# Call:
# lm(formula = dep.var1 ~ x1 + x2, data = my_data)
#
# Coefficients:
# (Intercept) x1 x2
# -4.7450 2.3398 0.2747
#
#
# $dep.var2
#
# Call:
# lm(formula = dep.var2 ~ x1 + x2, data = my_data)
#
# Coefficients:
# (Intercept) x1 x2
# 148.725 -11.699 -1.373
这允许您获取对象的 summary()，这可能就是您想要的。
summary(res$dep.var1)
# Call:
# lm(formula = dep.var1 ~ x1 + x2, data = my_data)
#
# Residuals:
# Min 1Q Median 3Q Max
# -2.8830 -1.8345 -0.2326 1.4335 4.2452
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -4.7450 7.2884 -0.651 0.536
# x1 2.3398 0.2836 8.251 7.48e-05 ***
# x2 0.2747 0.7526 0.365 0.726
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 2.55 on 7 degrees of freedom
# Multiple R-squared: 0.9117, Adjusted R-squared: 0.8865
# F-statistic: 36.14 on 2 and 7 DF, p-value: 0.0002046
最后你可以将它包装在一个函数中
calc_models <- \(dv) {
lapply(dv, \(x) {
f <- reformulate(c('x1', 'x2'), x)
do.call('lm', list(f, quote(my_data)))
}) |>
setNames(dv)
}
calc_models(list('dep.var1', 'dep.var2'))
考虑 reformulate 使用 lm 调用的字符值动态更改模型公式：
# VECTOR OF COLUMN NAMES (NOT VALUES)
dep.vars <- c("dep.var1", "dep.var2")
# USER-DEFINED METHOD TO PROCESS DIFFERENT DEP VAR
run_model <- function(dep.var) {
fml <- reformulate(c("x1", "x2"), dep.var)
lm(fml, data=data)
}
# NAMED LIST OF MODELS
all_models <- sapply(dep.vars, run_model, simplify = FALSE)
# OUTPUT RESULTS
all_models$dep.var1
all_models$dep.var2
...
从那里，您可以运行跨模型对象进一步提取或处理：
# NAMED LIST OF MODEL SUMMARIES
all_summaries <- lapply(all_models, summary)
all_summaries$dep.var1
all_summaries$dep.var2
...
# NAMED LIST OF MODEL COEFFICIENTS
all_coefficients <- lapply(all_models, `[`, "coefficients")
all_coefficients$dep.var1
all_coefficients$dep.var2
...
这是一种迭代数据框并将函数应用于您定义的组（此处 dep.var）并将不同模型保存在数据框中的方法：
library(tidyverse)
library(broom)
my_data %>%
pivot_longer(
starts_with("dep"),
names_to = "group",
values_to = "dep.var"
) %>%
mutate(group = as.factor(group)) %>%
group_by(group) %>%
group_split() %>%
map_dfr(.f = function(df) {
lm(dep.var ~ x1 + x2, data = df) %>%
tidy() %>% # first output
#glance() %>% # second output
add_column(group = unique(df$group), .before=1)
})
数据帧输出：
# A tibble: 9 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var1 (Intercept) -5.29 11.6 -0.456 0.662
2 dep.var1 x1 2.11 0.268 7.87 0.000101
3 dep.var1 x2 0.538 1.23 0.437 0.675
4 dep.var2 (Intercept) 151. 57.9 2.61 0.0347
5 dep.var2 x1 -10.6 1.34 -7.87 0.000101
6 dep.var2 x2 -2.69 6.15 -0.437 0.675
7 dep.var3 (Intercept) -9.29 11.6 -0.802 0.449
8 dep.var3 x1 2.11 0.268 7.87 0.000101
9 dep.var3 x2 0.538 1.23 0.437 0.675
列表输出：
[[1]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var1 (Intercept) -5.29 11.6 -0.456 0.662
2 dep.var1 x1 2.11 0.268 7.87 0.000101
3 dep.var1 x2 0.538 1.23 0.437 0.675
[[2]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var2 (Intercept) 151. 57.9 2.61 0.0347
2 dep.var2 x1 -10.6 1.34 -7.87 0.000101
3 dep.var2 x2 -2.69 6.15 -0.437 0.675
[[3]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var3 (Intercept) -9.29 11.6 -0.802 0.449
2 dep.var3 x1 2.11 0.268 7.87 0.000101
3 dep.var3 x2 0.538 1.23 0.437 0.675
概览输出：
group r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 dep.var1 0.927 0.906 2.32 44.3 0.000106 2 -20.8 49.7 50.9 37.8 7 10
2 dep.var2 0.927 0.906 11.6 44.3 0.000106 2 -36.9 81.9 83.1 944. 7 10
3 dep.var3 0.927 0.906 2.32 44.3 0.000106 2 -20.8 49.7 50.9 37.8 7 10
我正在尝试编写一个函数，该函数将 运行 进行多重回归，然后将输出存储在一个向量中。我想要的是函数从我将提供的列表中选择因变量，然后 运行 对相同右侧变量的回归。不知道该怎么做。任何提示将不胜感激。
my_data <- data.frame(x1=(1:10) + rnorm(10, 3, 1.5), x2=25/3 + rnorm(10, 0, 1),
dep.var1=seq(5, 28, 2.5), dep.var2=seq(100, -20, -12.5),
dep.var3=seq(1, 25, 2.5))
## The following is a list that tells the function
dep.var <- list(dep.var1=my_data$dep.var1, dep.var2=my_data$dep.var2)
## which dependent variables to use from my_data
all_models <- function(dep.var){lm(dep.var ~ x1 + x2, data=my_data)}
model <- sapply(dep.var, all_models) ## The "sapply" here tells the function to
## take the dependent variables from the list dep.var.
我希望“模型”列表有两个对象：带有 dep.var1 的 model1 和带有 dep.var2 的 model2。然后根据需要，我将使用 summary(model#) 来查看回归输出。
我知道这在理论上适用于使用矢量（即 p）的情况：
p <- seq(0.25, 0.95, 0.05)
s <- function(p) {1 - pnorm(35, p*1*44, sqrt(44)*sqrt(p*(1 - p)))}
f <- sapply(p, s)
但我无法让整个事情按照我的回归模型的要求工作。它有点工作，因为你可以 运行 并检查“模型”，它会向你显示两个回归输出 - 但它太可怕了。并且“模型”没有显示回归规范，即 dep.var1 ~ x1 + x2.
您可以 sapply 覆盖您可以用 grep 很好地识别的依赖变量的名称。在 lm 中使用 reformulate 构建公式。
sapply(grep('^dep', names(my_data), value=TRUE), \(x)
lm(reformulate(c('x1', 'x2'), x), my_data))
# dep.var1 dep.var2 dep.var3
# coefficients numeric,3 numeric,3 numeric,3
# residuals numeric,10 numeric,10 numeric,10
# effects numeric,10 numeric,10 numeric,10
# rank 3 3 3
# fitted.values numeric,10 numeric,10 numeric,10
# assign integer,3 integer,3 integer,3
# qr qr,5 qr,5 qr,5
# df.residual 7 7 7
# xlevels list,0 list,0 list,0
# call expression expression expression
# terms dep.var1 ~ x1 + x2 dep.var2 ~ x1 + x2 dep.var3 ~ x1 + x2
# model data.frame,3 data.frame,3 data.frame,3
dep.var* 很好地出现在结果中。
但是，您可能想使用 lapply 并将其通过管道传输到 setNames() 以获取命名的列表元素。您当然可以手动定义因变量，而不是 grep。为了存储干净的公式调用，我们使用 do.call.
dv <- as.list(grep('^dep', names(my_data), value=TRUE)[1:2])
res <- lapply(dv, \(x) {
f <- reformulate(c('x1', 'x2'), x)
do.call('lm', list(f, quote(my_data)))
}) |>
setNames(dv)
res
# $dep.var1
#
# Call:
# lm(formula = dep.var1 ~ x1 + x2, data = my_data)
#
# Coefficients:
# (Intercept) x1 x2
# -4.7450 2.3398 0.2747
#
#
# $dep.var2
#
# Call:
# lm(formula = dep.var2 ~ x1 + x2, data = my_data)
#
# Coefficients:
# (Intercept) x1 x2
# 148.725 -11.699 -1.373
这允许您获取对象的 summary()，这可能就是您想要的。
summary(res$dep.var1)
# Call:
# lm(formula = dep.var1 ~ x1 + x2, data = my_data)
#
# Residuals:
# Min 1Q Median 3Q Max
# -2.8830 -1.8345 -0.2326 1.4335 4.2452
#
# Coefficients:
# Estimate Std. Error t value Pr(>|t|)
# (Intercept) -4.7450 7.2884 -0.651 0.536
# x1 2.3398 0.2836 8.251 7.48e-05 ***
# x2 0.2747 0.7526 0.365 0.726
# ---
# Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#
# Residual standard error: 2.55 on 7 degrees of freedom
# Multiple R-squared: 0.9117, Adjusted R-squared: 0.8865
# F-statistic: 36.14 on 2 and 7 DF, p-value: 0.0002046
最后你可以将它包装在一个函数中
calc_models <- \(dv) {
lapply(dv, \(x) {
f <- reformulate(c('x1', 'x2'), x)
do.call('lm', list(f, quote(my_data)))
}) |>
setNames(dv)
}
calc_models(list('dep.var1', 'dep.var2'))
考虑 reformulate 使用 lm 调用的字符值动态更改模型公式：
# VECTOR OF COLUMN NAMES (NOT VALUES)
dep.vars <- c("dep.var1", "dep.var2")
# USER-DEFINED METHOD TO PROCESS DIFFERENT DEP VAR
run_model <- function(dep.var) {
fml <- reformulate(c("x1", "x2"), dep.var)
lm(fml, data=data)
}
# NAMED LIST OF MODELS
all_models <- sapply(dep.vars, run_model, simplify = FALSE)
# OUTPUT RESULTS
all_models$dep.var1
all_models$dep.var2
...
从那里，您可以运行跨模型对象进一步提取或处理：
# NAMED LIST OF MODEL SUMMARIES
all_summaries <- lapply(all_models, summary)
all_summaries$dep.var1
all_summaries$dep.var2
...
# NAMED LIST OF MODEL COEFFICIENTS
all_coefficients <- lapply(all_models, `[`, "coefficients")
all_coefficients$dep.var1
all_coefficients$dep.var2
...
这是一种迭代数据框并将函数应用于您定义的组（此处 dep.var）并将不同模型保存在数据框中的方法：
library(tidyverse)
library(broom)
my_data %>%
pivot_longer(
starts_with("dep"),
names_to = "group",
values_to = "dep.var"
) %>%
mutate(group = as.factor(group)) %>%
group_by(group) %>%
group_split() %>%
map_dfr(.f = function(df) {
lm(dep.var ~ x1 + x2, data = df) %>%
tidy() %>% # first output
#glance() %>% # second output
add_column(group = unique(df$group), .before=1)
})
数据帧输出：
# A tibble: 9 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var1 (Intercept) -5.29 11.6 -0.456 0.662
2 dep.var1 x1 2.11 0.268 7.87 0.000101
3 dep.var1 x2 0.538 1.23 0.437 0.675
4 dep.var2 (Intercept) 151. 57.9 2.61 0.0347
5 dep.var2 x1 -10.6 1.34 -7.87 0.000101
6 dep.var2 x2 -2.69 6.15 -0.437 0.675
7 dep.var3 (Intercept) -9.29 11.6 -0.802 0.449
8 dep.var3 x1 2.11 0.268 7.87 0.000101
9 dep.var3 x2 0.538 1.23 0.437 0.675
列表输出：
[[1]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var1 (Intercept) -5.29 11.6 -0.456 0.662
2 dep.var1 x1 2.11 0.268 7.87 0.000101
3 dep.var1 x2 0.538 1.23 0.437 0.675
[[2]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var2 (Intercept) 151. 57.9 2.61 0.0347
2 dep.var2 x1 -10.6 1.34 -7.87 0.000101
3 dep.var2 x2 -2.69 6.15 -0.437 0.675
[[3]]
# A tibble: 3 x 6
group term estimate std.error statistic p.value
<fct> <chr> <dbl> <dbl> <dbl> <dbl>
1 dep.var3 (Intercept) -9.29 11.6 -0.802 0.449
2 dep.var3 x1 2.11 0.268 7.87 0.000101
3 dep.var3 x2 0.538 1.23 0.437 0.675
概览输出：
group r.squared adj.r.squared sigma statistic p.value df logLik AIC BIC deviance df.residual nobs
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int>
1 dep.var1 0.927 0.906 2.32 44.3 0.000106 2 -20.8 49.7 50.9 37.8 7 10
2 dep.var2 0.927 0.906 11.6 44.3 0.000106 2 -36.9 81.9 83.1 944. 7 10
3 dep.var3 0.927 0.906 2.32 44.3 0.000106 2 -20.8 49.7 50.9 37.8 7 10