我，一个R初学者，创建了以下可重现的示例代码，用于输出各种变量的ADF测试的测试统计信息：
library(urca)
data(nporg)
df <- nporg[complete.cases(nporg), ]
table_adf = function(x) {
for (i in 1:x) { #something is wrong here
out1 <- ur.df(x, type = "drift", selectlags = "AIC")@teststat[1]
out2 <- ur.df(x, type = "trend", selectlags = "AIC")@teststat[1]
out <- c(out1,out2)
return(data.frame(matrix(out, nrow=1, dimnames=list(NULL, names(out)))))
}
footnote <- rep("", 3)
footnote[1] <- c("Lag length determined with the AIC information criteria.")
footnote[2] <- c(paste("ADF critical values drift:", out1@cval[[1]], out1@cval[[2]], out1@cval[[3]]))
footnote[3] <- c(paste("ADF critical values trend:", out2@cval[[2]], out2@cval[[2]], out2@cval[[3]]))
print(footnote) # something is wrong here, too
}
rbind(
table_adf(df$emp),
table_adf(df$cpi),
table_adf(df$sp)
)
我的问题： 如何打印表格下方的脚注，如果可能，还要将每个变量名称添加到行中？
如果您在我的代码中发现任何其他错误或效率低下，请给我一个提示。谢谢。
更新：所需输出：
==============================================
drift trend
----------------------------------------------
emp -0.259 -4.841 ***
cpi 0.488 -1.326
sp 0.232 -3.831 **
... ... ...
----------------------------------------------
ADF critical values drift: -3.51 -2.89 -2.58
ADF critical values trend: -4.04 -3.45 -3.15
我更改了趋势中的数字，以便您可以看到重要性。
答案 0 :(得分：3)
stargazer()具有出色的报告功能。您可以使用notes= library(urca)
library(stargazer)
library(dplyr)
data(nporg)
df <- nporg[complete.cases(nporg), ]
table_adf = function(DF, ...){
var_list = list(...)
select_(DF, .dots = var_list) %>%
{invisible(Map(function(x, y){
out1 = ur.df(x, type = "drift", selectlags = "AIC")
out2 = ur.df(x, type = "trend", selectlags = "AIC")
est_df = data.frame(drift = out1@teststat[1], trend = out2@teststat[1],
row.names = y)
footnote1 = paste("ADF critical values drift:", out1@cval[1,1], out1@cval[1,2], out1@cval[1,3])
footnote2 = paste("ADF critical values trend:", out2@cval[1,1], out2@cval[1,2], out2@cval[1,3])
stargazer(est_df, type = "text", summary = FALSE,
notes = c(footnote1, footnote2))
},. , var_list))}
}
table_adf(df, "emp", "cpi", "sp")
参数作为脚注来执行以下操作：
...使用select_参数和.dots + select代替out1@cval以编程方式提供变量名称
请注意，我更改了您的out1@cval[[1]]索引，以正确引用＆＃34; tau＆＃34;关键价值观。虽然我不熟悉ADF测试，但是使用> out1@cval
1pct 5pct 10pct
tau2 -3.51 -2.89 -2.58
phi1 6.70 4.71 3.86
索引2x3矩阵的方式对我来说似乎很奇怪。所以我怀疑你真的想要那张桌子的第一行。
==============================================
drift trend
----------------------------------------------
emp -0.259 -3.041
----------------------------------------------
ADF critical values drift: -3.51 -2.89 -2.58
ADF critical values trend: -4.04 -3.45 -3.15
==============================================
drift trend
----------------------------------------------
cpi 0.488 -1.326
----------------------------------------------
ADF critical values drift: -3.51 -2.89 -2.58
ADF critical values trend: -4.04 -3.45 -3.15
==============================================
drift trend
----------------------------------------------
sp 0.232 -1.343
----------------------------------------------
ADF critical values drift: -3.51 -2.89 -2.58
ADF critical values trend: -4.04 -3.45 -3.15
结果：
@teststate现在，这需要更多参与，因为你必须......：
ur.df对象@cval）
%>% 注意我在此函数中使用了最少数量的管道运算符...，因为几乎每个步骤都被使用了不止一次。例如，我不能仅将lapply参数传递到tests row.names，因为我还需要变量列表来设置est_table步骤中的var_list = as.list(unlist(list(...)))。
c("emp", "cpi", "sp")似乎不必要地复杂，但它允许用户输入变量名称"emp", "cpi", "sp"，单个变量名称c("emp", "cpi"), "sp"或两者ifelse的向量。这为用户创造了额外的便利。最开始的table_adf = function(DF, ...){
# Stores ... arguments as list
var_list = as.list(unlist(list(...)))
# Subset DF based on var_list
if(length(var_list) == 0){
subset = DF
var_list = as.list(names(DF))
}else{
subset = select_(DF, .dots = var_list)
}
# Run model for each variable, and stored as list of lists, each element of the
# outer list contains a list of the variable's drift and trend estimates
tests = lapply(subset, function(x, y){
out1 = ur.df(x, type = "drift", selectlags = "AIC")
out2 = ur.df(x, type = "trend", selectlags = "AIC")
return(list(out1, out2))
})
# Store each model estimates in to a dataframe with corresponding variable names
est_df = lapply(tests, function(x){
data.frame(drift = x[[1]]@teststat[1], trend = x[[2]]@teststat[1])
})
# Combine the list into a single dataframe
est_table = do.call(rbind, est_df)
critical_vals = data.frame(tests[[1]][[1]]@cval[1,],
tests[[1]][[2]]@cval[1,])
# Check for significance level and add *'s accordingly
est_table = Map(function(x, y){
ifelse(x < y[1], paste(x, "***"),
ifelse(x < y[2], paste(x, "**"),
ifelse(x < y[3], paste(x, "*"), x)))
}, est_table, critical_vals) %>% data.frame(row.names = unlist(var_list))
# Set footnotes
footnote1 = paste("ADF critical values drift:", paste(critical_vals[[1]], collapse = " "))
footnote2 = paste("ADF critical values trend:", paste(critical_vals[[2]], collapse = " "))
# Create nice looking table with critical values
stargazer(est_table, type = "text", summary = FALSE,
notes = c(footnote1, footnote2))
}
步骤检查变量名称是否有零参数，如果是这种情况则使用整个数据帧。
> table_adf(df, "emp", "cpi", "sp")
==============================================
drift trend
----------------------------------------------
emp -0.259 -3.041
cpi 0.488 -1.326
sp 0.232 -1.343
----------------------------------------------
ADF critical values drift: -3.51 -2.89 -2.58
ADF critical values trend: -4.04 -3.45 -3.15
> table_adf(df, names(df)[-1])
==============================================
drift trend
----------------------------------------------
gnp.r 1.22473721812295 -1.308
gnp.n 4.2258196600637 2.195
gnp.pc -0.153914521574277 -2.628
ip 1.49350460242673 -0.924
emp -0.258523412908067 -3.041
ur -2.75061777744267 * -2.777
gnp.p 1.06909915298064 -0.800
cpi 0.488243115715459 -1.326
wg.n 2.81042091873464 0.399
wg.r 0.399440467221402 -2.445
M 1.45354783780152 -0.454
vel -2.50304919304012 -2.537
bnd 1.21565390985417 1.837
sp 0.231913401411228 -1.343
----------------------------------------------
ADF critical values drift: -3.51 -2.89 -2.58
ADF critical values trend: -4.04 -3.45 -3.15
> table_adf(df)
==============================================
drift trend
----------------------------------------------
year -1.73205080756904 -1.732
gnp.r 1.22473721812295 -1.308
gnp.n 4.2258196600637 2.195
gnp.pc -0.153914521574277 -2.628
ip 1.49350460242673 -0.924
emp -0.258523412908067 -3.041
ur -2.75061777744267 * -2.777
gnp.p 1.06909915298064 -0.800
cpi 0.488243115715459 -1.326
wg.n 2.81042091873464 0.399
wg.r 0.399440467221402 -2.445
M 1.45354783780152 -0.454
vel -2.50304919304012 -2.537
bnd 1.21565390985417 1.837
sp 0.231913401411228 -1.343
----------------------------------------------
ADF critical values drift: -3.51 -2.89 -2.58
ADF critical values trend: -4.04 -3.45 -3.15
下面演示了用户输入变量名称以获得所需估计值的方法：
{{1}}