如何向回归表添加额外的 GOF，例如 ivreg 诊断？
How to add additional GOFs such as ivreg diagnostics to regression tables?
对于我的 stargazer table 我想包括弱仪器诊断和 Wu Hausman 测试。
我阅读了以下答案：.
虽然，我不是很明白这一点。如何将其应用于具有 9 列或 10 列的更大模型？
我如何插入您在编码 summary(ivreg1, diagnostics=TRUE) 时获得的诊断信息？
我有以下用于观星者的代码 table:
ivreg1 <- ivreg(budget ~ policyfactor1 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg2 <- ivreg(budget ~ policyfactor2 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg3 <- ivreg(budget ~ policyfactor3 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg4 <- ivreg(budget ~ policyfactor4 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg5 <- ivreg(budget ~ policyfactor5 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg6 <- ivreg(budget ~ policyfactor6 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg7 <- ivreg(budget ~ policyfactor7 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg8 <- ivreg(budget ~ policyfactor8 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg9 <- ivreg(budget ~ policyfactor9 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
stargazer(ivreg1, ivreg2, ivreg3, ivreg4, ivreg5, ivreg6, ivreg7, ivreg8, ivreg9, title="Regression results", align=TRUE, column.sep.width = "-15pt", font.size = "small", type="latex")
我有以下数据框示例：
data.frame(
budget = c(10, 8, -7, 8, 3, 2, 0.5, 1.5, -2, -15, 30, -0.5, 12, 18),
policyfactor1 = c(1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L),
policyfactor2 = c(0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L),
policyfactor3 = c(0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L),
policyfactor4 = c(1L, 0L, 1L, 0L, 0L, 1L, NA, NA, 1L, 1L, 0L, 1L, 1L, 0L),
policyfactor5 = c(1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L),
policyfactor6 = c(0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, NA, 0L),
policyfactor7 = c(0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L),
policyfactor8 = c(1L, 0L, 1L, 1L, NA, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L),
policyfactor9 = c(0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, NA, 1L, 1L, 1L),
IV = c(17L,46L,18L,23L,35L,10L,0L,19L,
15L,12L,21L,6L,27L,10L),
control1 = c(29.4,27.4,33.2,27.1,27.4,34.2,
26.8,32.9,26,26.3,27.3,33.4,23.5,31.3),
control2 = c(1.3,20,-0.2,3,3.4,0.3,-1.1,1.9,
-2,-1.6,2.8,1.9,2,1.8)
)
#> budget policyfactor1 policyfactor2 policyfactor3 policyfactor4 policyfactor5
#> 1 10.0 1 0 0 1 1
#> 2 8.0 1 1 1 0 0
#> 3 -7.0 0 0 1 1 1
#> 4 8.0 0 1 0 0 0
#> 5 3.0 0 0 1 0 0
#> 6 2.0 1 0 1 1 1
#> 7 0.5 1 1 1 NA 1
#> 8 1.5 0 1 0 NA 0
#> 9 -2.0 0 0 0 1 0
#> 10 -15.0 0 0 1 1 1
#> 11 30.0 1 1 1 0 1
#> 12 -0.5 0 0 1 1 0
#> 13 12.0 1 1 0 1 1
#> 14 18.0 1 0 0 0 1
#> policyfactor6 policyfactor7 policyfactor8 policyfactor9 IV control1 control2
#> 1 0 0 1 0 17 29.4 1.3
#> 2 1 1 0 0 46 27.4 20.0
#> 3 1 1 1 1 18 33.2 -0.2
#> 4 0 1 1 0 23 27.1 3.0
#> 5 1 1 NA 1 35 27.4 3.4
#> 6 1 0 1 0 10 34.2 0.3
#> 7 0 1 1 1 0 26.8 -1.1
#> 8 1 0 0 1 19 32.9 1.9
#> 9 1 1 0 0 15 26.0 -2.0
#> 10 1 0 1 1 12 26.3 -1.6
#> 11 0 0 0 NA 21 27.3 2.8
#> 12 1 1 0 1 6 33.4 1.9
#> 13 NA 0 1 1 27 23.5 2.0
#> 14 0 0 0 1 10 31.3 1.8
由 reprex package (v0.3.0)
于 2020 年 6 月 20 日创建
如果您的全局环境中已有对象，您只需要list模型适合您可以使用的地方mget（不过，请参阅答案底部的替代方案）。
iv.fit <- mget(paste0("ivreg", 1:9))
然后在 stargazer::stargazer 中，在 函数中使用 lapply.
添加包含诊断的摘要
library(stargazer)
stargazer(iv.fit[1:3], title="Regression results", align=TRUE,
type="text", ## to get LaTeX output use `type="latex"`
column.sep.width="-15pt", font.size="small",
add.lines=
gaze.lines.ivreg.diagn(lapply(iv.fit[1:3], summary, diagnostics=TRUE), row=1:2))
结果stargazer
# Regression results
# ===========================================================
# Dependent variable:
# -----------------------------
# budget
# (1) (2) (3)
# -----------------------------------------------------------
# policyfactor1 1.217
# (15.134)
#
# policyfactor2 3.209
# (39.816)
#
# policyfactor3 3.302
# (44.912)
#
# control1 -0.394 -0.237 -0.513
# (0.984) (2.407) (1.687)
#
# control2 0.517 0.439 0.486
# (0.721) (1.496) (1.079)
#
# Constant 14.493 9.342 16.727
# (31.255) (82.795) (33.982)
#
# -----------------------------------------------------------
# Weak instruments 0.17 0.57 0.63
# Wu-Hausman 0.36 0.91 0.83
# Observations 14 14 14
# R2 0.154 0.159 -0.011
# Adjusted R2 -0.099 -0.094 -0.315
# Residual Std. Error (df = 10) 11.465 11.437 12.539
# ===========================================================
# Note: *p<0.1; **p<0.05; ***p<0.01
这也可以用 texreg::texreg。同样，我们将摘要放入列表中。
iv.gof2 <- lapply(iv.fit, function(x)
summary(x, diagnostics=T)$diagnostics[c("Weak instruments", "Wu-Hausman"), "p-value"])
然后我们使用texreg::extract提取相关模型细节来编辑GOF信息。这给出了包含 "texreg" 个对象的列表 iv.fit.ex。
library(texreg)
iv.fit.ex <- lapply(iv.fit, extract)
对于一个小函数，我们可以使用 Map 添加额外的 GOF。
addGof<- function(x, y) {
x@gof.names <- c(x@gof.names, names(y))
x@gof <- c(x@gof, y)
x@gof.decimal <- c(x@gof.decimal, rep(TRUE, length(y)))
x
}
iv.fit.ex <- Map(addGof, iv.fit.ex, iv.gof2)
然后我们 运行 texreg::texreg iv.fit.ex。 （我在这里显示控制台版本 texreg::screenreg）。
screenreg(iv.fit.ex[1:3], digits=3, column.spacing=1,
custom.model.names=sprintf(" (%s)", 1:length(iv.fit.ex[1:3])))
结果texreg
# ===========================================
# (1) (2) (3)
# -------------------------------------------
# (Intercept) 14.493 9.342 16.727
# (31.255) (82.795) (33.982)
# policyfactor1 1.217
# (15.134)
# control1 -0.394 -0.237 -0.513
# (0.984) (2.407) (1.687)
# control2 0.517 0.439 0.486
# (0.721) (1.496) (1.079)
# policyfactor2 3.209
# (39.816)
# policyfactor3 3.302
# (44.912)
# -------------------------------------------
# R^2 0.154 0.159 -0.011
# Adj. R^2 -0.099 -0.094 -0.315
# Num. obs. 14 14 14
# Weak instruments 0.168 0.567 0.628
# Wu-Hausman 0.357 0.911 0.825
# ===========================================
# *** p < 0.001; ** p < 0.01; * p < 0.05
顺便说一下， 你也可以用一种不那么乏味的方式列出你的回归模型，不必单独对每个回归进行编码:
fo <- sapply(paste0("policyfactor", 1:9), function(i)
as.formula(sprintf("budget ~ %s + control1 + control2 | iv + control1 + control2", i)))
iv.fit <- lapply(fo, function(fo) do.call(ivreg, list(formula=fo, data=quote(dat))))
数据：
dat <- structure(list(budget = c(10, 8, -7, 8, 3, 2, 0.5, 1.5, -2, -15,
30, -0.5, 12, 18), policyfactor1 = c(1L, 1L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L), policyfactor2 = c(0L, 1L, 0L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L), policyfactor3 = c(0L,
1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L), policyfactor4 = c(1L,
0L, 1L, 0L, 0L, 1L, NA, NA, 1L, 1L, 0L, 1L, 1L, 0L), policyfactor5 = c(1L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L), policyfactor6 = c(0L,
1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, NA, 0L), policyfactor7 = c(0L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L), policyfactor8 = c(1L,
0L, 1L, 1L, NA, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L), policyfactor9 = c(0L,
0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, NA, 1L, 1L, 1L), iv = c(17L,
46L, 18L, 23L, 35L, 10L, 0L, 19L, 15L, 12L, 21L, 6L, 27L, 10L
), control1 = c(29.4, 27.4, 33.2, 27.1, 27.4, 34.2, 26.8, 32.9,
26, 26.3, 27.3, 33.4, 23.5, 31.3), control2 = c(1.3, 20, -0.2,
3, 3.4, 0.3, -1.1, 1.9, -2, -1.6, 2.8, 1.9, 2, 1.8)), class = "data.frame", row.names = c(NA,
-14L))
对于我的 stargazer table 我想包括弱仪器诊断和 Wu Hausman 测试。
我阅读了以下答案：
虽然，我不是很明白这一点。如何将其应用于具有 9 列或 10 列的更大模型？
我如何插入您在编码 summary(ivreg1, diagnostics=TRUE) 时获得的诊断信息？
我有以下用于观星者的代码 table:
ivreg1 <- ivreg(budget ~ policyfactor1 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg2 <- ivreg(budget ~ policyfactor2 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg3 <- ivreg(budget ~ policyfactor3 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg4 <- ivreg(budget ~ policyfactor4 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg5 <- ivreg(budget ~ policyfactor5 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg6 <- ivreg(budget ~ policyfactor6 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg7 <- ivreg(budget ~ policyfactor7 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg8 <- ivreg(budget ~ policyfactor8 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
ivreg9 <- ivreg(budget ~ policyfactor9 + control1 + control2 + control3 | iv + control1 + control2 + control3, data=df)
stargazer(ivreg1, ivreg2, ivreg3, ivreg4, ivreg5, ivreg6, ivreg7, ivreg8, ivreg9, title="Regression results", align=TRUE, column.sep.width = "-15pt", font.size = "small", type="latex")
我有以下数据框示例：
data.frame(
budget = c(10, 8, -7, 8, 3, 2, 0.5, 1.5, -2, -15, 30, -0.5, 12, 18),
policyfactor1 = c(1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L),
policyfactor2 = c(0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L),
policyfactor3 = c(0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L),
policyfactor4 = c(1L, 0L, 1L, 0L, 0L, 1L, NA, NA, 1L, 1L, 0L, 1L, 1L, 0L),
policyfactor5 = c(1L, 0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L),
policyfactor6 = c(0L, 1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, NA, 0L),
policyfactor7 = c(0L, 1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L),
policyfactor8 = c(1L, 0L, 1L, 1L, NA, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L),
policyfactor9 = c(0L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, NA, 1L, 1L, 1L),
IV = c(17L,46L,18L,23L,35L,10L,0L,19L,
15L,12L,21L,6L,27L,10L),
control1 = c(29.4,27.4,33.2,27.1,27.4,34.2,
26.8,32.9,26,26.3,27.3,33.4,23.5,31.3),
control2 = c(1.3,20,-0.2,3,3.4,0.3,-1.1,1.9,
-2,-1.6,2.8,1.9,2,1.8)
)
#> budget policyfactor1 policyfactor2 policyfactor3 policyfactor4 policyfactor5
#> 1 10.0 1 0 0 1 1
#> 2 8.0 1 1 1 0 0
#> 3 -7.0 0 0 1 1 1
#> 4 8.0 0 1 0 0 0
#> 5 3.0 0 0 1 0 0
#> 6 2.0 1 0 1 1 1
#> 7 0.5 1 1 1 NA 1
#> 8 1.5 0 1 0 NA 0
#> 9 -2.0 0 0 0 1 0
#> 10 -15.0 0 0 1 1 1
#> 11 30.0 1 1 1 0 1
#> 12 -0.5 0 0 1 1 0
#> 13 12.0 1 1 0 1 1
#> 14 18.0 1 0 0 0 1
#> policyfactor6 policyfactor7 policyfactor8 policyfactor9 IV control1 control2
#> 1 0 0 1 0 17 29.4 1.3
#> 2 1 1 0 0 46 27.4 20.0
#> 3 1 1 1 1 18 33.2 -0.2
#> 4 0 1 1 0 23 27.1 3.0
#> 5 1 1 NA 1 35 27.4 3.4
#> 6 1 0 1 0 10 34.2 0.3
#> 7 0 1 1 1 0 26.8 -1.1
#> 8 1 0 0 1 19 32.9 1.9
#> 9 1 1 0 0 15 26.0 -2.0
#> 10 1 0 1 1 12 26.3 -1.6
#> 11 0 0 0 NA 21 27.3 2.8
#> 12 1 1 0 1 6 33.4 1.9
#> 13 NA 0 1 1 27 23.5 2.0
#> 14 0 0 0 1 10 31.3 1.8
由 reprex package (v0.3.0)
于 2020 年 6 月 20 日创建如果您的全局环境中已有对象，您只需要list模型适合您可以使用的地方mget（不过，请参阅答案底部的替代方案）。
iv.fit <- mget(paste0("ivreg", 1:9))
然后在 stargazer::stargazer 中，在 lapply.
library(stargazer)
stargazer(iv.fit[1:3], title="Regression results", align=TRUE,
type="text", ## to get LaTeX output use `type="latex"`
column.sep.width="-15pt", font.size="small",
add.lines=
gaze.lines.ivreg.diagn(lapply(iv.fit[1:3], summary, diagnostics=TRUE), row=1:2))
结果stargazer
# Regression results
# ===========================================================
# Dependent variable:
# -----------------------------
# budget
# (1) (2) (3)
# -----------------------------------------------------------
# policyfactor1 1.217
# (15.134)
#
# policyfactor2 3.209
# (39.816)
#
# policyfactor3 3.302
# (44.912)
#
# control1 -0.394 -0.237 -0.513
# (0.984) (2.407) (1.687)
#
# control2 0.517 0.439 0.486
# (0.721) (1.496) (1.079)
#
# Constant 14.493 9.342 16.727
# (31.255) (82.795) (33.982)
#
# -----------------------------------------------------------
# Weak instruments 0.17 0.57 0.63
# Wu-Hausman 0.36 0.91 0.83
# Observations 14 14 14
# R2 0.154 0.159 -0.011
# Adjusted R2 -0.099 -0.094 -0.315
# Residual Std. Error (df = 10) 11.465 11.437 12.539
# ===========================================================
# Note: *p<0.1; **p<0.05; ***p<0.01
这也可以用 texreg::texreg。同样，我们将摘要放入列表中。
iv.gof2 <- lapply(iv.fit, function(x)
summary(x, diagnostics=T)$diagnostics[c("Weak instruments", "Wu-Hausman"), "p-value"])
然后我们使用texreg::extract提取相关模型细节来编辑GOF信息。这给出了包含 "texreg" 个对象的列表 iv.fit.ex。
library(texreg)
iv.fit.ex <- lapply(iv.fit, extract)
对于一个小函数，我们可以使用 Map 添加额外的 GOF。
addGof<- function(x, y) {
x@gof.names <- c(x@gof.names, names(y))
x@gof <- c(x@gof, y)
x@gof.decimal <- c(x@gof.decimal, rep(TRUE, length(y)))
x
}
iv.fit.ex <- Map(addGof, iv.fit.ex, iv.gof2)
然后我们 运行 texreg::texreg iv.fit.ex。 （我在这里显示控制台版本 texreg::screenreg）。
screenreg(iv.fit.ex[1:3], digits=3, column.spacing=1,
custom.model.names=sprintf(" (%s)", 1:length(iv.fit.ex[1:3])))
结果texreg
# ===========================================
# (1) (2) (3)
# -------------------------------------------
# (Intercept) 14.493 9.342 16.727
# (31.255) (82.795) (33.982)
# policyfactor1 1.217
# (15.134)
# control1 -0.394 -0.237 -0.513
# (0.984) (2.407) (1.687)
# control2 0.517 0.439 0.486
# (0.721) (1.496) (1.079)
# policyfactor2 3.209
# (39.816)
# policyfactor3 3.302
# (44.912)
# -------------------------------------------
# R^2 0.154 0.159 -0.011
# Adj. R^2 -0.099 -0.094 -0.315
# Num. obs. 14 14 14
# Weak instruments 0.168 0.567 0.628
# Wu-Hausman 0.357 0.911 0.825
# ===========================================
# *** p < 0.001; ** p < 0.01; * p < 0.05
顺便说一下， 你也可以用一种不那么乏味的方式列出你的回归模型，不必单独对每个回归进行编码:
fo <- sapply(paste0("policyfactor", 1:9), function(i)
as.formula(sprintf("budget ~ %s + control1 + control2 | iv + control1 + control2", i)))
iv.fit <- lapply(fo, function(fo) do.call(ivreg, list(formula=fo, data=quote(dat))))
数据：
dat <- structure(list(budget = c(10, 8, -7, 8, 3, 2, 0.5, 1.5, -2, -15,
30, -0.5, 12, 18), policyfactor1 = c(1L, 1L, 0L, 0L, 0L, 1L,
1L, 0L, 0L, 0L, 1L, 0L, 1L, 1L), policyfactor2 = c(0L, 1L, 0L,
1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 0L), policyfactor3 = c(0L,
1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L), policyfactor4 = c(1L,
0L, 1L, 0L, 0L, 1L, NA, NA, 1L, 1L, 0L, 1L, 1L, 0L), policyfactor5 = c(1L,
0L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 1L, 1L), policyfactor6 = c(0L,
1L, 1L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 1L, NA, 0L), policyfactor7 = c(0L,
1L, 1L, 1L, 1L, 0L, 1L, 0L, 1L, 0L, 0L, 1L, 0L, 0L), policyfactor8 = c(1L,
0L, 1L, 1L, NA, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 0L), policyfactor9 = c(0L,
0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 1L, NA, 1L, 1L, 1L), iv = c(17L,
46L, 18L, 23L, 35L, 10L, 0L, 19L, 15L, 12L, 21L, 6L, 27L, 10L
), control1 = c(29.4, 27.4, 33.2, 27.1, 27.4, 34.2, 26.8, 32.9,
26, 26.3, 27.3, 33.4, 23.5, 31.3), control2 = c(1.3, 20, -0.2,
3, 3.4, 0.3, -1.1, 1.9, -2, -1.6, 2.8, 1.9, 2, 1.8)), class = "data.frame", row.names = c(NA,
-14L))