作者： 谢雁翔 (南开大学)
目录
- 1.简介
- 2. R 的安装下载及 Rcall 命令的安装
- 2.1 R 的安装下载
- 2.2 Rcall 命令的安装
- 3. Rcall 命令及 R 语言初识
- 3.1 Rcall 命令基本语法
- 3.2 R 语言基本的数据类型
- 3.3 R 语言基本的数据结构
- 4. Stata实例
- 4.1 Stata 与 R 语言的数据转换
- 4.2 在 Stata 中运行 R
- 4.3 拓展：在 R 中运行Stata
本文主要参考如下论文，特此致谢
Haghish E F . Seamless interactive language interfacing between R and Stata[J]. Stata Journal, 2019, 19(1):61-82.[PDF]
1.简介
R 是一门免费、开源的语言，具有体积小 (R 4.0.0 为83.5 M) 、功能多 (外部包达16052个，截止 2020.07.19) 等优势，其被广泛应用于数据挖掘、机器学习、数据可视化、计量经济学和空间统计等领域。那么如何在 Stata 中运行 R 语言？ Rcall ！
Rcall 安装包可将 R 集成在 Stata 中并允许 R 与 Stata 之间的数据衔接。其能够将 Stata 中的宏、标量、矩阵和数据集自动传输到 R，并将 R 中不同类别的对象 (数据框 data.frame ，列表 list ，矩阵 matrix ，向量 vector ，逻辑 logical 等) 自动导入到 Stata。Stata 与 R 的结合可使 Stata 用户在 R 环境中交互执行的其他编程语言：例如使用 Rcpp 的 C ++，或使用V8软件包的JavaScript。此外，Rcall 允许在 Stata 的 ado 程序中嵌入 R 语言，从而不仅可以调用 R 函数和程序包，还可以通过Stata和R之间的转换来对 Stata 程序包进行编程。
2. R 的安装下载及 Rcall 命令的安装
2.1 R 的安装下载
在 Rcall命令安装之前首先要安装 R 。由于 R 是免费的开源软件，下载十分方便，可直接百度搜索：R software，登录官网下载 。具体安装操作如下图所示：
进入官网下载
选择对应镜像
选择对应系统
选择初次安装
下载最新版本
2.2 Rcall 命令的安装
github package 是安装 rcall 的唯一推荐方式。由于 github 网络不稳定，该安装过程需要花费大量时间。
. net install github, from("https://haghish.github.io/github/")
checking github consistency and verifying not already installed...
installing into C:\Users\Aadministrator\ado\plus\...
installation complete.
. net install github, from("https://haghish.github.io/github/")
checking github consistency and verifying not already installed...
installing into C:\Users\Aadministrator\ado\plus\...
installation complete.一旦 github 被安装，输入：
github install haghish/rcall, stable
checking rcall consistency and verifying not already installed...
installing into C:\Users\Aadministrator\ado\plus\...
installation complete.
github install haghish/rcall, stable
checking rcall consistency and verifying not already installed...
installing into C:\Users\Aadministrator\ado\plus\...
installation complete.3. Rcall 命令及 R 语言初识
3.1 Rcall 命令基本语法
要从 Stata 调用 R ，请使用以下语法 (具体参见help rcall)：
rcall [mode] [:] [R-command]
mode 模式具体有：vanilla, sync, interactive, console 四种模式，一般默认交互模式不用指定。该软件包还包括一些子命令subcommand，以方便将R集成到Stata中：
rcall [subcommand]
以下函数可用于将数据从 Stata 传输到 R ：
函数 | 英文描述 | 中文描述 |
st.scalar(name) | passes a scalar to R | 传输标量 |
st.matrix(name) | passes a matrix to R | 传输矩阵 |
st.var(varname) | passes a numeric or string variable to R | 传输数值或字符变量 |
st.data(filename) | passes Stata data to R. without filename, the currently loaded data is used | 将Stata数据传输到R。不带文件名，使用当前加载数据 |
st.load(dataframe) | loads data from R dataframe to Stata | 将数据从 R 的数据框加载到 Stata |
3.2 R 语言基本的数据类型
- 1.数值型 (numeric) ，如：x=2
- 2.字符型 (character) ，如：x=“2”
- 3.复数型 (complex) ，如：x= 1 + 3i
- 4.逻辑型 (logical) ，如：x=1; y=x>2
- 5.因子型 (factor) ，如：genders
(该部分内容源自：游万海—— R 语言导学)
3.3 R 语言基本的数据结构
- 1.向量vector (一维) ：数据类型都可取，不允许出现不同数据类型
b = c(1,2,3,4)
- 2.矩阵matrix (二维) 数据类型都可取，不允许出现不同数据类型
matrix(c(1,2,3,4,5,6),nrow=3,byrow=T/F)
- 3.数组array (三维) 数据类型都可取，不允许出现不同数据类型
aa=array(1:24,dim=c(3,4,2))
- 4.数据框data.frame (二维) 数据类型都可取，不同列的数据类型可不同
M = data.frame(a1=c(1,2,3),b=c(“a”,”b”,”c”))
- 5.列表list (“万能胶”) 数据类型都可取，任何元素数据类型均可不同
x=1:3 y=c("A","B","c") z=c(TRUE,FALSE) a=c(x,y,z) LL=list(x,y,z,a)
- (该部分内容源自：游万海—— R 语言导学)
4. Stata实例
4.1 Stata 与 R 语言的数据转换
每当执行 rcall 时，Stata 都会自动将 R 对象作为rclass接收。如果R以交互方式运行 (即不使用vanilla子命令) ，则先前的对象仍可访问 Stata ，除非将它们从 R 中更改或删除。此外，从 Stata 中加载的R软件包将保持加载状态，直到终止。在 Stata 中访问 R 对象是同时进行的，这使得使用 rcall 变得方便。例如，可以在 Stata 中访问 R 中定义的数字或字符串向量，就像使用 rclass 调用该对象的名称一样简单，即r(objectname)。
- 数值型案例：
rcall: a display r(a)
100
rcall: a display r(a)
100如果没有vanilla子命令，则定义的对象将保留在R的内存中，因此，只要调用R便将其返回给Stata。
rcall: a
[1] 100
rcall: a
[1] 100- 字符型案例：
rcall: str display r(str)
Hello World
rcall: str display r(str)
"Hello" "World"
rcall: str display r(str)
Hello World
rcall: str display r(str)
"Hello" "World"- 向量案例:
rcall: v display r(v)
1 2 3 4 5
rcall: v display r(v)
1 2 3 4 5- 矩阵案例:
rcall: A = matrix(1:6, nrow=2, byrow = TRUE)
mat list r(A)
r(A)[2,3]
c1 c2 c3
r1 1 2 3
r2 4 5 6
rcall: A = matrix(1:6, nrow=2, byrow = TRUE)
mat list r(A)
r(A)[2,3]
c1 c2 c3
r1 1 2 3
r2 4 5 6- 列表案例:
rcall: mylist display r(mylist_a)
1 2 3 4 5 6 7 8 9 10
rcall: mylist display r(mylist_a)
1 2 3 4 5 6 7 8 9 10- 逻辑性数据案例:
rcall: l display r(l)
TRUE
rcall: l display r(l)
TRUE- 空值型数据案例:
rcall: n display r(n)
NULL
rcall: n display r(n)
NULL为了实现 Stata 和 R 之间的理想交互，rcall 可以将 Stata 中的变量传递给 R 。可以在 R 代码中传递局部宏和全局宏。如下例所示：
global a 99
rcall: (a
[1] 99
global a 99
rcall: (a
[1] 99为了将标量从 Stata 传递到 R ，可以使用st.scalar()函数，如下所示：
scalar a = 50
rcall: (a
[1] 50
scalar a = 50
rcall: (a
[1] 50同样，可以使用 st.matrix () 函数将 Stata 矩阵传输到 R ，如下所示：
matrix A = (1,2\3,4)
matrix B = (96,96\96,96)
rcall: C rcall: C
[,1] [,2]
[1,] 97 98
[2,] 99 100
matrix A = (1,2\3,4)
matrix B = (96,96\96,96)
rcall: C rcall: C
[,1] [,2]
[1,] 97 98
[2,] 99 100当然，也可以调用 Stata 中的 R 矩阵：
mat list r(C)
r(C)[2,2]
c1 c2
r1 97 98
r2 99 100
mat list r(C)
r(C)[2,2]
c1 c2
r1 97 98
r2 99 100使用st.var(varname)函数将变量从 Stata 传递到 R 很方便。因此，只需将分析所需的变量从 Stata 传递到 R ，即可在 R 中执行任何分析：
sysuse auto, clear
(1978 Automobile Data)
rcall: dep rcall: pre rcall: lm(dep~pre)
Call:
lm(formula = dep ~ pre)
Coefficients:
(Intercept) pre
11253.1 -238.9
sysuse auto, clear
(1978 Automobile Data)
rcall: dep rcall: pre rcall: lm(dep~pre)
Call:
lm(formula = dep ~ pre)
Coefficients:
(Intercept) pre
11253.1 -238.9rcall包还允许在 st.data (filename) 函数中将 Stata 数据传递到 R 。此功能依赖于 R 中 readstata13 软件包来加载 Stata 数据集，而无需将其转换为 csv 或类似格式。readstata13 的 R 软件包可以按以下方式安装在 Stata 中：
rcall: install.packages("readstata13", repos="http://cran.uk.r-project.org")
package 'readstata13' successfully unpacked and MD5 sums checked
The downloaded binary packages are in C:\Users\Administrator\AppData\Local\Temp\Rtmp0qfZTJ\downloaded_packages
rcall: install.packages("readstata13", repos="http://cran.uk.r-project.org")
package 'readstata13' successfully unpacked and MD5 sums checked
The downloaded binary packages are in C:\Users\Administrator\AppData\Local\Temp\Rtmp0qfZTJ\downloaded_packages指定数据集的相对或绝对路径，以将数据从 Stata 传输到 R 。例如：
rcall: data rcall: dim(data)
rcall: data rcall: dim(data)若未指定文件名，则该函数将当前加载的数据传递给R。
sysuse auto, clear
(1978 Automobile Data)
rcall: data rcall: dim(data)
[1] 74 12
sysuse auto, clear
(1978 Automobile Data)
rcall: data rcall: dim(data)
[1] 74 12最后，可以使用 st.load(dataframe)函数将数据从 R 自动导入到 Stata 中。此功能将自动从R中保存一个 Stata 数据集，并通过清除当前数据集(如果有的话)将其加载到 Stata 中。当然，如果在R中编写了适当的代码以导出 Stata 数据集，则可以更好地控制转换变量类型。该功能在大多数情况下应该可以正常工作：
clear
rcall: st.load(cars)
list in 1/2
+--------------+
| speed dist |
|--------------|
1. | 4 2 |
2. | 4 10 |
+--------------+
clear
rcall: st.load(cars)
list in 1/2
+--------------+
| speed dist |
|--------------|
1. | 4 2 |
2. | 4 10 |
+--------------+4.2 在 Stata 中运行 R
本部分以 Kleiber 和 Zeileis (2008) 的Grunfeld.dta数据集为例，分别使用 Stata 和 R 软件进行固定效应分析。对于面板数据的理解，请参考 Stata - 面板数据模型-一文读懂。
clear
webuse grunfeld,clear //利用webuse从网络读取数据
list in 1/10 // 显示该数据集的前10行
+--------------------------------------------------+
| company year invest mvalue kstock time |
|--------------------------------------------------|
1. | 1 1935 317.6 3078.5 2.8 1 |
2. | 1 1936 391.8 4661.7 52.6 2 |
3. | 1 1937 410.6 5387.1 156.9 3 |
4. | 1 1938 257.7 2792.2 209.2 4 |
5. | 1 1939 330.8 4313.2 203.4 5 |
|--------------------------------------------------|
6. | 1 1940 461.2 4643.9 207.2 6 |
7. | 1 1941 512 4551.2 255.2 7 |
8. | 1 1942 448 3244.1 303.7 8 |
9. | 1 1943 499.6 4053.7 264.1 9 |
10. | 1 1944 547.5 4379.3 201.6 10 |
+--------------------------------------------------+
xtset company year,yearly //设置面板数据格式
panel variable: company (strongly balanced)
time variable: year, 1935 to 1954
delta: 1 year
xtreg invest mvalue kstock ,fe //fe表示固定效应
Fixed-effects (within) regression Number of obs = 200
Group variable: company Number of groups = 10
R-sq: Obs per group:
within = 0.7668 min = 20
between = 0.8194 avg = 20.0
overall = 0.8060 max = 20
F(2,188) = 309.01
corr(u_i, Xb) = -0.1517 Prob > F = 0.0000
------------------------------------------------------------------------------
invest | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mvalue | .1101238 .0118567 9.29 0.000 .0867345 .1335131
kstock | .3100653 .0173545 17.87 0.000 .2758308 .3442999
_cons | -58.74393 12.45369 -4.72 0.000 -83.31086 -34.177
-------------+----------------------------------------------------------------
sigma_u | 85.732501
sigma_e | 52.767964
rho | .72525012 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(9, 188) = 49.18 Prob > F = 0.0000
rcall:install.packages("plm",repos ="http://cran.us.r-project.org")
package 'plm' successfully unpacked and MD5 sums checked
The downloaded binary packages are in C:\Users\Administrator\AppData\Local\Temp\RtmpwjNECq\downloaded_packages
rcall:library(plm)
rcall:data("Grunfeld", package="plm")
rcall:head(Grunfeld,10)
firm year inv value capital
1 1 1935 317.6 3078.5 2.8
2 1 1936 391.8 4661.7 52.6
3 1 1937 410.6 5387.1 156.9
4 1 1938 257.7 2792.2 209.2
5 1 1939 330.8 4313.2 203.4
6 1 1940 461.2 4643.9 207.2
7 1 1941 512.0 4551.2 255.2
8 1 1942 448.0 3244.1 303.7
9 1 1943 499.6 4053.7 264.1
10 1 1944 547.5 4379.3 201.6
rcall:zz rcall:summary(zz)
Oneway (individual) effect Within Model
Call:
plm(formula = inv ~ value + capital, data = Grunfeld, index = c("firm"))
Balanced Panel: n = 10, T = 20, N = 200
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-184.00857 -17.64316 0.56337 19.19222 250.70974
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
value 0.110124 0.011857 9.2879 < 2.2e-16 ***
capital 0.310065 0.017355 17.8666 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 2244400
Residual Sum of Squares: 523480
R-Squared: 0.76676
Adj. R-Squared: 0.75311
F-statistic: 309.014 on 2 and 188 DF, p-value: < 2.22e-16
.
end of do-file
clear
webuse grunfeld,clear //利用webuse从网络读取数据
list in 1/10 // 显示该数据集的前10行
+--------------------------------------------------+
| company year invest mvalue kstock time |
|--------------------------------------------------|
1. | 1 1935 317.6 3078.5 2.8 1 |
2. | 1 1936 391.8 4661.7 52.6 2 |
3. | 1 1937 410.6 5387.1 156.9 3 |
4. | 1 1938 257.7 2792.2 209.2 4 |
5. | 1 1939 330.8 4313.2 203.4 5 |
|--------------------------------------------------|
6. | 1 1940 461.2 4643.9 207.2 6 |
7. | 1 1941 512 4551.2 255.2 7 |
8. | 1 1942 448 3244.1 303.7 8 |
9. | 1 1943 499.6 4053.7 264.1 9 |
10. | 1 1944 547.5 4379.3 201.6 10 |
+--------------------------------------------------+
xtset company year,yearly //设置面板数据格式
panel variable: company (strongly balanced)
time variable: year, 1935 to 1954
delta: 1 year
xtreg invest mvalue kstock ,fe //fe表示固定效应
Fixed-effects (within) regression Number of obs = 200
Group variable: company Number of groups = 10
R-sq: Obs per group:
within = 0.7668 min = 20
between = 0.8194 avg = 20.0
overall = 0.8060 max = 20
F(2,188) = 309.01
corr(u_i, Xb) = -0.1517 Prob > F = 0.0000
------------------------------------------------------------------------------
invest | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
mvalue | .1101238 .0118567 9.29 0.000 .0867345 .1335131
kstock | .3100653 .0173545 17.87 0.000 .2758308 .3442999
_cons | -58.74393 12.45369 -4.72 0.000 -83.31086 -34.177
-------------+----------------------------------------------------------------
sigma_u | 85.732501
sigma_e | 52.767964
rho | .72525012 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(9, 188) = 49.18 Prob > F = 0.0000
rcall:install.packages("plm",repos ="http://cran.us.r-project.org")
package 'plm' successfully unpacked and MD5 sums checked
The downloaded binary packages are in C:\Users\Administrator\AppData\Local\Temp\RtmpwjNECq\downloaded_packages
rcall:library(plm)
rcall:data("Grunfeld", package="plm")
rcall:head(Grunfeld,10)
firm year inv value capital
1 1 1935 317.6 3078.5 2.8
2 1 1936 391.8 4661.7 52.6
3 1 1937 410.6 5387.1 156.9
4 1 1938 257.7 2792.2 209.2
5 1 1939 330.8 4313.2 203.4
6 1 1940 461.2 4643.9 207.2
7 1 1941 512.0 4551.2 255.2
8 1 1942 448.0 3244.1 303.7
9 1 1943 499.6 4053.7 264.1
10 1 1944 547.5 4379.3 201.6
rcall:zz rcall:summary(zz)
Oneway (individual) effect Within Model
Call:
plm(formula = inv ~ value + capital, data = Grunfeld, index = c("firm"))
Balanced Panel: n = 10, T = 20, N = 200
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-184.00857 -17.64316 0.56337 19.19222 250.70974
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
value 0.110124 0.011857 9.2879 < 2.2e-16 ***
capital 0.310065 0.017355 17.8666 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 2244400
Residual Sum of Squares: 523480
R-Squared: 0.76676
Adj. R-Squared: 0.75311
F-statistic: 309.014 on 2 and 188 DF, p-value: < 2.22e-16
.
end of do-file也可以利用 R 通过利用write.csv()函数直接将数据转换为 .csv格式的数据储存，再导入 Stata 进行面板数据回归进一步与 R 的回归结果进行对比。使用 R 软件 (panel linear model, plm) 包自带数据集 Produc (Munnell, 1990) 。
rcall:install.packages("plm")
rcall:library(plm)
rcall:data(Produc,package="plm")
rcall:aa rcall:summary(aa)
Oneway (individual) effect Within Model
Call:
plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state", "year"))
Balanced Panel: n = 48, T = 17, N = 816
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-0.120456 -0.023741 -0.002041 0.018144 0.174718
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
log(pcap) -0.02614965 0.02900158 -0.9017 0.3675
log(pc) 0.29200693 0.02511967 11.6246 < 2.2e-16 ***
log(emp) 0.76815947 0.03009174 25.5273 < 2.2e-16 ***
unemp -0.00529774 0.00098873 -5.3582 1.114e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 18.941
Residual Sum of Squares: 1.1112
R-Squared: 0.94134
Adj. R-Squared: 0.93742
F-statistic: 3064.81 on 4 and 764 DF, p-value: < 2.22e-16
rcall:library(plm)
rcall:data(Produc,package="plm")
rcall:write.csv(Produc,file="D:/produc.csv")
import delimited "D:\produc.csv", encoding(ISO-8859-9) clear
(12 vars, 816 obs)
encode state, gen (id)
gen lgsp = log(gsp)
gen lpcap = log(pcap)
gen lpc = log(pc)
gen lemp = log(emp)
xtset id year
panel variable: id (strongly balanced)
time variable: year, 1970 to 1986
delta: 1 unit
xtreg lgsp lpcap lpc lemp unemp ,fe
Fixed-effects (within) regression Number of obs = 816
Group variable: id Number of groups = 48
R-sq: Obs per group:
within = 0.9413 min = 17
between = 0.9921 avg = 17.0
overall = 0.9910 max = 17
F(4,764) = 3064.81
corr(u_i, Xb) = 0.0608 Prob > F = 0.0000
------------------------------------------------------------------------------
lgsp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lpcap | -.0261493 .0290016 -0.90 0.368 -.0830815 .0307829
lpc | .2920067 .0251197 11.62 0.000 .2426949 .3413185
lemp | .7681595 .0300917 25.53 0.000 .7090872 .8272318
unemp | -.0052977 .0009887 -5.36 0.000 -.0072387 -.0033568
_cons | 2.352898 .1748131 13.46 0.000 2.009727 2.696069
-------------+----------------------------------------------------------------
sigma_u | .09057293
sigma_e | .03813705
rho | .8494045 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(47, 764) = 75.82 Prob > F = 0.0000
.
end of do-file
rcall:install.packages("plm")
rcall:library(plm)
rcall:data(Produc,package="plm")
rcall:aa rcall:summary(aa)
Oneway (individual) effect Within Model
Call:
plm(formula = log(gsp) ~ log(pcap) + log(pc) + log(emp) + unemp,
data = Produc, index = c("state", "year"))
Balanced Panel: n = 48, T = 17, N = 816
Residuals:
Min. 1st Qu. Median 3rd Qu. Max.
-0.120456 -0.023741 -0.002041 0.018144 0.174718
Coefficients:
Estimate Std. Error t-value Pr(>|t|)
log(pcap) -0.02614965 0.02900158 -0.9017 0.3675
log(pc) 0.29200693 0.02511967 11.6246 < 2.2e-16 ***
log(emp) 0.76815947 0.03009174 25.5273 < 2.2e-16 ***
unemp -0.00529774 0.00098873 -5.3582 1.114e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Total Sum of Squares: 18.941
Residual Sum of Squares: 1.1112
R-Squared: 0.94134
Adj. R-Squared: 0.93742
F-statistic: 3064.81 on 4 and 764 DF, p-value: < 2.22e-16
rcall:library(plm)
rcall:data(Produc,package="plm")
rcall:write.csv(Produc,file="D:/produc.csv")
import delimited "D:\produc.csv", encoding(ISO-8859-9) clear
(12 vars, 816 obs)
encode state, gen (id)
gen lgsp = log(gsp)
gen lpcap = log(pcap)
gen lpc = log(pc)
gen lemp = log(emp)
xtset id year
panel variable: id (strongly balanced)
time variable: year, 1970 to 1986
delta: 1 unit
xtreg lgsp lpcap lpc lemp unemp ,fe
Fixed-effects (within) regression Number of obs = 816
Group variable: id Number of groups = 48
R-sq: Obs per group:
within = 0.9413 min = 17
between = 0.9921 avg = 17.0
overall = 0.9910 max = 17
F(4,764) = 3064.81
corr(u_i, Xb) = 0.0608 Prob > F = 0.0000
------------------------------------------------------------------------------
lgsp | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
lpcap | -.0261493 .0290016 -0.90 0.368 -.0830815 .0307829
lpc | .2920067 .0251197 11.62 0.000 .2426949 .3413185
lemp | .7681595 .0300917 25.53 0.000 .7090872 .8272318
unemp | -.0052977 .0009887 -5.36 0.000 -.0072387 -.0033568
_cons | 2.352898 .1748131 13.46 0.000 2.009727 2.696069
-------------+----------------------------------------------------------------
sigma_u | .09057293
sigma_e | .03813705
rho | .8494045 (fraction of variance due to u_i)
------------------------------------------------------------------------------
F test that all u_i=0: F(47, 764) = 75.82 Prob > F = 0.0000
.
end of do-file4.3 拓展：在 R 中运行Stata
> install.packages("RStata") ##在 R 中安装包“RStata”，首次运行需要选择合适镜像地址
将程序包安装入‘D:/Documents/R/win-library/4.0’
(因为‘lib’没有被指定)
--- 在此連線階段时请选用CRAN的鏡子 ---
试开URL’https://mirrors.tongji.edu.cn/CRAN/bin/windows/contrib/4.0/RStata_1.1.1.zip'
Content type 'application/zip' length 19796 bytes (19 KB)
downloaded 19 KB
程序包‘RStata’打开成功，MD5和检查也通过
下载的二进制程序包在
C:\Users\Administrator\AppData\Local\Temp\RtmpY1ixWU\downloaded_packages里
> library(RStata) ##加载包
> chooseStataBin() ##找出stata具体位置确定打开并复制显示路径
[1] "\"D:\\Stata16\\StataMP-64\""
> options("RStata.StataPath" = "\"D:\\Stata16\\StataMP-64\"")
> options("RStata.StataVersion" = 16) ##设置版本
> stata_src + sysuse auto, clear
+ reg mpg weight
+ "
> stata(stata_src)
.
. sysuse auto, clear
(1978 Automobile Data)
. reg mpg weight
Source | SS df MS Number of obs = 74
-------------+---------------------------------- F(1, 72) = 134.62
Model | 1591.9902 1 1591.9902 Prob > F = 0.0000
Residual | 851.469256 72 11.8259619 R-squared = 0.6515
-------------+---------------------------------- Adj R-squared = 0.6467
Total | 2443.45946 73 33.4720474 Root MSE = 3.4389
------------------------------------------------------------------------------
mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight | -.0060087 .0005179 -11.60 0.000 -.0070411 -.0049763
_cons | 39.44028 1.614003 24.44 0.000 36.22283 42.65774
------------------------------------------------------------------------------
>
> install.packages("RStata") ##在 R 中安装包“RStata”，首次运行需要选择合适镜像地址
将程序包安装入‘D:/Documents/R/win-library/4.0’
(因为‘lib’没有被指定)
--- 在此連線階段时请选用CRAN的鏡子 ---
试开URL’https://mirrors.tongji.edu.cn/CRAN/bin/windows/contrib/4.0/RStata_1.1.1.zip'
Content type 'application/zip' length 19796 bytes (19 KB)
downloaded 19 KB
程序包‘RStata’打开成功，MD5和检查也通过
下载的二进制程序包在
C:\Users\Administrator\AppData\Local\Temp\RtmpY1ixWU\downloaded_packages里
> library(RStata) ##加载包
> chooseStataBin() ##找出stata具体位置确定打开并复制显示路径
[1] "\"D:\\Stata16\\StataMP-64\""
> options("RStata.StataPath" = "\"D:\\Stata16\\StataMP-64\"")
> options("RStata.StataVersion" = 16) ##设置版本
> stata_src + sysuse auto, clear
+ reg mpg weight
+ "
> stata(stata_src)
.
. sysuse auto, clear
(1978 Automobile Data)
. reg mpg weight
Source | SS df MS Number of obs = 74
-------------+---------------------------------- F(1, 72) = 134.62
Model | 1591.9902 1 1591.9902 Prob > F = 0.0000
Residual | 851.469256 72 11.8259619 R-squared = 0.6515
-------------+---------------------------------- Adj R-squared = 0.6467
Total | 2443.45946 73 33.4720474 Root MSE = 3.4389
------------------------------------------------------------------------------
mpg | Coef. Std. Err. t P>|t| [95% Conf. Interval]
-------------+----------------------------------------------------------------
weight | -.0060087 .0005179 -11.60 0.000 -.0070411 -.0049763
_cons | 39.44028 1.614003 24.44 0.000 36.22283 42.65774
------------------------------------------------------------------------------
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