lm_cluster: Fitting Linear Models with Clustered Errors
In leesh0222/sanghoon:
lm_cluster R Documentation
Fitting Linear Models with Clustered Errors
Description
This function calculates standard errors with clustered erros and add them to the linear model fit object with a named vector "se_cluster".
This vector can be used in stargazer.
Usage
lm_cluster(data, formula, cluster, ...)
Examples
## Petersen's data
data("PetersenCL", package = "sandwich")
m <- lm(y ~ x, data = PetersenCL)
## clustered covariances
m1 <- lm_cluster(y ~ x, data = PetersenCL, cluster=firm)
summary(m1)
m1$se_cluster
m1$vcov_cluster
stargazer(m, m1, se=list(NULL, m1$se_cluster))
leesh0222/sanghoon documentation built on Nov. 24, 2024, 12:07 a.m.
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| lm_cluster | R Documentation |
Fitting Linear Models with Clustered Errors
Description
This function calculates standard errors with clustered erros and add them to the linear model fit object with a named vector "se_cluster". This vector can be used in stargazer.
Usage
lm_cluster(data, formula, cluster, ...)
Examples
## Petersen's data
data("PetersenCL", package = "sandwich")
m <- lm(y ~ x, data = PetersenCL)
## clustered covariances
m1 <- lm_cluster(y ~ x, data = PetersenCL, cluster=firm)
summary(m1)
m1$se_cluster
m1$vcov_cluster
stargazer(m, m1, se=list(NULL, m1$se_cluster))
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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