ncor_summary: A Negative Control Outcome Regression for Eliminating...
In yuyy-shandong/NCOR:
Description
Usage
Arguments
Value
Description
A Negative Control Outcome Regression for
Eliminating Unobserved Confounding in Time-series Studies
Usage
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ncor_summary(
data = data,
coef_y3_x = "coef1",
coef_y1_x = "coef2",
se = "se",
boot_no = 1000
)
Arguments
data
an optional data frame containing the variables in the model.
coef_y3_x
the coef of exposure on post-exposure outcome
coef_y1_x
the coef of exposure on pre-exposure outcome
se
the standard error of coef_y3_x
boots_no
the number of bootstrap for estimation
Value
causal the casual effect estimation
lag the lag causal effect estimation
yuyy-shandong/NCOR documentation built on April 26, 2020, 12:44 a.m.
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Description Usage Arguments Value
Description
A Negative Control Outcome Regression for Eliminating Unobserved Confounding in Time-series Studies
Usage
1 2 3 4 5 6 7 | ncor_summary(
data = data,
coef_y3_x = "coef1",
coef_y1_x = "coef2",
se = "se",
boot_no = 1000
)
|
Arguments
data |
an optional data frame containing the variables in the model. |
coef_y3_x |
the coef of exposure on post-exposure outcome |
coef_y1_x |
the coef of exposure on pre-exposure outcome |
se |
the standard error of coef_y3_x |
boots_no |
the number of bootstrap for estimation |
Value
causal the casual effect estimation
lag the lag causal effect estimation
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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