Nothing
README.md
In konfound: Quantify the Robustness of Causal Inferences
konfound
The goal of konfound is to carry out sensitivity analysis to help
analysts to quantify how robust inferences are to potential sources of
bias. This R package provides tools to carry out sensitivity analysis
as described in Frank, Maroulis, Duong, and Kelcey (2013) based on
Rubin’s (1974) causal model as well as in Frank (2000) based on the
impact threshold for a confounding variable.
Installation
You can install the CRAN version of konfound with:
install.packages("konfound")
You can install the development version from GitHub with:
install.packages("devtools")
devtools::install_github("konfound-project/konfound")
Use of konfound
pkonfound() for published studies
pkonfound(), for published studies, calculates (1) how much bias there
must be in an estimate to invalidate/sustain an inference, and
interprets in terms of how much data would need to be replaced to
nullify an inference (Robustness of an Inference to Replacement, RIR);
(2) the impact of an omitted variable necessary to invalidate/sustain an
inference for a regression coefficient (Impact Threshold of a
Confounding Variable, ITCV). RIR is reported by default. ITCV can be
generated by specifying index = "IT".
library(konfound)
#> Sensitivity analysis as described in Frank,
#> Maroulis, Duong, and Kelcey (2013) and in
#> Frank (2000).
#> For more information visit http://konfound-it.com.
pkonfound(est_eff = 2,
std_err = .4,
n_obs = 100,
n_covariates = 3)
#> Robustness of Inference to Replacement (RIR):
#> RIR = 60
#>
#> To invalidate the inference of an effect using the threshold of 0.794 for
#> statistical significance (with null hypothesis = 0 and alpha = 0.05), 60.295%
#> of the (2) estimate would have to be due to bias. This implies that to
#> invalidate the inference one would expect to have to replace 60 (60.295%)
#> observations with data points for which the effect is 0 (RIR = 60).
#>
#> See Frank et al. (2013) for a description of the method.
#>
#> Citation: Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. (2013).
#> What would it take to change an inference?
#> Using Rubin's causal model to interpret the robustness of causal inferences.
#> Education, Evaluation and Policy Analysis, 35 437-460.
#>
#> Accuracy of results increases with the number of decimals reported.
#> For other forms of output, run
#> ?pkonfound and inspect the to_return argument
#> For models fit in R, consider use of konfound().
pkonfound(est_eff = 2,
std_err = .4,
n_obs = 100,
n_covariates = 3,
index = "IT")
#> Impact Threshold for a Confounding Variable (ITCV):
#>
#> The minimum impact of an omitted variable to invalidate an inference for
#> a null hypothesis of an effect of nu (0) is based on a correlation of 0.566
#> with the outcome and 0.566 with the predictor of interest (conditioning
#> on all observed covariates in the model; signs are interchangeable). This is
#> based on a threshold effect of 0.2 for statistical significance (alpha = 0.05).
#>
#> Correspondingly the impact of an omitted variable (as defined in Frank 2000) must be
#> 0.566 X 0.566 = 0.321 to invalidate an inference for a null hypothesis of an effect of nu (0).
#>
#> For calculation of unconditional ITCV using pkonfound(), additionally include
#> the R2, sdx, and sdy as input, and request raw output.
#>
#> See Frank (2000) for a description of the method.
#>
#> Citation:
#> Frank, K. (2000). Impact of a confounding variable on the inference of a
#> regression coefficient. Sociological Methods and Research, 29 (2), 147-194
#>
#> Accuracy of results increases with the number of decimals reported.
#>
#> The ITCV analysis was originally derived for OLS standard errors. If the
#> standard errors reported in the table were not based on OLS, some caution
#> should be used to interpret the ITCV.
#> For other forms of output, run
#> ?pkonfound and inspect the to_return argument
#> For models fit in R, consider use of konfound().
konfound() for models fit in R
konfound() calculates the robustness of inferences for models fit in
R. For example, below are the coefficients for a linear model fit with
lm() using the built-in dataset mtcars:
m1 <- lm(mpg ~ wt + disp, data = mtcars)
m1
#>
#> Call:
#> lm(formula = mpg ~ wt + disp, data = mtcars)
#>
#> Coefficients:
#> (Intercept) wt disp
#> 34.96055 -3.35083 -0.01772
summary(m1)
#>
#> Call:
#> lm(formula = mpg ~ wt + disp, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.4087 -2.3243 -0.7683 1.7721 6.3484
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 34.96055 2.16454 16.151 4.91e-16 ***
#> wt -3.35082 1.16413 -2.878 0.00743 **
#> disp -0.01773 0.00919 -1.929 0.06362 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 2.917 on 29 degrees of freedom
#> Multiple R-squared: 0.7809, Adjusted R-squared: 0.7658
#> F-statistic: 51.69 on 2 and 29 DF, p-value: 2.744e-10
Sensitivity analysis for the effect for wt on mpg can be carried out
as follows, specifying the fitted model object:
konfound(m1, wt)
#> Robustness of Inference to Replacement (RIR):
#> RIR = 9
#>
#> To invalidate the inference of an effect using the threshold of -2.381 for
#> statistical significance (with null hypothesis = 0 and alpha = 0.05), 28.946%
#> of the (-3.351) estimate would have to be due to bias. This implies that to
#> invalidate the inference one would expect to have to replace 9 (28.946%)
#> observations with data points for which the effect is 0 (RIR = 9).
#>
#> See Frank et al. (2013) for a description of the method.
#>
#> Citation: Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. (2013).
#> What would it take to change an inference?
#> Using Rubin's causal model to interpret the robustness of causal inferences.
#> Education, Evaluation and Policy Analysis, 35 437-460.
#>
#> Accuracy of results increases with the number of decimals reported.
#> NULL
Similar to pkonfound, ITCV can be generated by specifying
index = "IT".
konfound(m1, wt, index = "IT")
#> Impact Threshold for a Confounding Variable (ITCV):
#>
#> The minimum (in absolute value) impact of an omitted variable to invalidate
#> an inference for a null hypothesis of an effect of nu (0) is based on
#> a correlation of -0.425 with the outcome and 0.425 with the predictor of
#> interest (conditioning on all observed covariates in the model; signs are
#> interchangeable). This is based on a threshold effect of -0.355 for statistical
#> significance (alpha = 0.05).
#>
#> Correspondingly the impact of an omitted variable (as defined in Frank 2000) must be
#> -0.425 X 0.425 = -0.18 to invalidate an inference for a null hypothesis of an effect of nu (0).
#>
#> See Frank (2000) for a description of the method.
#>
#> Citation:
#> Frank, K. (2000). Impact of a confounding variable on the inference of a
#> regression coefficient. Sociological Methods and Research, 29 (2), 147-194
#>
#> Accuracy of results increases with the number of decimals reported.
#>
#> The ITCV analysis was originally derived for OLS standard errors. If the
#> standard errors reported in the table were not based on OLS, some caution
#> should be used to interpret the ITCV.
#> NULL
mkonfound for meta-analyses including sensitivity analysis
mkonfound() supports sensitivity that can be compared or synthesized
across multiple analyses. We can use an existing (and built-in) dataset,
such as mkonfound_ex.
mkonfound_ex
#> # A tibble: 30 × 2
#> t df
#> <dbl> <dbl>
#> 1 7.08 178
#> 2 4.13 193
#> 3 1.89 47
#> 4 -4.17 138
#> 5 -1.19 97
#> 6 3.59 87
#> 7 0.282 117
#> 8 2.55 75
#> 9 -4.44 137
#> 10 -2.05 195
#> # ℹ 20 more rows
mkonfound(mkonfound_ex, t, df)
#> # A tibble: 30 × 7
#> t df action inference pct_bias_to_change_i…¹ itcv r_con
#> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 7.08 178 to_invalidate reject_null 68.8 0.378 0.614
#> 2 4.13 193 to_invalidate reject_null 50.6 0.168 0.41
#> 3 1.89 47 to_sustain fail_to_rejec… 5.47 -0.012 0.11
#> 4 -4.17 138 to_invalidate reject_null 50.3 0.202 0.449
#> 5 -1.19 97 to_sustain fail_to_rejec… 39.4 -0.065 0.255
#> 6 3.59 87 to_invalidate reject_null 41.9 0.19 0.436
#> 7 0.282 117 to_sustain fail_to_rejec… 85.5 -0.131 0.361
#> 8 2.55 75 to_invalidate reject_null 20.6 0.075 0.274
#> 9 -4.44 137 to_invalidate reject_null 53.0 0.225 0.475
#> 10 -2.05 195 to_invalidate reject_null 3.51 0.006 0.077
#> # ℹ 20 more rows
#> # ℹ abbreviated name: ¹pct_bias_to_change_inference
Other information
How to learn more about sensitivity analysis
To learn more about sensitivity analysis, please visit:
- The KonFound-It website, with latest news,
links to tools and support
- The Introduction to konfound
vignette,
with detailed information about each of the functions (
pkonfound(),
konfound(), and mkounfound())
- The Konfound-It! interactive web
application, with
links to PowerPoints and key publications
Issues, feature requests, and contributing
We prefer for issues to be filed via GitHub (link to the issues page for
konfound here)
though we also welcome questions or feedback requests via email (see the
DESCRIPTION file).
Contributing guidelines are
here.
Code of Conduct
Please note that the konfound project is released with a Contributor
Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
Try the konfound package in your browser
Any scripts or data that you put into this service are public.
konfound documentation built on Oct. 17, 2024, 5:08 p.m.
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.
konfound
The goal of konfound is to carry out sensitivity analysis to help
analysts to quantify how robust inferences are to potential sources of
bias. This R package provides tools to carry out sensitivity analysis
as described in Frank, Maroulis, Duong, and Kelcey (2013) based on
Rubin’s (1974) causal model as well as in Frank (2000) based on the
impact threshold for a confounding variable.
Installation
You can install the CRAN version of konfound with:
install.packages("konfound")
You can install the development version from GitHub with:
install.packages("devtools")
devtools::install_github("konfound-project/konfound")
Use of konfound
pkonfound() for published studies
pkonfound(), for published studies, calculates (1) how much bias there
must be in an estimate to invalidate/sustain an inference, and
interprets in terms of how much data would need to be replaced to
nullify an inference (Robustness of an Inference to Replacement, RIR);
(2) the impact of an omitted variable necessary to invalidate/sustain an
inference for a regression coefficient (Impact Threshold of a
Confounding Variable, ITCV). RIR is reported by default. ITCV can be
generated by specifying index = "IT".
library(konfound)
#> Sensitivity analysis as described in Frank,
#> Maroulis, Duong, and Kelcey (2013) and in
#> Frank (2000).
#> For more information visit http://konfound-it.com.
pkonfound(est_eff = 2,
std_err = .4,
n_obs = 100,
n_covariates = 3)
#> Robustness of Inference to Replacement (RIR):
#> RIR = 60
#>
#> To invalidate the inference of an effect using the threshold of 0.794 for
#> statistical significance (with null hypothesis = 0 and alpha = 0.05), 60.295%
#> of the (2) estimate would have to be due to bias. This implies that to
#> invalidate the inference one would expect to have to replace 60 (60.295%)
#> observations with data points for which the effect is 0 (RIR = 60).
#>
#> See Frank et al. (2013) for a description of the method.
#>
#> Citation: Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. (2013).
#> What would it take to change an inference?
#> Using Rubin's causal model to interpret the robustness of causal inferences.
#> Education, Evaluation and Policy Analysis, 35 437-460.
#>
#> Accuracy of results increases with the number of decimals reported.
#> For other forms of output, run
#> ?pkonfound and inspect the to_return argument
#> For models fit in R, consider use of konfound().
pkonfound(est_eff = 2,
std_err = .4,
n_obs = 100,
n_covariates = 3,
index = "IT")
#> Impact Threshold for a Confounding Variable (ITCV):
#>
#> The minimum impact of an omitted variable to invalidate an inference for
#> a null hypothesis of an effect of nu (0) is based on a correlation of 0.566
#> with the outcome and 0.566 with the predictor of interest (conditioning
#> on all observed covariates in the model; signs are interchangeable). This is
#> based on a threshold effect of 0.2 for statistical significance (alpha = 0.05).
#>
#> Correspondingly the impact of an omitted variable (as defined in Frank 2000) must be
#> 0.566 X 0.566 = 0.321 to invalidate an inference for a null hypothesis of an effect of nu (0).
#>
#> For calculation of unconditional ITCV using pkonfound(), additionally include
#> the R2, sdx, and sdy as input, and request raw output.
#>
#> See Frank (2000) for a description of the method.
#>
#> Citation:
#> Frank, K. (2000). Impact of a confounding variable on the inference of a
#> regression coefficient. Sociological Methods and Research, 29 (2), 147-194
#>
#> Accuracy of results increases with the number of decimals reported.
#>
#> The ITCV analysis was originally derived for OLS standard errors. If the
#> standard errors reported in the table were not based on OLS, some caution
#> should be used to interpret the ITCV.
#> For other forms of output, run
#> ?pkonfound and inspect the to_return argument
#> For models fit in R, consider use of konfound().
konfound() for models fit in R
konfound() calculates the robustness of inferences for models fit in
R. For example, below are the coefficients for a linear model fit with
lm() using the built-in dataset mtcars:
m1 <- lm(mpg ~ wt + disp, data = mtcars)
m1
#>
#> Call:
#> lm(formula = mpg ~ wt + disp, data = mtcars)
#>
#> Coefficients:
#> (Intercept) wt disp
#> 34.96055 -3.35083 -0.01772
summary(m1)
#>
#> Call:
#> lm(formula = mpg ~ wt + disp, data = mtcars)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -3.4087 -2.3243 -0.7683 1.7721 6.3484
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 34.96055 2.16454 16.151 4.91e-16 ***
#> wt -3.35082 1.16413 -2.878 0.00743 **
#> disp -0.01773 0.00919 -1.929 0.06362 .
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 2.917 on 29 degrees of freedom
#> Multiple R-squared: 0.7809, Adjusted R-squared: 0.7658
#> F-statistic: 51.69 on 2 and 29 DF, p-value: 2.744e-10
Sensitivity analysis for the effect for wt on mpg can be carried out
as follows, specifying the fitted model object:
konfound(m1, wt)
#> Robustness of Inference to Replacement (RIR):
#> RIR = 9
#>
#> To invalidate the inference of an effect using the threshold of -2.381 for
#> statistical significance (with null hypothesis = 0 and alpha = 0.05), 28.946%
#> of the (-3.351) estimate would have to be due to bias. This implies that to
#> invalidate the inference one would expect to have to replace 9 (28.946%)
#> observations with data points for which the effect is 0 (RIR = 9).
#>
#> See Frank et al. (2013) for a description of the method.
#>
#> Citation: Frank, K.A., Maroulis, S., Duong, M., and Kelcey, B. (2013).
#> What would it take to change an inference?
#> Using Rubin's causal model to interpret the robustness of causal inferences.
#> Education, Evaluation and Policy Analysis, 35 437-460.
#>
#> Accuracy of results increases with the number of decimals reported.
#> NULL
Similar to pkonfound, ITCV can be generated by specifying
index = "IT".
konfound(m1, wt, index = "IT")
#> Impact Threshold for a Confounding Variable (ITCV):
#>
#> The minimum (in absolute value) impact of an omitted variable to invalidate
#> an inference for a null hypothesis of an effect of nu (0) is based on
#> a correlation of -0.425 with the outcome and 0.425 with the predictor of
#> interest (conditioning on all observed covariates in the model; signs are
#> interchangeable). This is based on a threshold effect of -0.355 for statistical
#> significance (alpha = 0.05).
#>
#> Correspondingly the impact of an omitted variable (as defined in Frank 2000) must be
#> -0.425 X 0.425 = -0.18 to invalidate an inference for a null hypothesis of an effect of nu (0).
#>
#> See Frank (2000) for a description of the method.
#>
#> Citation:
#> Frank, K. (2000). Impact of a confounding variable on the inference of a
#> regression coefficient. Sociological Methods and Research, 29 (2), 147-194
#>
#> Accuracy of results increases with the number of decimals reported.
#>
#> The ITCV analysis was originally derived for OLS standard errors. If the
#> standard errors reported in the table were not based on OLS, some caution
#> should be used to interpret the ITCV.
#> NULL
mkonfound for meta-analyses including sensitivity analysis
mkonfound() supports sensitivity that can be compared or synthesized
across multiple analyses. We can use an existing (and built-in) dataset,
such as mkonfound_ex.
mkonfound_ex
#> # A tibble: 30 × 2
#> t df
#> <dbl> <dbl>
#> 1 7.08 178
#> 2 4.13 193
#> 3 1.89 47
#> 4 -4.17 138
#> 5 -1.19 97
#> 6 3.59 87
#> 7 0.282 117
#> 8 2.55 75
#> 9 -4.44 137
#> 10 -2.05 195
#> # ℹ 20 more rows
mkonfound(mkonfound_ex, t, df)
#> # A tibble: 30 × 7
#> t df action inference pct_bias_to_change_i…¹ itcv r_con
#> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <dbl>
#> 1 7.08 178 to_invalidate reject_null 68.8 0.378 0.614
#> 2 4.13 193 to_invalidate reject_null 50.6 0.168 0.41
#> 3 1.89 47 to_sustain fail_to_rejec… 5.47 -0.012 0.11
#> 4 -4.17 138 to_invalidate reject_null 50.3 0.202 0.449
#> 5 -1.19 97 to_sustain fail_to_rejec… 39.4 -0.065 0.255
#> 6 3.59 87 to_invalidate reject_null 41.9 0.19 0.436
#> 7 0.282 117 to_sustain fail_to_rejec… 85.5 -0.131 0.361
#> 8 2.55 75 to_invalidate reject_null 20.6 0.075 0.274
#> 9 -4.44 137 to_invalidate reject_null 53.0 0.225 0.475
#> 10 -2.05 195 to_invalidate reject_null 3.51 0.006 0.077
#> # ℹ 20 more rows
#> # ℹ abbreviated name: ¹pct_bias_to_change_inference
Other information
How to learn more about sensitivity analysis
To learn more about sensitivity analysis, please visit:
- The KonFound-It website, with latest news, links to tools and support
- The Introduction to konfound
vignette,
with detailed information about each of the functions (
pkonfound(),konfound(), andmkounfound()) - The Konfound-It! interactive web application, with links to PowerPoints and key publications
Issues, feature requests, and contributing
We prefer for issues to be filed via GitHub (link to the issues page for
konfound here)
though we also welcome questions or feedback requests via email (see the
DESCRIPTION file).
Contributing guidelines are here.
Code of Conduct
Please note that the konfound project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.
Try the konfound package in your browser
Any scripts or data that you put into this service are public.
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.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.