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| @@ -0,0 +1,3 @@ | ||
| Changes in 0.7.3 | ||
| * Removed 'dots' from argument section of the man page for the 'mlr.match' function. | ||
| * Removed autogenerated 'keyword' lines from man page for 'mlr.power'. |
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,15 +1,15 @@ | ||
| Package: MatchLinReg | ||
| Type: Package | ||
| Title: Combining Matching and Linear Regression for Causal Inference | ||
| Version: 0.7.0 | ||
| Date: 2015-07-11 | ||
| Version: 0.7.3 | ||
| Date: 2020-02-03 | ||
| Author: Alireza S. Mahani, Mansour T.A. Sharabiani | ||
| Maintainer: Alireza S. Mahani <alireza.s.mahani@gmail.com> | ||
| Description: Core functions as well as diagnostic and calibration tools for combining matching and linear regression for causal inference in observational studies. | ||
| License: GPL (>= 2) | ||
| Depends: R (>= 3.1.0) | ||
| Imports: Hmisc, Matching, graphics, stats | ||
| NeedsCompilation: no | ||
| Packaged: 2015-07-12 00:14:58 UTC; amahani | ||
| Repository: CRAN | ||
| Date/Publication: 2015-07-12 11:06:19 | ||
| Packaged: 2020-02-03 17:22:02 UTC; amahani | ||
| Date/Publication: 2020-02-03 18:00:06 UTC |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,88 +1,87 @@ | ||
| \name{mlr.match} | ||
| \alias{mlr.match} | ||
| %- Also NEED an '\alias' for EACH other topic documented here. | ||
| \title{ | ||
| Thin wrapper around \code{Match} function from \code{Matching} package | ||
| } | ||
| \description{ | ||
| Performs propensity score or Mahalanobis matching and return indexes of treatment and control groups. | ||
| } | ||
| \usage{ | ||
| mlr.match(tr, X, psm = TRUE, replace = F, caliper = Inf | ||
| , verbose = TRUE) | ||
| } | ||
| %- maybe also 'usage' for other objects documented here. | ||
| \arguments{ | ||
| \item{tr}{Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE.} | ||
| \item{X}{Covariates used in matching, either directly (Mahalanobis matching) or indirectly (propensity score).} | ||
| \item{psm}{Boolean flag, indicating whether propensity score matching should be used (\code{TRUE}) or Mahalanobis matching (\code{FALSE}).} | ||
| \item{replace}{Boolean flag, indicating whether matching must be done with or without replacement.} | ||
| \item{caliper}{Size of caliper (standardized distance of two observations) used in matching. Treatment and control observations with standardized distance larger than \code{caliper} will not be considered as eligible pairs duing matching.} | ||
| \item{verbose}{Boolean flag, indicating whether size of treatment and control groups before and after matching will be printed.} | ||
| \item{\dots}{Other arguments to be passed to \code{Match} function.} | ||
| } | ||
|
|
||
| \details{ | ||
| For propensity score matching, linear predictors from logistic regression are used (rather than predicted probabilities). | ||
| } | ||
|
|
||
| \value{ | ||
| A vector of matched indexes, containing both treatment and control groups. Also, the following attributes are attached: 1) \code{nt}: size of treatment group, 2) \code{nc}: size of control group, 3) \code{psm.reg}: logistic regression object used in generating propensity scores (\code{NA} if \code{psm} is \code{FALSE}), 4) \code{match.obj}: matching object returned by \code{Match} function. | ||
| } | ||
|
|
||
| %\references{ | ||
| %% ~put references to the literature/web site here ~ | ||
| %} | ||
|
|
||
| \author{ | ||
| Alireza S. Mahani, Mansour T.A. Sharabiani | ||
| } | ||
|
|
||
| %\note{ | ||
| %% ~~further notes~~ | ||
| %} | ||
|
|
||
| %% ~Make other sections like Warning with \section{Warning }{....} ~ | ||
|
|
||
| %\seealso{ | ||
| %% ~~objects to See Also as \code{\link{help}}, ~~~ | ||
| %} | ||
|
|
||
| \examples{ | ||
|
|
||
| data(lalonde) | ||
|
|
||
| tr <- lalonde$treat | ||
| Z.i <- as.matrix(lalonde[, c("age", "educ", "black" | ||
| , "hispan", "married", "nodegree", "re74", "re75")]) | ||
| Z.o <- model.matrix(~ I(age^2) + I(educ^2) + I(re74^2) + I(re75^2) - 1, lalonde) | ||
|
|
||
| # propensity score matching on all covariates | ||
| idx <- mlr.match(tr = tr, X = cbind(Z.i, Z.o), caliper = 1.0, replace = FALSE) | ||
|
|
||
| # improvement in maximum single-covariate bias due to matching | ||
| bias.obj.before <- mlr.bias(tr = tr, Z.i = Z.i, Z.o = Z.o) | ||
| bias.before <- bias.obj.before$subspace$bias | ||
| dir <- bias.obj.before$subspace$dir | ||
| bias.after <- as.numeric(mlr.bias(tr = tr[idx] | ||
| , Z.i = Z.i[idx, ], Z.o = dir[idx], gamma.o = 1.0)$single$bias) | ||
|
|
||
| # percentage bias-squared rediction | ||
| cat("normalized bias - before:", bias.before, "\n") | ||
| cat("normalized bias - after:", bias.after, "\n") | ||
| cat("percentage squared-bias reduction:" | ||
| , (bias.before^2 - bias.after^2)/bias.before^2, "\n") | ||
|
|
||
| # matching with replacement | ||
| idx.wr <- mlr.match(tr = tr, X = cbind(Z.i, Z.o), caliper = 1.0 | ||
| , replace = TRUE) | ||
| bias.after.wr <- as.numeric(mlr.bias(tr = tr | ||
| , Z.i = Z.i, Z.o = dir, gamma.o = 1.0, idx = idx.wr)$single$bias) | ||
| cat("normalized bias - after (with replacement):", bias.after.wr, "\n") | ||
|
|
||
| } | ||
|
|
||
| % Add one or more standard keywords, see file 'KEYWORDS' in the | ||
| % R documentation directory. | ||
| % \keyword{ ~kwd1 } | ||
| % \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line | ||
| \name{mlr.match} | ||
| \alias{mlr.match} | ||
| %- Also NEED an '\alias' for EACH other topic documented here. | ||
| \title{ | ||
| Thin wrapper around \code{Match} function from \code{Matching} package | ||
| } | ||
| \description{ | ||
| Performs propensity score or Mahalanobis matching and return indexes of treatment and control groups. | ||
| } | ||
| \usage{ | ||
| mlr.match(tr, X, psm = TRUE, replace = F, caliper = Inf | ||
| , verbose = TRUE) | ||
| } | ||
| %- maybe also 'usage' for other objects documented here. | ||
| \arguments{ | ||
| \item{tr}{Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE.} | ||
| \item{X}{Covariates used in matching, either directly (Mahalanobis matching) or indirectly (propensity score).} | ||
| \item{psm}{Boolean flag, indicating whether propensity score matching should be used (\code{TRUE}) or Mahalanobis matching (\code{FALSE}).} | ||
| \item{replace}{Boolean flag, indicating whether matching must be done with or without replacement.} | ||
| \item{caliper}{Size of caliper (standardized distance of two observations) used in matching. Treatment and control observations with standardized distance larger than \code{caliper} will not be considered as eligible pairs duing matching.} | ||
| \item{verbose}{Boolean flag, indicating whether size of treatment and control groups before and after matching will be printed.} | ||
| } | ||
|
|
||
| \details{ | ||
| For propensity score matching, linear predictors from logistic regression are used (rather than predicted probabilities). | ||
| } | ||
|
|
||
| \value{ | ||
| A vector of matched indexes, containing both treatment and control groups. Also, the following attributes are attached: 1) \code{nt}: size of treatment group, 2) \code{nc}: size of control group, 3) \code{psm.reg}: logistic regression object used in generating propensity scores (\code{NA} if \code{psm} is \code{FALSE}), 4) \code{match.obj}: matching object returned by \code{Match} function. | ||
| } | ||
|
|
||
| %\references{ | ||
| %% ~put references to the literature/web site here ~ | ||
| %} | ||
|
|
||
| \author{ | ||
| Alireza S. Mahani, Mansour T.A. Sharabiani | ||
| } | ||
|
|
||
| %\note{ | ||
| %% ~~further notes~~ | ||
| %} | ||
|
|
||
| %% ~Make other sections like Warning with \section{Warning }{....} ~ | ||
|
|
||
| %\seealso{ | ||
| %% ~~objects to See Also as \code{\link{help}}, ~~~ | ||
| %} | ||
|
|
||
| \examples{ | ||
|
|
||
| data(lalonde) | ||
|
|
||
| tr <- lalonde$treat | ||
| Z.i <- as.matrix(lalonde[, c("age", "educ", "black" | ||
| , "hispan", "married", "nodegree", "re74", "re75")]) | ||
| Z.o <- model.matrix(~ I(age^2) + I(educ^2) + I(re74^2) + I(re75^2) - 1, lalonde) | ||
|
|
||
| # propensity score matching on all covariates | ||
| idx <- mlr.match(tr = tr, X = cbind(Z.i, Z.o), caliper = 1.0, replace = FALSE) | ||
|
|
||
| # improvement in maximum single-covariate bias due to matching | ||
| bias.obj.before <- mlr.bias(tr = tr, Z.i = Z.i, Z.o = Z.o) | ||
| bias.before <- bias.obj.before$subspace$bias | ||
| dir <- bias.obj.before$subspace$dir | ||
| bias.after <- as.numeric(mlr.bias(tr = tr[idx] | ||
| , Z.i = Z.i[idx, ], Z.o = dir[idx], gamma.o = 1.0)$single$bias) | ||
|
|
||
| # percentage bias-squared rediction | ||
| cat("normalized bias - before:", bias.before, "\n") | ||
| cat("normalized bias - after:", bias.after, "\n") | ||
| cat("percentage squared-bias reduction:" | ||
| , (bias.before^2 - bias.after^2)/bias.before^2, "\n") | ||
|
|
||
| # matching with replacement | ||
| idx.wr <- mlr.match(tr = tr, X = cbind(Z.i, Z.o), caliper = 1.0 | ||
| , replace = TRUE) | ||
| bias.after.wr <- as.numeric(mlr.bias(tr = tr | ||
| , Z.i = Z.i, Z.o = dir, gamma.o = 1.0, idx = idx.wr)$single$bias) | ||
| cat("normalized bias - after (with replacement):", bias.after.wr, "\n") | ||
|
|
||
| } | ||
|
|
||
| % Add one or more standard keywords, see file 'KEYWORDS' in the | ||
| % R documentation directory. | ||
| % \keyword{ ~kwd1 } | ||
| % \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,57 +1,57 @@ | ||
| \name{mlr.power} | ||
| \alias{mlr.power} | ||
| %- Also NEED an '\alias' for EACH other topic documented here. | ||
| \title{ | ||
| Power analysis for causal inference using linear regression | ||
| } | ||
| \description{ | ||
| Monte Carlo based calculation of study power for treatment effect estimation using linear regression on treatment indicator and adjustment covariates. | ||
| } | ||
| \usage{ | ||
| mlr.power(tr, Z.i = NULL, d, sig.level = 0.05, niter = 1000 | ||
| , verbose = FALSE, idx = 1:length(tr), rnd = FALSE) | ||
| } | ||
| %- maybe also 'usage' for other objects documented here. | ||
| \arguments{ | ||
| \item{tr}{Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE.} | ||
| \item{Z.i}{Matrix of adjustment covariates included in linear regression. We must have \code{nrow(Z.i) == length(tr)}.} | ||
| \item{d}{Standardized effect size, equal to treatment effect divided by standard deviation of generative noise.} | ||
| \item{sig.level}{Significance level for rejecting null hypothesis.} | ||
| \item{niter}{Number of Monte Carlo simulations used for calculating power.} | ||
| \item{verbose}{If \code{TRUE}, calculated power is printed.} | ||
| \item{idx}{Subset of observations to use for power calculation.} | ||
| \item{rnd}{Boolean flag. If \code{TRUE}, power is also calculated for random subsampling of observations, using same treatment and control group sizes as indicated by \code{idx}.} | ||
| } | ||
|
|
||
| \details{ | ||
| In each Monte Carlo iteration, response variable is generated from a normal distribution whose mean is equal to \code{d * tr} (other coefficients are assumed to be zero since their value does not affect power calculation), and whose standard deviation is \code{1.0}. Then OLS-based regression is performed on data, and p-value for treatment effect is compared to \code{sig.level}, based on which null hypothesis (no effect) is rejected or accepted. The fraction of iterations where null hypothesis is rejected is taken to be power. Standard error is calculated using a binomial-distribution assumption. | ||
| } | ||
|
|
||
| \value{ | ||
| A numeric vector is returned. If \code{rnd} is \code{FALSE}, meand and standard error of calculated power is returned. If \code{rnd} is \code{TRUE}, mean and standard error of power calculated for random subsampling of observations is returned as well. | ||
| } | ||
|
|
||
| %\references{ | ||
| %% ~put references to the literature/web site here ~ | ||
| %} | ||
|
|
||
| \author{ | ||
| Alireza S. Mahani, Mansour T.A. Sharabiani | ||
| } | ||
|
|
||
| %\note{ | ||
| %% ~~further notes~~ | ||
| %} | ||
|
|
||
| %% ~Make other sections like Warning with \section{Warning }{....} ~ | ||
|
|
||
| %\seealso{ | ||
| %% ~~objects to See Also as \code{\link{help}}, ~~~ | ||
| %} | ||
|
|
||
| %\examples{ | ||
| %} | ||
| % Add one or more standard keywords, see file 'KEYWORDS' in the | ||
| % R documentation directory. | ||
| \keyword{ ~kwd1 } | ||
| \keyword{ ~kwd2 }% __ONLY ONE__ keyword per line | ||
| \name{mlr.power} | ||
| \alias{mlr.power} | ||
| %- Also NEED an '\alias' for EACH other topic documented here. | ||
| \title{ | ||
| Power analysis for causal inference using linear regression | ||
| } | ||
| \description{ | ||
| Monte Carlo based calculation of study power for treatment effect estimation using linear regression on treatment indicator and adjustment covariates. | ||
| } | ||
| \usage{ | ||
| mlr.power(tr, Z.i = NULL, d, sig.level = 0.05, niter = 1000 | ||
| , verbose = FALSE, idx = 1:length(tr), rnd = FALSE) | ||
| } | ||
| %- maybe also 'usage' for other objects documented here. | ||
| \arguments{ | ||
| \item{tr}{Binary treatment indicator vector (1=treatment, 0=control), whose coefficient in the linear regression model is TE.} | ||
| \item{Z.i}{Matrix of adjustment covariates included in linear regression. We must have \code{nrow(Z.i) == length(tr)}.} | ||
| \item{d}{Standardized effect size, equal to treatment effect divided by standard deviation of generative noise.} | ||
| \item{sig.level}{Significance level for rejecting null hypothesis.} | ||
| \item{niter}{Number of Monte Carlo simulations used for calculating power.} | ||
| \item{verbose}{If \code{TRUE}, calculated power is printed.} | ||
| \item{idx}{Subset of observations to use for power calculation.} | ||
| \item{rnd}{Boolean flag. If \code{TRUE}, power is also calculated for random subsampling of observations, using same treatment and control group sizes as indicated by \code{idx}.} | ||
| } | ||
|
|
||
| \details{ | ||
| In each Monte Carlo iteration, response variable is generated from a normal distribution whose mean is equal to \code{d * tr} (other coefficients are assumed to be zero since their value does not affect power calculation), and whose standard deviation is \code{1.0}. Then OLS-based regression is performed on data, and p-value for treatment effect is compared to \code{sig.level}, based on which null hypothesis (no effect) is rejected or accepted. The fraction of iterations where null hypothesis is rejected is taken to be power. Standard error is calculated using a binomial-distribution assumption. | ||
| } | ||
|
|
||
| \value{ | ||
| A numeric vector is returned. If \code{rnd} is \code{FALSE}, meand and standard error of calculated power is returned. If \code{rnd} is \code{TRUE}, mean and standard error of power calculated for random subsampling of observations is returned as well. | ||
| } | ||
|
|
||
| %\references{ | ||
| %% ~put references to the literature/web site here ~ | ||
| %} | ||
|
|
||
| \author{ | ||
| Alireza S. Mahani, Mansour T.A. Sharabiani | ||
| } | ||
|
|
||
| %\note{ | ||
| %% ~~further notes~~ | ||
| %} | ||
|
|
||
| %% ~Make other sections like Warning with \section{Warning }{....} ~ | ||
|
|
||
| %\seealso{ | ||
| %% ~~objects to See Also as \code{\link{help}}, ~~~ | ||
| %} | ||
|
|
||
| %\examples{ | ||
| %} | ||
| % Add one or more standard keywords, see file 'KEYWORDS' in the | ||
| % R documentation directory. | ||
| %\keyword{ ~kwd1 } | ||
| %\keyword{ ~kwd2 }% __ONLY ONE__ keyword per line |