Extract School-Level Covariates

Description

Given a vector of variables of interest for students in a single school, extracts a single value for the school

Usage

agg(x)

Arguments

Details

If the input is numeric, agg returns the mean; if the input is not numeric, an error will be thrown unless all values are the same, in which case the single unique value will be returned.

Value

A single value of the same type as the input vector.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

Collect Matched Samples

Description

After students and schools have both been matched separately, assembles the matched student samples corresponding to the school match into a single dataframe of student-level data.

Usage

assembleMatch(student.matches, school.match, school.id, treatment)

Arguments

Value

a dataframe containing the full set of matched samples for the multilevel match.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

Performs balance checking after multilevel matching.

Description

This function checks balance after multilevel balance. It checks balance on both level-one (student) and level-two (school) covariates.

Usage

balanceMulti(
  match.obj,
  student.cov = NULL,
  school.cov = NULL,
  include.tests = TRUE,
  single.table = FALSE
)

Arguments

Details

This function returns a list which include balance checks for before and after matching for both level-one and level-two covariates. Balance statistics include treated and control means, standardized differences, which is the difference in means divided by the pooled standard deviation before matching, and p-values for mean differences. It extracts the matched data and calls 'balanceTable' for student and school level covariates.

Value

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu Sam Pimentel, University of Pennsylvania, spi@wharton.upenn.edu

See Also

See also matchMulti, matchMultisens, matchMultioutcome, rematchSchools

Examples

	## Not run: 
# Load Catholic school data
data(catholic_schools)

student.cov <- c('minority','female','ses','mathach')

# Check balance student balance before matching
balanceTable(catholic_schools[c(student.cov,'sector')],  treatment = 'sector')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov)

## End(Not run)

Create Balance Table

Description

Given an unmatched sample of treated and control units and (optionally) a matched sample from the same data, produces a table with pre- and post-match measures of covariate balance.

Usage

balanceTable(
  df.orig,
  df.match = NULL,
  treatment,
  school.id = NULL,
  var.names = NULL,
  include.tests = FALSE,
  verbose = FALSE
)

Arguments

Details

This table can also include p-values for tests of whether the balance is statistically significant. These tests assume randomization at the cluster level. We recommend looking at the standardized differences rather than p-values to assess severity of imbalance, however.

The two tests, for each covariate are (1) Aggregation, where the covariates are averaged by each cluster, followed by a heteroskedastic robust t-test on the coefficient of a regression of these averages onto treatment (and intercept) and (2) cluster robust standard errors for the coefficient of treatment on a regression of covariate onto treatment (and intercept).

Value

A data.frame of balance measures, with one row for each covariate in df.orig except treatment, and columns for treated and control means, standardized differences in means, p-values from two types of regression for difference in the groups. See description for further details. If df.match is specified there are twice as many columns, one set for the pre-match samples and one set for the post-match samples.

References

Rosenbaum, Paul R. (2002). Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Construct propensity score caliper

Description

Fits a propensity score for an individual-level or group-level treatment, computes a caliper for the propensity score (based on a fractional number of standard deviations provided by the user), and creates a matrix containing information about which treated-control pairings are excluded by the caliper.

Usage

buildCaliper(data, treatment, ps.vars, group.id = NULL, caliper = 0.2)

Arguments

Details

The treatment variable should be binary with 1 indicating treated units and 0 indicating controls. When group.id is NULL, treatment is assumed to be at the individual level and the propensity score is fitted using the matrix data. When a group ID is specified, data frame data is first aggregated into groups, with variables in ps.vars replaced by their within-group means, and the propensity score is fitted on the group matrix.

Value

A matrix with nrow equal to the number of treated individuals or groups and ncol equal to the number of control individuals, with 0 entries indicating pairings permitted by the caliper and Inf entries indicating forbidden pairings.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

Examples

	## Not run: 
# Load Catholic school data
data(catholic_schools)

student.cov <- c('minority','female','ses','mathach')

# Check balance student balance before matching
balanceTable(catholic_schools[c(student.cov,'sector')],  treatment = 'sector')

#fit a propensity score caliper on mean values of student covariates within schools
school.caliper <- buildCaliper(data = catholic_schools, treatment = 'sector',
	ps.vars = student.cov, group.id = 'school')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector', 
	school.caliper = school.caliper, school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov)

## End(Not run)

1980 and 1982 High School and Beyond Data

Description

These data are a subset of the data used in Raudenbush and Bryk (1999) for multilevel modeling.

Format

A data.frame with 1595 observations on the following variables.

school: unique school level identifier

ses: student level socio-economic status scale ranges from approx. -3.578 to 2.692

mathach: senior year mathematics test score, outcome measure

female: student level indicator for sex

minority: student level indicator for minority

minority_mean: school level measure of percentage of student body that is minority

female_mean: school level measure of percentage of student body that is female

ses_mean: school level measure of average level of student socio-economic status

sector: treatment indicator 1 if catholic 0 if public

size: school level measure of total number of enrolled students

acad: school level measure of the percentage of students on the academic track

discrm: school level measure of disciplinary climate ranges from approx. -2.4 to 2.7

size_large: school level indicator for schools with more than 1000 students

minority_mean_large: school level indicator for schools with more than ten percent minority

Source

Raudenbush, S. W. and Bryk, A. (2002). Hierarchical Linear Models: Applications and Data Analysis Methods. Thousand Oaks, CA: Sage.

References

United States Department of Education. National Center for Education Statistics. High School and Beyond, 1980: Sophomore and Senior Cohort First Follow-Up (1982).

Outcome analysis.

Description

Calculates confidence interval via grid search.

Usage

ci_func(
  beta,
  obj,
  out.name = NULL,
  schl_id_name = NULL,
  treat.name = NULL,
  alpha,
  alternative = "less"
)

Arguments

Value

The endpoint of an estimated confidence interval.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

References

Rosenbaum, Paul R. (2002). Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Print out summary of student and school counts

Description

Given a school ID and treatment variable, count up number of schools and students, print out a summary of the counts of students and schools.

Usage

describe_data_counts(data, school.id, treatment)

Arguments

Value

List of three numbers, # control, # Tx, # Total

See Also

tally_schools

Optimal Subset Matching without Balance Constraints

Description

Conducts optimal subset matching as described in the reference.

Usage

elastic(mdist, n = 0, val = 0)

pairmatchelastic(mdist, n = 0, val = 0)

Arguments

Details

pairmatchelastic is the main function, which conducts an entire match. elastic is a helper function which augments the original distance matrix as described in the reference.

The original versions of these functions were written by Paul Rosenbaum and distributed in the supplemental material to the paper: "Optimal Matching of an Optimally Chosen Subset in Observational Studies," Paul R. Rosenbaum, Journal of Computational and Graphical Statistics, Vol. 21, Iss. 1, 2012.

Value

elastic returns an augmented version of the input matrix mdist. pairmatchelastic returns a matrix of 1 column whose values are the column numbers of matched controls and whose rownames are the row numbers of matched treated units.

Author(s)

Paul R. Rosenbaum (original forms), modifications by Luke Keele and Sam Pimentel

References

Rosenbaum, Paul R. (2012) "Optimal Matching of an Optimally Chosen Subset in Observational Studies." Journal of Computational and Graphical Statistics, 21.1, 57-71.

Handle Missing Values

Description

Preprocesses a dataframe of matching covariates so the Mahalanobis distance can be calculated.

Usage

handleNA(X, verbose = FALSE)

Arguments

Details

Preprocessing involves three main steps: (1) converting factors to matrices of dummy variables (2) for any variable with NAs, adding an additional binary variable indicating whether it is missing (3) imputing all NAs with the column mean. This follows the recommendations of Rosenbaum in section 9.4 of the referenced text.

Value

a matrix containing the preprocessed data.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

References

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Check if a variable is binary

Description

Examines a vector that is not coded as a logical to see if it contains only 0s and 1s.

Usage

is.binary(x)

Arguments

Value

a logical value, TRUE if the vector contains only 0s and 1s and FALSE otherwise.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

Compute School Distance from a Student Match

Description

Defines a distance between two schools whose students have been matched based on the size of the resulting matched sample and on the student-level covariate balance.

Usage

match2distance(
  matchFrame,
  treatFrame,
  ctrlFrame,
  student.vars,
  treatment,
  largeval
)

Arguments

Details

The distance is computed by (1) subtracting the harmonic mean of the treated and control counts in the matched sample from largeval (2) adding largeval for each covariate among studentvars that has an absolute standardized difference exceeding 0.2. This encourages the school match to choose larger schools with better balance.

Value

a numeric distance.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

A function that performs multilevel matching.

Description

This is the workhorse function in the package which matches groups and units within groups. For example, it will match both schools and students in schools, where the goal is to make units more comparable to estimate treatment effects.

Usage

matchMulti(
  data,
  treatment,
  school.id,
  match.students = TRUE,
  student.vars = NULL,
  school.caliper = NULL,
  school.fb = NULL,
  verbose = FALSE,
  keep.target = NULL,
  student.penalty.qtile = 0.05,
  min.keep.pctg = 0.8,
  school.penalty = NULL,
  save.first.stage = TRUE,
  tol = 10,
  solver = "rlemon"
)

Arguments

Details

matchMulti first matches students (or other individual units) within each pairwise combination of schools (or other groups); based on these matches a distance matrix is generated for the schools. Then schools are matched on this distance matrix and the student matches for the selected school pairs are combined into a single matched sample.

School covariates are not used to compute the distance matrix for schools (since it is generated from the student match). Instead imbalances in school covariates should be addressed through theschool.fb argument, which encodes a refined covariate balance constraint. School covariates in school.fb should be given in order of priority for balance, since the matching algorithm optimally balances the variables in the first list element, then attempts to further balance the those in the second element, and so on.

Value

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

See Also

See also matchMulti, matchMultisens, balanceMulti, matchMultioutcome, rematchSchools

Examples

#toy example with short runtime
library(matchMulti)

#Load Catholic school data
data(catholic_schools)

# Trim data to speed up example
catholic_schools <- catholic_schools[catholic_schools$female_mean >.45 &
 catholic_schools$female_mean < .60,]

#match on a single covariate
student.cov <- c('minority')

 match.simple <- 
matchMulti(catholic_schools, treatment = 'sector',
                             school.id = 'school', match.students = FALSE,
                             student.vars = student.cov, verbose=TRUE, tol=.01)

#Check balance after matching - this checks both student and school balance
  balanceMulti(match.simple, student.cov = student.cov)


## Not run: 
#larger example
data(catholic_schools)

student.cov <- c('minority','female','ses')

# Check balance student balance before matching
balanceTable(catholic_schools[c(student.cov,'sector')],  treatment = 'sector')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov)

#Estimate treatment effect
output <- matchMultioutcome(match.simple, out.name = "mathach",
schl_id_name = "school",  treat.name = "sector")

# Perform sensitivity analysis using Rosenbaum bound -- increase Gamma to increase effect of
# possible hidden confounder
matchMultisens(match.simple, out.name = "mathach",
          schl_id_name = "school",
          treat.name = "sector", Gamma = 1.3)


# Now match both schools and students within schools
match.out <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = TRUE, student.vars = student.cov)

# Check balance again
bal.tab <- balanceMulti(match.out, student.cov = student.cov)

# Now match with fine balance constraints on whether the school is large
# or has a high percentage of minority students
match.fb <- matchMulti(catholic_schools, treatment = 'sector', school.id = 'school',
match.students = TRUE, student.vars = student.cov,
school.fb = list( c('size_large'), c('minority_mean_large') )

# Estimate treatment effects
matchMultioutcome(match.fb, out.name = "mathach", schl_id_name = "school",  treat.name = "sector")

#Check Balance
balanceMulti(match.fb, student.cov = student.cov)


## End(Not run)

matchMultiResult object for results of power calculations

Description

The matchMultiResult object is an S3 class that holds the results from the matchMulti call.

matchMulti result objects have the matched datasets inside of them.

Usage

is.matchMultiResult(x)

## S3 method for class 'matchMultiResult'
print(x, ...)

## S3 method for class 'matchMultiResult'
summary(object, ...)

Arguments

Value

is.matchMultiResult: TRUE if object is a matchMultiResult object.

Performs an outcome analysis after multilevel matching.

Description

This function returns a point estimate, 95% confidence interval, and p-values for the matched multilevel data. All results are based on randomization inference.

Usage

matchMultioutcome(
  obj,
  out.name = NULL,
  schl_id_name = NULL,
  treat.name = NULL,
  end.1 = -1000,
  end.2 = 1000
)

Arguments

Details

It may be necessary to adjust the lower and upper bounds if one expects the treatment effect confidence interval to be outside the range of -1000 or 1000.

Value

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

References

Rosenbaum, Paul R. (2002) Observational Studies. Springer-Verlag.

See Also

See Also as matchMulti, matchMultisens

Examples

	## Not run: 
# Load Catholic school data
data(catholic_schools)

student.cov <- c('minority','female','ses','mathach')

# Check balance student balance before matching
balanceTable(catholic_schools[c(student.cov,'sector')],  treatment = 'sector')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov)

#Estimate treatment effect
output <- matchMultioutcome(match.simple, out.name = "mathach",
schl_id_name = "school",  treat.name = "sector")

  
## End(Not run)

Rosenbaum Bounds after Multilevel Matching

Description

Function to calculate Rosenbaum bounds for continuous outcomes after multilevel matching.

Usage

matchMultisens(
  obj,
  out.name = NULL,
  schl_id_name = NULL,
  treat.name = NULL,
  Gamma = 1
)

Arguments

Details

This function returns a single p-value, but actually conducts two tests. The first assumes that the treatment effect does not vary with cluster size. The second allows the treatment effect to vary with cluster size. The function returns a single p-value that is corrected for multiple testing. This p-value is the upper bound for a single Gamma value

Value

Author(s)

Luke Keele, University of Pennsylvania, luke.keele@gmail.com

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

References

Rosenbaum, Paul R. (2002) Observational Studies. Springer-Verlag.

See Also

See Also as matchMulti, matchMultioutcome

Examples

## Not run: 
# Load Catholic school data
data(catholic_schools)

student.cov <- c('minority','female','ses','mathach')

# Check balance student balance before matching
balanceTable(catholic_schools[c(student.cov,'sector')],  treatment = 'sector')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov)

#Estimate treatment effect
output <- matchMultioutcome(match.simple, out.name = "mathach",
schl_id_name = "school",  treat.name = "sector")

# Perform sensitivity analysis using Rosenbaum bound -- increase Gamma to increase effect of
# possible hidden confounder 
         
matchMultisens(match.simple, out.name = "mathach",
          schl_id_name = "school", 
          treat.name = "sector", Gamma=1.3)
          
          
## End(Not run)

Match Schools on Student-based Distance

Description

Takes in a school distance matrix created using information from the first-stage student match and matches schools optimally, potentially

Usage

matchSchools(
  dmat,
  students,
  treatment,
  school.id,
  school.fb,
  penalty,
  verbose,
  tol,
  solver = "rlemon"
)

Arguments

Details

The school.fb argument encodes a refined covariate balance constraint: the matching algorithm optimally balances the interaction of the variables in the first list element, then attempts to further balance the interaction in the second element, and so on. As such variables should be added in order of priority for balance.

Value

a dataframe with two columns, one containing treated school IDs and the other containing matched control school IDs.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

Compute Student Matches for all Pairs of Schools

Description

Iterates over all possible treated-control school pairs, optionally computes and stores an optimal student match for each one, and generates a distance matrix for schools based on the quality of each student match.

Usage

matchStudents(
  students,
  treatment,
  school.id,
  match.students,
  student.vars,
  school.caliper = NULL,
  verbose,
  penalty.qtile,
  min.keep.pctg
)

Arguments

Details

The penalty.qtile and min.keep.pctg control the rate at which students are trimmed from the match. If the quantile is high enough no students should be excluded in any match; if the quantile is very low the min.keep.pctg can still ensure a minimal sample size in each match.

Value

A list with two elements:

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

Mini-data set for illustration

Description

The Catholic schools dataset subset to a smaller number of schools (with only 6 Catholic schools). See full dataset documentation for more information.

Format

A data frame with 1500 rows and 12 variables, as described in the 'catholic_schools' dataset.

Source

See documentation page for 'catholic_schools' dataset.

See Also

catholic_schools

Outcome analysis.

Description

Calculates Hodges-Lehmann point estimate for treatment effect.

Usage

pe_func(beta, obj, out.name = NULL, schl_id_name = NULL, treat.name = NULL)

Arguments

Value

A point estimate for constant-additive treatment effect.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

References

Rosenbaum, Paul R. (2002). Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Outcome analysis.

Description

Calcualtes p-values for test of sharp null for treatment effect.

Usage

pval_func(
  obj,
  out.name = NULL,
  schl_id_name = NULL,
  treat.name = NULL,
  wt = TRUE
)

Arguments

Value

A p-value for constant-additive treatment effect.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

References

Rosenbaum, Paul R. (2002). Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Repeat School Match Only

Description

After matchMulti has been called, repeats the school match (with possibly different parameters) without repeating the more computationally intensive student match.

Usage

rematchSchools(
  match.out,
  students,
  school.fb = NULL,
  verbose = FALSE,
  keep.target = NULL,
  school.penalty = NULL,
  tol = 0.001
)

Arguments

Details

The school.fb argument encodes a refined covariate balance constraint: the matching algorithm optimally balances the interaction of the variables in the first list element, then attempts to further balance the interaction in the second element, and so on. As such variables should be added in order of priority for balance.

The keep.target and school.penalty parameters allow optimal subset matching within the school match. When the keep.target argument is specified, the school match is repeated for different values of the school.penalty parameter in a form of binary search until an optimal match is obtained with the desired number of treated schools or a stopping rule is reached. The tol parameter controls the stopping rule; smaller values provide a stronger guarantee of obtaining the exact number of treated schools desired but may lead to greater computational costs.

It is not recommended that users specify the school.penalty parameter directly in most cases. Instead the keep.target parameter provides an easier way to consider excluding schools.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

References

Rosenbaum, Paul R. (2002). Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2012) "Optimal Matching of an Optimally Chosen Subset in Observational Studies." Journal of Computational and Graphical Statistics, 21.1, 57-71.

See Also

matchMulti.

Examples

## Not run: 
# Load Catholic school data
data(catholic_schools)

student.cov <- c('minority','female','ses')
school.cov <- c('minority_mean','female_mean', 'ses_mean', 'size', 'acad')

#Match schools but not students within schools
match.simple <- matchMulti(catholic_schools, treatment = 'sector',
school.id = 'school', match.students = FALSE)

#Check balance after matching - this checks both student and school balance
balanceMulti(match.simple, student.cov = student.cov, school.cov = school.cov)

#now rematch excluding 2 schools
match.trimmed <- rematchSchools(match.simple, catholic_schools, keep.target = 13)
match.trimmed$dropped$schools.t

## End(Not run)

Ensure Dataframes Share Same Set Columns

Description

Takes in two dataframes. For each column name that is in the second frame but not in the first frame, a new column of zeroes is added to the first frame.

Usage

resolve.cols(df1, df2)

Arguments

Value

a dataframe

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

Balance Measures

Description

Balance assessment for individual variables, before and after matching

Usage

sdiff(varname, treatment, orig.data, match.data = NULL)

Arguments

Details

The sdiff function computes the standardized difference in means. The other functions perform different kinds of balance tests: t.balance does the 2-sample t-test, fisher.balance does Fisher's exact test for binary variable, and wilc.balance does Wilcoxon's signed rank test.

Value

a labeled vector. For sdiff, the vector has six elements if match.data is provided: treated and control means and standardized differences before and after matching. If match.data is not provided, the vector has only the three elements corresponding to the pre-match case.

For the other functions, if match.data is provided, the vector contains p-values for the test before and after matching. Otherwise a single p-value is given for the pre-match data.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

References

Rosenbaum, Paul R. (2002). Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Robust Mahalanobis Distance

Description

Computes robust Mahalanobis distance between treated and control units.

Usage

smahal(z, X)

Arguments

Details

For an explanation of the robust Mahalanobis distance, see section 8.3 of the first reference. This function was written by Paul Rosenbaum and distributed in the supplemental material to the second reference.

Value

a matrix of robust Mahalanobis distances, with a row for each treated unit and a column for each control.

Author(s)

Paul R. Rosenbaum.

References

Rosenbaum, Paul R. (2010). Design of Observational Studies. Springer-Verlag.

Rosenbaum, Paul R. (2012) "Optimal Matching of an Optimally Chosen Subset in Observational Studies." Journal of Computational and Graphical Statistics, 21.1, 57-71.

Aggregate Student Data into School Data

Description

Takes a dataframe of student-level covariates and aggregates selected columns into a dataframe of school covariates.

Usage

students2schools(students, school.cov, school.id)

Arguments

Details

Aggregation is either done by taking averages or by selecting the unique factor value when a school has only one value for a factor. As a result, school.covs should only include variables that are numeric or do not vary within schools.

Value

a dataframe of aggregated data, with one row for each school and columns in school.covs and school.id.

Author(s)

Luke Keele, Penn State University, ljk20@psu.edu

Sam Pimentel, University of California, Berkeley, spi@berkeley.edu

Tally schools and students in a given dataset

Description

Returns a count of schools, without printing anything.

Usage

tally_schools(data, school.id, treatment)

Arguments

Value

List of two things: school and student counts (invisible).

Author(s)

Luke Miratrix

See Also

describe_data_counts