| Title: | Propensity Score Matching of Non-Binary Treatments |
|---|---|
| Description: | Propensity score matching for non-binary treatments. |
| Authors: | Jason Bryer <[email protected]> |
| Maintainer: | Jason Bryer <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.9.9 |
| Built: | 2024-12-28 04:24:45 UTC |
| Source: | https://github.com/jbryer/trimatch |
This packages provides functions to estimate and visualize propensity score analyses including matching for non-binary treatments.
Jason Bryer [email protected]
PSAgraphics multilevelPSA
This function will convert a list of vectors to a data frame. This function
will handle three different types of lists of vectors. First, if all the elements
in the list are named vectors, the resulting data frame will have have a number
of columns equal to the number of unique names across all vectors. In cases
where some vectors do not have names in other vectors, those values will be
filled with NA.
## S3 method for class 'list' as.data.frame(x, row.names = NULL, optional = FALSE, ...)## S3 method for class 'list' as.data.frame(x, row.names = NULL, optional = FALSE, ...)
x |
a list to convert to a data frame. |
row.names |
a vector equal to |
optional |
not used. |
... |
other parameters passed to |
The second case is when all the vectors are of the same length. In this case,
the resulting data frame is equivalent to applying rbind across all elements.
The third case handled is when there are varying vector lengths and not all the
vectors are named. This condition should be avoided. However, the function will
attempt to convert this list to a data frame. The resulting data frame will have
a number of columns equal to the length of the longest vector. For vectors with
length less than this will fill the row with NAs. Note that this function
will print a warning if this condition occurs.
a data frame.
Jason Bryer [email protected]
http://stackoverflow.com/questions/4227223/r-list-to-data-frame
test1 <- list( c(a='a',b='b',c='c'), c(a='d',b='e',c='f')) as.data.frame(test1) test2 <- list( c('a','b','c'), c(a='d',b='e',c='f')) as.data.frame(test2) test3 <- list('Row1'=c(a='a',b='b',c='c'), 'Row2'=c(var1='d',var2='e',var3='f')) as.data.frame(test3) ## Not run: #This will print a warning. test4 <- list('Row1'=letters[1:5], 'Row2'=letters[1:7], 'Row3'=letters[8:14]) as.data.frame(test4) ## End(Not run) test5 <- list(letters[1:10], letters[11:20]) as.data.frame(test5) ## Not run: #This will throw an error. test6 <- list(list(letters), letters) as.data.frame(test6) ## End(Not run)test1 <- list( c(a='a',b='b',c='c'), c(a='d',b='e',c='f')) as.data.frame(test1) test2 <- list( c('a','b','c'), c(a='d',b='e',c='f')) as.data.frame(test2) test3 <- list('Row1'=c(a='a',b='b',c='c'), 'Row2'=c(var1='d',var2='e',var3='f')) as.data.frame(test3) ## Not run: #This will print a warning. test4 <- list('Row1'=letters[1:5], 'Row2'=letters[1:7], 'Row3'=letters[8:14]) as.data.frame(test4) ## End(Not run) test5 <- list(letters[1:10], letters[11:20]) as.data.frame(test5) ## Not run: #This will throw an error. test6 <- list(list(letters), letters) as.data.frame(test6) ## End(Not run)
If the covariate is numeric, boxplots will be drawn with red points for the mean and green error bars for the standard error. For non-numeric covariates a barplot will be drawn.
balance.plot(x, covar, model, nstrata = attr(attr(tmatch, "triangle.psa"), "nstrata"), label = "Covariate", ylab = "", xlab = NULL, se.ratio = 2, print = TRUE, legend.position = "top", x.axis.labels, x.axis.angle = -45, ...)balance.plot(x, covar, model, nstrata = attr(attr(tmatch, "triangle.psa"), "nstrata"), label = "Covariate", ylab = "", xlab = NULL, se.ratio = 2, print = TRUE, legend.position = "top", x.axis.labels, x.axis.angle = -45, ...)
x |
results from |
covar |
vector of the covariate to check balance of. |
model |
an integer between 1 and 3 indicating from which model the propensity scores will be used. |
nstrata |
number of strata to use. |
label |
label for the legend. |
ylab |
label of the y-axis. |
xlab |
label of the x-axis. |
se.ratio |
a multiplier for how large standard error bars will be. |
print |
print the output if the Freidman Rank Sum Test and repeated measures ANOVA (for continuous variables). |
legend.position |
the position of the legend. See |
x.axis.labels |
labels for the x-axis. |
x.axis.angle |
angle for x-axis labels. |
... |
parameters passed to |
A Friedman rank sum test will be performed for all covariate types, printed,
and stored as an attribute to the returned object named friedman. If
a continuous covariate a repeated measures ANOVA will also be performed, printed,
and returned as an attribute named rmanova.
a ggplot2 figure or a list of ggplot2 figures if covar
is a data frame.
ggplot2 box plot of the differences.A boxplot of differences between each pair of treatments.
boxdiff.plot(tmatch, out, plot.mean = TRUE, ordering = attr(tmatch, "match.order"), ci.width = 0.5)boxdiff.plot(tmatch, out, plot.mean = TRUE, ordering = attr(tmatch, "match.order"), ci.width = 0.5)
tmatch |
the results from |
out |
a vector of the outcome measure of interest. |
plot.mean |
logical indicating whether the means should be plotted. |
ordering |
specify the order for doing the paired analysis, that is
analysis will be conducted as:
|
ci.width |
the width for the confidence intervals. |
a ggplot2 boxplot of the differences.
This function is modified from the cv.bal.psa function in the
PSAgrpahics package.
covariateBalance(covariates, treatment, propensity, strata = NULL, int = NULL, tree = FALSE, minsize = 2, universal.psd = TRUE, trM = 0, absolute.es = TRUE, trt.value = NULL, use.trt.var = FALSE, verbose = FALSE, xlim = NULL, plot.strata = TRUE, ...)covariateBalance(covariates, treatment, propensity, strata = NULL, int = NULL, tree = FALSE, minsize = 2, universal.psd = TRUE, trM = 0, absolute.es = TRUE, trt.value = NULL, use.trt.var = FALSE, verbose = FALSE, xlim = NULL, plot.strata = TRUE, ...)
covariates |
dataframe of interest |
treatment |
binary vector of 0s and 1s (necessarily? what if character, or 1, 2?) |
propensity |
PS scores from some method or other. |
strata |
either a vector of strata number for each row of covariate, or one number n in which case it is attempted to group rows by ps scores into n strata of size approximately 1/n. This does not seem to work well in the case of few specific propensity values, as from a tree. |
int |
either a number m used to divide [0,1] into m equal length subintervals, or a vector of cut points between 0 an 1 defining the subintervals (perhaps as suggested by loess.psa). In either case these subintervals define strata, so strata can be of any size. |
tree |
logical, if unique ps scores are few, as from a recursively partitioned tree, then TRUE will force each ps value to define a stratum. |
minsize |
smallest allowable stratum-treatment size. If violated, strata is removed. |
universal.psd |
If 'TRUE', forces standard deviations used to be unadjusted for stratification. |
trM |
trimming proportion for mean calculations. |
absolute.es |
logical, if 'TRUE' routine uses absolute values of all effect sizes. |
trt.value |
allows user to specify which value is active treatment, if desired. |
use.trt.var |
logical, if true then Rubin-Stuart method using only treatment variance with be used in effect size calculations. |
verbose |
logical, controls output that is visibly returned. |
xlim |
limits for the x-axis. |
plot.strata |
logical indicating whether to print strata. |
... |
currently unused. |
Note: effect sizes are calculated as treatment 1 - treatment 0, or treatment B - treatment A.
Robert M. Pruzek [email protected]
James E. Helmreich [email protected]
KuangNan Xiong [email protected]
This function will convert a list of vectors to a data frame. This function
will handle three different types of lists of vectors. First, if all the elements
in the list are named vectors, the resulting data frame will have have a number
of columns equal to the number of unique names across all vectors. In cases
where some vectors do not have names in other vectors, those values will be
filled with NA.
data.frame.to.list(...)data.frame.to.list(...)
... |
other parameters passed to |
The second case is when all the vectors are of the same length. In this case,
the resulting data frame is equivalent to applying rbind across all elements.
The third case handled is when there are varying vector lengths and not all the
vectors are named. This condition should be avoided. However, the function will
attempt to convert this list to a data frame. The resulting data frame will have
a number of columns equal to the length of the longest vector. For vectors with
length less than this will fill the row with NAs. Note that this function
will print a warning if this condition occurs.
a data frame.
http://stackoverflow.com/questions/4227223/r-list-to-data-frame
This method uses a simple Euclidean distance calculation for determining the distances between two matches. That is, |ps1 - ps2|.
distance.euclid(x, grouping, id, groups, caliper, nmatch = Inf)distance.euclid(x, grouping, id, groups, caliper, nmatch = Inf)
x |
vector of propensity scores. |
grouping |
vector or factor identifying group membership. |
id |
vector corresponding to unique identifer for each element in
|
groups |
vector of length two indicating the unique groups to calculate the distance between. The first element will be the rows, the second columns. |
caliper |
a scaler indicating the caliper to use for matching within each step. |
nmatch |
number of smallest distances to retain. |
a list of length equal to x. Each element of the list is a
named numeric vector where the values correspond to the distance and the
name to the id.
Barplot for the sum of distances.
distances.plot(tmatch, caliper = 0.25, label = FALSE)distances.plot(tmatch, caliper = 0.25, label = FALSE)
tmatch |
the results of |
caliper |
a vector indicating where vertical lines should be drawn as a factor of the standard deviation. Rosenbaum and Rubin (1985) suggested one quarter of one standard deviation. |
label |
label the bars that exceed the minimum caliper. |
triangle.match
This function will create a ggplot2 figure with propensity scores on the
x-axis and the outcome on the y-axis. Three Loess regression lines will be plotted
based upon the propensity scores from model. Since each model produces
propensity scores for two of the three groups, the propensity score for the third
group in each matched triplet will be the mean of the other two. If model
is not specified, the default will be to use the model that estimates the propensity
scores for the first two groups in the matching order.
loess3.plot(tmatch, outcome, model, ylab = "Outcome", plot.connections = FALSE, connections.color = "black", connections.alpha = 0.2, plot.points = geom_point, points.alpha = 0.1, points.palette = "Dark2", ...)loess3.plot(tmatch, outcome, model, ylab = "Outcome", plot.connections = FALSE, connections.color = "black", connections.alpha = 0.2, plot.points = geom_point, points.alpha = 0.1, points.palette = "Dark2", ...)
tmatch |
the results of |
outcome |
a vector representing the outcomes. |
model |
an integer between 1 and 3 indicating from which model the propensity scores will be used. |
ylab |
the label for the y-axis. |
plot.connections |
boolean indicating whether lines will be drawn connecting each matched triplet. |
connections.color |
the line color of connections. |
connections.alpha |
number between 0 and 1 representing the alpha levels for connection lines. |
plot.points |
a |
points.alpha |
number between 0 and 1 representing the alpha level for the points. |
points.palette |
the color palette to use. See |
... |
other parameters passed to |
a ggplot2 figure.
This method will attempt to return enough rows to use each treatment (the first two groups in the matching order) at least once. Assuming treat1 is the first group in the match order and treat2 the second, all duplicate treat1 rows are removed. Next, all treat2 units not in present in after removing duplicate treat1 units are identified. For each of those treat2 units, the matched triplet with the smallest overall distances where treat2 is one of the mathched units is retained.
maximumTreat(tmatch, ...)maximumTreat(tmatch, ...)
tmatch |
initial results from |
... |
currently unused. |
The y parameter should be a subset of the original data used.
## S3 method for class 'triangle.matches' merge(x, y, ...)## S3 method for class 'triangle.matches' merge(x, y, ...)
x |
the result of |
y |
another data frame or vector to merge with. |
... |
unused |
x with the additional column(s) added.
trips.The y parameter should be a subset of the original data used.
## S3 method for class 'triangle.psa' merge(x, y, ...)## S3 method for class 'triangle.psa' merge(x, y, ...)
x |
the result of |
y |
another data frame or vector to merge with. |
... |
unused |
x with the additional column(s) added.
A graphic based upon cv.bal.psa function in the PSAgraphics
package. This graphic plots the effect sizes for multiple covariates before and
after propensity score adjustment.
multibalance.plot(tpsa, tmatch, grid = TRUE, cols)multibalance.plot(tpsa, tmatch, grid = TRUE, cols)
tpsa |
results of |
tmatch |
results of |
grid |
if TRUE, then a grid of three plots for each model will be displayed. |
cols |
character vector of covariates (i.e. column names) from the original data to include in the plot. By default all covariates used in the logistic regression model are used. |
a ggplot2 figure.
This file was originally prepared by Anders Corr ([email protected]) who reports on December 8, 2007 that the resulting numbers closely match with those reported in the published article. It was later modified by Jason Bryer ([email protected]) to an R data object to be included in this package. See http://imai.princeton.edu/research/pscore.html for more information
a data frame with 9,708 observations of 12 variables.
United States Department of Health and Human Services. Agency for Health Care Policy and Research
http://imai.princeton.edu/research/pscore.html
National Center For Health Services Research, 1987. National Medical Expenditure Survey. Methods II. Questionnaires and data collection methods for the household survey and the Survey of American Indians and Alaska Natives. National Center for Health Services Research and Health Technology Assessment.
Imai, K., & van Dyk, D.A. (2004). Causal Inference With General Treatment Regimes: Generalizing the Propensity Score, Journal of the American Statistical Association, 99(467), pp. 854-866.
Elizabeth Johnson, E., Dominici, F., Griswold, M., & Zeger, S.L. (2003). Disease cases and their medical costs attributable to smoking: An analysis of the national medical expenditure survey. Journal of Econometrics, 112.
In this method, M2 corresponds to the number of times a treat1 unit can be
matched with a treat2 unit. The M1 parameter corresponds to the number of
times a treat1 unit can be used in total.
OneToN(tmatch, M1 = 2, M2 = 1, ...)OneToN(tmatch, M1 = 2, M2 = 1, ...)
tmatch |
initial results from |
M1 |
a scaler indicating the number of unique subjects in group one to retain. This applies only to the first group in the matching order. |
M2 |
a scaler indicating the number of unique matches to retain. This applies to the first two groups in the matching order. |
... |
currently unused. |
Creates a ggplot2 figure of a parallel coordinate plot.
parallel.plot(tmatch, outcome)parallel.plot(tmatch, outcome)
tmatch |
results from |
outcome |
vector of the outcome |
Internal method for plotting. Finds a point d distance from x, y
perpPt(x, y, d = 0.05)perpPt(x, y, d = 0.05)
x |
x coordinate |
y |
y coordinate |
d |
the distance |
Prints a grid of balance plots.
## S3 method for class 'balance.plots' plot(x, rows, cols, byrow = TRUE, plot.sequence = seq_along(bplots), ...)## S3 method for class 'balance.plots' plot(x, rows, cols, byrow = TRUE, plot.sequence = seq_along(bplots), ...)
x |
the results of |
rows |
if |
cols |
if |
byrow |
if TRUE (default), plots will be drawn by rows, otherwise by columns. |
plot.sequence |
the sequence (or subset) of plots to draw. |
... |
currently unused. |
This plot function adds a layer to plot.triangle.psa drawing
matched triplets. If p is supplied, this function will simply draw on
top of the pre-existing plot, otherwise plot.triangle.psa will
be called first.
## S3 method for class 'triangle.matches' plot(x, sample = 0.05, rows = sample(nrow(tmatch), nrow(tmatch) * sample), line.color = "black", line.alpha = 0.5, point.color = "black", point.size = 3, p, ...)## S3 method for class 'triangle.matches' plot(x, sample = 0.05, rows = sample(nrow(tmatch), nrow(tmatch) * sample), line.color = "black", line.alpha = 0.5, point.color = "black", point.size = 3, p, ...)
x |
matched triplets from |
sample |
an number between 0 and 1 representing the percentage of matched triplets to draw. |
rows |
an integer vector corresponding to the rows in |
line.color |
the line color. |
line.alpha |
the alpha for the lines. |
point.color |
color of matched triplet points. |
point.size |
point size for matched triplets. |
p |
a ggplot to add the match lines. If NULL, then |
... |
other parameters passed to |
If this function calls plot.triangle.psa, it will only draw
line segments and points for those data rows that were used in the matching
procedure. That is, data elements not matched will be excluded from the
figure. To plot all segments and points regardless if used in matching, set
p = plot(tpsa).
a ggplot2 graphic.
plot.triangle.psa
triangle.match
Triangle plot showing the fitted values (propensity scores) for three different models.
## S3 method for class 'triangle.psa' plot(x, point.alpha = 0.3, point.size = 1.5, legend.title = "Treatment", text.size = 4, draw.edges = FALSE, draw.segments = TRUE, edge.alpha = 0.2, edge.color = "grey", edge.labels = c("Model 1", "Model 2", "Model 3"), sample = c(1), ...)## S3 method for class 'triangle.psa' plot(x, point.alpha = 0.3, point.size = 1.5, legend.title = "Treatment", text.size = 4, draw.edges = FALSE, draw.segments = TRUE, edge.alpha = 0.2, edge.color = "grey", edge.labels = c("Model 1", "Model 2", "Model 3"), sample = c(1), ...)
x |
the results from |
point.alpha |
alpha level for points. |
point.size |
point size. |
legend.title |
title for the legend. |
text.size |
text size. |
draw.edges |
draw edges of the triangle. |
draw.segments |
draw segments connecting points across two models. |
edge.alpha |
alpha level for edges if drawn. |
edge.color |
the color for edges if drawn. |
edge.labels |
the labels to use for each edge of the triangle. |
sample |
a vector of length 1 or 3 representing the sample of points to plot.
The position of each element corresponds to the groups as returned
by |
... |
currently unused. |
ggplot2 figure
triangle.psa
balance.plot for a data frame of covariates.Print the results of balance.plot for a data frame of covariates.
## S3 method for class 'balance.plots' print(x, ...)## S3 method for class 'balance.plots' print(x, ...)
x |
the results of |
... |
parameters passed to |
plot.triangle.psa. The primary purpose is to
suppress the "Removed n rows containing missing values" warning printed
by ggplot2.Print method for plot.triangle.psa. The primary purpose is to
suppress the "Removed n rows containing missing values" warning printed
by ggplot2.
## S3 method for class 'triangle.plot' print(x, ...)## S3 method for class 'triangle.plot' print(x, ...)
x |
a plot from |
... |
other parameters passed to ggplot2. |
summary.triangle.matches.This is an S3 generic function to print the results of
summary.triangle.matches.
## S3 method for class 'trimatch.summary' print(x, ...)## S3 method for class 'trimatch.summary' print(x, ...)
x |
results of |
... |
multiple results of |
Internal method for plotting. Position along the left side segment
segment1(d)segment1(d)
d |
the distance |
Internal method for plotting. Position along the right side segment
segment2(d)segment2(d)
d |
the distance |
Returns the significance level as stars, or NA if a non-numeric value is passed in.
star(x)star(x)
x |
p-value. |
The balance.plot function will create a grid of balance plots
if a data frame is provided. The returned object is a list of ggplot2
figures with the statistical tests (i.e. Friedmen Rank Sum tests and if a
continuous variable, repeated measures ANOVA as well) saved as attributes.
This function will return a data frame combining all of those results.
## S3 method for class 'balance.plots' summary(object, ...)## S3 method for class 'balance.plots' summary(object, ...)
object |
the results of |
... |
currently unused. |
a data frame
If an outcome measure is provided this function will perform a Freidman
Rank Sum Test and repeated measures ANOVA. If either test has a statistically
significant difference (as determined by the value of the p parameter),
a Pairwise Wilcoxon Rank Sum Test will also be provided.
## S3 method for class 'triangle.matches' summary(object, outcome, p = 0.05, ordering = attr(object, "match.order"), ...)## S3 method for class 'triangle.matches' summary(object, outcome, p = 0.05, ordering = attr(object, "match.order"), ...)
object |
result of |
outcome |
vector representing the outcome measure. |
p |
threshold of the p value to perform a |
ordering |
specify the order for doing the paired analysis, that is
analysis will be conducted as:
|
... |
parameters passed to other statistical tests. |
a trimatch.summary object.
friedman.test, ezANOVA,
pairwise.wilcox.test
The trips function estimates three separate logistic regression
models for each pair of groups. This function will print a combined table
of the three summaries.
## S3 method for class 'triangle.psa' summary(object, ...)## S3 method for class 'triangle.psa' summary(object, ...)
object |
the results of |
... |
currently unused. |
Will return as a list and print the percentage of total unmatched rows and percent by treatment.
## S3 method for class 'unmatched' summary(object, digits = 3, ...)## S3 method for class 'unmatched' summary(object, digits = 3, ...)
object |
results of |
digits |
number of digits to print. |
... |
currently unused. |
a list of summary results.
Create matched triplets by minimizing the total distance between matched triplets within a specified caliper.
trimatch(tpsa, caliper = 0.25, nmatch = c(15), match.order, exact, method = maximumTreat, ...)trimatch(tpsa, caliper = 0.25, nmatch = c(15), match.order, exact, method = maximumTreat, ...)
tpsa |
the results from |
caliper |
a vector of length one or three indicating the caliper to use for matching within each step. This is expressed in standardized units such that .25 means that matches must be within .25 of one standard deviation to be kept, otherwise the match is dropped. |
nmatch |
number of closest matches to retain before moving to next edge. This can
be |
match.order |
character vector of length three indicating the order in which the matching algorithm will processes. The default is to use start with the group the middle number of subjects, followed by the smallest, and then the largest. |
exact |
a vector or data frame of representing covariates for exact matching. That is, matched triplets will first be matched exactly on these covariates before evaluating distances. |
method |
This is a function that specifies which matched triplets will be
retained. If |
... |
other parameters passed to |
The trips function will estimate the propensity scores
for three models. This method will then find the best matched triplets based
upon minimizing the summed differences between propensity scores across the
three models. That is, the algorithm works as follows:
The first subject from model 1 is selected.
The nmatch[1] smallest distances are selected using propensity scores from
model 1.
For each of the matches identified, the subjects propensity score from model 2 is retrieved.
The nmatch[2] smallest distances are selected using propensity score from
model 3.
For each of those matches identified, the subjects propensity score from model 2 is retrieved.
The distances is calculated from the first and last subjects propensity scores from model 2.
The three distances are summed.
The triplet with the smallest overall distance is selected and returned.
## Not run: data(turoing) formu <- ~ Gender + Ethnicity + Military + ESL + EdMother + EdFather + Age + Employment + Income + Transfer + GPA tpsa <- trips(tutoring, tutoring$treat, formu) tmatch <- trimatch(tpsa, status=FALSE) ## End(Not run)## Not run: data(turoing) formu <- ~ Gender + Ethnicity + Military + ESL + EdMother + EdFather + Age + Employment + Income + Transfer + GPA tpsa <- trips(tutoring, tutoring$treat, formu) tmatch <- trimatch(tpsa, status=FALSE) ## End(Not run)
Internal method. This version does not use the exact matching. Instead, this function should be called separately for each grouping.
trimatch.apply2(tpsa, caliper, nmatch, match.order, sd1, sd2, sd3)trimatch.apply2(tpsa, caliper, nmatch, match.order, sd1, sd2, sd3)
tpsa |
the results from |
caliper |
a vector of length one or three indicating the caliper to use for matching within each step. This is expressed in standardized units such that .25 means that matches must be within .25 of one standard deviation to be kept, otherwise the match is dropped. |
nmatch |
number of closest matches to retain before moving to next edge. This can
be |
match.order |
character vector of length three indicating the order in which the matching algorithm will processes. The default is to use start with the group the middle number of subjects, followed by the smallest, and then the largest. |
sd1 |
standard deviation for propensity scores from model 1. |
sd2 |
standard deviation for propensity scores from model 2. |
sd3 |
standard deviation for propensity scores from model 3. |
The propensity score is
This function will estimate the propensity scores for each pair of groups (e.g. two treatments and one control).
trips(thedata, treat, formu = ~., groups = unique(treat), nstrata = 5, method = "logistic", ...)trips(thedata, treat, formu = ~., groups = unique(treat), nstrata = 5, method = "logistic", ...)
thedata |
the data frame. |
treat |
vector or factor indicating the treatment/control assignment for
|
formu |
the logistic regression formula. Note that the dependent variable should not be specified and will be modified. |
groups |
a vector of exactly length three corresponding the values in
|
nstrata |
the number of strata marks to plot on the edge. |
method |
the method to use to estimate the propensity scores. Current options are logistic or randomForest. |
... |
other parameters passed to |
## Not run: data(tutoring) formu <- ~ Gender + Ethnicity + Military + ESL + EdMother + EdFather + Age + Employment + Income + Transfer + GPA tpsa <- trips(tutoring, tutoring$treat, formu) head(tpsa) ## End(Not run)## Not run: data(tutoring) formu <- ~ Gender + Ethnicity + Military + ESL + EdMother + EdFather + Age + Employment + Income + Transfer + GPA tpsa <- trips(tutoring, tutoring$treat, formu) head(tpsa) ## End(Not run)
treat Treatment indicator.
Course The course id the student was enrolled in.
Grade The course grade the student earned (4=A, 3=B, 2=C, 1=D, 0=F or W).
Gender Gender of the student.
Ethnicity Ethnicity of the student, either White, Black, or Other.
Military Is the student an active military student.
ESL English second language student.
EdMother Education level of the mother (1 = did not finish high school;
2 = high school grad; 3 = some college; 4 = earned associate degree;
5 = earned baccalaureate degree; 6 = Earned Master's degree; 7 = earned doctorate).
EdFather Education level of the father (levels same as EdMother).
Age Age at the start of the course.
Employment Employment level at college enrollment (1 = No; 2 = part-time;
3 = full-time).
Income Household income level at college enrollment (1 = <25K; 2 = <35K; 3 = <45K;
4 = <55K; 5 = <70K; 6 = <85K; 7 = <100K; 8 = <120K; 9 = >120K).
Transfer Number of transfer credits at the start of the course.
GPA GPA as of the start of the course.
GradeCode Letter grade.
Level Level of the course, either Lower or Upper.
ID Randomly assigned student ID.
a data frame with 17 variables.
trips that were not matched by trimatch.This function returns a subset of trips that were not matched by
trimatch. All data frame methods work with the returned object
but special summary function will provided relevant information.
unmatched(tmatch)unmatched(tmatch)
tmatch |
the results of |
a data frame of unmatched rows.