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, ...)results from trimatch.
vector of the covariate to check balance of.
an integer between 1 and 3 indicating from which model the propensity scores will be used.
number of strata to use.
label for the legend.
label of the y-axis.
label of the x-axis.
a multiplier for how large standard error bars will be.
print the output if the Freidman Rank Sum Test and repeated measures ANOVA (for continuous variables).
the position of the legend. See theme.
labels for the x-axis.
angle for x-axis labels.
parameters passed to plot.balance.plots.
a ggplot2 figure or a list of ggplot2 figures if covar
is a data frame.
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.