Movatterモバイル変換

[0]ホーム
URL:

Type:Package
Title:Visualization and Estimation of Effect Sizes
Version:0.3.1
Description:A variety of methods are provided to estimate and visualize distributional differences in terms of effect sizes. Particular emphasis is upon evaluating differences between two or more distributions across the entire scale, rather than at a single point (e.g., differences in means). For example, Probability-Probability (PP) plots display the difference between two or more distributions, matched by their empirical CDFs (see Ho and Reardon, 2012; <doi:10.3102/1076998611411918>), allowing for examinations of where on the scale distributional differences are largest or smallest. The area under the PP curve (AUC) is an effect-size metric, corresponding to the probability that a randomly selected observation from the x-axis distribution will have a higher value than a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributions are split into bins (set by the user) and separate effect sizes (Cohen's d) are produced for each bin - again providing a means to evaluate the consistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. The following effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; <doi:10.3102/1076998609332755>), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified.
Depends:R (≥ 3.1)
Imports:sfsmisc, ggplot2, magrittr, dplyr, rlang, tidyr (≥ 1.0.0),purrr, Hmisc, tibble
URL:https://github.com/datalorax/esvis
BugReports:https://github.com/datalorax/esvis/issues
License:MIT + file LICENSE
LazyData:true
RoxygenNote:7.0.2
Suggests:testthat, viridisLite
NeedsCompilation:no
Packaged:2020-04-30 20:05:17 UTC; daniel
Author:Daniel Anderson [aut, cre]
Maintainer:Daniel Anderson <daniela@uoregon.edu>
Repository:CRAN
Date/Publication:2020-04-30 23:20:02 UTC

esvis: Visualization and Estimation of Effect Sizes

Description

A variety of methods are provided to estimate and visualizedistributional differences in terms of effect sizes. Particular emphasisis upon evaluating differences between two or more distributions acrossthe entire scale, rather than at a single point (e.g., differences inmeans). For example, Probability-Probability (PP) plots display thedifference between two or more distributions, matched by their empiricalCDFs (see Ho and Reardon, 2012; <doi:10.3102/1076998611411918>), allowingfor examinations of where on the scale distributional differences arelargest or smallest. The area under the PP curve (AUC) is an effect-sizemetric, corresponding to the probability that a randomly selectedobservation from the x-axis distribution will have a higher valuethan a randomly selected observation from the y-axis distribution. Binned effect size plots are also available, in which the distributionsare split into bins (set by the user) and separate effect sizes (Cohen'sd) are produced for each bin - again providing a means to evaluate theconsistency (or lack thereof) of the difference between two or more distributions at different points on the scale. Evaluation of empirical CDFs is also provided, with built-in arguments for providing annotations to help evaluate distributional differences at specific points (e.g., semi-transparent shading). All function take a consistent argument structure. Calculation of specific effect sizes is also possible. Thefollowing effect sizes are estimable: (a) Cohen's d, (b) Hedges' g, (c) percentage above a cut, (d) transformed (normalized) percentage above a cut, (e) area under the PP curve, and (f) the V statistic (see Ho, 2009; <doi:10.3102/1076998609332755>), which essentially transforms the area under the curve to standard deviation units. By default, effect sizes are calculated for all possible pairwise comparisons, but a reference group (distribution) can be specified.

Author(s)

Maintainer: Daniel Andersondaniela@uoregon.edu

See Also

Useful links:

Compute the Area Under thepp_plot CurveCalculates the area under thepp curve. The area under the curve is also a useful effect-size like statistic, representing the probability that a randomly selected individual from thex distribution will have a higher value than a randomly selected individual from they distribution.

Description

Compute the Area Under thepp_plot CurveCalculates the area under thepp curve. The area under the curve is also a useful effect-size like statistic, representing the probability that a randomly selected individual from thex distribution will have a higher value than a randomly selected individual from they distribution.

Usage

auc(data, formula, ref_group = NULL, rename = TRUE)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

rename

Used primarily for internal purposes. Should the column names be renamed to reference the focal and reference groups? Defaults toTRUE.

Value

By default the area under the curve for all possible pairings ofthe grouping factor are returned.

Examples

# Calculate AUC for all pairwise comparisonsauc(star, reading ~ condition) # Report only relative to regular-sized classroomsauc(star,     reading ~ condition,     ref_group = "reg")# Report by ELL and FRL groups for each season, compare to non-ELL students# who were not eligible for free or reduced price lunch in the fall (using# the formula interface for reference group referencing).## Not run: auc(benchmarks,       math ~ ell + frl + season,      ref_group = ~`Non-ELL` + `Non-FRL` + Fall)# Same thing but with character vector supplied, rather than a formulaauc(benchmarks,       math ~ ell + frl + season,      ref_group = c("Non-ELL", "Non-FRL", "Fall"))## End(Not run)

Synthetic benchmark screening data

Description

Across the country many schools engage in seasonal benchmark screenings to monitor to progress of their students. These are relatively briefassessments administered to "check-in" on students' progress throughoutthe year. This dataset was simulated from a real dataset from one largeschool district using the terrificsynthpopR package. Overall characteristics of the synthetic data are remarkablysimilar to the real data.

Usage

benchmarks

Format

A data frame with 10240 rows and 9 columns.

sid

Integer. Student identifier.

cohort

Integer. Identifies the cohort from which the student wassampled (1-3).

sped

Character. Special Education status: "Non-Sped" or "Sped"

ethnicity

Character. The race/ethnicity to which the studentidentified. Takes on one of seven values: "Am. Indian", "Asian","Black", "Hispanic", "Native Am.", "Two or More", and "White"

frl

Character. Student's eligibility for free or reduced pricelunch. Takes on the values "FRL" and "Non-FRL".

ell

Character. Students' English language learner status. Takes on one of values: "Active", "Monitor", and "Non-ELL". Studentscoded "Active" were actively receiving English language servicesat the time of testing. Students coded "Monitor" had previously received services, but not at the time of testing. Students coded"Non-ELL" did not receive services at any time.

season

Character. The season during which the assessment wasadministered: "Fall", "Winter", or "Spring"

reading

Integer. Reading scale score.

math

Integer. Mathematics scale score.

Calculate binned effect sizes

Description

Calculate binned effect sizes

Usage

binned_es(  data,  formula,  ref_group = NULL,  qtile_groups = 3,  es = "g",  rename = TRUE)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

qtile_groups

The number of quantile bins to split the data by and calculate effect sizes. Defaults to 3 bins (lower, middle, upper).

es

The effect size to calculate. Currently the only options are "d" or "g".

rename

Logical. Should the column names be relabeled according tothe reference and focal groups. Defaults toTRUE.

Value

A data frame with the corresponding effect sizes.

Quantile-binned effect size plot

Description

Plots the effect size between focal and reference groups by matched (binned) quantiles (i.e., the results frombinned_es), with the matchedquantiles plotted along the x-axis and the effect size plotted along the y-axis. The intent is to examine how (if) the magnitude of the effect sizevaries at different points of the distributions. The mean differences withineach quantile bin are divided by the overall pooled standard deviation for the two groups being compared.

Usage

binned_plot(  data,  formula,  ref_group = NULL,  qtile_groups = 3,  es = "g",  lines = TRUE,  points = TRUE,  shade = TRUE,  shade_alpha = 0.4,  rects = TRUE,  rect_fill = "gray20",  rect_alpha = 0.35,  refline = TRUE,  refline_col = "gray40",  refline_lty = "solid",  refline_lwd = 1.1)

Arguments

data

The data frame to be plotted

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate plots by the secondary ortertiary variable of interest (e.g.,out ~ group + characteristic1 + characteristic2). No more than two additional characteristics can be supplied at this time.

ref_group

Optional character vector (of length 1) naming thereference group. Defaults to the group with the highest mean score.

qtile_groups

The number of quantile bins to split the data by and calculate effect sizes. Defaults to 3 bins (lower, middle, upper).

es

The effect size to plot. Defaults to"g", in which case Hedge's g is plotted, which is better for small samples. At present, the only other option is"d" for Cohen's D.

lines

Logical. Should the PP Lines be plotted? Defaults toTRUE.

points

Logical. Should points be plotted for eachqtiles be plotted? Defaults toTRUE.

shade

Logical. Should the standard errors around the effect size pointestimates be displayed? Defaults toTRUE, with the uncertainty displayed with shading.

shade_alpha

Transparency level of the standard error shading.Defaults to 0.40.

rects

Logical. Should semi-transparent rectangles be plotted in the background to show the binning? Defaults toTRUE.

rect_fill

Color fill of rectangles to be plotted in the background, ifrects == TRUE. Defaults to "gray20".

rect_alpha

Transparency level of the rectangles in the background whenrects == TRUE. Defaults to 0.35.

refline

Logical. Defaults toTRUE. Should a diagonalreference line, representing the point of equal probabilities, be plotted?

refline_col

The color of the reference line. Defaults to"gray40"

refline_lty

Line type of the reference line. Defaults to"solid".

refline_lwd

Line width of the reference line. Defaults to1.1.

Examples

# Binned Effect Size Plot: Defaults to Hedges' Gbinned_plot(star, math ~ condition) # Same plot, separated by sexbinned_plot(star, math ~ condition + sex)# Same plot by sex and race## Not run:   pp_plot(star, math ~ condition + sex + race)## End(Not run)## Evaluate with simulated data: Plot is most interesting when variance# in the distributions being compared differ.library(tidyr)library(ggplot2)# simulate data with different variancesset.seed(100)common_vars <- data.frame(low  = rnorm(1000, 10, 1),                        high = rnorm(1000, 12, 1),                        vars = "common")diff_vars <- data.frame(low  = rnorm(1000, 10, 1),                      high = rnorm(1000, 12, 2),                      vars = "diff")d <- rbind(common_vars, diff_vars)# Plot distributions d <- d %>% gather(group, value, -vars) ggplot(d, aes(value, color = group)) + geom_density() + facet_wrap(~vars)# Note that the difference between the distributions depends on where you're # evaluating from on the x-axis. The binned plot helps us visualize this. # The below shows the binned plots when there is a common versus different# variancebinned_plot(d, value ~ group + vars)

Cohen's d

Description

Wraps the equation into a function

Usage

coh(n1, n2, mn1, mn2, vr1, vr2)

Arguments

n1

The sample size for group 1

n2

The sample size for group 2

mn1

The mean for group 1

mn2

The mean for group 2

vr1

The variance for group 1

vr2

The variance for group 2

Compute Cohen'sd

Description

This function calculates effect sizes in terms of Cohen'sd, alsocalled the uncorrected effect size. Seehedg_g for the samplesize corrected version. Also seeLakens (2013)for a discussion on different types of effect sizes and theirinterpretation. Note that missing data are removed from the calculations of the means and standard deviations.

Usage

coh_d(data, formula, ref_group = NULL, se = TRUE)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

se

Logical. Should the standard error of the effect size be estimated and returned in the resulting data frame? Defaults toTRUE.

Value

By default the Cohen'sd for all possible pairings ofthe grouping factor(s) are returned.

Examples

# Calculate Cohen's d for all pairwise comparisonscoh_d(star, reading ~ condition) # Report only relative to regular-sized classroomscoh_d(star,      reading ~ condition,    ref_group = "reg")# Report by ELL and FRL groups for each season, compare to non-ELL students# who were not eligible for free or reduced price lunch in the fall (using# the formula interface for reference group referencing).coh_d(benchmarks,       math ~ ell + frl + season,      ref_group = ~`Non-ELL` + `Non-FRL` + Fall)# Same thing but with character vector supplied, rather than a formulacoh_d(benchmarks,       math ~ ell + frl + season,      ref_group = c("Non-ELL", "Non-FRL", "Fall"))

Report descriptive stats for all possible pairings on the rhs of the formula.

Description

Report descriptive stats for all possible pairings on the rhs of the formula.

Usage

descrip_stats(data, formula, ..., qtile_groups = NULL)

Arguments

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce descriptive stats by the secondary or tertiary variable of interest (e.g.,out ~ group + characteristic1 + characteristic2).

Computes the empirical cummulative distribution function for all groupssupplied by the formula.

Description

Computes the empirical cummulative distribution function for all groupssupplied by the formula.

Usage

ecdf_fun(data, formula, cuts = NULL)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

cuts

Optional vector of cut scores. If supplied, the ECDF will beguaranteed to include these points. Otherwise, there could be gaps in the ECDF at those particular points (used in plotting the cut scores).

Empirical Cumulative Distribution Plot

Description

This is a wrapper function for thestat_ecdf function and helps make it easy to directly compare distributions at specificlocations along the scale.

Usage

ecdf_plot(  data,  formula,  cuts = NULL,  linewidth = 1.2,  ref_line_cols = "gray40",  ref_linetype = "solid",  center = FALSE,  ref_rect = TRUE,  ref_rect_col = "gray40",  ref_rect_alpha = 0.15)

Arguments

data

A tidy data frame containing the data to be plotted.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate plots by the secondary ortertiary varaible (e.g.,out ~ group + characteristic1 + characteristic2). No more than two additional characteristics can be supplied at this time.

cuts

Optional numeric vector stating the location of reference line(s) and/or rectangle(s).

linewidth

Width of ECDF lines. Note that the color of the lines can be controlled through additional functions (e.g.,scale_color_brewer, scale_color_manual).

ref_line_cols

Optional vector (or single value) of colors forcuts lines.

ref_linetype

Optional vector (or single value) of line types forcuts lines. Takes any of the arguments supplied bylinetype.

center

Logical. Should the functions be centered prior to plotting? Defaults toFALSE. Note that if paneled/faceted plots are produced, the centering occurs by group.

ref_rect

Logical, defaults toTRUE whencuts takes any non-null value. Should semi-transparent rectangle(s) be plotted at the locations ofcuts?

ref_rect_col

Color of the fill for the reference rectangles. Defaults to a dark gray.

ref_rect_alpha

Transparency of the fill for the reference rectangles. Defaults to 0.7.

Examples

ecdf_plot(benchmarks, math ~ ell,           cuts = c(190, 205, 210),           ref_line_cols = c("#D68EE3", "#9BE38E", "#144ECA"))# Customize the plot with ggplot2 functionslibrary(ggplot2)ecdf_plot(benchmarks, math ~ ell,           cuts = c(190, 205, 210),           ref_line_cols = c("#D68EE3", "#9BE38E", "#144ECA")) +  theme_minimal() +  theme(legend.position = "bottom")ecdf_plot(seda, mean ~ grade) +  scale_fill_brewer(palette = "Set2") +  theme_minimal()  # Use within the dplyr pipelinelibrary(dplyr)benchmarks %>%   mutate(season = factor(season,                          levels = c("Fall", "Winter", "Spring"))) %>%   ecdf_plot(math ~ ell + season + frl)

Hedge's g

Description

Wraps the equation into a function

Usage

hedg(n1, n2, d)

Arguments

n1

The sample size for group 1

n2

The sample size for group 2

d

The value of Cohen's d

Compute Hedges'gThis function calculates effect sizes in terms of Hedges'g, alsocalled the corrected (for sample size) effect size. Seecoh_d for the uncorrected version. Also seeLakens (2013)for a discussion on different types of effect sizes and theirinterpretation. Note that missing data are removed from the calculations of the means and standard deviations.

Description

Compute Hedges'gThis function calculates effect sizes in terms of Hedges'g, alsocalled the corrected (for sample size) effect size. Seecoh_d for the uncorrected version. Also seeLakens (2013)for a discussion on different types of effect sizes and theirinterpretation. Note that missing data are removed from the calculations of the means and standard deviations.

Usage

hedg_g(data, formula, ref_group = NULL, keep_d = TRUE)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

keep_d

Logical. Should Cohen'sd be reported along with Hedge'sg? Defaults toTRUE.

Value

By default the Hedges'g for all possible pairings ofthe grouping factor are returned as a tidy data frame.

Examples

# Calculate Hedges' g for all pairwise comparisonshedg_g(star, reading ~ condition) # Report only relative to regular-sized classroomshedg_g(star,        reading ~ condition,        ref_group = "reg")# Report by ELL and FRL groups for each season, compare to non-ELL students# who were not eligible for free or reduced price lunch in the fall (using# the formula interface for reference group referencing).hedg_g(benchmarks,       math ~ ell + frl + season,      ref_group = ~`Non-ELL` + `Non-FRL` + Fall)# Same thing but with character vector supplied, rather than a formulahedg_g(benchmarks,       math ~ ell + frl + season,      ref_group = c("Non-ELL", "Non-FRL", "Fall"))

Compute the proportion above a specific cut location

Description

Computes the proportion of the corresponding group, as specified by theformula, scoring above the specifiedcuts.

Usage

pac(data, formula, cuts, ref_group = NULL)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

cuts

Optional vector of cut scores. If supplied, the ECDF will beguaranteed to include these points. Otherwise, there could be gaps in the ECDF at those particular points (used in plotting the cut scores).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

Value

Tidy data frame of the proportion above the cutoff for each (or selected) groups.

See Also

[esvis::pac_compare(), esvis::tpac(), esvis::tpac_diff()]

Examples

# Compute differences for all pairwise comparisons for each of three cutspac(star,    reading ~ condition, cuts = c(450, 500, 550)) pac(star,    reading ~ condition + freelunch + race,  cuts = c(450, 500))pac(star,    reading ~ condition + freelunch + race,  cuts = c(450, 500), ref_group = ~small + no + white)

Compute the difference in the proportion above a specific cut location

Description

Computes the difference in the proportion above the specifiedcuts for all possible pairwise comparisons of the groups specified by theformula.

Usage

pac_compare(data, formula, cuts, ref_group = NULL)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

cuts

Optional vector of cut scores. If supplied, the ECDF will beguaranteed to include these points. Otherwise, there could be gaps in the ECDF at those particular points (used in plotting the cut scores).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

Value

Tidy data frame of the proportion above the cutoff for each (or selected) groups.

See Also

[esvis::pac(), esvis::tpac(), esvis::tpac_diff()]

Examples

# Compute differences for all pairwise comparisons for each of three cutspac_compare(star,    reading ~ condition,  cuts = c(450, 500, 550))  pac_compare(star,    reading ~ condition + freelunch + race,  cuts = c(450, 500))pac_compare(star,    reading ~ condition + freelunch + race,  cuts = c(450, 500), ref_group = ~small + no + white)

Pairs empirical cummulative distribution functions for all groupssupplied by the formula.

Description

Pairs empirical cummulative distribution functions for all groupssupplied by the formula.

Usage

paired_ecdf(data, formula, cuts = NULL)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

cuts

Optional vector of cut scores. If supplied, the ECDF will beguaranteed to include these points. Otherwise, there could be gaps in the ECDF at those particular points (used in plotting the cut scores).

Produces the paired probability plot for two groups

Description

The paired probability plot maps the probability of obtaining a specificscore for each of two groups. The area under the curve (auc) corresponds to the probability that a randomlyselected observation from the x-axis group will have a higher score thana randomly selected observation from the y-axis group. This functionextends the basic pp-plot by allowing multiple curves and faceting tofacilitate a variety of comparisons. Note that because the plotting isbuilt on top ofggplot2, additional customization can be made on top of the plots, as illustrated in the examples.

Usage

pp_plot(  data,  formula,  ref_group = NULL,  cuts = NULL,  cut_labels = TRUE,  cut_label_x = 0.02,  cut_label_size = 3,  lines = TRUE,  linetype = "solid",  linewidth = 1.1,  shade = TRUE,  shade_alpha = 0.2,  refline = TRUE,  refline_col = "gray40",  refline_type = "dashed",  refline_width = 1.1)

Arguments

data

The data frame to be plotted

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate plots by the secondary ortertiary variable of interest (e.g.,out ~ group + characteristic1 + characteristic2). No more than two additional characteristics can be supplied at this time.

ref_group

Optional character vector (of length 1) naming thereference group. Defaults to the group with the highest mean score.

cuts

Integer. Optional vector (or single number) of scores used to annotate the plot. If supplied, line segments will extend from the corresponding x and y axes and meet at the PP curve.

cut_labels

Logical. Should the reference lines corresponding tocuts be labeled? Defaults toTRUE.

cut_label_x

The x-axis location of the cut labels. Defaults to 0.02.

cut_label_size

The size of the cut labels. Defaults to 3.

lines

Logical. Should the PP Lines be plotted? Defaults toTRUE.

linetype

Thelinetype for the PP lines. Defaults to "solid".

linewidth

The width of the PP lines. Defaults to 1.1 (justmarginally larger than the default ggplot2 lines).

shade

Logical. Should the area under the curve be shaded? Defaults toTRUE.

shade_alpha

Transparency of the shading. Defaults to 0.2.

refline

Logical. Should a diagonal reference line be plotted, representing the value at which no difference is observed between thereference and focal distributions? Defaults toTRUE.

refline_col

Color of the reference line. Defaults to a dark gray.

refline_type

Thelinetype for the reference line.Defaults to "dashed".

refline_width

The width of the reference line. Defaults to 1, or just slightly thinner than the PP lines.

Value

Aggplot2 object displaying the specified PP plot.

Examples

# PP plot examining differences by conditionpp_plot(star, math ~ condition)# The sample size gets very small in the above within cells (e.g., wild # changes within the "other" group in particular). Overall, the effect doesn't# seem to change much by condition.# Look at something a little more interesting## Not run: pp_plot(benchmarks, math ~ ell + season + frl)## End(Not run)# Add some cut scorespp_plot(benchmarks, math ~ ell, cuts = c(190, 210, 215))## Make another interesting plot. Use ggplot to customize## Not run: library(tidyr)library(ggplot2)benchmarks %>%   gather(subject, score, reading, math) %>%   pp_plot(score ~ ell + subject + season,          ref_group = "Non-ELL") +  scale_fill_brewer(name = "ELL Status", palette = "Pastel2") +  scale_color_brewer(name = "ELL Status", palette = "Pastel2") +  labs(title = "Differences among English Language Learning Groups",       subtitle = "Note crossing of reference line") +  theme_minimal()## End(Not run)

Pooled Standard Deviation

Description

The denominator for Cohen's d

Usage

psd(n1, n2, vr1, vr2)

Arguments

n1

The sample size for group 1

n2

The sample size for group 2

vr1

The variance for group 1

vr2

The variance for group 2

Portion of the Stanford Educational Data Archive (SEDA).

Description

The full SEDA dataset contains mean test scores on statewide testing data inreading and math for every school district in the United States. See adescription of the datahere. The data represented in this package represent a random sample of 10cases in the full dataset. To access the full data, please visit the data archive in the above link.

Usage

seda

Format

A data frame with 32625 rows and 8 columns.

leaid

Integer. Local education authority identifier.

leaname

Character. Local education authority name.

stateabb

Character. State abbreviation.

year

Integer. Year the data were collected.

grade

Integer. Grade level the data were collected.

subject

Character. Whether the data were from reading ormathematics.

mean

Double. Mean test score for the LEA in the correspondingsubject/grade/year.

se

Double. Standard error of the mean.

Source

Sean F. Reardon, Demetra Kalogrides, Andrew Ho, Ben Shear, Kenneth Shores,Erin Fahle. (2016). Stanford Education Data Archive.http://purl.stanford.edu/db586ns4974. For more information, please visithttps://edopportunity.org.

Data from the Tennessee class size experiment

Description

These data come from the Ecdat package and represent a cross-section ofdata from Project STAR (Student/Teacher Achievement Ratio), where studentswere randomly assigned to classrooms.

Usage

star

Format

A data frame with 5748 rows and 9 columns.

sid

Integer. Student identifier.

schid

Integer. School identifier.

condition

Character. Classroom type the student was enrolled in (randomly assigned to).

tch_experience

Integer. Number of years of teaching experiencefor the teacher in the classroom in which the student wasenrolled.

sex

Character. Sex of student: "girl" or "boy".

freelunch

Character. Eligibility of the student for free orreduced price lunch: "no" or "yes"

race

Character. The identified race of the student: "white","black", or "other"

math

Integer. Math scale score.

reading

Integer. Reading scale score.

Transformed proportion above the cut

Description

This function transforms calls topac into standard deviation units.Function assumes that each distribution is distributed normally with common variances. SeeHo &Reardon, 2012

Usage

tpac(data, formula, cuts, ref_group = NULL)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

cuts

Optional vector of cut scores. If supplied, the ECDF will beguaranteed to include these points. Otherwise, there could be gaps in the ECDF at those particular points (used in plotting the cut scores).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

Value

Tidy data frame of the proportion above the cutoff for each (or selected) groups.

See Also

[esvis::pac(), esvis::pac_diff(), esvis::tpac_compare()]

Examples

# Compute differences for all pairwise comparisons for each of three cutstpac(star,    reading ~ condition,  cut = c(450, 500, 550))  tpac(star,    reading ~ condition + freelunch + race,  cut = c(450, 500))tpac(star,    reading ~ condition + freelunch + race,  cut = c(450, 500), ref_group = ~small + no + white)

Compare Transformed Proportion Above the Cut

Description

This function compares all possible pairwise comparisons, as supplied byformula, in terms of the transformed proportion above the cut. Thisis an effect-size like measure of the differences between two groups as thecut point(s) in the distribution. SeeHo &Reardon, 2012

Usage

tpac_compare(data, formula, cuts, ref_group = NULL)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

cuts

Optional vector of cut scores. If supplied, the ECDF will beguaranteed to include these points. Otherwise, there could be gaps in the ECDF at those particular points (used in plotting the cut scores).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

Value

Tidy data frame of the proportion above the cutoff for each (or selected) groups.

See Also

[esvis::pac(), esvis::pac_diff(), esvis::tpac()]

Examples

# Compute differences for all pairwise comparisons for each of three cutstpac_compare(star,    reading ~ condition,  cut = c(450, 500, 550))  tpac_compare(star,    reading ~ condition + freelunch + race,  cut = c(450, 500))tpac_compare(star,    reading ~ condition + freelunch + race,  cut = c(450, 500), ref_group = ~small + no + white)

Calculate the V effect size statistic

Description

This function calculates the effect size V, as discussed byHo, 2009. The Vstatistic is a transformation ofauc, interpreted as the average difference between the distributions in standard deviation units.

Usage

v(data, formula, ref_group = NULL)

Arguments

data

The data frame used for estimation - ideally structured in a tidy format.

formula

A formula of the typeout ~ group whereout isthe outcome variable andgroup is the grouping variable. Note thisvariable can include any arbitrary number of groups. Additional variables can be included with+ to produce separate estimates by the secondary or tertiary variables of interest (e.g.,out ~ group + characteristic1 + characteristic2).

ref_group

Optional. A character vector or forumla listing the reference group levels for each variable on the right hand side of the formula, supplied in the same order as the formula. Note that if using theformula version, levels that are numbers, or include hyphens, spaces, etc., should be wrapped in back ticks (e.g.,ref_group = ~ Active + `Non-FRL`, orref_group = ~`8`). When in doubt, it is safest to use the back ticks, as they will not interfere with anything if they are not needed. See examples below for more details.

Value

By default the V statistic for all possible pairings ofthe grouping factor are returned as a tidy data frame. Alternatively, a vector can be returned, and/or only the V corresponding to a specificreference group can be returned.

Examples

# Calculate V for all pairwise comparisonsv(star, reading ~ condition) # Report only relative to regular-sized classroomsv(star,     reading ~ condition,     ref_group = "reg")# Report by ELL and FRL groups for each season, compare to non-ELL students# who were not eligible for free or reduced price lunch in the fall (using# the formula interface for reference group referencing).## Not run: v(benchmarks,       math ~ ell + frl + season,      ref_group = ~`Non-ELL` + `Non-FRL` + Fall)# Same thing but with character vector supplied, rather than a formulav(benchmarks,       math ~ ell + frl + season,      ref_group = c("Non-ELL", "Non-FRL", "Fall"))## End(Not run)
[8]ページ先頭
©2009-2025 Movatter.jp