Package 'ViSe'

Adjust coefficient for confounders

Description

This function calculates the adjusted effect after controlling for confounding effects. You can use d values or standardized regression coefficients.

Usage

adjusted_coef(effect_xz, effect_uxz, effect_d)

Arguments

effect_xz	Effect of x on y given z
effect_uxz	Effect of u on y given x and z
effect_d	Effect size difference of interest

Value

Adjusted effect size of x on y given u and z

Examples

adjusted_coef(effect_xz = .2,
  effect_uxz = .4,
  effect_d = .12)

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APA Format

Description

A function that formats decimals and leading zeroes for creating reports in scientific style.

Usage

apa(value, decimals = 3, leading = TRUE)

Arguments

value	A set of numeric values, either a single number, vector, or set of columns.
decimals	The number of decimal points desired in the output.
leading	Logical value: TRUE for leading zeroes on decimals and FALSE for no leading zeroes on decimals. The default is TRUE.

Details

This function creates "pretty" character vectors from numeric variables for printing as part of a report. The value can take a single number, matrix, vector, or multiple columns from a data frame, as long as they are numeric. The values will be coerced into numeric if they are characters or logical values, but this process may result in an error if values are truly alphabetical.

Value

Returns a nicely formatted character vector for numbers for reporting purposes.

Examples

apa(value = 0.54674, decimals = 3, leading = TRUE)

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$d_s$ for Between Subjects with Pooled SD Denominator

Description

This function displays d for two between subjects groups and gives the central and non-central confidence interval using the pooled standard deviation as the denominator.

Usage

calculate_d(
  m1 = NULL,
  m2 = NULL,
  sd1 = NULL,
  sd2 = NULL,
  n1 = NULL,
  n2 = NULL,
  t = NULL,
  model = NULL,
  df = NULL,
  x_col = NULL,
  y_col = NULL,
  d = NULL,
  a = 0.05,
  lower = TRUE
)

Arguments

m1	mean group one
m2	mean group two
sd1	standard deviation group one
sd2	standard deviation group two
n1	sample size group one
n2	sample size group two
t	optional, calculate d from independent t, you must include n1 and n2 for degrees of freedom
model	optional, calculate d from t.test for independent t, you must still include n1 and n2
df	optional dataframe that includes the x_col and y_col
x_col	name of the column that contains the factor levels OR a numeric vector of group 1 scores
y_col	name of the column that contains the dependent score OR a numeric vector of group 2 scores
d	a previously calculated d value from a study
a	significance level
lower	Use this to indicate if you want the lower or upper bound of d for one sided confidence intervals. If d is positive, you generally want lower = TRUE, while negative d values should enter lower = FALSE for the upper bound that is closer to zero.

Details

To calculate $d_s$, mean two is subtracted from mean one and divided by the pooled standard deviation.

$d_s = \frac{M_1 - M_2}{S_{pooled}}$

You should provide one combination of the following:

1: m1 through n2

2: t, n1, n2

3: model, n1, n2

4: df, "x_col", "y_col"

5: x_col, y_col as numeric vectors

6: d, n1, n2

You must provide alpha and lower to ensure the right confidence interval is provided for you.

Value

Provides the effect size (Cohen's *d*) with associated central and non-central confidence intervals, the *t*-statistic, the confidence intervals associated with the means of each group, as well as the standard deviations and standard errors of the means for each group. The one-tailed confidence interval is also included for sensitivity analyses.

d	effect size
dlow	noncentral lower level confidence interval of d value
dhigh	noncentral upper level confidence interval of d value
dlow_central	central lower level confidence interval of d value
dhigh_central	central upper level confidence interval of d value
done_low	noncentral lower bound of one tailed confidence interval
done_low_central	central lower bound of one tailed confidence interval
M1	mean of group one
sd1	standard deviation of group one mean
se1	standard error of group one mean
M1low	lower level confidence interval of group one mean
M1high	upper level confidence interval of group one mean
M2	mean of group two
sd2	standard deviation of group two mean
se2	standard error of group two mean
M2low	lower level confidence interval of group two mean
M2high	upper level confidence interval of group two mean
spooled	pooled standard deviation
sepooled	pooled standard error
n1	sample size of group one
n2	sample size of group two
df	degrees of freedom (n1 - 1 + n2 - 1)
t	t-statistic
p	p-value
estimate	the d statistic and confidence interval in APA style for markdown printing
statistic	the t-statistic in APA style for markdown printing

Examples

calculate_d(m1 = 14.37, # neglect mean
   sd1 = 10.716, # neglect sd
   n1 = 71, # neglect n
   m2 = 10.69, # none mean
   sd2 = 8.219, # none sd
   n2 = 3653, # none n
   a = .05, # alpha/confidence interval
   lower = TRUE) # lower or upper bound

---

Convert d to Cohen's f

Description

This function allows you to convert d to Cohen's f and $f^2$ statistics.

Usage

d_to_f2(d)

Arguments

d	the effect size to convert

Value

Both Cohen's f and f^2 statistics

f	d values translated into f
f2	d values translated into $f^2$

Examples

d_to_f2(.25)

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Convert d to Number Needed to Treat

Description

This function calculates the number needed to treat from continuous measures (Cohen's d) using Kraemer and Kupfer (2006) formula.

Usage

d_to_nnt(d = NULL)

Arguments

d	the effect size

Value

nnt values from d

References

Kraemer H.C., Kupfer D.J. (2006) Size of treatment effects and their importance to clinical research and practice. Biolological Psychiatry, 59, 990–996. https://doi.org/10.1016/j.biopsych.2005.09.014

Examples

d_to_nnt(d = .25)

---

Convert d to correlation coefficient

Description

This function allows you to convert d to Pearson's correlation coefficient.

Usage

d_to_r(d)

Arguments

d	the effect size to convert

Value

correlation coefficient

Examples

d_to_r(.25)

---

Visualization for Estimating $d_s$

Description

This function displays a visualization of effect sizes.

Usage

estimate_d(
  m1 = NULL,
  m2 = NULL,
  sd1 = NULL,
  sd2 = NULL,
  n1 = NULL,
  n2 = NULL,
  d = NULL,
  fill_1 = "lightblue",
  fill_2 = "pink",
  text_color = "black"
)

Arguments

m1	mean from first group
m2	mean from second group
sd1	standard deviation from first group
sd2	standard deviation from second group
n1	sample size for first group
n2	sample size for the second group
d	estimate of the effect size
fill_1	a color code or name to fill the first distribution
fill_2	a color code or name to fill the second distribution
text_color	a color code or name for the graph text

Value

Returns a pretty graph

d	effect size
graph	A graph of the distributions of the effect size

Examples

estimate_d(d = .25)

estimate_d(m1 = 10, m2 = 8, sd1 = 5, sd2 = 4,
 n1 = 100, n2 = 75)

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Visualization for Estimating $r$

Description

This function displays a visualization of effect sizes.

Usage

estimate_r(r = NULL)

Arguments

r	a correlation to visualize

Value

Returns a pretty graph

graph

A graph of the effect size

Examples

estimate_r(r = .4)

---

Convert other statistics to d

Description

This function allows you to convert other effect sizes to d including f, f squared, number needed to treat, correlation coefficient, probability of superiority, proportion overlap (u1, u2, u3, and proportion distribution overlap). Please note these are approximations.

Usage

other_to_d(
  f = NULL,
  f2 = NULL,
  nnt = NULL,
  r = NULL,
  prob = NULL,
  prop_u1 = NULL,
  prop_u2 = NULL,
  prop_u3 = NULL,
  prop_overlap = NULL
)

Arguments

f	Cohen's f
f2	Cohen's f squared
nnt	Number needed to treat
r	Correlation coefficient
prob	Probability superiority
prop_u1	Proportion Overlap U1
prop_u2	Proportion Overlap U2
prop_u3	Proportion Overlap U3
prop_overlap	Proportion Overlap of Distributions

Value

d effect size

Examples

other_to_d(f = .1)

---

Probability of Superiority Calculation

Description

This function calculates the probability of superiority from independent samples Cohen's d calculation.

Usage

probability_superiority(
  d = NULL,
  m1 = NULL,
  m2 = NULL,
  sd1 = NULL,
  sd2 = NULL,
  n1 = NULL,
  n2 = NULL,
  a = 0.05,
  t = NULL,
  model = NULL,
  df = NULL,
  x_col = NULL,
  y_col = NULL
)

Arguments

d	the effect size
m1	mean group one
m2	mean group two
sd1	standard deviation group one
sd2	standard deviation group two
n1	sample size group one
n2	sample size group two
a	significance level
t	optional, calculate d from independent t, you must include n1 and n2 for degrees of freedom
model	optional, calculate d from t.test for independent t, you must still include n1 and n2
df	optional dataframe that includes the x_col and y_col
x_col	name of the column that contains the factor levels OR a numeric vector of group 1 scores
y_col	name of the column that contains the dependent score OR a numeric vector of group 2 scores

Details

You should provide one combination of the following:

1: d

2: m1 through n2

3: t, n1, n2

4: model, n1, n2

5: df, "x_col", "y_col"

6: x_col, y_col as numeric vectors

Value

The probability of superiority.

Examples

probability_superiority(d = .25)

---

Proportion Overlap Calculations for Cohen's d

Description

This function calculates the proportion overlap from two independent group d effect size calculations. Cohen's u1, u2, u3 and proportion overlap are provided.

Usage

proportion_overlap(
  model = NULL,
  x_col = NULL,
  y_col = NULL,
  df = NULL,
  d = NULL
)

Arguments

model	a saved independent t-test model
x_col	name of the column that contains the factor levels OR a numeric vector of group 1 scores
y_col	name of the column that contains the dependent score OR a numeric vector of group 2 scores
df	optional dataframe that includes the x_col and y_col
d	previously calculated d value

Value

A list of the following:

u1	Proportion of non-overlap across both distributions
u2	Proportion that one group is more than the same proportion in the other group
u3	Proportion of one group that is smaller than the median of the other group
p_o	Proportional overlap of distributions

Examples

proportion_overlap(d = .25)

---

Run Shiny App

Description

This function is a convenience function to help you easily run the shiny app for the package.

Usage

runExample()

Value

Opens the shiny app version of the package to use interactively.

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Visualization for Estimating c Bias

Description

This function displays a visualization of the possible bias c that allows for a non-zero effect in sensitivity.

Usage

visualize_c(dlow, lower = TRUE, ribbon_color = "lightblue")

Arguments

dlow	The lower limit of the possible effect size
lower	Use this to indicate if you want the lower or upper bound of d for one sided confidence intervals. If d is positive, you generally want lower = TRUE, while negative d values should enter lower = FALSE for the upper bound that is closer to zero.
ribbon_color	background coloring for c values that support a non-zero effect in sensitivity

Value

Returns a pretty graph

graph

The graph of possible values for c

Examples

visualize_c(dlow = .25, lower = TRUE)

---

Visualization for Estimating c Bias + Estimates

Description

This function displays a visualization of the possible bias c that allows for a non-zero effect in sensitivity. This function includes the ability to add values of effect size and correlation to see how they map onto the proposed c value.

Usage

visualize_c_map(
  dlow,
  r_values,
  d_values = NULL,
  f_values = NULL,
  f2_values = NULL,
  nnt_values = NULL,
  prob_values = NULL,
  prop_u1_values = NULL,
  prop_u2_values = NULL,
  prop_u3_values = NULL,
  prop_overlap_values = NULL,
  point_colors = c("red", "green", "blue"),
  size = 2,
  shape_1 = 2,
  shape_2 = 3,
  ribbon_color = "lightblue",
  lower = TRUE
)

Arguments

dlow	The lower limit of the possible effect size (required).
r_values	A vector of correlation values that are possible (required).
d_values	A vector of effect size values that are possible.
f_values	A vector of f effect size values that are possible.
f2_values	A vector of f2 effect size values that are possible.
nnt_values	A vector of number needed to treat effect size values that are possible.
prob_values	A vector of probability of superiority effect size values that are possible.
prop_u1_values	A vector of proportion of overlap u1 effect size values that are possible.
prop_u2_values	A vector of proportion of overlap u2 effect size values that are possible.
prop_u3_values	A vector of proportion of overlap u3 effect size values that are possible.
prop_overlap_values	A vector of proportion of distribution overlap effect size values that are possible.
point_colors	A vector of color names or codes to plot the effect sizes on the graph. You should use as many color names/codes as you have max of an effect size (i.e, if r has 4, d has 3, and prob has 5, then use 5 as the max number of colors).
size	The size of the symbols on the chart.
shape_1	a numeric value of one of the ggplot2 shapes
shape_2	a numeric value of one of the ggplot2 shapes - if you use different numbers, the two shapes are overlaid, as we found this effect made it easier to read with many effect sizes plotted on the same graph.
ribbon_color	a color name or code to shade the area that shows a non-zero effect in sensitivity.
lower	Use this to indicate if you want the lower or upper bound of d for one sided confidence intervals. If d is positive, you generally want lower = TRUE, while negative d values should enter lower = FALSE for the upper bound that is closer to zero (required).

Value

Returns a pretty graph of the possible effect size and correlation combinations with the region of effect colored in. Note that all effect sizes are converted to d for the graph.

graph

The graph of possible values for c

Examples

visualize_c_map(dlow = .25,
  d_values = c(.2, .3, .8),
  r_values = c(.1, .4, .3),
  lower = TRUE)

---

Visualization for Conversions of Effect Sizes

Description

This function displays a visualization the same effect in various effect sizes including d, f, $f^2$, proportion overlap, correlation, number needed to treat, and more.

Usage

visualize_effects(
  d,
  circle_color = "lightblue",
  circle_fill = "grey",
  percent_color = "black",
  percent_size = 12,
  text_color = "black",
  font_family = "Times"
)

Arguments

d	d effect size to convert to other numbers
circle_color	a color name or code for the highlighted part of the donut circle
circle_fill	a color name or code for the rest of the circle
percent_color	a color name or code for the text of the effect size
percent_size	a numeric value representing the font size of the larger effect size text inside the circle
text_color	a color name or code that changes the color of the effect size text label
font_family	A font family name for the font of the effect size text label

Value

Returns a pretty graph of all the effects

graph

ggplot object of converted effect sizes

Examples

visualize_effects(d = .25)

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