Package 'scipub'

Format simple statistic test results for scientific publication

Description

The apastat function summarizes statistic test results scientific publication. This currently will take stats::t.test, stats::cor.test, or stats::lm results as input. The output is intended to be included as in-text parenthetical statistics in publication.

Usage

apastat(test, roundN = 2, es = c(TRUE, FALSE), ci = c(TRUE, FALSE), var = NULL)

Arguments

test	The stats::t.test, stats::cor.test, or stats::lm object to be formatted.
roundN	The number of decimal places to round all output to (default=2).
es	Include effect side (Cohen's d for t-test or 2-level factor lm variable), default to TRUE.
ci	Include confidence interval of estimate, default to TRUE.
var	Only for lm object, select name of variable to summarize (default=NULL), if NULL, will summarize overall model fit.

Value

Output formatted statistics

Examples

apastat(stats::cor.test(psydat$Age, psydat$Height))
apastat(stats::t.test(Height ~ Sex, data = psydat))
apastat(stats::lm(data = psydat, Height ~ Age + Sex))
apastat(stats::lm(data = psydat, Height ~ Age + Sex), var = "Age")

---

Create correlation table (with stars for significance) for scientific publication

Description

The correltable function can be used to create correlation table (with stars for significance) for scientific publication This is intended to summarize correlations between (vars) from an input dataset (data). Correlations are based on stats::cor, use and method follow from that function. Stars indicate significance: ⁠*p<.05, **p<.01, ***p<.001⁠ For formatting, variables can be renamed, numbers can be rounded, upper or lower triangle only can be selected (or whole matrix), and empty columns/rows can be dropped if using triangles. For more compact columns, variable names can be numbered in the rows and column names will be corresponding numbers. If only cross-correlation between two sets of variables is desired (no correlations within a set of variables), vars2 and var_names can be used. This function will drop any non-numeric variables by default. Requires tidyverse and stats libraries.

Usage

correltable(
  data,
  vars = NULL,
  var_names = vars,
  vars2 = NULL,
  var_names2 = vars2,
  method = c("pearson", "spearman"),
  use = c("pairwise", "complete"),
  round_n = 2,
  tri = c("upper", "lower", "all"),
  cutempty = c(FALSE, TRUE),
  colnum = c(FALSE, TRUE),
  html = c(FALSE, TRUE),
  strata = NULL
)

Arguments

data	The input dataset.
vars	A list of the names of variables to correlate, e.g. c("Age","height","WASI"), if NULL, all variables in data will be used.
var_names	An optional list to rename the vars colnames in the output table, e.g. c("Age (years)","Height (inches)","IQ"). Must match vars in length. If not supplied, vars will be printed as is.
vars2	If cross-correlation between two sets of variables is desired, add a second list of variables to correlate with vars; Overrides tri, cutempty, and colnum.
var_names2	An optional list to rename the vars2 colnames in the output table If not supplied, vars2 will be printed as is.
method	Type of correlation to calculate c("pearson", "spearman"), based on stats::cor, default = "pearson".
use	Use pairwise.complete.obs or restrict to complete cases c("pairwise", "complete"), based on stats::cor, default = "pairwise".
round_n	The number of decimal places to round all output to (default=2).
tri	Select output formatting c("upper", "lower","all"); KEEP the upper triangle, lower triangle, or all values, default ="upper.
cutempty	If keeping only upper/lower triangle with tri, cut empty row/column, default=FALSE.
colnum	For more concise column names, number row names and just use corresponding numbers as column names, default=FALSE, if TRUE overrides cutempty.
html	Format as html in viewer or not (default=F, print in console), needs library(htmlTable) installed.
strata	Split table by a 2-level factor variable with level1 in the upper and level2 in the lower triangle must have 2+ cases per level, cannot be combined with vars2

Value

Output Table 1

Examples

correltable(data = psydat)
correltable(
  data = psydat, vars = c("Age", "Height", "iq"),
  tri = "lower", html = TRUE
)
correltable(
  data = psydat, vars = c("Age", "Height", "iq"),
  tri = "lower", html = TRUE, strata = "Sex"
)
correltable(
  data = psydat, vars = c("Age", "Height", "iq"),
  var_names = c("Age (months)", "Height (inches)", "IQ"),
  tri = "upper", colnum = TRUE, html = TRUE
)
correltable(
  data = psydat, vars = c("Age", "Height", "iq"),
  var_names = c("Age (months)", "Height (inches)", "IQ"),
  vars2 = c("depressT", "anxT"),
  var_names2 = c("Depression T", "Anxiety T"), html = TRUE
)

---

Create Table1 of group summary with stats for scientific publication

Description

The FullTable1 function can be used to create a Table1 for scientific publication. This is intended to summarize demographic and other variables (vars) split by a grouping variable (strata) from an input dataset (data). Continuous variables will be summarized as mean (SD) and tested across groups using t-test or ANOVA (for 3+ level strata). Categorical variables will be summarized as N (%) and tested across groups as chi-squared. Effect sizes for group differences will be calculated as Cohen's d, partial eta-squared, Odds Ratio, Cramer's V depending on the test. Requires tidyverse and stats libraries.

Usage

FullTable1(
  data,
  strata = NULL,
  vars = NULL,
  var_names = vars,
  factor_vars = NULL,
  round_n = 2,
  es_col = c(TRUE, FALSE),
  p_col = c(TRUE, FALSE),
  stars = c("col", "name", "stat", "none"),
  html = c(FALSE, TRUE)
)

Arguments

data	The input dataset (will be converted to tibble).
strata	The grouping variable of interest (converted to factor), if NULL will make one column table.
vars	A list of variables to summarize, e.g. c("Age","sex","WASI").
var_names	An optional list to rename the variable colnames in the output table, e.g. c("Age (years)","Sex","IQ"). Must match vars in length. If not supplied, vars will be printed as is.
factor_vars	An optional list of variables from vars to use as class factor, e.g. c("sex"). Note that any character, factor, or logical class variables will be summarized as categorical by default.
round_n	The number of decimal places to round output to (default=2).
es_col	Include a column for effect size of group difference? (default=T).
p_col	Include a column for p-value of group difference? (default=TRUE).
stars	Where to include stars indicating significance of group differences. Options: "col"=separate column (default), "name"= append to variable name, "stat"= append to group difference statistic, "none" for no stars.
html	Format as html in viewer or not (default=FALSE, print in console), needs library(htmlTable) installed.

Value

Output Table 1

Examples

FullTable1(
  data = psydat,
  vars = c("Age", "Height", "depressT"), strata = "Sex"
)
FullTable1(
  data = psydat,
  vars = c("Age", "Height", "depressT"), strata = "Sex"
)
FullTable1(
  data = psydat, vars = c("Age", "Sex", "Height", "depressT"),
  var_names = c("Age (months)", "Sex", "Height (inches)", "Depression T"),
  strata = "Income", stars = "name", p_col = FALSE
)
tmp <- FullTable1(data = psydat,
  vars = c("Age", "Height", "depressT"), strata = "Sex")
  tmp$caption <- "Write your own caption"
  #print(htmlTable(x$table, useViewer=T, rnames=F,caption=x$caption, pos.caption="bottom"))

---

Create ggplot to display group differences (box+point+hist)

Description

The gg_groupplot function can be used to create group difference plots for scientific publication. This is intended to summarize a continuous outcome (y) based on a factor ('x') from an input dataset (data). The plot will include standard ggplot2::geom_boxplot indicating 25th, median, and 75th percentile for the box and 1.5 * IQR for the whiskers. Outliers are not highlighted. Raw data is displayed with standard ggplot2::geom_point and lateral but not vertical jittering. Histograms are shown with gghalves::geom_half_violin to the right of each boxplot. If meanline = = TRUE (default), gray dots will indicate the mean for each variable (vs. median in boxplot) connected by a gray line. This function will drop any NA values. Requires ggplot2 and gghalves libraries.

Usage

gg_groupplot(data, x, y, meanline = c(TRUE, FALSE))

Arguments

data	The input dataset.
x	The grouping factor, e.g. Sex
y	The numeric outcome variable, e.g. Age
meanline	Optional indicator of means

Value

Output group plot

Examples

gg_groupplot(data = psydat, x = Sex, y = depressT, meanline = TRUE)

---

Create partial correlation table (with stars for significance) for scientific publication

Description

The partial_correltable function can be used to create partial correlation table (with stars for significance) for scientific publication This is intended to summarize partial correlations between (vars) from an input dataset (data), residualizing all vars by partialvars. This function allows for numeric, binary, and factor variables as partialvars. but only numeric vars are used and any non-numeric vars will be dropped. All other flags follow from scipub::correltable. Correlations are based on stats::cor, use and method follow from that function. Stars indicate significance: ⁠*p<.05, **p<.01, ***p<.001⁠ For formatting, variables can be renamed, numbers can be rounded, upper or lower triangle only can be selected (or whole matrix), and empty columns/rows can be dropped if using triangles. For more compact columns, variable names can be numbered in the rows and column names will be corresponding numbers. Requires tidyverse and stats libraries.

Usage

partial_correltable(
  data,
  vars = NULL,
  var_names = vars,
  partialvars = NULL,
  partialvar_names = partialvars,
  method = c("pearson", "spearman"),
  use = c("pairwise", "complete"),
  round_n = 2,
  tri = c("upper", "lower", "all"),
  cutempty = c(FALSE, TRUE),
  colnum = c(FALSE, TRUE),
  html = c(FALSE, TRUE)
)

Arguments

data	The input dataset.
vars	A list of the names of 2+ variables to correlate, e.g. c("Age","height","WASI"). All variables must be numeric.
var_names	An optional list to rename the vars colnames in the output table, e.g. c("Age (years)","Height (inches)","IQ"). Must match vars in length. If not supplied, vars will be printed as is.
partialvars	A list of the names of 1+ variables to partial out, e.g. c("iq","Sex","Income"). Can include numeric, binary, factor variables.
partialvar_names	An optional list to rename the partialvars colnames in the output table, e.g. c("IQ (WASI)","Sex","Income"). Must match partialvar_names in length. If not supplied, partialvar_names will be printed as is.
method	Type of correlation to calculate c("pearson", "spearman"), based on stats::cor, default = "pearson".
use	Use pairwise.complete.obs or restrict to complete cases c("pairwise", "complete"), based on stats::cor, default = "pairwise".
round_n	The number of decimal places to round all output to (default=2).
tri	Select output formatting c("upper", "lower","all"); KEEP the upper triangle, lower triangle, or all values, default ="upper.
cutempty	If keeping only upper/lower triangle with tri, cut empty row/column, default=FALSE.
colnum	For more concise column names, number row names and just use corresponding numbers as column names, default=FALSE, if TRUE overrides cutempty.
html	Format as html in viewer or not (default=F, print in console), needs library(htmlTable) installed.

Value

Output Table 1

Examples

partial_correltable(
  data = psydat, vars = c("Age", "Height", "iq"),
  partialvars = c("Sex", "Income"),
  tri = "lower", html = TRUE
)

partial_correltable(
  data = psydat, vars = c("Age", "Height", "iq"),
  var_names = c("Age (months)", "Height (inches)", "IQ"),
  partialvars = c("Sex", "Income"),
  tri = "upper", colnum = TRUE, html = TRUE
)

partial_correltable(
  data = psydat, vars = c("Age", "Height", "iq"),
  var_names = c("Age (months)", "Height (inches)", "IQ"),
  partialvars = c("anxT"),
  partialvar_names = "Anxiety",
  tri = "all", html = TRUE
)

---

Sample demographic and clinical data for 5,000 children

Description

An example dataset containing demographic and clinical data for 5,000 children. The variables are as follows:

Usage

data(psydat)

Format

A data frame with 5000 rows and 7 variables:

Age

age in months (107.2–136.4)

Sex

biological sex, 4 missing value (M, F)

Income

reported family income, 404 missing values (<50K, >=100K, >=50K&<100K)

Height

height in inches, 7 missing values (36.05–84.51)

iq

cognition test, 179 missing values (34.86–222.99)

depressT

depression symptom severity T-score, 8 missing values (48.53–91.32)

anxT

anxiety symptom severity T-score, 8 missing values (48.76–93,67)

---

Winsorize outliers based on z-score cutoff to next most extreme non-outlier value

Description

The winsorZ function identifies outliers based on Z-score cutoff and replaces with the next most extreme non-outlier value. This involves z-scoring the variable and identifying/replacing any cases beyond the z-score threshold. The winsorZ_find function is an optional companion to flag any Z-score outliers to tally as needed.

Usage

winsorZ(x, zbound = 3)

Arguments

x	The input variable to Winsorize.
zbound	The Z-score cutoff (default=3, i.e. outliers are Z>3 | Z<-3).

Value

Output Winsorized variable

Examples

winsorZ(psydat$iq)
## Not run: 
psydat %>%
  dplyr::select(c(iq, anxT)) %>%
  map(winsorZ)
psydat %>% mutate_at(c("iq", "anxT"), list(~ winsorZ(.)))
psydat %>% mutate_if(is.double, list(~ winsorZ(.)))

## End(Not run)

---

Identify outliers based on z-score cutoff that are Winsorized by the winsorZ function

Description

The winsorZ_find function is an optional companion to the winsorZ function. The winsorZ function identifies Z-score outliers and replaces with the next most extreme non-outlier value. The winsorZ_find function finds/identifies these Z-score outliers (outliers=1, non-outliers=0).

Usage

winsorZ_find(x, zbound = 3)

Arguments

x	The input variable to check for Z-score outliers.
zbound	The Z-score cutoff (default=3, i.e. outliers are Z>3 | Z<-3).

Value

Output logical variable of Z-score outliers

Examples

summary(winsorZ_find(psydat$iq))
## Not run: 
psydat %>% mutate_at(c("iq", "anxT"), list(out = ~ winsorZ_find(.)))

## End(Not run)

---