umxAPA {umx}

Creates nicely formatted journal-style summaries of models, p-values, data-frames and much more.

Description

umxAPA creates APA-style reports from a range of statistical models, or to summarize data. I wrote it to suit me.

Nice alternatives include jtools::summ.

Example functionality includes:

1. Given an stats::lm() model, umxAPA will return a formatted effect, including 95% CI. e.g.: umxAPA(lm(mpg~wt, data=mtcars), "wt") yields: \beta = -5.34 [-6.48, -4.20], p < 0.001. here "wt" restricts the output to just the named effect.

2. umxAPA also supports t.test(), stats::glm(), cor.test(), and others as I need them.

3. Get a CI from obj=beta and se=se : umxAPA(-0.30, .03) returns \beta = -0.3 [-0.36, -0.24]

4. Back out an SE from \beta and CI: umxAPA(-0.030, c(-0.073, 0.013)) returns \beta = -0.03, se = 0.02

5. Given only a number as obj, will be treated as a p-value, and returned in APA format.

6. Given a dataframe, umxAPA will return a table of correlations with means and SDs in the last row. e.g.: ⁠umxAPA(mtcars[,c("cyl", "wt", "mpg", )]⁠ yields:

   	cyl	wt	mpg
   cyl	1	0.78	-0.85
   wt	0.78	1	-0.87
   mpg	-0.85	-0.87	1
   mean_sd	6.19 (1.79)	3.22 (0.98)	20.09 (6.03)

Usage

umxAPA(
  obj = .Last.value,
  se = NULL,
  p = NULL,
  std = FALSE,
  digits = 2,
  use = "complete",
  min = 0.001,
  addComparison = NA,
  report = c("markdown", "html", "none", "expression"),
  lower = TRUE,
  test = c("Chisq", "LRT", "Rao", "F", "Cp"),
  SEs = TRUE,
  means = TRUE,
  suffix = "",
  cols = NA
)

Arguments

obj

A model (e.g. lm(), lme(), glm(), t.test()), beta-value, or data.frame

se

If obj is a beta, se treated as standard-error (returning a CI). If obj is a model, used to select effect of interest (blank for all effects). Finally, set se to the CI c(lower, upper), to back out the SE.

p

If obj is a beta, use p-value to compute SE (returning a CI).

std

Whether to report std betas (re-runs model on standardized data).

digits

How many digits to round output.

use

If obj is a data.frame, how to handle NAs (default = "complete")

min

For a p-value, the smallest value to report numerically (default .001)

addComparison

For a p-value, whether to add "</=" default (NA) adds "<" if necessary

report

What to return (default = 'markdown'). Use 'html' to open a web table. none doesn't print. expression can contain plotmath()

lower

Whether to not show the lower triangle of correlations for a data.frame (Default TRUE)

test

If obj is a glm, which test to use to generate p-values options = "Chisq", "LRT", "Rao", "F", "Cp"

SEs

Whether or not to show correlations with their SE (Default TRUE)

means

Whether or not to show means in a correlation table (Default TRUE)

suffix

A string to append to the result. Mostly used with report = "expression"

cols

Optional, pass in a list of column names when using umxAPA with a dataframe input.

Value

• string

References

• https://stats.oarc.ucla.edu/r/dae/logit-regression/

See Also

SE_from_p()

Other Reporting Functions: umxFactorScores(), umxGetLatents(), umxGetManifests(), umxGetModel(), umxGetParameters(), umxParameters(), umx_aggregate(), umx_time(), umx

Examples

# ========================================
# = Report lm (regression/anova) results =
# ========================================
umxAPA(lm(mpg ~ wt + disp, mtcars)) # Report all parameters
umxAPA(lm(mpg ~ wt + disp, mtcars), "wt") # Just effect of weight
umxAPA(lm(mpg ~ wt + disp, mtcars), std = TRUE) # Standardize model!

###############
# GLM example #
###############

df = mtcars
df$mpg_thresh = 0
df$mpg_thresh[df$mpg > 16] = 1
m1 = glm(mpg_thresh ~ wt + gear,data = df, family = binomial)
umxAPA(m1)

###############
# A t-Test    #
###############

umxAPA(t.test(x = 1:10, y = c(7:20)))
umxAPA(t.test(extra ~ group, data = sleep))

# ======================================================
# = Summarize DATA FRAME: Correlations + Means and SDs =
# ======================================================
umxAPA(mtcars[,1:3])
umxAPA(mtcars[,1:3], digits = 3)
umxAPA(mtcars[,1:3], lower = FALSE)
## Not run: 
umxAPA(mtcars[,1:3], report = "html")

## End(Not run)

# ==========================================
# = CONFIDENCE INTERVAL from effect and se =
# ==========================================
umxAPA(.4, .3) # parameter 2 interpreted as SE

# Input beta and CI, and back out the SE
umxAPA(-0.030, c(-0.073, 0.013), digits = 3)

# ====================
# = Format a p-value =
# ====================
umxAPA(.0182613)   #   0.02
umxAPA(.00018261) # < 0.001
umxAPA(.00018261, addComparison = FALSE) # 0.001

# ========================
# = Report a correlation =
# ========================
data(twinData)
tmp = subset(twinData, zygosity %in% c("MZFF", "MZMM"))
m1 = cor.test(~ wt1 + wt2, data = tmp)
umxAPA(m1)