apaTables (version 2.0.8)

apa.ezANOVA.table: Creates an ANOVA table in APA style based output of ezANOVA command from ez package

Description

Creates an ANOVA table in APA style based output of ezANOVA command from ez package

Usage

apa.ezANOVA.table(
  ez.output,
  correction = "GG",
  table.title = "",
  filename,
  table.number = NA
)

Arguments

ez.output

Output object from ezANOVA command from ez package

correction

Type of sphercity correction: "none", "GG", or "HF" corresponding to none, Greenhouse-Geisser and Huynh-Feldt, respectively.

table.title

String containing text for table title

filename

(optional) Output filename document filename (must end in .rtf or .doc only)

table.number

Integer to use in table number output line

Value

APA table object

Examples

# NOT RUN {
# ** Example 1: Between Participant Predictors
#

library(apaTables)
library(ez)

# See format where one row represents one PERSON
# Note that participant, gender, and alcohol are factors

print(goggles)


# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits

options(digits = 10)
goggles_results <- ezANOVA(data = goggles,
                          dv = attractiveness,
                          between = .(gender, alcohol),
                          participant ,
                          detailed = TRUE)


# Make APA table

goggles_table <- apa.ezANOVA.table(goggles_results,
                                  filename="ex1_ez_independent.doc")

print(goggles_table)



#
# ** Example 2: Within Participant Predictors
#

library(apaTables)
library(tidyr)
library(forcats)
library(ez)

# See initial wide format where one row represents one PERSON
print(drink_attitude_wide)

# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.

drink_attitude_long <- gather(data = drink_attitude_wide,
                              key = cell, value = attitude,
                              beer_positive:water_neutral,
                              factor_key=TRUE)

drink_attitude_long <- separate(data = drink_attitude_long,
                                col = cell, into = c("drink","imagery"),
                                sep = "_", remove = TRUE)

drink_attitude_long$drink <- as_factor(drink_attitude_long$drink)
drink_attitude_long$imagery <- as_factor(drink_attitude_long$imagery)

# See new long format of data, where one row is one OBSERVATION.
# As well, notice that we have two columns (drink, imagery)
# drink, imagery, and participant are factors
print(drink_attitude_long)


# Set contrasts to match Field et al. (2012) textbook output

alcohol_vs_water <- c(1, 1, -2)
beer_vs_wine <- c(-1, 1, 0)
negative_vs_other <- c(1, -2, 1)
positive_vs_neutral <- c(-1, 0, 1)
contrasts(drink_attitude_long$drink) <- cbind(alcohol_vs_water, beer_vs_wine)
contrasts(drink_attitude_long$imagery) <- cbind(negative_vs_other, positive_vs_neutral)


# Use ezANOVA
# Be sure use the options command, as below, to ensure sufficient digits

options(digits = 10)
drink_attitude_results <- ezANOVA(data = drink_attitude_long,
                   dv = .(attitude), wid = .(participant),
                   within = .(drink, imagery),
                   type = 3, detailed = TRUE)


# Make APA table

drink_table <- apa.ezANOVA.table(drink_attitude_results,
                                 filename="ex2_repeated_table.doc")

print(drink_table)


#
# ** Example 3: Between and Within Participant Predictors
#

library(apaTables)
library(tidyr)
library(forcats)
library(ez)

# See initial wide format where one row represents one PERSON
print(dating_wide)


# Convert data from wide format to long format where one row represents one OBSERVATION.
# Wide format column names MUST represent levels of each variable separated by an underscore.
# See vignette for further details.

dating_long <- gather(data = dating_wide,
                     key = cell, value = date_rating,
                     attractive_high:ugly_none,
                     factor_key = TRUE)

dating_long <- separate(data = dating_long,
                       col = cell, into = c("looks","personality"),
                       sep = "_", remove = TRUE)

dating_long$looks <- as_factor(dating_long$looks)
dating_long$personality <- as_factor(dating_long$personality)


# See new long format of data, where one row is one OBSERVATION.
# As well, notice that we have two columns (looks, personality)
# looks, personality, and participant are factors

print(dating_long)

# Set contrasts to match Field et al. (2012) textbook output

some_vs_none <- c(1, 1, -2)
hi_vs_av <- c(1, -1, 0)
attractive_vs_ugly <- c(1, 1, -2)
attractive_vs_average <- c(1, -1, 0)
contrasts(dating_long$personality) <- cbind(some_vs_none, hi_vs_av)
contrasts(dating_long$looks) <- cbind(attractive_vs_ugly, attractive_vs_average)


# Use ezANOVA

library(ez)
options(digits = 10)
dating_results <-ezANOVA(data = dating_long, dv = .(date_rating), wid = .(participant),
                        between = .(gender), within = .(looks, personality),
                        type = 3, detailed = TRUE)


# Make APA table

dating_table <- apa.ezANOVA.table(dating_results,
                                 filename = "ex3_mixed_table.doc")
print(dating_table)
# }