# Creates an ANOVA table in APA style based output of ezANOVA command from ez package ```r 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 ## Returns APA table object ## Examples ```r ## 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) ## End(Not run) ```

apa.ezANOVA.table function