apa.aov.table: Creates a fixed-effects ANOVA table in APA style
In apaTables: Create American Psychological Association (APA) Style Tables

Description Usage Arguments Value References Examples

Creates a fixed-effects ANOVA table in APA style

apa.aov.table(
  lm_output,
  filename,
  table.number = NA,
  conf.level = 0.9,
  type = 3
)

`lm_output`	Regression (i.e., lm) result objects. Typically, one for each block in the regression.
`filename`	(optional) Output filename document filename (must end in .rtf or .doc only)
`table.number`	Integer to use in table number output line
`conf.level`	Level of confidence for interval around partial eta-squared (.90 or .95). A value of .90 is the default, this helps to create consistency between the CI overlapping with zero and conclusions based on the p-value.
`type`	Sum of Squares Type. Type II or Type III; specify, 2 or 3, respectively. Default value is 3.

APA table object

Smithson, M. (2001). Correct confidence intervals for various regression effect sizes and parameters: The importance of noncentral distributions in computing intervals. Educational and Psychological Measurement, 61(4), 605-632.

Fidler, F., & Thompson, B. (2001). Computing correct confidence intervals for ANOVA fixed-and random-effects effect sizes. Educational and Psychological Measurement, 61(4), 575-604.

## Not run: 
#Example 1: 1-way from Field et al. (2012) Discovery Statistics Using R
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(libido ~ dose, data = viagra)
apa.aov.table(lm_output, filename = "ex1_anova_table.doc")

# Example 2: 2-way from Fidler & Thompson (2001)
# You must set these contrasts to ensure values match SPSS
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(dv ~ a*b, data = fidler_thompson)
apa.aov.table(lm_output,filename = "ex2_anova_table.doc")

#Example 3: 2-way from Field et al. (2012) Discovery Statistics Using R
# You must set these contrasts to ensure values match SPSS
options(contrasts = c("contr.helmert", "contr.poly"))
lm_output <- lm(attractiveness ~ gender*alcohol, data = goggles)
apa.aov.table(lm_output, filename = "ex3_anova_table.doc")

## End(Not run)

ANOVA results using libido as the dependent variable
 

   Predictor     SS df     MS     F    p partial_eta2 CI_90_partial_eta2
 (Intercept) 180.27  1 180.27 91.66 .000                                
        dose  20.13  2  10.06  5.12 .025          .46         [.04, .62]
       Error  23.60 12   1.97                                           

Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 



ANOVA results using dv as the dependent variable
 

   Predictor     SS df     MS      F    p partial_eta2 CI_90_partial_eta2
 (Intercept) 150.00  1 150.00 150.00 .000                                
           a   1.50  1   1.50   1.50 .238          .09         [.00, .32]
           b  12.00  3   4.00   4.00 .027          .43         [.04, .57]
       a x b   4.50  3   1.50   1.50 .253          .22         [.00, .38]
       Error  16.00 16   1.00                                            

Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared 



ANOVA results using attractiveness as the dependent variable
 

        Predictor        SS df        MS       F    p partial_eta2
      (Intercept) 163333.33  1 163333.33 1967.03 .000             
           gender    168.75  1    168.75    2.03 .161          .05
          alcohol   3332.29  2   1666.14   20.07 .000          .49
 gender x alcohol   1978.12  2    989.06   11.91 .000          .36
            Error   3487.50 42     83.04                          
 CI_90_partial_eta2
                   
         [.00, .18]
         [.28, .60]
         [.15, .49]
                   

Note: Values in square brackets indicate the bounds of the 90% confidence interval for partial eta-squared