apa.reg.table: Creates a regresion table in APA style
In apaTables: Create American Psychological Association (APA) Style Tables
Description
Usage
Arguments
Value
References
Examples
View source: R/apaRegressionTable.R
Description
Creates a regresion table in APA style
Usage
1
2
3
4
5
6
apa.reg.table(
...,
filename = NA,
table.number = NA,
prop.var.conf.level = 0.95
)
Arguments
...
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
prop.var.conf.level
Level of confidence (.90 or .95, default .95) for interval around sr2, R2, and Delta R2. Use of .90 confidence level helps to create consistency between the CI overlapping with zero and conclusions based on the p-value for that block (or block difference).
Value
APA table object
References
sr2 and delta R2 confidence intervals calculated via:
Alf Jr, E. F., & Graf, R. G. (1999). Asymptotic confidence limits for the difference between two squared multiple correlations: A simplified approach. Psychological Methods, 4(1), 70.
Note that Algina, Keselman, & Penfield (2008) found this approach can under some circumstances lead to inaccurate CIs on proportion of variance values.
You might consider using the Algina, Keselman, & Penfield (2008) approach via the apa.reg.boot.table function
Examples
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
## Not run:
# View top few rows of goggles data set
# from Discovering Statistics Using R
head(album)
# Single block example
blk1 <- lm(sales ~ adverts + airplay, data=album)
apa.reg.table(blk1)
apa.reg.table(blk1,filename="exRegTable.doc")
# Two block example, more than two blocks can be used
blk1 <- lm(sales ~ adverts, data=album)
blk2 <- lm(sales ~ adverts + airplay + attract, data=album)
apa.reg.table(blk1,blk2,filename="exRegBlocksTable.doc")
# Interaction product-term test with blocks
blk1 <- lm(sales ~ adverts + airplay, data=album)
blk2 <- lm(sales ~ adverts + airplay + I(adverts * airplay), data=album)
apa.reg.table(blk1,blk2,filename="exInteraction1.doc")
# Interaction product-term test with blocks and additional product terms
blk1<-lm(sales ~ adverts + airplay, data=album)
blk2<-lm(sales ~ adverts + airplay + I(adverts*adverts) + I(airplay*airplay), data=album)
blk3<-lm(sales~adverts+airplay+I(adverts*adverts)+I(airplay*airplay)+I(adverts*airplay),data=album)
apa.reg.table(blk1,blk2,blk3,filename="exInteraction2.doc")
#Interaction product-term test with single regression (i.e., semi-partial correlation focus)
blk1 <- lm(sales ~ adverts + airplay + I(adverts * airplay), data=album)
apa.reg.table(blk1,filename="exInteraction3.doc")
## End(Not run)
Example output
adverts sales airplay attract
1 10.256 330 43 10
2 985.685 120 28 7
3 1445.563 360 35 7
4 1188.193 270 33 7
5 574.513 220 44 5
6 568.954 170 19 5
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI r
(Intercept) 41.12** [22.72, 59.53]
adverts 0.09** [0.07, 0.10] 0.52 [0.44, 0.61] .27 [.18, .36] .58**
airplay 3.59** [3.02, 4.15] 0.55 [0.46, 0.63] .29 [.20, .38] .60**
Fit
R2 = .629**
95% CI[.55,.69]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI r
(Intercept) 41.12** [22.72, 59.53]
adverts 0.09** [0.07, 0.10] 0.52 [0.44, 0.61] .27 [.18, .36] .58**
airplay 3.59** [3.02, 4.15] 0.55 [0.46, 0.63] .29 [.20, .38] .60**
Fit
R2 = .629**
95% CI[.55,.69]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI r
(Intercept) 134.14** [119.28, 149.00]
adverts 0.10** [0.08, 0.12] 0.58 [0.46, 0.69] .33 [.23, .43] .58**
(Intercept) -26.61 [-60.83, 7.60]
adverts 0.08** [0.07, 0.10] 0.51 [0.43, 0.59] .26 [.17, .34] .58**
airplay 3.37** [2.82, 3.92] 0.51 [0.43, 0.60] .25 [.17, .33] .60**
attract 11.09** [6.28, 15.89] 0.19 [0.11, 0.27] .04 [.00, .07] .33**
Fit Difference
R2 = .335**
95% CI[.23,.43]
R2 = .665** Delta R2 = .330**
95% CI[.59,.72] 95% CI[.24, .42]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2
(Intercept) 41.12** [22.72, 59.53]
adverts 0.09** [0.07, 0.10] 0.52 [0.44, 0.61] .27
airplay 3.59** [3.02, 4.15] 0.55 [0.46, 0.63] .29
(Intercept) 28.30* [1.09, 55.50]
adverts 0.11** [0.07, 0.16] 0.69 [0.42, 0.96] .05
airplay 4.02** [3.14, 4.91] 0.61 [0.48, 0.75] .15
I(adverts * airplay) -0.00 [-0.00, 0.00] -0.19 [-0.49, 0.11] .00
sr2_95%_CI r Fit Difference
[.18, .36] .58**
[.20, .38] .60**
R2 = .629**
95% CI[.55,.69]
[.01, .08] .58**
[.08, .22] .60**
[-.01, .01]
R2 = .632** Delta R2 = .003
95% CI[.55,.69] 95% CI[-.01, .01]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2
(Intercept) 41.12** [22.72, 59.53]
adverts 0.09** [0.07, 0.10] 0.52 [0.44, 0.61] .27
airplay 3.59** [3.02, 4.15] 0.55 [0.46, 0.63] .29
(Intercept) 48.77** [19.73, 77.82]
adverts 0.10** [0.05, 0.14] 0.58 [0.33, 0.83] .04
airplay 2.65** [0.67, 4.63] 0.40 [0.10, 0.70] .01
I(adverts * adverts) -0.00 [-0.00, 0.00] -0.05 [-0.31, 0.20] .00
I(airplay * airplay) 0.02 [-0.02, 0.05] 0.15 [-0.15, 0.45] .00
(Intercept) 37.30* [2.84, 71.77]
adverts 0.12** [0.06, 0.18] 0.72 [0.38, 1.06] .03
airplay 3.07** [0.98, 5.15] 0.47 [0.15, 0.78] .02
I(adverts * adverts) -0.00 [-0.00, 0.00] -0.03 [-0.29, 0.23] .00
I(airplay * airplay) 0.02 [-0.02, 0.05] 0.15 [-0.15, 0.45] .00
I(adverts * airplay) -0.00 [-0.00, 0.00] -0.19 [-0.49, 0.12] .00
sr2_95%_CI r Fit Difference
[.18, .36] .58**
[.20, .38] .60**
R2 = .629**
95% CI[.55,.69]
[.01, .07] .58**
[-.01, .03] .60**
[-.00, .00]
[-.01, .01]
R2 = .631** Delta R2 = .002
95% CI[.55,.69] 95% CI[-.01, .01]
[.00, .06] .58**
[-.01, .04] .60**
[-.00, .00]
[-.01, .01]
[-.01, .01]
R2 = .634** Delta R2 = .003
95% CI[.55,.69] 95% CI[-.01, .01]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI
(Intercept) 28.30* [1.09, 55.50]
adverts 0.11** [0.07, 0.16] 0.69 [0.42, 0.96] .05 [.01, .08]
airplay 4.02** [3.14, 4.91] 0.61 [0.48, 0.75] .15 [.08, .22]
I(adverts * airplay) -0.00 [-0.00, 0.00] -0.19 [-0.49, 0.11] .00 [-.01, .01]
r Fit
.58**
.60**
R2 = .632**
95% CI[.55,.69]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
apaTables documentation built on Jan. 13, 2021, 11:22 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value References Examples
View source: R/apaRegressionTable.R
Description
Creates a regresion table in APA style
Usage
1 2 3 4 5 6 | apa.reg.table(
...,
filename = NA,
table.number = NA,
prop.var.conf.level = 0.95
)
|
Arguments
... |
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 |
prop.var.conf.level |
Level of confidence (.90 or .95, default .95) for interval around sr2, R2, and Delta R2. Use of .90 confidence level helps to create consistency between the CI overlapping with zero and conclusions based on the p-value for that block (or block difference). |
Value
APA table object
References
sr2 and delta R2 confidence intervals calculated via:
Alf Jr, E. F., & Graf, R. G. (1999). Asymptotic confidence limits for the difference between two squared multiple correlations: A simplified approach. Psychological Methods, 4(1), 70.
Note that Algina, Keselman, & Penfield (2008) found this approach can under some circumstances lead to inaccurate CIs on proportion of variance values. You might consider using the Algina, Keselman, & Penfield (2008) approach via the apa.reg.boot.table function
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ## Not run:
# View top few rows of goggles data set
# from Discovering Statistics Using R
head(album)
# Single block example
blk1 <- lm(sales ~ adverts + airplay, data=album)
apa.reg.table(blk1)
apa.reg.table(blk1,filename="exRegTable.doc")
# Two block example, more than two blocks can be used
blk1 <- lm(sales ~ adverts, data=album)
blk2 <- lm(sales ~ adverts + airplay + attract, data=album)
apa.reg.table(blk1,blk2,filename="exRegBlocksTable.doc")
# Interaction product-term test with blocks
blk1 <- lm(sales ~ adverts + airplay, data=album)
blk2 <- lm(sales ~ adverts + airplay + I(adverts * airplay), data=album)
apa.reg.table(blk1,blk2,filename="exInteraction1.doc")
# Interaction product-term test with blocks and additional product terms
blk1<-lm(sales ~ adverts + airplay, data=album)
blk2<-lm(sales ~ adverts + airplay + I(adverts*adverts) + I(airplay*airplay), data=album)
blk3<-lm(sales~adverts+airplay+I(adverts*adverts)+I(airplay*airplay)+I(adverts*airplay),data=album)
apa.reg.table(blk1,blk2,blk3,filename="exInteraction2.doc")
#Interaction product-term test with single regression (i.e., semi-partial correlation focus)
blk1 <- lm(sales ~ adverts + airplay + I(adverts * airplay), data=album)
apa.reg.table(blk1,filename="exInteraction3.doc")
## End(Not run)
|
Example output
adverts sales airplay attract
1 10.256 330 43 10
2 985.685 120 28 7
3 1445.563 360 35 7
4 1188.193 270 33 7
5 574.513 220 44 5
6 568.954 170 19 5
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI r
(Intercept) 41.12** [22.72, 59.53]
adverts 0.09** [0.07, 0.10] 0.52 [0.44, 0.61] .27 [.18, .36] .58**
airplay 3.59** [3.02, 4.15] 0.55 [0.46, 0.63] .29 [.20, .38] .60**
Fit
R2 = .629**
95% CI[.55,.69]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI r
(Intercept) 41.12** [22.72, 59.53]
adverts 0.09** [0.07, 0.10] 0.52 [0.44, 0.61] .27 [.18, .36] .58**
airplay 3.59** [3.02, 4.15] 0.55 [0.46, 0.63] .29 [.20, .38] .60**
Fit
R2 = .629**
95% CI[.55,.69]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI r
(Intercept) 134.14** [119.28, 149.00]
adverts 0.10** [0.08, 0.12] 0.58 [0.46, 0.69] .33 [.23, .43] .58**
(Intercept) -26.61 [-60.83, 7.60]
adverts 0.08** [0.07, 0.10] 0.51 [0.43, 0.59] .26 [.17, .34] .58**
airplay 3.37** [2.82, 3.92] 0.51 [0.43, 0.60] .25 [.17, .33] .60**
attract 11.09** [6.28, 15.89] 0.19 [0.11, 0.27] .04 [.00, .07] .33**
Fit Difference
R2 = .335**
95% CI[.23,.43]
R2 = .665** Delta R2 = .330**
95% CI[.59,.72] 95% CI[.24, .42]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2
(Intercept) 41.12** [22.72, 59.53]
adverts 0.09** [0.07, 0.10] 0.52 [0.44, 0.61] .27
airplay 3.59** [3.02, 4.15] 0.55 [0.46, 0.63] .29
(Intercept) 28.30* [1.09, 55.50]
adverts 0.11** [0.07, 0.16] 0.69 [0.42, 0.96] .05
airplay 4.02** [3.14, 4.91] 0.61 [0.48, 0.75] .15
I(adverts * airplay) -0.00 [-0.00, 0.00] -0.19 [-0.49, 0.11] .00
sr2_95%_CI r Fit Difference
[.18, .36] .58**
[.20, .38] .60**
R2 = .629**
95% CI[.55,.69]
[.01, .08] .58**
[.08, .22] .60**
[-.01, .01]
R2 = .632** Delta R2 = .003
95% CI[.55,.69] 95% CI[-.01, .01]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2
(Intercept) 41.12** [22.72, 59.53]
adverts 0.09** [0.07, 0.10] 0.52 [0.44, 0.61] .27
airplay 3.59** [3.02, 4.15] 0.55 [0.46, 0.63] .29
(Intercept) 48.77** [19.73, 77.82]
adverts 0.10** [0.05, 0.14] 0.58 [0.33, 0.83] .04
airplay 2.65** [0.67, 4.63] 0.40 [0.10, 0.70] .01
I(adverts * adverts) -0.00 [-0.00, 0.00] -0.05 [-0.31, 0.20] .00
I(airplay * airplay) 0.02 [-0.02, 0.05] 0.15 [-0.15, 0.45] .00
(Intercept) 37.30* [2.84, 71.77]
adverts 0.12** [0.06, 0.18] 0.72 [0.38, 1.06] .03
airplay 3.07** [0.98, 5.15] 0.47 [0.15, 0.78] .02
I(adverts * adverts) -0.00 [-0.00, 0.00] -0.03 [-0.29, 0.23] .00
I(airplay * airplay) 0.02 [-0.02, 0.05] 0.15 [-0.15, 0.45] .00
I(adverts * airplay) -0.00 [-0.00, 0.00] -0.19 [-0.49, 0.12] .00
sr2_95%_CI r Fit Difference
[.18, .36] .58**
[.20, .38] .60**
R2 = .629**
95% CI[.55,.69]
[.01, .07] .58**
[-.01, .03] .60**
[-.00, .00]
[-.01, .01]
R2 = .631** Delta R2 = .002
95% CI[.55,.69] 95% CI[-.01, .01]
[.00, .06] .58**
[-.01, .04] .60**
[-.00, .00]
[-.01, .01]
[-.01, .01]
R2 = .634** Delta R2 = .003
95% CI[.55,.69] 95% CI[-.01, .01]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
Regression results using sales as the criterion
Predictor b b_95%_CI beta beta_95%_CI sr2 sr2_95%_CI
(Intercept) 28.30* [1.09, 55.50]
adverts 0.11** [0.07, 0.16] 0.69 [0.42, 0.96] .05 [.01, .08]
airplay 4.02** [3.14, 4.91] 0.61 [0.48, 0.75] .15 [.08, .22]
I(adverts * airplay) -0.00 [-0.00, 0.00] -0.19 [-0.49, 0.11] .00 [-.01, .01]
r Fit
.58**
.60**
R2 = .632**
95% CI[.55,.69]
Note. A significant b-weight indicates the beta-weight and semi-partial correlation are also significant.
b represents unstandardized regression weights. beta indicates the standardized regression weights.
sr2 represents the semi-partial correlation squared. r represents the zero-order correlation.
Square brackets are used to enclose the lower and upper limits of a confidence interval.
* indicates p < .05. ** indicates p < .01.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.