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Last data update: 2014.03.03

R: Tests for significant differences among all pairs of slopes...

compSlopes

R Documentation

Tests for significant differences among all pairs of slopes in a dummy variable regression (DVR).

Description

Tests for significant differences among all pairs of slopes in a dummy variable regression where the dummy variables all stem from one factor.

Usage

compSlopes(mdl, method = stats::p.adjust.methods, conf.level = 0.95,
  order.slopes = TRUE, digits = getOption("digits"))

## S3 method for class 'compSlopes'
print(x, ...)

Arguments

`mdl`	A `lm` object.
`method`	A string indicating the method of p-value adjustment to use. See details and `p.adjust.methods`.
`conf.level`	A single number that represents the level of confidence to use for constructing confidence intervals.
`order.slopes`	A logical indicating whether the slopes should be ordered from smallest to largest upon output.
`digits`	A numeric that controls the number of digits to print.
`x`	A `compSlopes` object (i.e., returned from `compSlopes`).
`...`	Other arguments sent to `print`.

Details

In a dummy variable regression the coefficient for the interaction between the covariate (x) and a dummy variable tests for a difference in slopes between the level of the dummy variable and the reference level. Thus, all dummy variables from a particular linear model fit only compare slopes with the reference level. Other slope comparisons may be found by changing the reference level, which requires refitting the model. This function automates this sequential process and produces a data.frame that shows the estimated difference, an unadjusted confidence interval for the difference, and the unadjusted and adjusted (for multiplce comparisons) p-values for testing that the difference in slopes is equal to zero for each pair of levels. The adjusted p-values may be computed with any of the methods coded in p.adjust (see p.adjust.methods).

Value

A list with three components. The first component contains the p-value adjustment method in method. The second component, called comparisons, is a data.frame that contains the following:

`comparison`	Description of how the difference in levels was computed.
`diff`	The estimated difference in slope values.
`lwr`	Lower confidence bound for difference in slope values.
`upr`	Upper confidence bound for difference in slope values.
`p.unadj`	Unadjusted p-value for testing the difference in slopes is zero.
`p.adj`	Adjusted p-value for testing the difference in slopes is zero.

The third component, called slopes, is a data.frame that contains the following:

`level`	A level name.
`slope`	The estimated slope value for the given level.
`XX LCI`	Lower confidence bound for difference in slope values.
`XX UCI`	Upper confidence bound for difference in slope values.
`p.unadj`	Unadjusted p-value for testing the slope is zero.
`p.adj`	Adjusted p-value for testing the slope is zero.

The print function prints the results nicely.

Note

This function only works for linear models with one factor variable.

Author(s)

Derek H. Ogle, derek@derekogle.com

Examples

data(Mirex)
Mirex$year <- factor(Mirex$year)
# fit a dummy variable regression, see that slopes differ
lm1 <- lm(mirex~weight*year,data=Mirex)
anova(lm1)
# compare all pairs of slopes using default Holm control
compSlopes(lm1)
# compare all pairs of slopes using the false discovery rate control
compSlopes(lm1,method="fdr")
# visualize the results
fitPlot(lm1)

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(FSA)


 ############################################
 ##      FSA package, version 0.8.7        ##
 ##    Derek H. Ogle, Northland College    ##
 ##                                        ##
 ## Run ?FSA for documentation.            ##
 ## Run citation('FSA') for citation ...   ##
 ##   please cite if used in publication.  ##
 ##                                        ##
 ## See derekogle.com/fishR/ for more      ##
 ##   thorough analytical vignettes.       ##
 ############################################


> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/FSA/compSlopes.Rd_%03d_medium.png", width=480, height=480)
> ### Name: compSlopes
> ### Title: Tests for significant differences among all pairs of slopes in a
> ###   dummy variable regression (DVR).
> ### Aliases: compSlopes print.compSlopes
> ### Keywords: htest
> 
> ### ** Examples
> 
> data(Mirex)
> Mirex$year <- factor(Mirex$year)
> # fit a dummy variable regression, see that slopes differ
> lm1 <- lm(mirex~weight*year,data=Mirex)
> anova(lm1)
Analysis of Variance Table

Response: mirex
             Df  Sum Sq  Mean Sq F value    Pr(>F)    
weight        1 0.22298 0.222980 60.2061   4.7e-12 ***
year          5 0.50667 0.101333 27.3607 < 2.2e-16 ***
weight:year   5 0.09351 0.018703  5.0499 0.0003326 ***
Residuals   110 0.40740 0.003704                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
> # compare all pairs of slopes using default Holm control
> compSlopes(lm1)
Multiple Slope Comparisons (using the 'holm' adjustment)
   comparison     diff  95% LCI  95% UCI p.unadj   p.adj
1   1982-1977 -0.00202 -0.01678  0.01273 0.78627 1.00000
2   1986-1977 -0.00331 -0.01670  0.01008 0.62556 1.00000
3   1992-1977 -0.00233 -0.01676  0.01211 0.75010 1.00000
4   1996-1977 -0.01661 -0.02903 -0.00418 0.00929 0.09394
5   1999-1977 -0.02500 -0.03964 -0.01036 0.00099 0.01386
6   1986-1982 -0.00128 -0.01393  0.01137 0.84111 1.00000
7   1992-1982 -0.00030 -0.01405  0.01345 0.96535 1.00000
8   1996-1982 -0.01458 -0.02621 -0.00295 0.01445 0.11790
9   1999-1982 -0.02297 -0.03694 -0.00901 0.00149 0.01788
10  1992-1986  0.00098 -0.01130  0.01326 0.87451 1.00000
11  1996-1986 -0.01330 -0.02314 -0.00346 0.00854 0.09394
12  1999-1986 -0.02169 -0.03421 -0.00917 0.00084 0.01260
13  1996-1992 -0.01428 -0.02550 -0.00306 0.01310 0.11790
14  1999-1992 -0.02267 -0.03630 -0.00904 0.00132 0.01716
15  1999-1996 -0.00839 -0.01988  0.00309 0.15049 1.00000

Slope Information (using the 'holm' adjustment)
  level  slopes  95% LCI 95% UCI p.unadj  p.adj
6  1999 0.00386 -0.00593 0.01366  0.4362 0.4362
5  1996 0.01225  0.00625 0.01826  0.0001 0.0002
3  1986 0.02555  0.01775 0.03335  0.0000 0.0000
4  1992 0.02653  0.01705 0.03602  0.0000 0.0000
2  1982 0.02684  0.01688 0.03680  0.0000 0.0000
1  1977 0.02886  0.01798 0.03974  0.0000 0.0000
> # compare all pairs of slopes using the false discovery rate control
> compSlopes(lm1,method="fdr")
Multiple Slope Comparisons (using the 'fdr' adjustment)
   comparison     diff  95% LCI  95% UCI p.unadj   p.adj
1   1982-1977 -0.00202 -0.01678  0.01273 0.78627 0.93698
2   1986-1977 -0.00331 -0.01670  0.01008 0.62556 0.93698
3   1992-1977 -0.00233 -0.01676  0.01211 0.75010 0.93698
4   1996-1977 -0.01661 -0.02903 -0.00418 0.00929 0.02322
5   1999-1977 -0.02500 -0.03964 -0.01036 0.00099 0.00559
6   1986-1982 -0.00128 -0.01393  0.01137 0.84111 0.93698
7   1992-1982 -0.00030 -0.01405  0.01345 0.96535 0.96535
8   1996-1982 -0.01458 -0.02621 -0.00295 0.01445 0.02709
9   1999-1982 -0.02297 -0.03694 -0.00901 0.00149 0.00559
10  1992-1986  0.00098 -0.01130  0.01326 0.87451 0.93698
11  1996-1986 -0.01330 -0.02314 -0.00346 0.00854 0.02322
12  1999-1986 -0.02169 -0.03421 -0.00917 0.00084 0.00559
13  1996-1992 -0.01428 -0.02550 -0.00306 0.01310 0.02709
14  1999-1992 -0.02267 -0.03630 -0.00904 0.00132 0.00559
15  1999-1996 -0.00839 -0.01988  0.00309 0.15049 0.25082

Slope Information (using the 'fdr' adjustment)
  level  slopes  95% LCI 95% UCI p.unadj   p.adj
6  1999 0.00386 -0.00593 0.01366  0.4362 0.43620
5  1996 0.01225  0.00625 0.01826  0.0001 0.00012
3  1986 0.02555  0.01775 0.03335  0.0000 0.00000
4  1992 0.02653  0.01705 0.03602  0.0000 0.00000
2  1982 0.02684  0.01688 0.03680  0.0000 0.00000
1  1977 0.02886  0.01798 0.03974  0.0000 0.00000
> # visualize the results
> fitPlot(lm1)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>