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Ubuntu Diego

2011-Apr-13 00:10 UTC

[R] is this an ANOVA ?

Hi all,
	I have a very easy questions (I hope). I had measure a property of plants,
growing in three different substrates (A, B and C). The rest of the conditions
remained constant. There was very high variation on the results.
	I want to do address, whether there is any difference in the response (my
measurement) from substrate to substrate?

x<-c('A','A','A','A','A','B','B','B','B','B','C','C','C','C','C')
# Substrate type
y <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15) # Results of the measurement
MD<-data.frame(x,y)

	I wrote a linear model for this:

summary(lm(y~x,data=MD))

	This is the output:
	
Call:
lm(formula = y ~ x, data = MD)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.000e+00 -1.000e+00  5.551e-17  1.000e+00  2.000e+00 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.0000     0.7071   4.243 0.001142 ** 
xB            5.0000     1.0000   5.000 0.000309 ***
xC           10.0000     1.0000  10.000 3.58e-07 ***
---
Signif. codes:  0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1 

Residual standard error: 1.581 on 12 degrees of freedom
Multiple R-squared: 0.8929,	Adjusted R-squared: 0.875 
F-statistic:    50 on 2 and 12 DF,  p-value: 1.513e-06 

	I conclude that there is an effect of substrate type (x). 
	NOW the questions :
		1) Do the fact that the all p-values are significant means that all the groups
are different from each other ?
		2) Is there a (easy) way to plot,  mean plus/minus 2*sd for each substrate
type ? (with asterisks denoting significant differences ?)
	

	THANKS !

version
platform       x86_64-apple-darwin9.8.0     
arch           x86_64                       
os             darwin9.8.0                  
system         x86_64, darwin9.8.0          
status                                      
major          2                            
minor          11.1                         
year           2010                         
month          05                           
day            31                           
svn rev        52157                        
language       R                            
version.string R version 2.11.1 (2010-05-31)

skfglades at gmail.com

2011-Apr-13 00:35 UTC

head link

[R] is this an ANOVA ?

The response variable "y" does not make sense to me. What does it
represent?



Friedman.steve@gmail.com
517-648-6290

-----Original message-----
From: Ubuntu Diego <ubuntu.diego@gmail.com>
To: r-help@r-project.org
Sent: Wed, Apr 13, 2011 00:12:07 GMT+00:00
Subject: [R] is this an ANOVA ?

Hi all,
	I have a very easy questions (I hope). I had measure a property of plants,
growing in three different substrates (A, B and C). The rest of the conditions
remained constant. There was very high variation on the results.
	I want to do address, whether there is any difference in the response (my
measurement) from substrate to substrate?

x<-c('A','A','A','A','A','B','B','B','B','B','C','C','C','C','C')
# Substrate type
y <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15) # Results of the measurement
MD<-data.frame(x,y)

	I wrote a linear model for this:

summary(lm(y~x,data=MD))

	This is the output:
	
Call:
lm(formula = y ~ x, data = MD)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.000e+00 -1.000e+00  5.551e-17  1.000e+00  2.000e+00 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.0000     0.7071   4.243 0.001142 ** 
xB            5.0000     1.0000   5.000 0.000309 ***
xC           10.0000     1.0000  10.000 3.58e-07 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 

Residual standard error: 1.581 on 12 degrees of freedom
Multiple R-squared: 0.8929,	Adjusted R-squared: 0.875 
F-statistic:    50 on 2 and 12 DF,  p-value: 1.513e-06 

	I conclude that there is an effect of substrate type (x). 
	NOW the questions :
		1) Do the fact that the all p-values are significant means that all the groups
are different from each other ?
		2) Is there a (easy) way to plot,  mean plus/minus 2*sd for each substrate
type ? (with asterisks denoting significant differences ?)
	

	THANKS !

version
platform       x86_64-apple-darwin9.8.0     
arch           x86_64                       
os             darwin9.8.0                  
system         x86_64, darwin9.8.0          
status                                      
major          2                            
minor          11.1                         
year           2010                         
month          05                           
day            31                           
svn rev        52157                        
language       R                            
version.string R version 2.11.1 (2010-05-31)

______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

	[[alternative HTML version deleted]]

Steven McKinney

2011-Apr-13 02:54 UTC

head link

[R] is this an ANOVA ?

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at
r-project.org] On Behalf Of Ubuntu Diego
> Sent: April-12-11 5:10 PM
> To: r-help at r-project.org
> Subject: [R] is this an ANOVA ?
> 
> Hi all,
> 	I have a very easy questions (I hope). I had measure a property of plants,
growing in three
> different substrates (A, B and C). The rest of the conditions remained
constant. There was very high
> variation on the results.
> 	I want to do address, whether there is any difference in the response (my
measurement) from
> substrate to substrate?
> 
>
x<-c('A','A','A','A','A','B','B','B','B','B','C','C','C','C','C')
# Substrate type
> y <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15) # Results of the measurement
> MD<-data.frame(x,y)
> 
> 	I wrote a linear model for this:
> 
> summary(lm(y~x,data=MD))
> 
> 	This is the output:
> 
> Call:
> lm(formula = y ~ x, data = MD)
> 
> Residuals:
>        Min         1Q     Median         3Q        Max
> -2.000e+00 -1.000e+00  5.551e-17  1.000e+00  2.000e+00
> 
> Coefficients:
>             Estimate Std. Error t value Pr(>|t|)
> (Intercept)   3.0000     0.7071   4.243 0.001142 **
> xB            5.0000     1.0000   5.000 0.000309 ***
> xC           10.0000     1.0000  10.000 3.58e-07 ***
> ---
> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1
> 
> Residual standard error: 1.581 on 12 degrees of freedom
> Multiple R-squared: 0.8929,	Adjusted R-squared: 0.875
> F-statistic:    50 on 2 and 12 DF,  p-value: 1.513e-06
> 
> 	I conclude that there is an effect of substrate type (x).
> 	NOW the questions :
> 		1) Do the fact that the all p-values are significant means that all the
groups are
> different from each other ?
No, the small p-values indicate that the associated estimate appears to be
significantly different from zero.

You can use the package "multcomp" to do multiple comparisons.

     > require("multcomp")
     > lma <- aov(y ~ x, data = MD)
     > lmamc <- glht(lma, linfct = mcp(x = "Tukey"))
     > ci.lma <- confint(lmamc)
     > ci.lma

              Simultaneous Confidence Intervals

     Multiple Comparisons of Means: Tukey Contrasts
     
     
     Fit: aov(formula = y ~ x, data = MD)

     Quantile = 2.667
     95% family-wise confidence level
 

     Linear Hypotheses:
                Estimate lwr     upr    
     B - A == 0  5.0000   2.3330  7.6670
     C - A == 0 10.0000   7.3330 12.6670
     C - B == 0  5.0000   2.3330  7.6670

     > lmacld <- cld(lmamc)
     > plot(lmacld)
     > plot(ci.lma)
     


> 		2) Is there a (easy) way to plot,  mean plus/minus 2*sd for each
substrate type ? (with
> asterisks denoting significant differences ?)
Not that I know of, though the multcomp tables and plots yield logically
equivalent results and plots.
Writing a few lines of code to accomplish your graph is fairly straightforward.

HTH

Steven McKinney

> 
> 
> 	THANKS !
> 
> version
> platform       x86_64-apple-darwin9.8.0
> arch           x86_64
> os             darwin9.8.0
> system         x86_64, darwin9.8.0
> status
> major          2
> minor          11.1
> year           2010
> month          05
> day            31
> svn rev        52157
> language       R
> version.string R version 2.11.1 (2010-05-31)
> 
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

Lao Meng

2011-Apr-13 06:51 UTC

head link

[R] is this an ANOVA ?

You may try the following to perform anova:
 anova(lm(y~x))
or
summary(aov(y~x))




2011/4/13 Ubuntu Diego <ubuntu.diego@gmail.com>
> Hi all,
>        I have a very easy questions (I hope). I had measure a property of
> plants, growing in three different substrates (A, B and C). The rest of the
> conditions remained constant. There was very high variation on the results.
>        I want to do address, whether there is any difference in the
> response (my measurement) from substrate to substrate?
>
>
x<-c('A','A','A','A','A','B','B','B','B','B','C','C','C','C','C')
#
> Substrate type
> y <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15) # Results of the measurement
> MD<-data.frame(x,y)
>
>        I wrote a linear model for this:
>
> summary(lm(y~x,data=MD))
>
>        This is the output:
>
> Call:
> lm(formula = y ~ x, data = MD)
>
> Residuals:
>       Min         1Q     Median         3Q        Max
> -2.000e+00 -1.000e+00  5.551e-17  1.000e+00  2.000e+00
>
> Coefficients:
>            Estimate Std. Error t value Pr(>|t|)
> (Intercept)   3.0000     0.7071   4.243 0.001142 **
> xB            5.0000     1.0000   5.000 0.000309 ***
> xC           10.0000     1.0000  10.000 3.58e-07 ***
> ---
> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
> Residual standard error: 1.581 on 12 degrees of freedom
> Multiple R-squared: 0.8929,     Adjusted R-squared: 0.875
> F-statistic:    50 on 2 and 12 DF,  p-value: 1.513e-06
>
>        I conclude that there is an effect of substrate type (x).
>        NOW the questions :
>                1) Do the fact that the all p-values are significant means
> that all the groups are different from each other ?
>                2) Is there a (easy) way to plot,  mean plus/minus 2*sd for
> each substrate type ? (with asterisks denoting significant differences ?)
>
>
>        THANKS !
>
> version
> platform       x86_64-apple-darwin9.8.0
> arch           x86_64
> os             darwin9.8.0
> system         x86_64, darwin9.8.0
> status
> major          2
> minor          11.1
> year           2010
> month          05
> day            31
> svn rev        52157
> language       R
> version.string R version 2.11.1 (2010-05-31)
>
> ______________________________________________
> R-help@r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide
> http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
	[[alternative HTML version deleted]]

Bill.Venables at csiro.au

2011-Apr-13 22:21 UTC

head link

[R] is this an ANOVA ?

You probably want to do something like this:
> fm <- lm(y ~ x, MD)
> anova(fm)Analysis of Variance Table

Response: y
          Df Sum Sq Mean Sq F value    Pr(>F)
x          2    250   125.0      50 1.513e-06
Residuals 12     30     2.5   


Answers to questions:

1. No.
2. Yes.

(whoever you are). 

-----Original Message-----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On Behalf Of Ubuntu Diego
Sent: Wednesday, 13 April 2011 10:10 AM
To: r-help at r-project.org
Subject: [R] is this an ANOVA ?

Hi all,
	I have a very easy questions (I hope). I had measure a property of plants,
growing in three different substrates (A, B and C). The rest of the conditions
remained constant. There was very high variation on the results.
	I want to do address, whether there is any difference in the response (my
measurement) from substrate to substrate?

x<-c('A','A','A','A','A','B','B','B','B','B','C','C','C','C','C')
# Substrate type
y <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15) # Results of the measurement
MD<-data.frame(x,y)

	I wrote a linear model for this:

summary(lm(y~x,data=MD))

	This is the output:
	
Call:
lm(formula = y ~ x, data = MD)

Residuals:
       Min         1Q     Median         3Q        Max 
-2.000e+00 -1.000e+00  5.551e-17  1.000e+00  2.000e+00 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)   3.0000     0.7071   4.243 0.001142 ** 
xB            5.0000     1.0000   5.000 0.000309 ***
xC           10.0000     1.0000  10.000 3.58e-07 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05
'.' 0.1 ' ' 1

Residual standard error: 1.581 on 12 degrees of freedom
Multiple R-squared: 0.8929,	Adjusted R-squared: 0.875 
F-statistic:    50 on 2 and 12 DF,  p-value: 1.513e-06 

	I conclude that there is an effect of substrate type (x). 
	NOW the questions :
		1) Do the fact that the all p-values are significant means that all the groups
are different from each other ?
		2) Is there a (easy) way to plot,  mean plus/minus 2*sd for each substrate
type ? (with asterisks denoting significant differences ?)
	

	THANKS !

version
platform       x86_64-apple-darwin9.8.0     
arch           x86_64                       
os             darwin9.8.0                  
system         x86_64, darwin9.8.0          
status                                      
major          2                            
minor          11.1                         
year           2010                         
month          05                           
day            31                           
svn rev        52157                        
language       R                            
version.string R version 2.11.1 (2010-05-31)

______________________________________________
R-help at r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

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