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Rich Shepard

2011-Nov-09 17:04 UTC

[R] Interpreting Multiple Linear Regression Summary

I would appreciate pointers on what I should read to understand this
output:

  summary(lm(TDS ~ Cond + Ca + Cl + Mg + Na + SO4))

Call:
lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4)

Residuals:
ALL 1 residuals are 0: no residual degrees of freedom!

Coefficients: (6 not defined because of singularities)
             Estimate Std. Error t value Pr(>|t|)
(Intercept)      125         NA      NA       NA
Cond              NA         NA      NA       NA
Ca                NA         NA      NA       NA
Cl                NA         NA      NA       NA
Mg                NA         NA      NA       NA
Na                NA         NA      NA       NA
SO4               NA         NA      NA       NA

Residual standard error: NaN on 0 degrees of freedom
   (63 observations deleted due to missingness)

   When I look at the summary for the data frame used for this model I do not
see an excessive number of missing values or indications why there are no
residual degrees of freedom. The same model applied to 8 other data frames
did not produce similar results.

Puzzled,

Rich

B77S

2011-Nov-09 17:23 UTC

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[R] Interpreting Multiple Linear Regression Summary

Please see ?dput

use dput(your data) and paste the output into a reply, thanks.

This way we know what you are working with. 




Rich Shepard wrote:> 
> I would appreciate pointers on what I should read to understand this
> output:
> 
>   summary(lm(TDS ~ Cond + Ca + Cl + Mg + Na + SO4))
> 
> Call:
> lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4)
> 
> Residuals:
> ALL 1 residuals are 0: no residual degrees of freedom!
> 
> Coefficients: (6 not defined because of singularities)
>              Estimate Std. Error t value Pr(>|t|)
> (Intercept)      125         NA      NA       NA
> Cond              NA         NA      NA       NA
> Ca                NA         NA      NA       NA
> Cl                NA         NA      NA       NA
> Mg                NA         NA      NA       NA
> Na                NA         NA      NA       NA
> SO4               NA         NA      NA       NA
> 
> Residual standard error: NaN on 0 degrees of freedom
>    (63 observations deleted due to missingness)
> 
>    When I look at the summary for the data frame used for this model I do
> not
> see an excessive number of missing values or indications why there are no
> residual degrees of freedom. The same model applied to 8 other data frames
> did not produce similar results.
> 
> Puzzled,
> 
> Rich
> 
> ______________________________________________
> R-help@ 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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Daniel Nordlund

2011-Nov-09 17:37 UTC

head link

[R] Interpreting Multiple Linear Regression Summary

> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at
r-project.org]
> On Behalf Of Rich Shepard
> Sent: Wednesday, November 09, 2011 9:05 AM
> To: r-help at r-project.org
> Subject: [R] Interpreting Multiple Linear Regression Summary
> 
>    I would appreciate pointers on what I should read to understand this
> output:
> 
>   summary(lm(TDS ~ Cond + Ca + Cl + Mg + Na + SO4))
> 
> Call:
> lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4)
> 
> Residuals:
> ALL 1 residuals are 0: no residual degrees of freedom!
> 
> Coefficients: (6 not defined because of singularities)
>              Estimate Std. Error t value Pr(>|t|)
> (Intercept)      125         NA      NA       NA
> Cond              NA         NA      NA       NA
> Ca                NA         NA      NA       NA
> Cl                NA         NA      NA       NA
> Mg                NA         NA      NA       NA
> Na                NA         NA      NA       NA
> SO4               NA         NA      NA       NA
> 
> Residual standard error: NaN on 0 degrees of freedom
>    (63 observations deleted due to missingness)
> 
>    When I look at the summary for the data frame used for this model I do
> not
> see an excessive number of missing values or indications why there are no
> residual degrees of freedom. The same model applied to 8 other data frames
> did not produce similar results.
> 
> Puzzled,
> 
> Rich
> 
Rich,

I don't see a 'data=' parameter in your call to lm().  How does lm()
know where to find the variables referenced in the model parameter?

If that is not the problem, then we need to see str() output for the data frame
that you are analyzing.

Dan

Daniel Nordlund
Bothell, WA USA

David Winsemius

2011-Nov-09 17:37 UTC

head link

[R] Interpreting Multiple Linear Regression Summary

On Nov 9, 2011, at 12:04 PM, Rich Shepard wrote:
>  I would appreciate pointers on what I should read to understand this
> output:
>
> summary(lm(TDS ~ Cond + Ca + Cl + Mg + Na + SO4))
I don't see a data= argument specified, so you are telling lm() that  
your workspace has individual vectors by those names in the formula.  
That is not what is implied by hte rest of your message.
>
> Call:
> lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4)
>
> Residuals:
> ALL 1 residuals are 0: no residual degrees of freedom!
>
> Coefficients: (6 not defined because of singularities)
>            Estimate Std. Error t value Pr(>|t|)
> (Intercept)      125         NA      NA       NA
> Cond              NA         NA      NA       NA
> Ca                NA         NA      NA       NA
> Cl                NA         NA      NA       NA
> Mg                NA         NA      NA       NA
> Na                NA         NA      NA       NA
> SO4               NA         NA      NA       NA
>
> Residual standard error: NaN on 0 degrees of freedom
>  (63 observations deleted due to missingness)
>
>  When I look at the summary for the data frame used for this model I  
> do not
> see an excessive number of missing values or indications why there  
> are no
> residual degrees of freedom. The same model applied to 8 other data  
> frames
> did not produce similar results.
>
> Puzzled,
>
> Rich
>
> ______________________________________________
> 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.
David Winsemius, MD
West Hartford, CT

B77S

2011-Nov-09 21:00 UTC

head link

[R] Interpreting Multiple Linear Regression Summary

This is the output of dput(your data)


structure(list(Ca = c(NA, NA, 24.4, NA, 21.4, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 28, 32, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, 34.7, NA, 42.5, NA, 26, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.6, 21.4, NA, 48.3, 
63.5, NA, NA, 28.7, NA, NA, NA, NA, 64.3, 23), Cl = c(1.58, 5.6, 
3, NA, 1, 5, 1.2, 4, 4, 8.4, 1, 1.4, 4.9, 1.7, 2, 1.6, 3.3, 2.2, 
9, 1, 2, 1, 1, 5, 4, 3, 2.27, 1.76, 5.81, 4.23, 4.23, 6.25, 6.72, 
4, NA, 5, 5.8, 5.8, 2.2, 5.4, 5.4, 4.8, 8, 1, 4.8, 5.9, 5.9, 
13, 5.6, 1.2, NA, NA, NA, 3, 7, NA, NA, 2, NA, NA, NA, NA, 7, 
4.1), Cond = c(NA, NA, 190, 187, 184, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 248, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, 304, 354, 379, NA, 300, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, 2.2, 187, 285, 378, 533, 
207, 262, 244, 238, 280, 380, 402, 636, 300), Mg = c(NA, NA, 
10, NA, 9.1, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, 11, 12, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
17.4, NA, 21.1, NA, 24, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, 9.5, NA, 22.1, 29.9, NA, NA, 12.6, NA, NA, NA, NA, 
32.4, 21), Na = c(NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 4L, 4L, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 
NA, NA, NA, NA, NA), SO4 = c(9.4, 6.5, 9, NA, 7, 55, 6.8, 105, 
15.6, 8.4, 8.8, 19.4, 37, 12, 10, 9.1, 34, 11, 69, 18, 9, 13, 
9, 7, 6, 5, 7.8, 7.8, 7.5, 6, 7.3, 7, 7.5, 6, 7, 7, 5.6, 5.6, 
5.4, 11, 10.5, 9.9, 11.7, 8.4, 12.1, 16, 20, 7.6, 17, 6.5, NA, 
8, 22, 24, 44, NA, 13, 13, 12, 18, 23, 23, 73, 4), TDS = c(105L, 
181L, 112L, 144L, 114L, 308L, 96L, 430L, 108L, 108L, 125L, 129L, 
360L, 140L, 95L, 120L, 280L, 130L, 352L, 148L, 107L, 125L, 139L, 
188L, 201L, 178L, 197L, 187L, 182L, 165L, 186L, 191L, 190L, 176L, 
175L, 220L, 163L, 163L, 152L, 221L, 171L, 204L, 174L, 190L, 174L, 
210L, 190L, 180L, 200L, 180L, NA, 120L, 135L, 228L, 14L, NA, 
156L, 140L, 128L, 160L, 215L, 230L, 316L, 163L)), .Names = c("Ca", 
"Cl", "Cond", "Mg", "Na",
"SO4", "TDS"), class = "data.frame", row.names
c(NA,
-64L))



B77S wrote:> 
> Please see ?dput
> 
> use dput(your data) and paste the output into a reply, thanks.
> 
> This way we know what you are working with. 
> 
> 
> 
> 
> Rich Shepard wrote:
>> 
>> I would appreciate pointers on what I should read to understand this
>> output:
>> 
>>   summary(lm(TDS ~ Cond + Ca + Cl + Mg + Na + SO4))
>> 
>> Call:
>> lm(formula = TDS ~ Cond + Ca + Cl + Mg + Na + SO4)
>> 
>> Residuals:
>> ALL 1 residuals are 0: no residual degrees of freedom!
>> 
>> Coefficients: (6 not defined because of singularities)
>>              Estimate Std. Error t value Pr(>|t|)
>> (Intercept)      125         NA      NA       NA
>> Cond              NA         NA      NA       NA
>> Ca                NA         NA      NA       NA
>> Cl                NA         NA      NA       NA
>> Mg                NA         NA      NA       NA
>> Na                NA         NA      NA       NA
>> SO4               NA         NA      NA       NA
>> 
>> Residual standard error: NaN on 0 degrees of freedom
>>    (63 observations deleted due to missingness)
>> 
>>    When I look at the summary for the data frame used for this model I
do
>> not
>> see an excessive number of missing values or indications why there are
no
>> residual degrees of freedom. The same model applied to 8 other data
>> frames
>> did not produce similar results.
>> 
>> Puzzled,
>> 
>> Rich
>> 
>> ______________________________________________
>> R-help@ 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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