From: Dimitrios Rizopoulos <Dimitris.Rizopoulos_at_med.kuleuven.be>

Date: Sat 13 Jan 2007 - 19:14:37 GMT

Dimitris Rizopoulos

Ph.D. Student

Biostatistical Centre

School of Public Health

Catholic University of Leuven

R-help@stat.math.ethz.ch 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. Received on Sun Jan 14 06:21:16 2007

Date: Sat 13 Jan 2007 - 19:14:37 GMT

try this

Out <- lm(A ~ data$B + data$C + data$D) summary(Out)

moreover, by typing 'summary.lm' in your R console you may see how the t-values are computed; check also ?summary.lm.

Another way, though less efficient, to obtain the standard errors is the following

summ.Out <- summary(Out)

X <- model.matrix(Out) # the design matrix
var.betas <- solve(crossprod(X)) * summ.Out$sigma^2
# standard errors

sqrt(diag(var.betas))

I hope it helps.

Best,

Dimitris

Dimitris Rizopoulos

Ph.D. Student

Biostatistical Centre

School of Public Health

Catholic University of Leuven

Address: Kapucijnenvoer 35, Leuven, Belgium

Tel: +32/(0)16/336899 Fax: +32/(0)16/337015 Web: http://med.kuleuven.be/biostat/ http://www.student.kuleuven.be/~m0390867/dimitris.htm

Quoting algorithms@gmx.de:

> Hello,

*>
**> I'd like to ask for the exact definition of the t-value, which R
**> uses in its summaries of a linear model for judging the importance
**> of an independent variable in explaining the dependent variable.
**> I searched the documentation, some groups, and the web for quite a
**> long time, but the best I could come up with is the following from
**>
**> www.answers.com/topic/value
**>
**> which reads:
**>
**> Measure of the statistical significance of an independent variable b
**> in explaining the dependent variable y. It is determined by
**> dividing the estimated regression coefficient b by its standard
**> error Sb. That is
**>
**> t-Value = b/Sb
**>
**> Thus, the t-statistic measures how many standard errors the
**> coefficient is away from zero. Generally, any t-value greater than
**> +2 or less than - 2 is acceptable. The higher the t-value, the
**> greater the confidence we have in the coefficient as a predictor.
**> Low t-values are indications of low reliability of the predictive
**> power of that coefficient.
**>
**>
**> My problem is that I do not know how to compute the standard error
**> Sb of some regression coefficient, when I have done nothing more
**> than to use the lm command in this manner:
**>
**> Out = lm(A~ data$B + data$C + data$D)
**>
**>
**> Does anyone know in detail, how R computes the t-value displayed in
**> summaries?
**>
**>
**> Thank you very much,
**>
**> Peter
**> --
**>
**> ______________________________________________
**> R-help@stat.math.ethz.ch 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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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. Received on Sun Jan 14 06:21:16 2007

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