[R] regression analyses using a vector of means and a variance-covariance matrix
R 2.2.0
windows XP
How can I perform a regression analyses using a vector of means, a
variance-covariance matrix? I looked at the help screen for lm and did
not see any option for using the afore mentioned structures as input to
lm.
Thanks,
John
John Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
Baltimore VA Medical Center GRECC,
University of Maryland School of Medicine Claude D. Pepper OAIC,
University of Maryland Clinical Nutrition Research Unit, and
Baltimore VA Center Stroke of Excellence
University of Maryland School of Medicine
Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)
[EMAIL PROTECTED]
Confidentiality Statement:
This email message, including any attachments, is for the so...{{dropped}}
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Re: [R] regression analyses using a vector of means and a variance-covariance matrix
Here is another approach using the same data as in
John Fox's reply. His is probably superior but this
does have the advantage that its very simple. Note
that it gives the same coefficients and R squared
to several decimal places. We just simulate a
data set with the given means and variance covariance
matrix
library(car) # for Duncan
library(MASS) # for mvrnorm
set.seed(1)
DF - mvrnorm(1, colMeans(data), var(data), empirical = TRUE)
summary(lm(prestige ~ income + education, as.data.frame(DF)))
Call:
lm(formula = prestige ~ income + education, data = as.data.frame(DF))
Residuals:
Min 1Q Median 3Q Max
-49.0377 -8.7332 0.2068 8.8342 50.2737
Coefficients:
Estimate Std. Error t value Pr(|t|)
(Intercept) -6.064663 0.277576 -21.85 2e-16 ***
income 0.598733 0.007756 77.19 2e-16 ***
education0.545834 0.006368 85.71 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.06 on 9997 degrees of freedom
Multiple R-Squared: 0.8282, Adjusted R-squared: 0.8281
F-statistic: 2.409e+04 on 2 and 9997 DF, p-value: 2.2e-16
On 10/14/06, John Sorkin [EMAIL PROTECTED] wrote:
R 2.2.0
windows XP
How can I perform a regression analyses using a vector of means, a
variance-covariance matrix? I looked at the help screen for lm and did
not see any option for using the afore mentioned structures as input to
lm.
Thanks,
John
John Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
Baltimore VA Medical Center GRECC,
University of Maryland School of Medicine Claude D. Pepper OAIC,
University of Maryland Clinical Nutrition Research Unit, and
Baltimore VA Center Stroke of Excellence
University of Maryland School of Medicine
Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)
[EMAIL PROTECTED]
Confidentiality Statement:
This email message, including any attachments, is for the so...{{dropped}}
__
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.
__
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.
Re: [R] regression analyses using a vector of means and a variance-covariance matrix
There was a missing line:
On 10/14/06, Gabor Grothendieck [EMAIL PROTECTED] wrote:
Here is another approach using the same data as in
John Fox's reply. His is probably superior but this
does have the advantage that its very simple. Note
that it gives the same coefficients and R squared
to several decimal places. We just simulate a
data set with the given means and variance covariance
matrix
library(car) # for Duncan
library(MASS) # for mvrnorm
set.seed(1)
data - Duncan[,c(prestige, income, education)] # as in John Fox's post
DF - mvrnorm(1, colMeans(data), var(data), empirical = TRUE)
summary(lm(prestige ~ income + education, as.data.frame(DF)))
Call:
lm(formula = prestige ~ income + education, data = as.data.frame(DF))
Residuals:
Min 1Q Median 3Q Max
-49.0377 -8.7332 0.2068 8.8342 50.2737
Coefficients:
Estimate Std. Error t value Pr(|t|)
(Intercept) -6.064663 0.277576 -21.85 2e-16 ***
income 0.598733 0.007756 77.19 2e-16 ***
education0.545834 0.006368 85.71 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 13.06 on 9997 degrees of freedom
Multiple R-Squared: 0.8282, Adjusted R-squared: 0.8281
F-statistic: 2.409e+04 on 2 and 9997 DF, p-value: 2.2e-16
On 10/14/06, John Sorkin [EMAIL PROTECTED] wrote:
R 2.2.0
windows XP
How can I perform a regression analyses using a vector of means, a
variance-covariance matrix? I looked at the help screen for lm and did
not see any option for using the afore mentioned structures as input to
lm.
Thanks,
John
John Sorkin M.D., Ph.D.
Chief, Biostatistics and Informatics
Baltimore VA Medical Center GRECC,
University of Maryland School of Medicine Claude D. Pepper OAIC,
University of Maryland Clinical Nutrition Research Unit, and
Baltimore VA Center Stroke of Excellence
University of Maryland School of Medicine
Division of Gerontology
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing)
[EMAIL PROTECTED]
Confidentiality Statement:
This email message, including any attachments, is for the so...{{dropped}}
__
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.
__
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.
