Re: [R] logistic regression - glm() - example in Dalgaard's book ISwR
You may find both of Alan Agresti's books on categorcial data analysis
useful. Try googling both books and then
search the word grouped within each book. Agresti refers to the
difference you describe as
grouped versus ungrouped data. The likelihoods differ and all
summaries based on the likelihood will
also differ.
On Fri, Jul 2, 2010 at 11:33 PM, Paulo Barata pbar...@infolink.com.br wrote:
Dear R-list members,
I would like to pose a question about the use and results
of the glm() function for logistic regression calculations.
The question is based on an example provided on p. 229
in P. Dalgaard, Introductory Statistics with R, 2nd. edition,
Springer, 2008. By means of this example, I was trying to
practice the different ways of entering data in glm().
In his book, Dalgaard provides data from a case-based study
about hypertension summarized in the form of a table. He shows
two ways of entering the response (dependent) variable data
in glm(): (1) as a matrix of successes/failures (diseased/
healthy); (2) as the proportion of people diseased for each
combination of independent variable's categories.
I tried to enter the response variable in glm() in a third
way: by reconstructing, from the table, the original data
in a case-based format, that is, a data frame in which
each row shows the data for one person. In this situation,
the response variable would be coded as a numeric 0/1 vector,
0=failure, 1=success, and so it would be entered in glm() as
a numeric 0/1 vector.
The program below presents the calculations for each of the
three ways of entering data - the first and second methods
were just copied from Dalgaard's book.
The three methods produce the same results with regard
to the estimated coefficients, when the output is seen
with five decimals (although regression 3 seems to have
produced slightly different std.errors).
My main question is: Why are the residual deviance, its
degrees of freedom and the AIC produced by regression 3
completely different when compared to those produced by
regressions 1 and 2 (which seem to produce equal results)?
It seems that the degrees of freedom in regressions 1
and 2 are based on the size (number of rows) of table d
(see the output of the program below), but this table is
just a way of summarizing the data. The degrees of
freedom in regressions 1 and 2 are not based on the
actual number of cases (people) examined, which is n=433.
I understand that no matter the way of entering the data
in glm(), we are always analyzing the same data, which
are those presented in table format on Dalgaard's page
229 (these data are in data.frame d in the program below).
So I understand that the three ways of entering data
in glm() should produce the same results.
Secondarily, why are the std.errors in regression 3 slightly
different from those calculated in regressions 1 and 2?
I am using R version 2.11.1 on Windows XP.
Thank you very much.
Paulo Barata
##== begin =
## data in: P. Dalgaard, Introductory Statistics with R,
## 2nd. edition, Springer, 2008
## logistic regression - example in Dalgaard's Section 13.2,
## page 229
rm(list=ls())
## data provided on Dalgaard's page 229:
no.yes - c(No,Yes)
smoking - gl(2,1,8,no.yes)
obesity - gl(2,2,8,no.yes)
snoring - gl(2,4,8,no.yes)
n.tot - c(60,17,8,2,187,85,51,23)
n.hyp - c(5,2,1,0,35,13,15,8)
d - data.frame(smoking,obesity,snoring,n.tot,n.hyp)
## d is the data to be analyzed, in table format
## d is the first table on Dalgaard's page 229
## n.tot = total number of cases
## n.hyp = people with hypertension
d
## regression 1: Dalgaard's page 230
## response variable entered in glm() as a matrix of
## successes/failures
hyp.tbl - cbind(n.hyp,n.tot-n.hyp)
regression1 - glm(hyp.tbl~smoking+obesity+snoring,
family=binomial(logit))
## regression 2: Dalgaard's page 230
## response variable entered in glm() as proportions
prop.hyp - n.hyp/n.tot
regression2 - glm(prop.hyp~smoking+obesity+snoring,
weights=n.tot,family=binomial(logit))
## regression 3 (well below): data entered in glm()
## by means of 'reconstructed' variables
## variables with names beginning with 'r' are
## 'reconstructed' from data in data.frame d.
## The objective is to reconstruct the original
## data from which the table on Dalgaard's page 229
## has been produced
rsmoking - c(rep('No',d[1,4]),rep('Yes',d[2,4]),
rep('No',d[3,4]),rep('Yes',d[4,4]),
rep('No',d[5,4]),rep('Yes',d[6,4]),
rep('No',d[7,4]),rep('Yes',d[8,4]))
rsmoking - factor(rsmoking)
length(rsmoking) # just a check
robesity - c(rep('No', d[1,4]),rep('No', d[2,4]),
rep('Yes',d[3,4]),rep('Yes',d[4,4]),
rep('No', d[5,4]),rep('No', d[6,4]),
rep('Yes',d[7,4]),rep('Yes',d[8,4]))
robesity - factor(robesity)
length(robesity) # just a check
Re: [R] logistic regression - glm() - example in Dalgaard's book ISwR
On Jul 2, 2010, at 11:33 PM, Paulo Barata wrote:
Dear R-list members,
I would like to pose a question about the use and results
of the glm() function for logistic regression calculations.
The question is based on an example provided on p. 229
in P. Dalgaard, Introductory Statistics with R, 2nd. edition,
Springer, 2008. By means of this example, I was trying to
practice the different ways of entering data in glm().
In his book, Dalgaard provides data from a case-based study
about hypertension summarized in the form of a table. He shows
two ways of entering the response (dependent) variable data
in glm(): (1) as a matrix of successes/failures (diseased/
healthy); (2) as the proportion of people diseased for each
combination of independent variable's categories.
I tried to enter the response variable in glm() in a third
way: by reconstructing, from the table, the original data
in a case-based format, that is, a data frame in which
each row shows the data for one person. In this situation,
the response variable would be coded as a numeric 0/1 vector,
0=failure, 1=success, and so it would be entered in glm() as
a numeric 0/1 vector.
The program below presents the calculations for each of the
three ways of entering data - the first and second methods
were just copied from Dalgaard's book.
The three methods produce the same results with regard
to the estimated coefficients, when the output is seen
with five decimals (although regression 3 seems to have
produced slightly different std.errors).
My main question is: Why are the residual deviance, its
degrees of freedom and the AIC produced by regression 3
completely different when compared to those produced by
regressions 1 and 2 (which seem to produce equal results)?
It seems that the degrees of freedom in regressions 1
and 2 are based on the size (number of rows) of table d
(see the output of the program below), but this table is
just a way of summarizing the data. The degrees of
freedom in regressions 1 and 2 are not based on the
actual number of cases (people) examined, which is n=433.
I first encountered this phenomenon 25 years ago when using GLIM. The
answer from my statistical betters was that the deviance is actually
only established up to a constant and that it is only differences in
deviance that can be properly interpreted. The same situation exists
with indefinite integrals in calculus.
I understand that no matter the way of entering the data
in glm(), we are always analyzing the same data, which
are those presented in table format on Dalgaard's page
229 (these data are in data.frame d in the program below).
So I understand that the three ways of entering data
in glm() should produce the same results.
The differences between equivalent nested models should remain the
same (up to numerical accuracy).
411.42 on 432 degrees of freedom
-398.92 on 429
-
12.5 3
14.1259 on 7 degrees of freedom
-1.6184 on 4
--
12.50753
Secondarily, why are the std.errors in regression 3 slightly
different from those calculated in regressions 1 and 2?
You mean the differences 4 places to the right of the decimal???
I am using R version 2.11.1 on Windows XP.
Thank you very much.
Paulo Barata
##== begin =
## data in: P. Dalgaard, Introductory Statistics with R,
## 2nd. edition, Springer, 2008
## logistic regression - example in Dalgaard's Section 13.2,
## page 229
rm(list=ls())
Personally, I rather annoyed when people post this particular line
without commenting it out. It is basically saying that your code is
terribly much more important than whatever else might be in a user's
workspace.
## data provided on Dalgaard's page 229:
no.yes - c(No,Yes)
smoking - gl(2,1,8,no.yes)
obesity - gl(2,2,8,no.yes)
snoring - gl(2,4,8,no.yes)
n.tot - c(60,17,8,2,187,85,51,23)
n.hyp - c(5,2,1,0,35,13,15,8)
d - data.frame(smoking,obesity,snoring,n.tot,n.hyp)
## d is the data to be analyzed, in table format
## d is the first table on Dalgaard's page 229
## n.tot = total number of cases
## n.hyp = people with hypertension
d
## regression 1: Dalgaard's page 230
## response variable entered in glm() as a matrix of
## successes/failures
hyp.tbl - cbind(n.hyp,n.tot-n.hyp)
regression1 - glm(hyp.tbl~smoking+obesity+snoring,
family=binomial(logit))
## regression 2: Dalgaard's page 230
## response variable entered in glm() as proportions
prop.hyp - n.hyp/n.tot
regression2 - glm(prop.hyp~smoking+obesity+snoring,
weights=n.tot,family=binomial(logit))
## regression 3 (well below): data entered in glm()
## by means of 'reconstructed' variables
## variables with names beginning with 'r' are
## 'reconstructed' from data in data.frame d.
## The objective is to reconstruct the original
## data from which the table on Dalgaard's page 229
## has been produced
rsmoking - c(rep('No',d[1,4]),rep('Yes',d[2,4]),
Re: [R] logistic regression - glm() - example in Dalgaard's book ISwR
On Jul 3, 2010, at 9:00 AM, David Winsemius wrote: On Jul 2, 2010, at 11:33 PM, Paulo Barata wrote: Dear R-list members, I would like to pose a question about the use and results of the glm() function for logistic regression calculations. The question is based on an example provided on p. 229 in P. Dalgaard, Introductory Statistics with R, 2nd. edition, Springer, 2008. By means of this example, I was trying to practice the different ways of entering data in glm(). In his book, Dalgaard provides data from a case-based study about hypertension summarized in the form of a table. He shows two ways of entering the response (dependent) variable data in glm(): (1) as a matrix of successes/failures (diseased/ healthy); (2) as the proportion of people diseased for each combination of independent variable's categories. I tried to enter the response variable in glm() in a third way: by reconstructing, from the table, the original data in a case-based format, that is, a data frame in which each row shows the data for one person. In this situation, the response variable would be coded as a numeric 0/1 vector, 0=failure, 1=success, and so it would be entered in glm() as a numeric 0/1 vector. The program below presents the calculations for each of the three ways of entering data - the first and second methods were just copied from Dalgaard's book. The three methods produce the same results with regard to the estimated coefficients, when the output is seen with five decimals (although regression 3 seems to have produced slightly different std.errors). My main question is: Why are the residual deviance, its degrees of freedom and the AIC produced by regression 3 completely different when compared to those produced by regressions 1 and 2 (which seem to produce equal results)? It seems that the degrees of freedom in regressions 1 and 2 are based on the size (number of rows) of table d (see the output of the program below), but this table is just a way of summarizing the data. The degrees of freedom in regressions 1 and 2 are not based on the actual number of cases (people) examined, which is n=433. I first encountered this phenomenon 25 years ago when using GLIM. The answer from my statistical betters was that the deviance is actually only established up to a constant and that it is only differences in deviance that can be properly interpreted. The same situation exists with indefinite integrals in calculus. I understand that no matter the way of entering the data in glm(), we are always analyzing the same data, which are those presented in table format on Dalgaard's page 229 (these data are in data.frame d in the program below). So I understand that the three ways of entering data in glm() should produce the same results. The differences between equivalent nested models should remain the same (up to numerical accuracy). 411.42 on 432 degrees of freedom -398.92 on 429 - 12.5 3 14.1259 on 7 degrees of freedom -1.6184 on 4 -- 12.50753 Secondarily, why are the std.errors in regression 3 slightly different from those calculated in regressions 1 and 2? You mean the differences 4 places to the right of the decimal??? I am using R version 2.11.1 on Windows XP. Thank you very much. Paulo Barata ##== begin = ## data in: P. Dalgaard, Introductory Statistics with R, ## 2nd. edition, Springer, 2008 ## logistic regression - example in Dalgaard's Section 13.2, ## page 229 rm(list=ls()) Personally, I rather annoyed when people post this particular line without commenting it out. It is basically saying that your code is terribly much more important than whatever else might be in a user's workspace. ## data provided on Dalgaard's page 229: no.yes - c(No,Yes) smoking - gl(2,1,8,no.yes) obesity - gl(2,2,8,no.yes) snoring - gl(2,4,8,no.yes) n.tot - c(60,17,8,2,187,85,51,23) n.hyp - c(5,2,1,0,35,13,15,8) d - data.frame(smoking,obesity,snoring,n.tot,n.hyp) ## d is the data to be analyzed, in table format ## d is the first table on Dalgaard's page 229 ## n.tot = total number of cases ## n.hyp = people with hypertension d ## regression 1: Dalgaard's page 230 ## response variable entered in glm() as a matrix of ## successes/failures hyp.tbl - cbind(n.hyp,n.tot-n.hyp) regression1 - glm(hyp.tbl~smoking+obesity+snoring, family=binomial(logit)) ## regression 2: Dalgaard's page 230 ## response variable entered in glm() as proportions prop.hyp - n.hyp/n.tot regression2 - glm(prop.hyp~smoking+obesity+snoring, weights=n.tot,family=binomial(logit)) ## regression 3 (well below): data entered in glm() ## by means of 'reconstructed' variables ## variables with names beginning with 'r' are ## 'reconstructed' from data in data.frame d. ## The objective is to
[R] logistic regression - glm() - example in Dalgaard's book ISwR
Dear R-list members,
I would like to pose a question about the use and results
of the glm() function for logistic regression calculations.
The question is based on an example provided on p. 229
in P. Dalgaard, Introductory Statistics with R, 2nd. edition,
Springer, 2008. By means of this example, I was trying to
practice the different ways of entering data in glm().
In his book, Dalgaard provides data from a case-based study
about hypertension summarized in the form of a table. He shows
two ways of entering the response (dependent) variable data
in glm(): (1) as a matrix of successes/failures (diseased/
healthy); (2) as the proportion of people diseased for each
combination of independent variable's categories.
I tried to enter the response variable in glm() in a third
way: by reconstructing, from the table, the original data
in a case-based format, that is, a data frame in which
each row shows the data for one person. In this situation,
the response variable would be coded as a numeric 0/1 vector,
0=failure, 1=success, and so it would be entered in glm() as
a numeric 0/1 vector.
The program below presents the calculations for each of the
three ways of entering data - the first and second methods
were just copied from Dalgaard's book.
The three methods produce the same results with regard
to the estimated coefficients, when the output is seen
with five decimals (although regression 3 seems to have
produced slightly different std.errors).
My main question is: Why are the residual deviance, its
degrees of freedom and the AIC produced by regression 3
completely different when compared to those produced by
regressions 1 and 2 (which seem to produce equal results)?
It seems that the degrees of freedom in regressions 1
and 2 are based on the size (number of rows) of table d
(see the output of the program below), but this table is
just a way of summarizing the data. The degrees of
freedom in regressions 1 and 2 are not based on the
actual number of cases (people) examined, which is n=433.
I understand that no matter the way of entering the data
in glm(), we are always analyzing the same data, which
are those presented in table format on Dalgaard's page
229 (these data are in data.frame d in the program below).
So I understand that the three ways of entering data
in glm() should produce the same results.
Secondarily, why are the std.errors in regression 3 slightly
different from those calculated in regressions 1 and 2?
I am using R version 2.11.1 on Windows XP.
Thank you very much.
Paulo Barata
##== begin =
## data in: P. Dalgaard, Introductory Statistics with R,
## 2nd. edition, Springer, 2008
## logistic regression - example in Dalgaard's Section 13.2,
## page 229
rm(list=ls())
## data provided on Dalgaard's page 229:
no.yes - c(No,Yes)
smoking - gl(2,1,8,no.yes)
obesity - gl(2,2,8,no.yes)
snoring - gl(2,4,8,no.yes)
n.tot - c(60,17,8,2,187,85,51,23)
n.hyp - c(5,2,1,0,35,13,15,8)
d - data.frame(smoking,obesity,snoring,n.tot,n.hyp)
## d is the data to be analyzed, in table format
## d is the first table on Dalgaard's page 229
## n.tot = total number of cases
## n.hyp = people with hypertension
d
## regression 1: Dalgaard's page 230
## response variable entered in glm() as a matrix of
## successes/failures
hyp.tbl - cbind(n.hyp,n.tot-n.hyp)
regression1 - glm(hyp.tbl~smoking+obesity+snoring,
family=binomial(logit))
## regression 2: Dalgaard's page 230
## response variable entered in glm() as proportions
prop.hyp - n.hyp/n.tot
regression2 - glm(prop.hyp~smoking+obesity+snoring,
weights=n.tot,family=binomial(logit))
## regression 3 (well below): data entered in glm()
## by means of 'reconstructed' variables
## variables with names beginning with 'r' are
## 'reconstructed' from data in data.frame d.
## The objective is to reconstruct the original
## data from which the table on Dalgaard's page 229
## has been produced
rsmoking - c(rep('No',d[1,4]),rep('Yes',d[2,4]),
rep('No',d[3,4]),rep('Yes',d[4,4]),
rep('No',d[5,4]),rep('Yes',d[6,4]),
rep('No',d[7,4]),rep('Yes',d[8,4]))
rsmoking - factor(rsmoking)
length(rsmoking) # just a check
robesity - c(rep('No', d[1,4]),rep('No', d[2,4]),
rep('Yes',d[3,4]),rep('Yes',d[4,4]),
rep('No', d[5,4]),rep('No', d[6,4]),
rep('Yes',d[7,4]),rep('Yes',d[8,4]))
robesity - factor(robesity)
length(robesity) # just a check
rsnoring - c(rep('No', d[1,4]),rep('No', d[2,4]),
rep('No', d[3,4]),rep('No', d[4,4]),
rep('Yes',d[5,4]),rep('Yes',d[6,4]),
rep('Yes',d[7,4]),rep('Yes',d[8,4]))
rsnoring - factor(rsnoring)
length(rsnoring) # just a check
rhyp - c(rep(1,d[1,5]),rep(0,d[1,4]-d[1,5]),
rep(1,d[2,5]),rep(0,d[2,4]-d[2,5]),
rep(1,d[3,5]),rep(0,d[3,4]-d[3,5]),
rep(1,d[4,5]),rep(0,d[4,4]-d[4,5]),
