[R] Statitics Textbook - any recommendation?
I would like to buy a basic statistics book (experimental design, sampling, ANOVA, regression, etc.) with examples in R. Or download it in PDF or html format. I went to the CRAN contributed documentation, but there were only R textbooks, that is, textbooks where R is the focus, not the statistics. And I would like to find the opposite. Other text I am trying to find is multivariate data analysis (EFA, cluster, mult regression, MANOVA, etc.) with examples with R. Any recommendation? Thank you in advance, Iuri. __ 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] Variance Components in R
Reading Bates' article on R News, I see that random effects require a
grouping variable. As, by convention, all variables in G-studies are
supposed random, what could be a grouping variable in that case? I see that
the model I wrote before (if ever ran...) would take all effects as fixed.
Is it possible to use lmer() without fixed effects?
Anything would help.
Iuri.
On 8/18/06, Iuri Gavronski [EMAIL PROTECTED] wrote:
Harold,
I don't have a grouping variable. And yes, persons can be an important
source of variance, and they are the resp variable. rating is the
response.variable in the model you specified below. aov perhaps could
give me distorted results, because of unbalanced data (what estimation
method it uses? ANOVA?): not all respondents evaluated all stores.
I have five variables: resp (persons, the respondents), aspect (the
construct), item (the question), chain (the store the person is
rating) and sector (the economic sector where chain belongs, e.g.
groceries). And one response, rating.
The model would be?
fm - lmer(rating ~ resp + aspect + item + chain + sector + sector*resp +
sector*aspect + sector*item + chain*resp + chain*aspect + chain*item +
resp*aspect + resp*item + sector*resp*aspect + sector*resp*item +
chain*resp*aspect)
Regards,
Iuri.
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
Iuri:
Here is an example of how a model would be specified using lmer using a
couple of your factors:
fm - lmer(response.variable ~ chain*sector*resp
+(chain*sector*resp|GroupingID), data)
This will give you a main effect for each factor and all possible
interactions. However, do you have a grouping variable? I wonder if aov
might be the better tool for your G-study?
As a side note, I don't see that you have a factor for persons. Isn't
this also a variance component of interest for your study?
--
*From:* [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] *On Behalf Of *Iuri
Gavronski
*Sent:* Thursday, August 17, 2006 1:26 PM
*To:* Doran, Harold
*Cc:* r-help@stat.math.ethz.ch
*Subject:* Re: [R] Variance Components in R
I am trying to replicate Finn and Kayandé (1997) study on G-theory
application on Marketing. The idea is to have people evaluate some aspects
of service quality for chains on different economy sectors. Then, conduct a
G-study to identify the generalizability coefficient estimates for different
D-study designs.
I have persons rating 3 different items on 3 different aspects of
service quality on 3 chains on 3 sectors. It is normally assumed on
G-studies that the factors are random. So I have to specify a model to
estimate the variance components of CHAIN SECTOR RESP ASPECT ITEM, and
the interaction of SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP
CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT
SECTOR*RESP*ITEM CHAIN*RESP*ASPECT. '*' in VARCOMP means a crossed design.
Evaluating only the two dimensions interactions (x*y) ran in few minutes
with the full database. Including three interactions (x*y*z) didn't complete
the execution at all. I have the data and script sent to a professor of the
department of Statistics on my university and he could not run it on either
SPSS or SAS (we don't have SAS licenses here at the business school, only
SPSS). Nobody here at the business school has any experience with R, so I
don't have anyone to ask for help.
Ì am not sure if I have answered you question, but feel free to ask it
again, and I will try to restate the problem.
Best regards,
Iuri
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
This will (should) be a piece of cake for lmer. But, I don't speak
SPSS. Can you write your model out as a linear model and give a brief
description of the data and your problem?
In addition to what Spencer noted as help below, you should also check
out the vignette in the mlmRev package. This will give you many examples.
vignette('MlmSoftRev')
--
*From:* [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] *On Behalf Of
*Iuri Gavronski
*Sent:* Thursday, August 17, 2006 11:16 AM
*To:* Doran, Harold
*Subject:* Re: [R] Variance Components in R
9500 records. It didn`t run in SPSS or SAS on Windows machines, so I
am trying to convert the SPSS script to R to run in a RISC station at the
university.
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
Iuri:
The lmer function is optimal for large data with crossed random effects.
How large are your data?
-Original Message-
From: [EMAIL PROTECTED]
[mailto: [EMAIL PROTECTED] On Behalf Of Iuri Gavronski
Sent: Thursday, August 17, 2006 11:08 AM
To: Spencer Graves
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Variance Components in R
Thank you for your reply.
VARCOMP is available at SPSS advanced models, I'm not sure
Re: [R] Variance Components in R
Harold, I have tried to adapt your syntax and got some problems. Some
responses from lmer:
On this one, I have tried to use 1 as a grouping variable. As I understood
from Bates (2005), grouping variables are like nested design, which is not
the case.
fm - lmer(RATING ~ CHAIN*SECTOR*RESP +(CHAIN*SECTOR*RESP|1), gt)
Erro em lmer(RATING ~ CHAIN * SECTOR * RESP + (CHAIN * SECTOR * RESP | :
Ztl[[1]] must have 1 columns
Then I have tried to ommit the fixed effects...
fm - lmer(RATING ~ (CHAIN*SECTOR*RESP|1), gt)
Erro em x[[3]] : não é possível dividir o objeto em subconjuntos
(the error message would be something like not possible to divide the
object in subsets... I don't know the original wording of message because
my R is in Portuguese...)
Then... I have tried to specify RESP (the persons) as the grouping variable
(which doesn't make any sense to me, but...)
fm - lmer(RATING ~ CHAIN*SECTOR*RESP +(CHAIN*SECTOR|RESP), gt)
Warning message:
nlminb returned message false convergence (8)
in: LMEoptimize-(`*tmp*`, value = list(maxIter = 200, tolerance =
1.49011611938477e-08,
Any idea?
Regards,
Iuri.
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
Iuri:
Here is an example of how a model would be specified using lmer using a
couple of your factors:
fm - lmer(response.variable ~ chain*sector*resp
+(chain*sector*resp|GroupingID), data)
This will give you a main effect for each factor and all possible
interactions. However, do you have a grouping variable? I wonder if aov
might be the better tool for your G-study?
As a side note, I don't see that you have a factor for persons. Isn't this
also a variance component of interest for your study?
--
*From:* [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] *On Behalf Of *Iuri
Gavronski
*Sent:* Thursday, August 17, 2006 1:26 PM
*To:* Doran, Harold
*Cc:* r-help@stat.math.ethz.ch
*Subject:* Re: [R] Variance Components in R
I am trying to replicate Finn and Kayandé (1997) study on G-theory
application on Marketing. The idea is to have people evaluate some aspects
of service quality for chains on different economy sectors. Then, conduct a
G-study to identify the generalizability coefficient estimates for different
D-study designs.
I have persons rating 3 different items on 3 different aspects of service
quality on 3 chains on 3 sectors. It is normally assumed on G-studies that
the factors are random. So I have to specify a model to estimate the
variance components of CHAIN SECTOR RESP ASPECT ITEM, and the interaction
of SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM
RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT.
'*' in VARCOMP means a crossed design.
Evaluating only the two dimensions interactions (x*y) ran in few minutes
with the full database. Including three interactions (x*y*z) didn't complete
the execution at all. I have the data and script sent to a professor of the
department of Statistics on my university and he could not run it on either
SPSS or SAS (we don't have SAS licenses here at the business school, only
SPSS). Nobody here at the business school has any experience with R, so I
don't have anyone to ask for help.
Ì am not sure if I have answered you question, but feel free to ask it
again, and I will try to restate the problem.
Best regards,
Iuri
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
This will (should) be a piece of cake for lmer. But, I don't speak
SPSS. Can you write your model out as a linear model and give a brief
description of the data and your problem?
In addition to what Spencer noted as help below, you should also check
out the vignette in the mlmRev package. This will give you many examples.
vignette('MlmSoftRev')
--
*From:* [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] *On Behalf Of *Iuri
Gavronski
*Sent:* Thursday, August 17, 2006 11:16 AM
*To:* Doran, Harold
*Subject:* Re: [R] Variance Components in R
9500 records. It didn`t run in SPSS or SAS on Windows machines, so I am
trying to convert the SPSS script to R to run in a RISC station at the
university.
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
Iuri:
The lmer function is optimal for large data with crossed random effects.
How large are your data?
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Iuri Gavronski
Sent: Thursday, August 17, 2006 11:08 AM
To: Spencer Graves
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Variance Components in R
Thank you for your reply.
VARCOMP is available at SPSS advanced models, I'm not sure
for how long it exists... I only work with SPSS for the last
4 years...
My model only has crossed random effects, what perhaps would
drive me to lmer().
However, as I have unbalanced data (why it is normally called
'unbalanced design'? the data
Re: [R] Variance Components in R
Harold, I have tried the following syntax: fm - lmer(RATING ~ CHAIN*SECTOR*RESP +(1|CHAIN*SECTOR*RESP), gt) summary(fm) Linear mixed-effects model fit by REML Formula: RATING ~ CHAIN * SECTOR * RESP + (1 | CHAIN * SECTOR * RESP) Data: gt AIC BIClogLik MLdeviance REMLdeviance 2767.466 2807.717 -1374.733 2710.253 2749.466 Random effects: GroupsNameVariance Std.Dev. CHAIN * SECTOR * RESP (Intercept) 5.7119 2.3900 Residual 2.8247 1.6807 number of obs: 647, groups: CHAIN * SECTOR * RESP, 71 Fixed effects: Estimate Std. Error t value (Intercept)4.576 2.6193950 1.74697 CHAIN -0.2014603 0.7984752 -0.25231 SECTOR-0.1093434 2.3516722 -0.04650 RESP 0.0184237 0.0276326 0.66674 CHAIN:SECTOR 0.1423668 0.3005919 0.47362 CHAIN:RESP 0.0024786 0.0083782 0.29584 SECTOR:RESP -0.0046001 0.0240517 -0.19126 CHAIN:SECTOR:RESP -0.0011219 0.0030762 -0.36470 Correlation of Fixed Effects: (Intr) CHAIN SECTOR RESP CHAIN:SECTOR CHAIN:R SECTOR: CHAIN -0.435 SECTOR-0.845 -0.050 RESP -0.778 0.345 0.645 CHAIN:SECTOR 0.886 -0.732 -0.635 -0.680 CHAIN:RESP 0.351 -0.782 0.038 -0.466 0.566 SECTOR:RESP0.666 0.038 -0.786 -0.822 0.500 -0.046 CHAIN:SECTOR: -0.709 0.586 0.500 0.879 -0.789 -0.729 -0.635 Again, my problem is: there are no fixed effects... The same dataset, when running at SPSS (I have a subset with 647 records), using the syntax I showed somewhere before, gives me the following output: Variance Components Estimation Variance Estimates Component Estimate Var(CHAIN) ,530 Var(SECTOR),000(a) Var(RESP) 2,734 Var(ASPECT),788 Var(ITEM) ,000(a) Var(SECTOR * ,061 RESP) Var(SECTOR * ,000(a) ASPECT) Var(SECTOR * ,031 ITEM) Var(CHAIN *2,183 RESP) Var(CHAIN *,038 ASPECT) Var(CHAIN *,003 ITEM) Var(RESP * ,467 ASPECT) Var(RESP * ,279 ITEM) Var(SECTOR * ,000(a) RESP * ASPECT) Var(SECTOR * ,077 RESP * ITEM) Var(CHAIN *,773 RESP * ASPECT) Var(Error) ,882 Dependent Variable: RATING Method: Restricted Maximum Likelihood Estimation a This estimate is set to zero because it is redundant. That's what I would like to get from R. Any help would be appreciated. Best regards, Iuri On 8/20/06, Iuri Gavronski [EMAIL PROTECTED] wrote: Harold, I have tried to adapt your syntax and got some problems. Some responses from lmer: On this one, I have tried to use 1 as a grouping variable. As I understood from Bates (2005), grouping variables are like nested design, which is not the case. fm - lmer(RATING ~ CHAIN*SECTOR*RESP +(CHAIN*SECTOR*RESP|1), gt) Erro em lmer(RATING ~ CHAIN * SECTOR * RESP + (CHAIN * SECTOR * RESP | : Ztl[[1]] must have 1 columns Then I have tried to ommit the fixed effects... fm - lmer(RATING ~ (CHAIN*SECTOR*RESP|1), gt) Erro em x[[3]] : não é possível dividir o objeto em subconjuntos (the error message would be something like not possible to divide the object in subsets... I don't know the original wording of message because my R is in Portuguese...) Then... I have tried to specify RESP (the persons) as the grouping variable (which doesn't make any sense to me, but...) fm - lmer(RATING ~ CHAIN*SECTOR*RESP +(CHAIN*SECTOR|RESP), gt) Warning message: nlminb returned message false convergence (8) in: LMEoptimize-(`*tmp*`, value = list(maxIter = 200, tolerance = 1.49011611938477e-08, Any idea? Regards, Iuri. On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote: Iuri: Here is an example of how a model would be specified using lmer using a couple of your factors: fm - lmer(response.variable ~ chain*sector*resp +(chain*sector*resp|GroupingID), data) This will give you a main effect for each factor and all possible interactions. However, do you have a grouping variable? I wonder if aov might be the better tool for your G-study? As a side note, I don't see that you have a factor for persons. Isn't this also a variance component of interest for your study? From: [EMAIL PROTECTED][mailto:[EMAIL PROTECTED] On Behalf Of IuriGavronski Sent: Thursday, August 17, 2006 1:26 PM To:Doran, Harold Cc: r-help@stat.math.ethz.ch Subject: Re:[R] Variance Components in R I am trying to replicate Finn and Kayandé (1997) study on G-theory application on Marketing. The idea is to have people evaluate some aspects ofservice quality for chains on different economy sectors. Then, conduct aG-study to identify the generalizability coefficient estimates for differentD-study designs. I have persons rating 3 different items on 3 differentaspects of service quality on 3 chains on 3 sectors. It is normally assumed on G-studies
Re: [R] Variance Components in R
Dear Harold and others, I have changed the syntax for lmer() and used this one: require(lme4) gt - read.table(gt5.txt) sink(GT output.txt) attach(gt) system.time(fm - lmer(RATING ~ 1 +(1|CHAIN) +(1|SECTOR) +(1|RESP) +(1|ASPECT) +(1|ITEM) +(1|SECTOR*RESP) +(1|SECTOR*ASPECT) +(1|SECTOR*ITEM) +(1|CHAIN*RESP) +(1|CHAIN*ASPECT) +(1|CHAIN*ITEM) +(1|RESP*ASPECT) +(1|RESP*ITEM) +(1|SECTOR*RESP*ASPECT) +(1|SECTOR*RESP*ITEM) +(1|CHAIN*RESP*ASPECT), gt) ) options(digits = 4) options(OutDec = ,) summary(fm, digits = 4) sink() Then the output I got from summary(lm) was: Linear mixed-effects model fit by REML Formula: RATING ~ 1 + (1 | CHAIN) + (1 | SECTOR) + (1 | RESP) + (1 | ASPECT) + (1 | ITEM) + (1 | SECTOR * RESP) + (1 | SECTOR * ASPECT) + (1 | SECTOR * ITEM) + (1 | CHAIN * RESP) + (1 | CHAIN * ASPECT) + (1 | CHAIN * ITEM) + (1 | RESP * ASPECT) + (1 | RESP * ITEM) + (1 | SECTOR * RESP * ASPECT) + (1 | SECTOR * RESP * ITEM) + (1 | CHAIN * RESP * ASPECT) Data: gt AIC BIC logLik MLdeviance REMLdeviance 2386 2462 -1176 2353 2352 Random effects: Groups NameVariance Std.Dev. CHAIN * RESP * ASPECT (Intercept) 5,89e-01 0,7675133 SECTOR * RESP * ITEM (Intercept) 4,91e-02 0,2216137 RESP * ITEM(Intercept) 2,75e-01 0,5242572 CHAIN * RESP (Intercept) 1,98e+00 1,4068696 SECTOR * RESP * ASPECT (Intercept) 5,17e-10 0,227 CHAIN * ITEM (Intercept) 5,17e-10 0,227 RESP * ASPECT (Intercept) 4,77e-01 0,6908419 SECTOR * RESP (Intercept) 3,42e-01 0,5848027 CHAIN * ASPECT (Intercept) 1,61e-02 0,1269306 SECTOR * ITEM (Intercept) 2,24e-02 0,1495102 ITEM (Intercept) 5,17e-10 0,227 CHAIN (Intercept) 8,88e-01 0,9424441 RESP (Intercept) 2,80e+00 1,6747970 SECTOR * ASPECT(Intercept) 5,17e-10 0,227 ASPECT (Intercept) 8,07e-01 0,8984151 SECTOR (Intercept) 5,17e-10 0,227 Residual 1,03e+00 1,0172221 number of obs: 647, groups: CHAIN * RESP * ASPECT, 138; SECTOR * RESP * ITEM, 138; RESP * ITEM, 70; CHAIN * RESP, 70; SECTOR * RESP * ASPECT, 47; CHAIN * ITEM, 36; RESP * ASPECT, 24; SECTOR * RESP, 24; CHAIN * ASPECT, 18; SECTOR * ITEM, 18; ITEM, 9; CHAIN, 9; RESP, 8; SECTOR * ASPECT, 6; ASPECT, 3; SECTOR, 3 Fixed effects: Estimate Std. Error t value (Intercept)5,797 0,8916,51 Comparing the output I had from R and SPSS, for the same database: Component Estimate SPSS R Var(CHAIN) ,530 0,888 Var(SECTOR),000(a) 0,000 Var(RESP) 2,7342,800 Var(ASPECT),788 0,807 Var(ITEM) ,000(a) 0,000 Var(SECTOR * ,061 0,342 RESP) Var(SECTOR * ,000(a) 0,000 ASPECT) Var(SECTOR * ,031 0,022 ITEM) Var(CHAIN *2,1831,980 RESP) Var(CHAIN *,038 0,016 ASPECT) Var(CHAIN *,003 0,000 ITEM) Var(RESP * ,467 0,477 ASPECT) Var(RESP * ,279 0,275 ITEM) Var(SECTOR * ,000(a) 0,000 RESP * ASPECT) Var(SECTOR * ,077 0,049 RESP * ITEM) Var(CHAIN *,773 0,589 RESP * ASPECT) Var(Error) ,882 1,030 As can be seen on the previous table, the results are different. Am I specifing a different model on R and SPSS? Is it possible to have the output from summary(lmer()) in #,### format, instead of scientific format? Best regards, Iuri. On 8/20/06, Iuri Gavronski [EMAIL PROTECTED] wrote: Harold, I have tried the following syntax: fm - lmer(RATING ~ CHAIN*SECTOR*RESP +(1|CHAIN*SECTOR*RESP), gt) summary(fm) Linear mixed-effects model fit by REML Formula: RATING ~ CHAIN * SECTOR * RESP + (1 | CHAIN * SECTOR * RESP) Data: gt AIC BIClogLik MLdeviance REMLdeviance 2767.466 2807.717 -1374.733 2710.253 2749.466 Random effects: GroupsNameVariance Std.Dev. CHAIN * SECTOR * RESP (Intercept) 5.7119 2.3900 Residual 2.8247 1.6807 number of obs: 647, groups: CHAIN * SECTOR * RESP, 71 Fixed effects: Estimate Std. Error t value (Intercept)4.576 2.6193950 1.74697 CHAIN -0.2014603 0.7984752 -0.25231 SECTOR-0.1093434 2.3516722 -0.04650 RESP 0.0184237 0.0276326 0.66674 CHAIN:SECTOR 0.1423668 0.3005919 0.47362 CHAIN:RESP 0.0024786 0.0083782 0.29584 SECTOR:RESP -0.0046001 0.0240517 -0.19126 CHAIN:SECTOR:RESP -0.0011219 0.0030762 -0.36470 Correlation of Fixed Effects: (Intr) CHAIN SECTOR RESP CHAIN:SECTOR CHAIN:R SECTOR: CHAIN -0.435 SECTOR-0.845 -0.050 RESP -0.778 0.345 0.645
Re: [R] Variance Components in R
Harold,
I don't have a grouping variable. And yes, persons can be an important
source of variance, and they are the resp variable. rating is the
response.variable in the model you specified below. aov perhaps could give
me distorted results, because of unbalanced data (what estimation method it
uses? ANOVA?): not all respondents evaluated all stores.
I have five variables: resp (persons, the respondents), aspect (the
construct), item (the question), chain (the store the person is
rating) and sector (the economic sector where chain belongs, e.g.
groceries). And one response, rating.
The model would be?
fm - lmer(rating ~ resp + aspect + item + chain + sector + sector*resp +
sector*aspect + sector*item + chain*resp + chain*aspect + chain*item +
resp*aspect + resp*item + sector*resp*aspect + sector*resp*item +
chain*resp*aspect)
Regards,
Iuri.
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
Iuri:
Here is an example of how a model would be specified using lmer using a
couple of your factors:
fm - lmer(response.variable ~ chain*sector*resp
+(chain*sector*resp|GroupingID), data)
This will give you a main effect for each factor and all possible
interactions. However, do you have a grouping variable? I wonder if aov
might be the better tool for your G-study?
As a side note, I don't see that you have a factor for persons. Isn't this
also a variance component of interest for your study?
--
*From:* [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] *On Behalf Of *Iuri
Gavronski
*Sent:* Thursday, August 17, 2006 1:26 PM
*To:* Doran, Harold
*Cc:* r-help@stat.math.ethz.ch
*Subject:* Re: [R] Variance Components in R
I am trying to replicate Finn and Kayandé (1997) study on G-theory
application on Marketing. The idea is to have people evaluate some aspects
of service quality for chains on different economy sectors. Then, conduct a
G-study to identify the generalizability coefficient estimates for different
D-study designs.
I have persons rating 3 different items on 3 different aspects of service
quality on 3 chains on 3 sectors. It is normally assumed on G-studies that
the factors are random. So I have to specify a model to estimate the
variance components of CHAIN SECTOR RESP ASPECT ITEM, and the interaction
of SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM
RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT.
'*' in VARCOMP means a crossed design.
Evaluating only the two dimensions interactions (x*y) ran in few minutes
with the full database. Including three interactions (x*y*z) didn't complete
the execution at all. I have the data and script sent to a professor of the
department of Statistics on my university and he could not run it on either
SPSS or SAS (we don't have SAS licenses here at the business school, only
SPSS). Nobody here at the business school has any experience with R, so I
don't have anyone to ask for help.
Ì am not sure if I have answered you question, but feel free to ask it
again, and I will try to restate the problem.
Best regards,
Iuri
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
This will (should) be a piece of cake for lmer. But, I don't speak
SPSS. Can you write your model out as a linear model and give a brief
description of the data and your problem?
In addition to what Spencer noted as help below, you should also check
out the vignette in the mlmRev package. This will give you many examples.
vignette('MlmSoftRev')
--
*From:* [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] *On Behalf Of *Iuri
Gavronski
*Sent:* Thursday, August 17, 2006 11:16 AM
*To:* Doran, Harold
*Subject:* Re: [R] Variance Components in R
9500 records. It didn`t run in SPSS or SAS on Windows machines, so I am
trying to convert the SPSS script to R to run in a RISC station at the
university.
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
Iuri:
The lmer function is optimal for large data with crossed random effects.
How large are your data?
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Iuri Gavronski
Sent: Thursday, August 17, 2006 11:08 AM
To: Spencer Graves
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Variance Components in R
Thank you for your reply.
VARCOMP is available at SPSS advanced models, I'm not sure
for how long it exists... I only work with SPSS for the last
4 years...
My model only has crossed random effects, what perhaps would
drive me to lmer().
However, as I have unbalanced data (why it is normally called
'unbalanced design'? the data was not intended to be
unbalanced, only I could not get responses for all cells...),
I'm afraid that REML would take too much CPU, memory and time
to execute, and MINQUE would be faster and provide similar
variance estimates (please, correct me if I'm wrong on that point
Re: [R] Variance Components in R
Thank you for your reply. VARCOMP is available at SPSS advanced models, I'm not sure for how long it exists... I only work with SPSS for the last 4 years... My model only has crossed random effects, what perhaps would drive me to lmer(). However, as I have unbalanced data (why it is normally called 'unbalanced design'? the data was not intended to be unbalanced, only I could not get responses for all cells...), I'm afraid that REML would take too much CPU, memory and time to execute, and MINQUE would be faster and provide similar variance estimates (please, correct me if I'm wrong on that point). I only found MINQUE on the maanova package, but as my study is very far from genetics, I'm not sure I can use this package. Any comment would be appreciated. Iuri On 8/16/06, Spencer Graves [EMAIL PROTECTED] wrote: I used SPSS over 25 years ago, but I don't recall ever fitting a variance components model with it. Are all your random effects nested? If they were, I would recommend you use 'lme' in the 'nlme' package. However, if you have crossed random effects, I suggest you try 'lmer' associated with the 'lme4' package. For 'lmer', documentation is available in Douglas Bates. Fitting linear mixed models in R. /R News/, 5(1):27-30, May 2005 (www.r-project.org - newsletter). I also recommend you try the vignette available with the 'mlmRev' package (see, e.g., http://finzi.psych.upenn.edu/R/Rhelp02a/archive/81375.html). Excellent documentation for both 'lme' (and indirectly for 'lmer') is available in Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). I have personally recommended this book so many times on this listserve that I just now got 234 hits for RSiteSearch(graves pinheiro). Please don't hesitate to pass this recommendation to your university library. This book is the primary documentation for the 'nlme' package, which is part of the standard R distribution. A subdirectory ~library\nlme\scripts of your R installation includes files named ch01.R, ch02.R, ..., ch06.R, ch08.R, containing the R scripts described in the book. These R script files make it much easier and more enjoyable to study that book, because they make it much easier to try the commands described in the book, one line at a time, testing modifications to check you comprehension, etc. In addition to avoiding problems with typographical errors, it also automatically overcomes a few minor but substantive changes in the notation between S-Plus and R. Also, the MINQUE method has been obsolete for over 25 years. I recommend you use method = REML except for when you want to compare two nested models with different fixed effects; in that case, you should use method = ML, as explained in Pinheiro and Bates (2000). Hope this helps. Spencer Graves Iuri Gavronski wrote: Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN = CHAIN SECTOR RESP ASPECT ITEM SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT /INTERCEPT = INCLUDE. VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = REML /DESIGN = CHAIN SECTOR RESP ASPECT ITEM SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT /INTERCEPT = INCLUDE. Thank you for your help. Best regards, Iuri. ___ Iuri Gavronski - [EMAIL PROTECTED] doutorando UFRGS/PPGA/NITEC - www.ppga.ufrgs.br Brazil __ 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. [[alternative HTML version deleted]] __ 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] Fwd: Variance Components in R
9500 records. It didn`t run in SPSS or SAS on Windows machines, so I am trying to convert the SPSS script to R to run in a RISC station at the university. On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote: Iuri: The lmer function is optimal for large data with crossed random effects. How large are your data? -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Iuri Gavronski Sent: Thursday, August 17, 2006 11:08 AM To: Spencer Graves Cc: r-help@stat.math.ethz.ch Subject: Re: [R] Variance Components in R Thank you for your reply. VARCOMP is available at SPSS advanced models, I'm not sure for how long it exists... I only work with SPSS for the last 4 years... My model only has crossed random effects, what perhaps would drive me to lmer(). However, as I have unbalanced data (why it is normally called 'unbalanced design'? the data was not intended to be unbalanced, only I could not get responses for all cells...), I'm afraid that REML would take too much CPU, memory and time to execute, and MINQUE would be faster and provide similar variance estimates (please, correct me if I'm wrong on that point). I only found MINQUE on the maanova package, but as my study is very far from genetics, I'm not sure I can use this package. Any comment would be appreciated. Iuri On 8/16/06, Spencer Graves [EMAIL PROTECTED] wrote: I used SPSS over 25 years ago, but I don't recall ever fitting a variance components model with it. Are all your random effects nested? If they were, I would recommend you use 'lme' in the 'nlme' package. However, if you have crossed random effects, I suggest you try 'lmer' associated with the 'lme4' package. For 'lmer', documentation is available in Douglas Bates. Fitting linear mixed models in R. /R News/, 5(1):27-30, May 2005 (www.r-project.org - newsletter). I also recommend you try the vignette available with the 'mlmRev' package (see, e.g., http://finzi.psych.upenn.edu/R/Rhelp02a/archive/81375.html). Excellent documentation for both 'lme' (and indirectly for 'lmer') is available in Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). I have personally recommended this book so many times on this listserve that I just now got 234 hits for RSiteSearch(graves pinheiro). Please don't hesitate to pass this recommendation to your university library. This book is the primary documentation for the 'nlme' package, which is part of the standard R distribution. A subdirectory ~library\nlme\scripts of your R installation includes files named ch01.R, ch02.R, ..., ch06.R, ch08.R, containing the R scripts described in the book. These R script files make it much easier and more enjoyable to study that book, because they make it much easier to try the commands described in the book, one line at a time, testing modifications to check you comprehension, etc. In addition to avoiding problems with typographical errors, it also automatically overcomes a few minor but substantive changes in the notation between S-Plus and R. Also, the MINQUE method has been obsolete for over 25 years. I recommend you use method = REML except for when you want to compare two nested models with different fixed effects; in that case, you should use method = ML, as explained in Pinheiro and Bates (2000). Hope this helps. Spencer Graves Iuri Gavronski wrote: Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN = CHAIN SECTOR RESP ASPECT ITEM SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT /INTERCEPT = INCLUDE. VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = REML /DESIGN = CHAIN SECTOR RESP ASPECT ITEM SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT /INTERCEPT = INCLUDE. Thank you for your help. Best regards, Iuri. ___ Iuri Gavronski - [EMAIL PROTECTED] doutorando UFRGS/PPGA/NITEC - www.ppga.ufrgs.br Brazil __ R-help@stat.math.ethz.ch mailing list https
Re: [R] Fwd: Variance Components in R
We have tried on many machines, from my laptop to a dual core Intel processor with 1GB of RAM. On 8/17/06, Spencer Graves [EMAIL PROTECTED] wrote: Hi, Iuri: How much RAM and how fast a microprocessor (and what version of Windows) do you have? You might still try it in R under Windows. The results might be comparable or dramatically better in R than in SPSS or SAS. hope this helps. Spencer Graves Iuri Gavronski wrote: 9500 records. It didn`t run in SPSS or SAS on Windows machines, so I am trying to convert the SPSS script to R to run in a RISC station at the university. On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote: Iuri: The lmer function is optimal for large data with crossed random effects. How large are your data? -Original Message- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Iuri Gavronski Sent: Thursday, August 17, 2006 11:08 AM To: Spencer Graves Cc: r-help@stat.math.ethz.ch Subject: Re: [R] Variance Components in R Thank you for your reply. VARCOMP is available at SPSS advanced models, I'm not sure for how long it exists... I only work with SPSS for the last 4 years... My model only has crossed random effects, what perhaps would drive me to lmer(). However, as I have unbalanced data (why it is normally called 'unbalanced design'? the data was not intended to be unbalanced, only I could not get responses for all cells...), I'm afraid that REML would take too much CPU, memory and time to execute, and MINQUE would be faster and provide similar variance estimates (please, correct me if I'm wrong on that point). I only found MINQUE on the maanova package, but as my study is very far from genetics, I'm not sure I can use this package. Any comment would be appreciated. Iuri On 8/16/06, Spencer Graves [EMAIL PROTECTED] wrote: I used SPSS over 25 years ago, but I don't recall ever fitting a variance components model with it. Are all your random effects nested? If they were, I would recommend you use 'lme' in the 'nlme' package. However, if you have crossed random effects, I suggest you try 'lmer' associated with the 'lme4' package. For 'lmer', documentation is available in Douglas Bates. Fitting linear mixed models in R. /R News/, 5(1):27-30, May 2005 (www.r-project.org - newsletter). I also recommend you try the vignette available with the 'mlmRev' package (see, e.g., http://finzi.psych.upenn.edu/R/Rhelp02a/archive/81375.html). Excellent documentation for both 'lme' (and indirectly for 'lmer') is available in Pinheiro and Bates (2000) Mixed-Effects Models in S and S-Plus (Springer). I have personally recommended this book so many times on this listserve that I just now got 234 hits for RSiteSearch(graves pinheiro). Please don't hesitate to pass this recommendation to your university library. This book is the primary documentation for the 'nlme' package, which is part of the standard R distribution. A subdirectory ~library\nlme\scripts of your R installation includes files named ch01.R, ch02.R, ..., ch06.R, ch08.R, containing the R scripts described in the book. These R script files make it much easier and more enjoyable to study that book, because they make it much easier to try the commands described in the book, one line at a time, testing modifications to check you comprehension, etc. In addition to avoiding problems with typographical errors, it also automatically overcomes a few minor but substantive changes in the notation between S-Plus and R. Also, the MINQUE method has been obsolete for over 25 years. I recommend you use method = REML except for when you want to compare two nested models with different fixed effects; in that case, you should use method = ML, as explained in Pinheiro and Bates (2000). Hope this helps. Spencer Graves Iuri Gavronski wrote: Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN = CHAIN SECTOR RESP ASPECT ITEM SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT /INTERCEPT = INCLUDE. VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = REML /DESIGN = CHAIN SECTOR RESP ASPECT ITEM SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM
Re: [R] Variance Components in R
I am trying to replicate Finn and Kayandé (1997) study on G-theory
application on Marketing. The idea is to have people evaluate some aspects
of service quality for chains on different economy sectors. Then, conduct a
G-study to identify the generalizability coefficient estimates for different
D-study designs.
I have persons rating 3 different items on 3 different aspects of service
quality on 3 chains on 3 sectors. It is normally assumed on G-studies that
the factors are random. So I have to specify a model to estimate the
variance components of CHAIN SECTOR RESP ASPECT ITEM, and the interaction of
SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM
RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT.
'*' in VARCOMP means a crossed design.
Evaluating only the two dimensions interactions (x*y) ran in few minutes
with the full database. Including three interactions (x*y*z) didn't complete
the execution at all. I have the data and script sent to a professor of the
department of Statistics on my university and he could not run it on either
SPSS or SAS (we don't have SAS licenses here at the business school, only
SPSS). Nobody here at the business school has any experience with R, so I
don't have anyone to ask for help.
Ì am not sure if I have answered you question, but feel free to ask it
again, and I will try to restate the problem.
Best regards,
Iuri
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
This will (should) be a piece of cake for lmer. But, I don't speak SPSS.
Can you write your model out as a linear model and give a brief description
of the data and your problem?
In addition to what Spencer noted as help below, you should also check out
the vignette in the mlmRev package. This will give you many examples.
vignette('MlmSoftRev')
--
*From:* [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] *On Behalf Of *Iuri
Gavronski
*Sent:* Thursday, August 17, 2006 11:16 AM
*To:* Doran, Harold
*Subject:* Re: [R] Variance Components in R
9500 records. It didn`t run in SPSS or SAS on Windows machines, so I am
trying to convert the SPSS script to R to run in a RISC station at the
university.
On 8/17/06, Doran, Harold [EMAIL PROTECTED] wrote:
Iuri:
The lmer function is optimal for large data with crossed random effects.
How large are your data?
-Original Message-
From: [EMAIL PROTECTED]
[mailto:[EMAIL PROTECTED] On Behalf Of Iuri Gavronski
Sent: Thursday, August 17, 2006 11:08 AM
To: Spencer Graves
Cc: r-help@stat.math.ethz.ch
Subject: Re: [R] Variance Components in R
Thank you for your reply.
VARCOMP is available at SPSS advanced models, I'm not sure
for how long it exists... I only work with SPSS for the last
4 years...
My model only has crossed random effects, what perhaps would
drive me to lmer().
However, as I have unbalanced data (why it is normally called
'unbalanced design'? the data was not intended to be
unbalanced, only I could not get responses for all cells...),
I'm afraid that REML would take too much CPU, memory and time
to execute, and MINQUE would be faster and provide similar
variance estimates (please, correct me if I'm wrong on that point).
I only found MINQUE on the maanova package, but as my study
is very far from genetics, I'm not sure I can use this package.
Any comment would be appreciated.
Iuri
On 8/16/06, Spencer Graves [EMAIL PROTECTED] wrote:
I used SPSS over 25 years ago, but I don't recall
ever fitting a
variance components model with it. Are all your random
effects nested?
If they were, I would recommend you use 'lme' in the 'nlme' package.
However, if you have crossed random effects, I suggest you
try 'lmer'
associated with the 'lme4' package.
For 'lmer', documentation is available in Douglas
Bates. Fitting
linear mixed models in R. /R News/, 5(1):27-30, May 2005
(www.r-project.org - newsletter). I also recommend you try the
vignette available with the 'mlmRev' package (see, e.g.,
http://finzi.psych.upenn.edu/R/Rhelp02a/archive/81375.html).
Excellent documentation for both 'lme' (and indirectly for
'lmer') is available in Pinheiro and Bates (2000)
Mixed-Effects Models
in S and S-Plus (Springer). I have personally recommended
this book
so many times on this listserve that I just now got 234 hits for
RSiteSearch(graves pinheiro). Please don't hesitate to pass this
recommendation to your university library. This book is
the primary
documentation for the 'nlme' package, which is part of the
standard R
distribution. A subdirectory ~library\nlme\scripts of your R
installation includes files named ch01.R, ch02.R, ...,
ch06.R,
ch08.R, containing the R scripts described in the book. These R
script files make it much easier and more enjoyable to study
[R] Variance Components in R
Hi, I'm trying to fit a model using variance components in R, but if very new on it, so I'm asking for your help. I have imported the SPSS database onto R, but I don't know how to convert the commands... the SPSS commands I'm trying to convert are: VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = MINQUE (1) /DESIGN = CHAIN SECTOR RESP ASPECT ITEM SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT /INTERCEPT = INCLUDE. VARCOMP RATING BY CHAIN SECTOR RESP ASPECT ITEM /RANDOM = CHAIN SECTOR RESP ASPECT ITEM /METHOD = REML /DESIGN = CHAIN SECTOR RESP ASPECT ITEM SECTOR*RESP SECTOR*ASPECT SECTOR*ITEM CHAIN*RESP CHAIN*ASPECT CHAIN*ITEM RESP*ASPECT RESP*ITEM SECTOR*RESP*ASPECT SECTOR*RESP*ITEM CHAIN*RESP*ASPECT /INTERCEPT = INCLUDE. Thank you for your help. Best regards, Iuri. ___ Iuri Gavronski - [EMAIL PROTECTED] doutorando UFRGS/PPGA/NITEC - www.ppga.ufrgs.br Brazil __ 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.
