*Dear Bert, * Thank you very much for your feedback and the useful link https://rseek.org/ and https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/. Actually, I want to know different performance between Stata and R, in multilevel logistic regression. For this purposes, I replicate ".do" file use Stata in http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153778. The nice journal not only gives ".do" file but also ".dta" file for Stata user. Fyi, I succeed running my dataset accordingly approach that use in the nice article. In Model ii, by adding the random effects of the clusters (neighbourhoods) only, I perform a multilevel logistic regression. Adding the neighbourhoods a random effect considerably increase the DA of the model. If the AU-ROC increases. This means the neighbourhoods have a relevant (observational) General Contextual Effect on the individual outcome. That is, knowledge on the clusters where the individuals reside is relevant for classifying individuals. That is, for distinguishing those with from those without the outcome. In Model iii, as expected, the AU-ROC remains similar to Model ii after including a neighbourhoods level variable as a fixed effect. Model ii represents the ?ceiling? of the explanatory power of the clusters. However, particularly both syntax in the steps below, the process taking time very long, particularly for a data set with the 130,585 observation that I have. I trust can be replicate the syntax Stata below into the script language of R under your advice. I want to to know the performance of R to analyse in the syntax " roccomp". Unfortunately, the process still not finished in this step yet at the moment. . * FIGURE 1 - AU-ROC . ******************************************************************************** . roccomp Y r1m1p r1m2p, graph summary . * FIGURE 3 - AU-ROC . ******************************************************************************** . roccomp Y r2m1p r2m2p, graph summary Abbreviations Y = The dependent (or responding) variable AUC = Area Under the Curve. AUROC = Area Under the Receiver Operating Characteristic curve. ROC = Receiver operating characteristic r1m1p = Pr(malaria) r1m2p = Predicted mean r1m3p = Predicted mean In addition, I saw in https://cran.r-project.org/web/packages/auRoc/index.html, and in https://www.rdocumentation.org/packages/limma/versions/3.28.14/topics/auROC , there is an issue regarding auROC. Hopefully with install "CRAN - Package auRoc", I can be running the "roccomp" used the plug-in. In addition, I can compute areas under ROC curves using the R Commander. However, the R Commander doesn't include ROC curves, but a Google search suggests that the RcmdrPlugin.EZR, a plug-in package for the R Commander, includes ROC curves and may do what I want. Isn't it? Again thank you very much, I appreciate it. Best wishes Hamzah On 8 April 2018 at 16:41, Bert Gunter <bgunter.4567 at gmail.com> wrote:> 1. *If* this is homework, we do not do homework here. > > 2. Please read and follow the posting guide linked below to get a > useful answer. In general, we expect posters to provide code showing > their attempt to solve the problem, rather than expecting to be > provided complete solutions. See also instructions for providing a > small reproducible example. > > 3. Search! e.g. on the rseek.org site, inputting "AUC" gave this, among > others: > https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/ > > Cheers, > Bert > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along > and sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > > > On Sat, Apr 7, 2018 at 5:01 PM, Hamzah Hasyim <hamzah.hasyim at gmail.com> > wrote: > > Dear User R > > > > > > It's been a pleasure talking with you. I am newcomer use R. Would you > > please help me how to translate the script below to "R" script? > > > > > > * Area under receiver operating characteristic (AU-ROC) > > predict r1m1p, p > > roctab malaria r1m1p, graph summary > > > > > > * Area under receiver operating characteristic (AU-ROC) curve > > predict r1m2p, mu > > roctab malaria r1m2p, graph summary > > > > ************************************************************ > > ******************** > > * FIGURE 1 - AU-ROC > > ************************************************************ > > ******************** > > > > roccomp malaria r1m1p r1m2p, graph > > > > > > * Area under receiver operating characteristic (AU-ROC) > > predict r2m1p, p > > roctab malaria r2m1p, graph summary > > > > > > * Area under receiver operating characteristic (AU-ROC) curve > > predict r1m2p, mu > > roctab malaria r1m2p, graph summary > > > > > > ************************************************************ > > ******************** > > * FIGURE 3 - AU-ROC > > ************************************************************ > > ******************** > > roccomp malaria r2m1p r2m2p, graph > > > > > > > > Best regards, > > > > > > > > Hamzah > > > > Description of data-set > > > > ------------------------------------------------------------ > > ---------------------- > > storage display value > > variable name type format label variable label > > ------------------------------------------------------------ > > ---------------------- > > > > malaria float %48.0g malaria Participants who had > diagnosed > > malaria by health > > professionals > > _est_r1m1 byte %8.0g esample() from estimates > store > > r1m1p float %9.0g Pr(malaria) > > r1m2p float %9.0g Predicted mean > > r1m3p float %9.0g Predicted mean > > _est_r1m2 byte %8.0g esample() from estimates > store > > r1m2use float %9.0g S.E. of empirical Bayes > means > > for > > _cons[district] > > r1m2u float %9.0g empirical Bayes means for > > _cons[district] > > pickone byte %8.0g tag(district) > > r1m2urank float %9.0g rank of (r1m2u) > > _est_r1m3 byte %8.0g esample() from estimates > store > > r1m3use float %9.0g S.E. of empirical Bayes > means > > for > > _cons[district] > > r1m3u float %9.0g empirical Bayes means for > > _cons[district] > > r1m3urank float %9.0g rank of (r1m3u) > > _est_r2m1 byte %8.0g esample() from estimates > store > > r2m1p float %9.0g Pr(malaria) > > r2m2p float %9.0g Predicted mean > > r2m3p float %9.0g Predicted mean > > _est_r2m2 byte %8.0g esample() from estimates > store > > r2m2use float %9.0g S.E. of empirical Bayes > means > > for > > _cons[district] > > r2m2u float %9.0g empirical Bayes means for > > _cons[district] > > r2m2urank float %9.0g rank of (r2m2u) > > _est_r2m3 byte %8.0g esample() from estimates > store > > r2m3use float %9.0g S.E. of empirical Bayes > means > > for > > _cons[district] > > r2m3u float %9.0g empirical Bayes means for > > _cons[district] > > r2m3urank float %9.0g rank of (r2m3u) > > ------------------------------------------------------------ > > > > > > > > > > > > Respectfully, > > > > > > > > Hamzah > > > > ______________________________________________ > > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see > > 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]]
I do not provide consulting services, so you're on your own. As I said earlier, you need to follow the posting guide and show us what efforts you have made. "None" will probably not succeed in getting anyone to help. Nor should you expect anyone on this list to be familiar with Stata and/or to do your "translation" work for you. -- Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Sun, Apr 8, 2018 at 1:39 PM, Hamzah Hasyim <hamzah.hasyim at gmail.com> wrote:> Dear Bert, > > Thank you very much for your feedback and the useful link https://rseek.org/ > and https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/. > Actually, I want to know different performance between Stata and R, in > multilevel logistic regression. For this purposes, I replicate ".do" file > use Stata in > http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153778. > The nice journal not only gives ".do" file but also ".dta" file for Stata > user. Fyi, I succeed running my dataset accordingly approach that use in the > nice article. > > In Model ii, by adding the random effects of the clusters (neighbourhoods) > only, I perform a multilevel logistic regression. Adding the neighbourhoods > a random effect considerably increase the DA of the model. If the AU-ROC > increases. This means the neighbourhoods have a relevant (observational) > General Contextual Effect on the individual outcome. That is, knowledge on > the clusters where the individuals reside is relevant for classifying > individuals. That is, for distinguishing those with from those without the > outcome. In Model iii, as expected, the AU-ROC remains similar to Model ii > after including a neighbourhoods level variable as a fixed effect. Model ii > represents the ?ceiling? of the explanatory power of the clusters. However, > particularly both syntax in the steps below, the process taking time very > long, particularly for a data set with the 130,585 observation that I have. > > I trust can be replicate the syntax Stata below into the script language of > R under your advice. I want to to know the performance of R to analyse in > the syntax " roccomp". Unfortunately, the process still not finished in > this step yet at the moment. > > > . * FIGURE 1 - AU-ROC > . > ******************************************************************************** > . roccomp Y r1m1p r1m2p, graph summary > > . * FIGURE 3 - AU-ROC > . > ******************************************************************************** > . roccomp Y r2m1p r2m2p, graph summary > > > Abbreviations > Y = The dependent (or responding) variable > AUC = Area Under the Curve. > AUROC = Area Under the Receiver Operating Characteristic curve. > ROC = Receiver operating characteristic > r1m1p = Pr(malaria) > r1m2p = Predicted mean > r1m3p = Predicted mean > > > In addition, I saw in > https://cran.r-project.org/web/packages/auRoc/index.html, and in > https://www.rdocumentation.org/packages/limma/versions/3.28.14/topics/auROC, > there is an issue regarding auROC. > > Hopefully with install "CRAN - Package auRoc", I can be running the > "roccomp" used the plug-in. In addition, I can compute areas under ROC > curves using the R Commander. However, the R Commander doesn't include ROC > curves, but a Google search suggests that the RcmdrPlugin.EZR, a plug-in > package for the R Commander, includes ROC curves and may do what I want. > Isn't it? > > > Again thank you very much, I appreciate it. > > > Best wishes > > > Hamzah > > > On 8 April 2018 at 16:41, Bert Gunter <bgunter.4567 at gmail.com> wrote: >> >> 1. *If* this is homework, we do not do homework here. >> >> 2. Please read and follow the posting guide linked below to get a >> useful answer. In general, we expect posters to provide code showing >> their attempt to solve the problem, rather than expecting to be >> provided complete solutions. See also instructions for providing a >> small reproducible example. >> >> 3. Search! e.g. on the rseek.org site, inputting "AUC" gave this, among >> others: >> https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/ >> >> Cheers, >> Bert >> >> >> Bert Gunter >> >> "The trouble with having an open mind is that people keep coming along >> and sticking things into it." >> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) >> >> >> On Sat, Apr 7, 2018 at 5:01 PM, Hamzah Hasyim <hamzah.hasyim at gmail.com> >> wrote: >> > Dear User R >> > >> > >> > It's been a pleasure talking with you. I am newcomer use R. Would you >> > please help me how to translate the script below to "R" script? >> > >> > >> > * Area under receiver operating characteristic (AU-ROC) >> > predict r1m1p, p >> > roctab malaria r1m1p, graph summary >> > >> > >> > * Area under receiver operating characteristic (AU-ROC) curve >> > predict r1m2p, mu >> > roctab malaria r1m2p, graph summary >> > >> > ************************************************************ >> > ******************** >> > * FIGURE 1 - AU-ROC >> > ************************************************************ >> > ******************** >> > >> > roccomp malaria r1m1p r1m2p, graph >> > >> > >> > * Area under receiver operating characteristic (AU-ROC) >> > predict r2m1p, p >> > roctab malaria r2m1p, graph summary >> > >> > >> > * Area under receiver operating characteristic (AU-ROC) curve >> > predict r1m2p, mu >> > roctab malaria r1m2p, graph summary >> > >> > >> > ************************************************************ >> > ******************** >> > * FIGURE 3 - AU-ROC >> > ************************************************************ >> > ******************** >> > roccomp malaria r2m1p r2m2p, graph >> > >> > >> > >> > Best regards, >> > >> > >> > >> > Hamzah >> > >> > Description of data-set >> > >> > ------------------------------------------------------------ >> > ---------------------- >> > storage display value >> > variable name type format label variable label >> > ------------------------------------------------------------ >> > ---------------------- >> > >> > malaria float %48.0g malaria Participants who had >> > diagnosed >> > malaria by health >> > professionals >> > _est_r1m1 byte %8.0g esample() from estimates >> > store >> > r1m1p float %9.0g Pr(malaria) >> > r1m2p float %9.0g Predicted mean >> > r1m3p float %9.0g Predicted mean >> > _est_r1m2 byte %8.0g esample() from estimates >> > store >> > r1m2use float %9.0g S.E. of empirical Bayes >> > means >> > for >> > _cons[district] >> > r1m2u float %9.0g empirical Bayes means for >> > _cons[district] >> > pickone byte %8.0g tag(district) >> > r1m2urank float %9.0g rank of (r1m2u) >> > _est_r1m3 byte %8.0g esample() from estimates >> > store >> > r1m3use float %9.0g S.E. of empirical Bayes >> > means >> > for >> > _cons[district] >> > r1m3u float %9.0g empirical Bayes means for >> > _cons[district] >> > r1m3urank float %9.0g rank of (r1m3u) >> > _est_r2m1 byte %8.0g esample() from estimates >> > store >> > r2m1p float %9.0g Pr(malaria) >> > r2m2p float %9.0g Predicted mean >> > r2m3p float %9.0g Predicted mean >> > _est_r2m2 byte %8.0g esample() from estimates >> > store >> > r2m2use float %9.0g S.E. of empirical Bayes >> > means >> > for >> > _cons[district] >> > r2m2u float %9.0g empirical Bayes means for >> > _cons[district] >> > r2m2urank float %9.0g rank of (r2m2u) >> > _est_r2m3 byte %8.0g esample() from estimates >> > store >> > r2m3use float %9.0g S.E. of empirical Bayes >> > means >> > for >> > _cons[district] >> > r2m3u float %9.0g empirical Bayes means for >> > _cons[district] >> > r2m3urank float %9.0g rank of (r2m3u) >> > ------------------------------------------------------------ >> > >> > >> > >> > >> > >> > Respectfully, >> > >> > >> > >> > Hamzah >> > >> > ______________________________________________ >> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see >> > 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. >> >
You really should? read the posting guide (link at the bottom of the e-mail) and
also read one or both of http://stackoverflow.com/questions/5963269
/how-to-make-a-great-r-reproducible-example and
http://adv-r.had.co.nz/Reproducibility.html
As it stands your posts are very close to unreadable. The R-help list does not
accept HTML files nor embedded images so your text is garbled and none of the
images are coming through.
Your two attached pdf files are coming through but? are useless without a
readable message.
Two key elements in posting are: Alway post in plain text and provide sample
data in dput() format. See ?dput for more information on this function.
Here is a example data set in dput format.
mydata? <- structure(list(aa = structure(c(2L, 6L, 3L, 7L, 2L, 15L, 1L,
14L, 2L, 9L, 1L, 4L, 15L, 19L, 4L, 3L, 20L, 21L, 11L, 22L, 3L,
12L, 14L, 1L, 19L, 10L, 16L, 1L, 3L, 14L, 2L, 8L, 1L, 11L, 8L,
19L, 22L, 8L, 10L, 18L, 16L, 11L, 7L, 24L, 12L, 16L, 6L, 12L,
22L, 12L, 12L, 10L, 8L, 20L, 3L, 21L, 9L, 23L, 5L, 17L, 5L, 8L,
24L, 19L, 6L, 8L, 23L, 8L, 15L, 23L, 18L, 20L, 5L, 4L, 18L, 26L,
24L, 24L, 24L, 2L, 13L, 23L, 20L, 1L, 19L, 12L, 5L, 25L, 2L,
22L, 10L, 9L, 3L, 8L, 9L, 2L, 23L, 13L, 19L, 1L), .Label = c("A",
"B", "C", "D", "E", "F",
"G", "H", "I", "J", "K",
"L", "M", "N",
"O", "P", "Q", "R", "S",
"T", "U", "V", "W", "X",
"Y", "Z"), class = "factor"),
??? bb = c(19L, 13L, 14L, 6L, 15L, 13L, 11L, 20L, 20L, 3L, 3L,
??? 6L, 4L, 7L, 19L, 18L, 12L, 19L, 12L, 8L, 3L, 2L, 10L, 9L,
??? 9L, 9L, 13L, 5L, 16L, 12L, 9L, 8L, 10L, 7L, 14L, 10L, 6L,
??? 12L, 8L, 13L, 13L, 13L, 10L, 10L, 10L, 4L, 12L, 6L, 9L, 13L,
??? 7L, 20L, 3L, 5L, 20L, 17L, 4L, 8L, 14L, 9L, 5L, 4L, 5L, 1L,
??? 13L, 2L, 5L, 11L, 9L, 16L, 3L, 6L, 7L, 14L, 18L, 6L, 19L,
??? 2L, 12L, 9L, 10L, 20L, 17L, 13L, 9L, 7L, 3L, 1L, 14L, 12L,
??? 11L, 15L, 15L, 15L, 10L, 9L, 2L, 11L, 11L, 16L)), .Names = c("aa",
"bb"), row.names = c(NA, -100L), class = "data.frame")
On Sunday, April 8, 2018, 4:39:39 p.m. EDT, Hamzah Hasyim <hamzah.hasyim
at gmail.com> wrote:
*Dear Bert, *
Thank you very much for your feedback and the useful link https://rseek.org/
and https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/.
Actually, I want to know different performance between Stata and R, in
multilevel logistic regression. For this purposes, I replicate ".do"
file
use Stata in
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0153778. The
nice journal not only gives ".do" file but also ".dta" file
for Stata
user. Fyi,
I succeed running my dataset accordingly approach that use in the nice
article.
In Model ii, by adding the random effects of the clusters (neighbourhoods)
only, I perform a multilevel logistic regression. Adding the neighbourhoods
a random effect considerably increase the DA of the model. If the AU-ROC
increases. This means the neighbourhoods have a relevant (observational)
General Contextual Effect on the individual outcome. That is, knowledge on
the clusters where the individuals reside is relevant for classifying
individuals. That is, for distinguishing those with from those without the
outcome. In Model iii, as expected, the AU-ROC remains similar to Model ii
after including a neighbourhoods level variable as a fixed effect. Model ii
represents the ?ceiling? of the explanatory power of the clusters. However,
particularly both syntax in the steps below, the process taking time very
long, particularly for a data set with the 130,585 observation that I have.
I trust can be replicate the syntax Stata below into the script language of
R under your advice. I want to to know the performance of R to analyse in
the syntax " roccomp".? Unfortunately, the process still not finished
in
this step yet at the moment.
. * FIGURE 1 - AU-ROC
.
********************************************************************************
.? roccomp Y? r1m1p r1m2p, graph summary
. * FIGURE 3 - AU-ROC
.
********************************************************************************
. roccomp Y? r2m1p r2m2p, graph summary
Abbreviations
Y? ? ? = The dependent (or responding) variable
AUC? ? = Area Under the Curve.
AUROC? = Area Under the Receiver Operating Characteristic curve.
ROC? ? = Receiver operating characteristic
r1m1p? =? Pr(malaria)
r1m2p? = Predicted mean
r1m3p? = Predicted mean
In addition, I saw in
https://cran.r-project.org/web/packages/auRoc/index.html, and in
https://www.rdocumentation.org/packages/limma/versions/3.28.14/topics/auROC
,? there is an issue regarding auROC.
Hopefully with install "CRAN - Package auRoc", I can be running the
"roccomp" used the plug-in. In addition, I can compute areas under ROC
curves using the R Commander. However, the R Commander doesn't include ROC
curves, but a Google search suggests that the RcmdrPlugin.EZR, a plug-in
package for the R Commander, includes ROC curves and may do what I want.
Isn't it?
Again thank you very much, I appreciate it.
Best wishes
Hamzah
On 8 April 2018 at 16:41, Bert Gunter <bgunter.4567 at gmail.com> wrote:
> 1. *If* this is homework, we do not do homework here.
>
> 2. Please read and follow the posting guide linked below to get a
> useful answer. In general, we expect posters to provide code showing
> their attempt to solve the problem, rather than expecting to be
> provided complete solutions. See also instructions for providing a
> small reproducible example.
>
> 3. Search! e.g. on the rseek.org site, inputting "AUC" gave this,
among
> others:
> https://www.r-bloggers.com/calculating-auc-the-area-under-a-roc-curve/
>
> Cheers,
> Bert
>
>
> Bert Gunter
>
> "The trouble with having an open mind is that people keep coming along
> and sticking things into it."
> -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip
)
>
>
> On Sat, Apr 7, 2018 at 5:01 PM, Hamzah Hasyim <hamzah.hasyim at
gmail.com>
> wrote:
> > Dear User R
> >
> >
> > It's been a pleasure talking with you. I am newcomer use R. Would
you
> > please help me how to translate the script below to "R"
script?
> >
> >
> > * Area under receiver operating characteristic (AU-ROC)
> > predict r1m1p, p
> > roctab malaria r1m1p, graph summary
> >
> >
> > * Area under receiver operating characteristic (AU-ROC) curve
> > predict r1m2p, mu
> > roctab malaria r1m2p, graph summary
> >
> > ************************************************************
> > ********************
> > * FIGURE 1 - AU-ROC
> > ************************************************************
> > ********************
> >
> > roccomp malaria r1m1p r1m2p, graph
> >
> >
> > * Area under receiver operating characteristic (AU-ROC)
> > predict r2m1p, p
> > roctab malaria r2m1p, graph summary
> >
> >
> > * Area under receiver operating characteristic (AU-ROC) curve
> > predict r1m2p, mu
> > roctab malaria r1m2p, graph summary
> >
> >
> > ************************************************************
> > ********************
> > * FIGURE 3 - AU-ROC
> > ************************************************************
> > ********************
> > roccomp malaria r2m1p r2m2p, graph
> >
> >
> >
> > Best regards,
> >
> >
> >
> > Hamzah
> >
> > Description of data-set
> >
> > ------------------------------------------------------------
> > ----------------------
> >? ? ? ? ? ? ? storage? display? ? value
> > variable name? type? ? format? ? label? ? ? variable label
> > ------------------------------------------------------------
> > ----------------------
> >
> > malaria? ? ? ? float? %48.0g? ? malaria? ? Participants who had
> diagnosed
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? malaria by health
> > professionals
> > _est_r1m1? ? ? byte? ? %8.0g? ? ? ? ? ? ? ? esample() from estimates
> store
> > r1m1p? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? Pr(malaria)
> > r1m2p? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? Predicted mean
> > r1m3p? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? Predicted mean
> > _est_r1m2? ? ? byte? ? %8.0g? ? ? ? ? ? ? ? esample() from estimates
> store
> > r1m2use? ? ? ? float? %9.0g? ? ? ? ? ? ? ? S.E. of empirical Bayes
> means
> > for
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? _cons[district]
> > r1m2u? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? empirical Bayes means for
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? _cons[district]
> > pickone? ? ? ? byte? ? %8.0g? ? ? ? ? ? ? ? tag(district)
> > r1m2urank? ? ? float? %9.0g? ? ? ? ? ? ? ? rank of (r1m2u)
> > _est_r1m3? ? ? byte? ? %8.0g? ? ? ? ? ? ? ? esample() from estimates
> store
> > r1m3use? ? ? ? float? %9.0g? ? ? ? ? ? ? ? S.E. of empirical Bayes
> means
> > for
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? _cons[district]
> > r1m3u? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? empirical Bayes means for
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? _cons[district]
> > r1m3urank? ? ? float? %9.0g? ? ? ? ? ? ? ? rank of (r1m3u)
> > _est_r2m1? ? ? byte? ? %8.0g? ? ? ? ? ? ? ? esample() from estimates
> store
> > r2m1p? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? Pr(malaria)
> > r2m2p? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? Predicted mean
> > r2m3p? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? Predicted mean
> > _est_r2m2? ? ? byte? ? %8.0g? ? ? ? ? ? ? ? esample() from estimates
> store
> > r2m2use? ? ? ? float? %9.0g? ? ? ? ? ? ? ? S.E. of empirical Bayes
> means
> > for
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? _cons[district]
> > r2m2u? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? empirical Bayes means for
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? _cons[district]
> > r2m2urank? ? ? float? %9.0g? ? ? ? ? ? ? ? rank of (r2m2u)
> > _est_r2m3? ? ? byte? ? %8.0g? ? ? ? ? ? ? ? esample() from estimates
> store
> > r2m3use? ? ? ? float? %9.0g? ? ? ? ? ? ? ? S.E. of empirical Bayes
> means
> > for
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? _cons[district]
> > r2m3u? ? ? ? ? float? %9.0g? ? ? ? ? ? ? ? empirical Bayes means for
> >? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? ? _cons[district]
> > r2m3urank? ? ? float? %9.0g? ? ? ? ? ? ? ? rank of (r2m3u)
> > ------------------------------------------------------------
> >
> >
> >
> >
> >
> > Respectfully,
> >
> >
> >
> > Hamzah
> >
> > ______________________________________________
> > R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> > 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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______________________________________________
R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
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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