date:20200712

Re: [R] Multi-level (nested) correlation structures via geepack package

2020-07-12 Thread dulcalma dulcalma


Hi

Your choice of package should partly depend on the type of dependent 
variable or Y that you are going to be dealing with
categorical/ordinal data may involve different packages than continuous 
or binary data see multgee for one.
The number of samples can also make a difference GEE with the "correct 
model" should normally have no problems with numbers 30-40; 25 or less 
would normally require corrections and a diffence package.


The doi for multgee  paper is 10./biom.12054 and Touloumis paper in 
Journal of Statistical Software


For longitudinal data there is the following doi:
10.2307/2531248
and
10.1097/EDE.0b013e3181caeb90
10.1093/biomet/90.1.29
10.1007/s00362-017-0881-0
10.1002/sim.2368

a search for gee in the list of available packages should show you the 
alternatives.


As a check of the result do the statistics on another package. I 
remember doing a simple gee with an example
from a book using 4 different packages 2 of which gave poor or 
unreasonable answers


Regards

Duncan

Duncan Mackay
Department of Agronomy and Soil Science
University of New England
ARMIDALE NSW 2351



-- Original Message --
From: "Phat Chau" 
To: "r-help@R-project.org" ; "sor...@math.aau.dk" 


Sent: Sunday, 12 Jul, 2020 At 11:52 PM
Subject: Re: [R]  Multi-level (nested) correlation structures via 
geepack package


Hello,

I have a multi-level, cohort dataset with three levels: repeat measures 
of a response (level 1), that are collected from individual participants 
(level 2) who are students within a school (level 3). I would like to do 
a generalized estimating equation (GEE) analysis of this clustered data, 
but to do so I need to specify ‘nested’ correlation structures (e.g. 
exchangeable, compound symmetric, Toeplitz) to account for the 
within-individual and within-cluster correlations.


Here is a reference paper that describes a nested exchangeable 
correlation structure and nested compound symmetry: 
doi:10./j.1541-0420.2009.01374.x.


The geepack is available in R to do GEE analyses, but it seems to me 
that it only allows the user to specify a correlation structure via the 
geepack(…‘corstr = ‘) option which only accounts for the 
within-individual correlations (that arise from repeated measures). 
Would it be possible to specify the nested correlation structures that I 
refer to here to also account for the within-cluster correlations using 
this package?


Thank you,

Edward


[[alternative HTML version deleted]]

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Re: [R] Using Rvest to scrape pages

2020-07-12 Thread David Winsemius




On 7/12/20 10:42 AM, Tiffany Adekola wrote:

Dear All,

I am just learning how to use R programming. I want to extract reviews
from a page and loop till I extract for all pages:

#specify the first page URL
fpURL <- 'https://wordpress.org/support/plugin/easyrecipe/reviews/'

#read the HTML contents in the first page URL
contentfpURL <- read_html(fpURL)

#identify the anchor tags in the first page URL
fpAnchors <- html_nodes(contentfpURL, css='a.bbp-topic-permalink')

#extract the HREF attribute value of each anchor tag
fpHREF <- html_attr(fpAnchors, 'href')

#create empty lists to store titles & contents found in the HREF
attribute value of each anchor tag
titles = c()
contents = c()

#loop the following actions for each HREF found firstpage
for (u in fpHREF) {

#read the HTML content of the review page
fpURL = read_html(u)

   #identify the title anchor and read the title text
   fpreviewT = html_text(html_nodes(fpURL, css='h1.page-title'))

   #identify the content anchor and read the content text
   fpreviewC = html_text(html_nodes(fpURL, css='div.bbp-topic-content'))

   #store the review titles and contents in the previous lists
   titles = c(titles, fpreviewT)
   contents = c(contents, fpreviewC)
}
#identify the anchor tag pointing to the next summary page
npAnchor <- html_text(html_node(contentfpURL, css='a.next page-numbers'))

#extract the HREF attribute value of the anchor tag pointing to the
next summary page
npHREF <- html_attr(npAnchor, 'href')



The error occurs with the line above, but if you look at the argument to 
`html_attr` you see that the problem is higher up


str(npAnchor)
# chr NA

Perhaps the problem occurs here:


html_node(contentfpURL, css='a.next page-numbers')
#{xml_missing}
#

--

David.



#loop the following actions for every next summary page HREF attribute
for (u in npHREF) {

   #specify the URL of the summary page
   spURL <- read_html('npHREF')

   #identify all the anchor tags on that summary page
   spAnchors <- html_nodes(spURL, css='a.bbp-topic-permalink')

   #extract the HREF attribute value of each anchor tag
   spHREF <- html_attr(spAnchors, 'href')

   #loop the following actions for each HREF found on that summarypage

for (u in fpHREF) {
  #read the HTML contents of the review page
  spURL = read_html(u)

   #identify the title anchor and read the title text
   spreviewT = html_text(html_nodes(spURL, css='h1.page-title'))

   #identify the content anchor and read the content text
   spreviewC = html_text(html_nodes(spURL, css='div.bbp-topic-content'))

   #store the review titles and contents in the previous lists
   titles = c(titles, spreviewT)
   contents = c(contents, spreviewC)
   }
}

I got stuck at the step to extract the HREF attribute value of the
anchor tag pointing to the next summary page with the error: Error in
UseMethod("xml_attr") :
   no applicable method for 'xml_attr' applied to an object of class "character"

  I will appreciate any help with this task.
Thanks in advance.

---Tiffany

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Re: [R] Multi-level (nested) correlation structures via geepack package

2020-07-12 Thread Bert Gunter

You may get lucky, but generally such package-specific questions don't get
responses here. There are about 2 packages after all. You might do
better posting on the r-sig-mixed-models list or by asking the package
maintainer (?maintainer) whether there is some sort of support list for the
package.

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, Jul 12, 2020 at 9:59 AM Phat Chau 
wrote:

> Hello,
>
> I have a multi-level, cohort dataset with three levels: repeat measures of
> a response (level 1), that are collected from individual participants
> (level 2) who are students within a school (level 3). I would like to do a
> generalized estimating equation (GEE) analysis of this clustered data, but
> to do so I need to specify ‘nested’ correlation structures (e.g.
> exchangeable, compound symmetric, Toeplitz) to account for the
> within-individual and within-cluster correlations.
>
> Here is a reference paper that describes a nested exchangeable correlation
> structure and nested compound symmetry:
> doi:10./j.1541-0420.2009.01374.x.
>
> The geepack is available in R to do GEE analyses, but it seems to me that
> it only allows the user to specify a correlation structure via the
> geepack(…‘corstr = ‘) option which only accounts for the within-individual
> correlations (that arise from repeated measures). Would it be possible to
> specify the nested correlation structures that I refer to here to also
> account for the within-cluster correlations using this package?
>
> Thank you,
>
> Edward
>
>
> [[alternative HTML version deleted]]
>
> __
> R-help@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]]

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Re: [R] How to differ stats_cor labels by group on a ggplot

2020-07-12 Thread Rui Barradas


Hello,

The code below puts the group names as subscripts to 'R'.
The trick is to create each of the labels with those names before 
pasting the ..p.label..

This is done by Map, calling a function f defined outside the for loop.

I have also changed your code a bit, simplifying parts of it.

1. I have named the data set df1, since df already is a base R function.
2. Instead of redefining theme() elements in the loop, I have created a 
custom one.
3. The for loop runs directly through the names of the data set df1, not 
through indices into them.

4. df1[[i]] is the same as unlist(df1[i]). And simpler.
5. After testing the code with df1[[i]], there were warnings, telling to 
use the pronoun .data, which I do.
6. The code repeated itself a lot, if you define aesthetics in the 
initial call to ggplot, there is no need to redefine them in the layers 
calls unless you are changing them. For instance, after


ggplot(df, mapping=aes(x = Age, y = unlist(df[i]), color=factor(AgeGroup)))


you don't have to define the same y and color again:

stat_cor(aes(color = factor(AgeGroup), y = unlist(df[i]))


7. Some arguments (method = "pearson" and others) didn't change their 
default values and were removed.



Now the code.
Note that before writing to file, the filename is output to stdin, 
remove it, it's not part of your original code.




theme_custom_ps <- function(){

  theme_classic() %+replace%# replace elements we want to change

theme(axis.text.x = element_text(face = "bold", size = 14),
  axis.text.y = element_text(face = "bold", size = 14),
  axis.title.x = element_text(face = "bold", size = 14),
  axis.title.y = element_text(face = "bold", size = 14),
  legend.text = element_text(face = "bold", size = 14, colour = 
"black"),

  legend.title = element_text(size = 12, colour = "black")
)
}


library(ggplot2)
library(ggpubr)

df1 <- read.table("sim_data.txt", header = TRUE)

group_name <- c("old", "young")
f <- function(grp, rlab) sub("R", paste0("R[", grp, "]"), rlab)

for (i in names(df1)[3:6]) {
  p1 <- ggplot(df1, mapping = aes(x = Age, y = .data[[i]], color = 
factor(AgeGroup))) +

geom_point(size = 4) +
geom_smooth(method = "lm", formula = y ~ x) +
geom_smooth(mapping = aes(group = 1), method = "lm", formula = y ~ x) +
stat_cor(aes(label = paste(Map(f, group_name, ..r.label..), 
..p.label.., sep = "~`,`~")),

 label.x.npc = "center",
 show.legend = FALSE) +
stat_cor(aes(label = paste(sub("R", expression("R"[overall]), 
..r.label..), ..p.label.., sep = "~`,`~"),

 group = 1, color = "black"),
 label.y = 0.5,
 label.x.npc = "center",
 position = position_nudge(y = 0.015 * 0.5),
 show.legend = FALSE) +
scale_colour_discrete(name = "Group", labels = c("Young", "Old", 
"Overall")) +

scale_x_continuous(breaks = seq(20, 90, by = 10)) +
scale_y_continuous(breaks = seq(0.1, 0.5, by = 0.1)) +
expand_limits(y = 0.5) +
ylab(i) +
theme_custom_ps()

  pngfile <- paste0("TestAge_", i, ".png")
  cat("filename:", pngfile, "\n")
  ggsave(p1, file = pngfile, scale = 1, width = 16, height = 10, units 
= "cm")

}


So it seems this is worth the effort after all.
Just trickier than expected.


Hope this helps,

Rui Barradas

Às 02:20 de 12/07/20, Paulina Skolasinska escreveu:

Sorry for misspelling your name, Jim. Well, it seems this is not worth the 
effort then. If my advisor decides this is absolutely essential, I�ll add it in 
gimp or something. Thanks for giving it a go, Jim.

Get Outlook for iOS

From: Jim Lemon 
Sent: Saturday, July 11, 2020 6:32:13 PM
To: Paulina Skolasinska 
Cc: r-help@r-project.org 
Subject: Re: [R] How to differ stats_cor labels by group on a ggplot

Hi Paulina,
Thanks for the data. Even after downloading a ton of packages along
with the "ggpubr" package, I get a namespace error when I try to load
it. After a bit of wrangling, I did get the code to run without the
"stat_cor" function, producing the four PNG images. My best guess is
to try the geom_label function with stat="cor", but I can't see any
argument to format the actual label. I think this is about as far as
I'm going to get.

Jim

On Sun, Jul 12, 2020 at 12:09 AM Paulina Skolasinska
 wrote:


Thanks Tim, here is the link to the data: https://we.tl/t-c4x9Lw7LeR
I'm posting my code again so it better matches the modified data, and because 
I've added several improvements in the meantime.

for (i in 3:6) {
 p1 <- ggplot(df, mapping=aes(x = Age, y = unlist(df[i]), 
color=factor(AgeGroup))) +
 geom_smooth(method="lm") +
 geom_point(size = 4) +
 scale_x_continuous(breaks = seq(20,90, by=10)) +
 scale_y_continuous(breaks = seq(0.1,0.5, by=0.1)) +
 theme_classic() +
 expand_limits(y = 0.5) +
 ylab(names(df)[i]) +
 theme(axis.text.x = element_te

[R] Using Rvest to scrape pages

2020-07-12 Thread Tiffany Adekola

Dear All,

I am just learning how to use R programming. I want to extract reviews
from a page and loop till I extract for all pages:

#specify the first page URL
fpURL <- 'https://wordpress.org/support/plugin/easyrecipe/reviews/'

#read the HTML contents in the first page URL
contentfpURL <- read_html(fpURL)

#identify the anchor tags in the first page URL
fpAnchors <- html_nodes(contentfpURL, css='a.bbp-topic-permalink')

#extract the HREF attribute value of each anchor tag
fpHREF <- html_attr(fpAnchors, 'href')

#create empty lists to store titles & contents found in the HREF
attribute value of each anchor tag
titles = c()
contents = c()

#loop the following actions for each HREF found firstpage
for (u in fpHREF) {

   #read the HTML content of the review page
   fpURL = read_html(u)

  #identify the title anchor and read the title text
  fpreviewT = html_text(html_nodes(fpURL, css='h1.page-title'))

  #identify the content anchor and read the content text
  fpreviewC = html_text(html_nodes(fpURL, css='div.bbp-topic-content'))

  #store the review titles and contents in the previous lists
  titles = c(titles, fpreviewT)
  contents = c(contents, fpreviewC)
}
#identify the anchor tag pointing to the next summary page
npAnchor <- html_text(html_node(contentfpURL, css='a.next page-numbers'))

#extract the HREF attribute value of the anchor tag pointing to the
next summary page
npHREF <- html_attr(npAnchor, 'href')

#loop the following actions for every next summary page HREF attribute
for (u in npHREF) {

  #specify the URL of the summary page
  spURL <- read_html('npHREF')

  #identify all the anchor tags on that summary page
  spAnchors <- html_nodes(spURL, css='a.bbp-topic-permalink')

  #extract the HREF attribute value of each anchor tag
  spHREF <- html_attr(spAnchors, 'href')

  #loop the following actions for each HREF found on that summarypage

   for (u in fpHREF) {
 #read the HTML contents of the review page
 spURL = read_html(u)

  #identify the title anchor and read the title text
  spreviewT = html_text(html_nodes(spURL, css='h1.page-title'))

  #identify the content anchor and read the content text
  spreviewC = html_text(html_nodes(spURL, css='div.bbp-topic-content'))

  #store the review titles and contents in the previous lists
  titles = c(titles, spreviewT)
  contents = c(contents, spreviewC)
  }
}

I got stuck at the step to extract the HREF attribute value of the
anchor tag pointing to the next summary page with the error: Error in
UseMethod("xml_attr") :
  no applicable method for 'xml_attr' applied to an object of class "character"

 I will appreciate any help with this task.
Thanks in advance.

---Tiffany

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Re: [R] Multi-level (nested) correlation structures via geepack package

2020-07-12 Thread Phat Chau

Hello,

I have a multi-level, cohort dataset with three levels: repeat measures of a 
response (level 1), that are collected from individual participants (level 2) 
who are students within a school (level 3). I would like to do a generalized 
estimating equation (GEE) analysis of this clustered data, but to do so I need 
to specify ‘nested’ correlation structures (e.g. exchangeable, compound 
symmetric, Toeplitz) to account for the within-individual and within-cluster 
correlations.

Here is a reference paper that describes a nested exchangeable correlation 
structure and nested compound symmetry: doi:10./j.1541-0420.2009.01374.x.

The geepack is available in R to do GEE analyses, but it seems to me that it 
only allows the user to specify a correlation structure via the 
geepack(…‘corstr = ‘) option which only accounts for the within-individual 
correlations (that arise from repeated measures). Would it be possible to 
specify the nested correlation structures that I refer to here to also account 
for the within-cluster correlations using this package?

Thank you,

Edward


[[alternative HTML version deleted]]

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Re: [R] Rmpfr correlation

2020-07-12 Thread tring

Thanks a lot - solved the issue!

BW
Troels

-Oprindelig meddelelse-
Fra: Rui Barradas  
Sendt: 12. juli 2020 12:59
Til: tr...@gvdnet.dk; r-help mailing list 
Emne: Re: [R] Rmpfr correlation

Hello,

Why not write a function COR? Not one as general purpose as stats::cor but a 
simple one, to compute the sample Pearson correlation only.


library(Rmpfr)

COR <- function(x, y){
   precBits <- getPrec(x)[1]
   n <- mpfr(length(x), precBits = precBits)
   x.bar <- mean(x)
   y.bar <- mean(y)
   numer <- sum(x*y) - n*x.bar*y.bar
   denom <- sqrt(sum(x*x) - n*x.bar*x.bar) * sqrt(sum(y*y) - n*y.bar*y.bar)
   numer/denom
}

set.seed(2020)
KA <- mpfr(10^-4.6, 128)
x <- rnorm(100)*KA
y <- rnorm(100)*x

cor(as.numeric(x), as.numeric(y)) # -0.1874986
#[1] -0.1874986

COR(x, y)
#1 'mpfr' number of precision  128   bits
#[1] -0.1874985950531874160800643775644747505073


Hope this helps,

Rui Barradas

Às 10:42 de 12/07/20, tr...@gvdnet.dk escreveu:
> Dear friends - I'm calculating buffer capacities by different methods and
> need very high precision and package Rmpfr is working beautifully. However,
> I have not been able to find out how to keep precision when finding
> correlations.
> 
> library(Rmpfr)
> 
> KA <- mpfr(10^-4.6, 128)
> 
> x <- rnorm(100)*KA
> 
> y <- rnorm(100)*x
> 
> cor(x,y) # "x" must be numeric
> 
> cor(as.numeric(x),as.numeric(y))# 0.2918954
> 
>   
> 
> In my concrete application I get cor = 1 for
> cor(as.numeric(dff$BB),as.numeric(BBVS)) even though I have
> 
>   
> 
> str(summary((dff$BB)-(BBVS)))
> Class 'summaryMpfr' [package "Rmpfr"] of length 6 and precision 128
>   4.61351010833e-8 7.33418976521e-7 1.31009046563e-5 3.76407022709e-5
> 5.72386764888e-5 ...
> 
>   
> 
> I am on windows 10
> 
> R version 3.6.1
>   
> Best wishes
> Troels Ring,
> Aalborg, Denmark
> 
>   
> 
>   
> 
> 
> This email has been scanned by BullGuard antivirus protection.
> For more info visit www.bullguard.com
>  p&url=/>
> 
>   [[alternative HTML version deleted]]
> 
> __
> R-help@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.
> 


This email has been scanned by BullGuard antivirus protection.
For more info visit www.bullguard.com

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Re: [R] Rmpfr correlation

2020-07-12 Thread Rui Barradas


Hello,

Why not write a function COR? Not one as general purpose as stats::cor 
but a simple one, to compute the sample Pearson correlation only.



library(Rmpfr)

COR <- function(x, y){
  precBits <- getPrec(x)[1]
  n <- mpfr(length(x), precBits = precBits)
  x.bar <- mean(x)
  y.bar <- mean(y)
  numer <- sum(x*y) - n*x.bar*y.bar
  denom <- sqrt(sum(x*x) - n*x.bar*x.bar) * sqrt(sum(y*y) - n*y.bar*y.bar)
  numer/denom
}

set.seed(2020)
KA <- mpfr(10^-4.6, 128)
x <- rnorm(100)*KA
y <- rnorm(100)*x

cor(as.numeric(x), as.numeric(y)) # -0.1874986
#[1] -0.1874986

COR(x, y)
#1 'mpfr' number of precision  128   bits
#[1] -0.1874985950531874160800643775644747505073


Hope this helps,

Rui Barradas

Às 10:42 de 12/07/20, tr...@gvdnet.dk escreveu:

Dear friends - I'm calculating buffer capacities by different methods and
need very high precision and package Rmpfr is working beautifully. However,
I have not been able to find out how to keep precision when finding
correlations.

library(Rmpfr)

KA <- mpfr(10^-4.6, 128)

x <- rnorm(100)*KA

y <- rnorm(100)*x

cor(x,y) # "x" must be numeric

cor(as.numeric(x),as.numeric(y))# 0.2918954

  


In my concrete application I get cor = 1 for
cor(as.numeric(dff$BB),as.numeric(BBVS)) even though I have

  


str(summary((dff$BB)-(BBVS)))
Class 'summaryMpfr' [package "Rmpfr"] of length 6 and precision 128
  4.61351010833e-8 7.33418976521e-7 1.31009046563e-5 3.76407022709e-5
5.72386764888e-5 ...

  


I am on windows 10

R version 3.6.1
  
Best wishes

Troels Ring,
Aalborg, Denmark

  

  



This email has been scanned by BullGuard antivirus protection.
For more info visit www.bullguard.com


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[R] Rmpfr correlation

2020-07-12 Thread tring

Dear friends - I'm calculating buffer capacities by different methods and
need very high precision and package Rmpfr is working beautifully. However,
I have not been able to find out how to keep precision when finding
correlations.

library(Rmpfr)

KA <- mpfr(10^-4.6, 128)

x <- rnorm(100)*KA

y <- rnorm(100)*x

cor(x,y) # "x" must be numeric

cor(as.numeric(x),as.numeric(y))# 0.2918954

 

In my concrete application I get cor = 1 for
cor(as.numeric(dff$BB),as.numeric(BBVS)) even though I have 

 

str(summary((dff$BB)-(BBVS)))
Class 'summaryMpfr' [package "Rmpfr"] of length 6 and precision 128
 4.61351010833e-8 7.33418976521e-7 1.31009046563e-5 3.76407022709e-5
5.72386764888e-5 ...

 

I am on windows 10

R version 3.6.1
 
Best wishes
Troels Ring,
Aalborg, Denmark

 

 


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Re: [R] Multi-level (nested) correlation structures via geepack package

Re: [R] Using Rvest to scrape pages

Re: [R] Multi-level (nested) correlation structures via geepack package

Re: [R] How to differ stats_cor labels by group on a ggplot

[R] Using Rvest to scrape pages

Re: [R] Multi-level (nested) correlation structures via geepack package

Re: [R] Rmpfr correlation

Re: [R] Rmpfr correlation

[R] Rmpfr correlation

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