Re: [R] Sensitivity Analysis for Moderated Mediation?

2016-12-05 Thread Bert Gunter 
This post actually has nothing to do with R programming per se, and
hence is off topic here. Please post elsewhere, e.g. on
stats.stackexchange.com or other list that discusses "mediation
models".

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 Mon, Dec 5, 2016 at 5:00 AM, Dominik Wyss  wrote:
> Dear group,
> medsens() is a fantastic method for testing sensitivity of mediation models
> estimated by the mediate(). (mediation package by Tingley et al; Version
> 4.4.5).
> However, I'm wondering whether medsens() is also appropriate for moderated
> mediation? And if not, is there an alternative procedure that allows to test
> sensitivity of moderated mediation analysis?
>
> In one of my study, I find strong theoretical and empirical support for
> moderated mediation (moderator is a dummy var; otherwise the model specs are
> similar to chapter 3.2
> ftp://cran.r-project.org/pub/R/web/packages/mediation/vignettes/mediation.pdf).
> However, my medsens() analysis finds only very poor Rho and R2 values.
> Before throwing away the model, I wonder whether medsens is appropriate for
> estimating sensitivity of moderated mediation models, normally entailing a
> moderation*mediator interaction term.
>
> And if medsens is not appropriate for moderated mediation: Would it be
> enough enlightening to test sensitivity of two unmoderated mediation models
> ran over two sub-datasets, split along the moderation dummy?
>
> many thanks for any advice.
> Dominik
>
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[R] Sensitivity Analysis for Moderated Mediation?

2016-12-05 Thread Dominik Wyss 

Dear group,
medsens() is a fantastic method for testing sensitivity of mediation 
models estimated by the mediate(). (mediation package by Tingley et al; 
Version 4.4.5).
However, I'm wondering whether medsens() is also appropriate for 
moderated mediation? And if not, is there an alternative procedure that 
allows to test sensitivity of moderated mediation analysis?


In one of my study, I find strong theoretical and empirical support for 
moderated mediation (moderator is a dummy var; otherwise the model specs 
are similar to chapter 3.2 
ftp://cran.r-project.org/pub/R/web/packages/mediation/vignettes/mediation.pdf). 
However, my medsens() analysis finds only very poor Rho and R2 values. 
Before throwing away the model, I wonder whether medsens is appropriate 
for estimating sensitivity of moderated mediation models, normally 
entailing a moderation*mediator interaction term.


And if medsens is not appropriate for moderated mediation: Would it be 
enough enlightening to test sensitivity of two unmoderated mediation 
models ran over two sub-datasets, split along the moderation dummy?


many thanks for any advice.
Dominik

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[R] Sensitivity analysis - minimum effect size detectable by a binomial test

2014-02-05 Thread Simone 
Hi all,

I have performed a binomial test to verify if the number of males in a study is 
significantly different from a null hypothesis (say, H0:p of being a male= 0.5).
For instancee:
binom.test(10, 30, p=0.5, alternative=two.sided, conf.level=0.95)

Exact binomial test

data:  10 and 30
number of successes = 10, number of trials =
30, p-value = 0.09874
alternative hypothesis: true probability of success is not equal to 0.5
95 percent confidence interval:
 0.1728742 0.5281200
sample estimates:
probability of success 
 0.333 

This way I get the estimated proportion of males (in this case p of success) 
that is equal to 0.33 and an associated p-value (this is not significant at 
alpha=0.05 with respect to the H0:P=0.5).

Now, I want to know, given a power of, say, 0.8, alpha=0.05 and the above 
sample size (30), what is the minimum proportion of males as low or as high 
(two sided) like to be significantly detected with respect to a H0 (not 
necessarily H0:P=0.5 - I am interested also in other null hypotheses). In other 
words, I would have been able to detect a significant deviation from the H0 for 
a given power, alpha and sample size if the proportion of males would have been 
more than Xhigh or less than Xlow.

I have had a look at the pwr package but it seems to me it doesn't allow to 
calculate this.
I would appreciate very much any suggestion.

  
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Re: [R] Sensitivity analysis - minimum effect size detectable by a binomial test

2014-02-05 Thread Marc Schwartz 
On Feb 5, 2014, at 10:05 AM, Simone misen...@hotmail.com wrote:

 Hi all,
 
 I have performed a binomial test to verify if the number of males in a study 
 is significantly different from a null hypothesis (say, H0:p of being a male= 
 0.5).
 For instancee:
 binom.test(10, 30, p=0.5, alternative=two.sided, conf.level=0.95)
 
Exact binomial test
 
 data:  10 and 30
 number of successes = 10, number of trials =
 30, p-value = 0.09874
 alternative hypothesis: true probability of success is not equal to 0.5
 95 percent confidence interval:
 0.1728742 0.5281200
 sample estimates:
 probability of success 
 0.333 
 
 This way I get the estimated proportion of males (in this case p of success) 
 that is equal to 0.33 and an associated p-value (this is not significant at 
 alpha=0.05 with respect to the H0:P=0.5).
 
 Now, I want to know, given a power of, say, 0.8, alpha=0.05 and the above 
 sample size (30), what is the minimum proportion of males as low or as high 
 (two sided) like to be significantly detected with respect to a H0 (not 
 necessarily H0:P=0.5 - I am interested also in other null hypotheses). In 
 other words, I would have been able to detect a significant deviation from 
 the H0 for a given power, alpha and sample size if the proportion of males 
 would have been more than Xhigh or less than Xlow.
 
 I have had a look at the pwr package but it seems to me it doesn't allow to 
 calculate this.
 I would appreciate very much any suggestion.


Take a look at ?power.prop.test, where you can specify that one of the 
proportions is NULL, yielding the value you seek:


 power.prop.test(n = 30, p1 = 0.5, p2 = NULL, power = 0.8, sig.level = 0.05) 

 Two-sample comparison of proportions power calculation 

  n = 30
 p1 = 0.5
 p2 = 0.834231
  sig.level = 0.05
  power = 0.8
alternative = two.sided

NOTE: n is number in *each* group


The value for 'p2' is your high value for the detectible difference from a 
proportion of 0.5, given the other parameters. 1 - p2 would be your low value.


Regards,

Marc Schwartz

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[R] Sensitivity Analysis

2013-04-22 Thread Gilson Carvalho 
Hi all,

Do nnet, neuralnet or RSNNS implemented any kind of sensitivity analysis?

Best Wishes,

-- 
Prof. Gilson Correia de Carvalho, M.Sc.

Pesquisador Associado
Laboratório de Ecologia Bentônica - LEB
Instituto de Biologia - UFBA
Universidade Federal da Bahia - UFBA

Professor Assistente
Departamento de Biointeração
Instituto de Ciências da Saúde - ICS
Universidade Federal da Bahia - UFBA
-
Diretor Técnico
Holos Soluções Ambientais Ltda
-
Skype: bio_gilson
GTalk: biogilson

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Re: [R] Sensitivity Analysis

2013-04-22 Thread Gilson Carvalho 
Hi all,

Do nnet, neuralnet or RSNNS implemented any kind of sensitivity analysis?

Best Wishes,


Em 22 de abril de 2013 11:46, Gilson Carvalho biogil...@gmail.comescreveu:

 Hi all,

 Do nnet, neuralnet or RSNNS implemented any kind of sensitivity analysis?

 Best Wishes,

 --
 Prof. Gilson Correia de Carvalho, M.Sc.
 
 Pesquisador Associado
 Laboratório de Ecologia Bentônica - LEB
 Instituto de Biologia - UFBA
 Universidade Federal da Bahia - UFBA
 
 Professor Assistente
 Departamento de Biointeração
 Instituto de Ciências da Saúde - ICS
 Universidade Federal da Bahia - UFBA
 -
 Diretor Técnico
 Holos Soluções Ambientais Ltda
 -
 Skype: bio_gilson
 GTalk: biogilson




-- 
Prof. Gilson Correia de Carvalho, M.Sc.

Pesquisador Associado
Laboratório de Ecologia Bentônica - LEB
Instituto de Biologia - UFBA
Universidade Federal da Bahia - UFBA

Professor Assistente
Departamento de Biointeração
Instituto de Ciências da Saúde - ICS
Universidade Federal da Bahia - UFBA
-
Diretor Técnico
Holos Soluções Ambientais Ltda
-
Skype: bio_gilson
GTalk: biogilson

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[R] Sensitivity analysis in case of correlated inputs

2012-03-18 Thread Jin Minming 
Dear All,

There are two packages which can be used for sensitivtiy analysis when the 
predictor variables are correlated. 
a: pcc (partial correlation coefficient) in R sensitivity package
b: varimp in R party package (conditional importance)

Are other R packages available for sensitivity anlaysis to handle the 
correlated inputs?

Thanks,

Jim

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Re: [R] Sensitivity analysis - looking a tool for epidemiologic research

2012-01-31 Thread Dominic Comtois 
Thanks for the reply. I am aware of those packages, as well as of a few
others (epiR, epinet, epibasix, epicalc). Unfortunately, they don't do this.
I'll try to get in touch with the authors of the Stata package, who knows.

Regards,

D.C.

-Message d'origine-
De : MacQueen, Don [mailto:macque...@llnl.gov] 
Envoyé : 30 janvier 2012 18:19
À : Dominic Comtois; r-help@r-project.org
Objet : Re: [R] Sensitivity analysis - looking a tool for epidemiologic
research

R has several packages for epidemiology. Maybe one of them has it. Take a
look.
To name just two:  Epi and epitools

-Don


--
Don MacQueen

Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062





On 1/27/12 9:01 PM, Dominic Comtois dominic.comt...@gmail.com wrote:

Stata users can rely on the very neat Episens package for sensitivity 
analysis. Briefly, it allows one to specify a diagnostic tool's 
sensitivity and specificity and take those into account when estimating 
a risk ratio, for instance. A full description of the package is 
available at 
http://www.stata-journal.com/sjpdf.html?articlenum=st0138
http://www.stata-journal.com/sjpdf.html?articlenum=st0138

 

Anyone aware of a similar package in R?

 

Thanks

 

DC


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Re: [R] Sensitivity analysis - looking a tool for epidemiologic research

2012-01-30 Thread MacQueen, Don 
R has several packages for epidemiology. Maybe one of them has it. Take a
look.
To name just two:  Epi and epitools

-Don


-- 
Don MacQueen

Lawrence Livermore National Laboratory
7000 East Ave., L-627
Livermore, CA 94550
925-423-1062





On 1/27/12 9:01 PM, Dominic Comtois dominic.comt...@gmail.com wrote:

Stata users can rely on the very neat Episens package for sensitivity
analysis. Briefly, it allows one to specify a diagnostic tool's
sensitivity
and specificity and take those into account when estimating a risk ratio,
for instance. A full description of the package is available at
http://www.stata-journal.com/sjpdf.html?articlenum=st0138
http://www.stata-journal.com/sjpdf.html?articlenum=st0138

 

Anyone aware of a similar package in R?

 

Thanks

 

DC


   [[alternative HTML version deleted]]

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[R] Sensitivity analysis - looking a tool for epidemiologic research

2012-01-27 Thread Dominic Comtois 
Stata users can rely on the very neat Episens package for sensitivity
analysis. Briefly, it allows one to specify a diagnostic tool's sensitivity
and specificity and take those into account when estimating a risk ratio,
for instance. A full description of the package is available at
http://www.stata-journal.com/sjpdf.html?articlenum=st0138
http://www.stata-journal.com/sjpdf.html?articlenum=st0138

 

Anyone aware of a similar package in R?

 

Thanks

 

DC


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[R] Sensitivity Analysis: Morris method - argument scale

2011-05-12 Thread Christoph Warkotsch 
Dear R-users,
 
I have a question on the logical argument scale in the morris-function
from the sensitivity package. Should it be set to TRUE or FALSE?
 
Thanks,
 
Chris

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[R] sensitivity analysis with external simulation model - decoupling in package sensitivity or alternative?

2011-02-04 Thread Rainer M Krug 
-BEGIN PGP SIGNED MESSAGE-
Hash: SHA1

Hi

I hava a simulation model, of which I want to do a sensitivity analysis.
I have identified a number of input variables and my response variable.
What I hava done so far:

1) I created a Latin Hypercube with the lhs package (10.000 simulations)
2) simulated these

and now I have a data frame with the input parameter and the response
variable. I looked around and found the package sensitivity which
seems to be the most suitable for my case. As the model is not in R, I
have to use the decoupling approach. But now I am stuck:

How can I do this? An example highlighting how to do the decoupling to
do a sensitivity analysis, would be very much appreciated.

Alternatively, ids there a different approach I could take?

Thanks,

Rainer

- -- 
Rainer M. Krug, PhD (Conservation Ecology, SUN), MSc (Conservation
Biology, UCT), Dipl. Phys. (Germany)

Centre of Excellence for Invasion Biology
Natural Sciences Building
Office Suite 2039
Stellenbosch University
Main Campus, Merriman Avenue
Stellenbosch
South Africa

Tel:+33 - (0)9 53 10 27 44
Cell:   +27 - (0)8 39 47 90 42
Fax (SA):   +27 - (0)8 65 16 27 82
Fax (D) :   +49 - (0)3 21 21 25 22 44
Fax (FR):   +33 - (0)9 58 10 27 44
email:  rai...@krugs.de

Skype:  RMkrug
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[R] sensitivity analysis, input factors

2010-04-13 Thread Szalai Márk 
Hi,


I'm trying to conduct sensitivity analysis in R using the 'sensitivity' 
package. Although the description of functions seem straightforward, I can’t 
succeed. The definition of input factors can be the problem. 

library(sensitivity)
#A simple model with 4 input factor to test the morris function:
model01=function(a1,a2,a3,a4)
{
Z-numeric(10)
Z[1]-runif(1)
Z[2]-runif(1,a1,30)
Z[3]-6*runif(1,min(a1,a2),max(a1,a3))
Z[4]-runif(1,2,5)*runif(1,min(a2,a4),max(a2,a4))
Z[5]-0.5*runif(1,min(a3,a4),max(a3,a4))
Z[6]-2*runif(1,min(a1,a4),max(a1,a4))
Z[7]-runif(1)
Z[8]-2*runif(1,min(2*a1,5*a4),max(10*a1,100*a4))
Z[9]-2.5*runif(1,min(a2,a3),max(a2,a3))
Z[10]-rnorm(1,10*a1,1)
mean(Z)
}

xx=morris(model = model01, factors=c(a1,a2,a3,a4), r=4,  
design=list(type=oat, levels = 5, grid.jump = 3), binf =1,bsup=20, scale=F)


Error message suggests that the second input factor is not used 
How should I define the input factors?


Thanks in advance,
Mark 
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Re: [R] sensitivity analysis, input factors

2010-04-13 Thread milton ruser 
Hi Szalai

I had used only src function, and on that case you need to
have a vector with your Y variable, and a data-frame with all
your X (i.e. explanatory) variables.

I have interest on stay in touch with others that have been using
sensitivity package!

bests

milton

On Tue, Apr 13, 2010 at 11:08 AM, Szalai Márk szalai.m...@mkk.szie.huwrote:

 Hi,


 I'm trying to conduct sensitivity analysis in R using the 'sensitivity'
 package. Although the description of functions seem straightforward, I can’t
 succeed. The definition of input factors can be the problem.

 library(sensitivity)
 #A simple model with 4 input factor to test the morris function:
 model01=function(a1,a2,a3,a4)
 {
 Z-numeric(10)
 Z[1]-runif(1)
 Z[2]-runif(1,a1,30)
 Z[3]-6*runif(1,min(a1,a2),max(a1,a3))
 Z[4]-runif(1,2,5)*runif(1,min(a2,a4),max(a2,a4))
 Z[5]-0.5*runif(1,min(a3,a4),max(a3,a4))
 Z[6]-2*runif(1,min(a1,a4),max(a1,a4))
 Z[7]-runif(1)
 Z[8]-2*runif(1,min(2*a1,5*a4),max(10*a1,100*a4))
 Z[9]-2.5*runif(1,min(a2,a3),max(a2,a3))
 Z[10]-rnorm(1,10*a1,1)
 mean(Z)
 }

 xx=morris(model = model01, factors=c(a1,a2,a3,a4), r=4,
 design=list(type=oat, levels = 5, grid.jump = 3), binf =1,bsup=20,
 scale=F)


 Error message suggests that the second input factor is not used
 How should I define the input factors?


 Thanks in advance,
 Mark
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Re: [R] sensitivity analysis, input factors

2010-04-13 Thread Szalai Márk 
Hi Milton,

Thanks for your rapid answer.

As far as I know the src is a regression based method and can be separated from 
model evaluations. However, a screening method like Morris requires that the 
input factors of  latter model evaluations would be determined by the results 
of previous evaluations. Thus, there is no vector of responses (and data frame 
of factors) before the sensitivity analysis itself.

Bests,
Mark

- Eredeti levél (Original Message) -
Feladó: milton ruser milton.ru...@gmail.com
Dátum: Kedd, Április 13, 2010 5:16 du
Tárgy: Re: [R] sensitivity analysis, input factors
Címzett: Szalai Márk szalai.m...@mkk.szie.hu
Másolat: r-help@r-project.org

 Hi Szalai
 
 I had used only src function, and on that case you need to
 have a vector with your Y variable, and a data-frame with all
 your X (i.e. explanatory) variables.
 
 I have interest on stay in touch with others that have been using
 sensitivity package!
 
 bests
 
 milton
 
 On Tue, Apr 13, 2010 at 11:08 AM, Szalai Márk 
 szalai.m...@mkk.szie.huwrote:
  Hi,
 
 
  I'm trying to conduct sensitivity analysis in R using the 
 'sensitivity' package. Although the description of functions 
 seem straightforward, I can’t
  succeed. The definition of input factors can be the problem.
 
  library(sensitivity)
  #A simple model with 4 input factor to test the morris function:
  model01=function(a1,a2,a3,a4)
  {
  Z-numeric(10)
  Z[1]-runif(1)
  Z[2]-runif(1,a1,30)
  Z[3]-6*runif(1,min(a1,a2),max(a1,a3))
  Z[4]-runif(1,2,5)*runif(1,min(a2,a4),max(a2,a4))
  Z[5]-0.5*runif(1,min(a3,a4),max(a3,a4))
  Z[6]-2*runif(1,min(a1,a4),max(a1,a4))
  Z[7]-runif(1)
  Z[8]-2*runif(1,min(2*a1,5*a4),max(10*a1,100*a4))
  Z[9]-2.5*runif(1,min(a2,a3),max(a2,a3))
  Z[10]-rnorm(1,10*a1,1)
  mean(Z)
  }
 
  xx=morris(model = model01, factors=c(a1,a2,a3,a4), r=4,
  design=list(type=oat, levels = 5, grid.jump = 3), binf =1,bsup=20,
  scale=F)
 
 
  Error message suggests that the second input factor is not used
  How should I define the input factors?
 
 
  Thanks in advance,
  Mark
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 project.org/posting-guide.html
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[R] sensitivity analysis

2008-04-16 Thread Eleni Christodoulou 
Hello list,

I am performing a sensitivity analysis using the package ROCR. I am using
the class prediction in this aim. My question is, could anyone tell me
what the vector cutoffs represent in the result?

Thank you all,
Eleni

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Re: [R] sensitivity analysis

2008-04-16 Thread Dieter Menne 
Eleni Christodoulou elenichri at gmail.com writes:

 I am performing a sensitivity analysis using the package ROCR. I am using
 the class prediction in this aim. My question is, could anyone tell me
 what the vector cutoffs represent in the result?
 

As the docs say:
cutoffs:
A list in which each element is a vector of all necessary cutoffs. Each
cutoff vector consists of the predicted scores (duplicates removed), in
descending order.


Dieter

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and provide commented, minimal, self-contained, reproducible code.