These is essentially a statistical question, which are generally
consider off topic here. So you may not get a satisfactory reply.
stats.stackexchange.com is probably a better venue for your post.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
On Fri, 4 May 2018, Allaisone 1 wrote:
Hi all ,
I have a dataframe (Hypertension) with following headers :-
Hypertension
ID Hypertension(before drug A) Hypertension(On drug A)On drug B?
Healthy diet?
1160
No, it's not homework, it's just some initial analysis, but still...
and thanks for recommendation.
On Thu, Nov 21, 2013 at 4:42 PM, Rolf Turner wrote:
>
> (1) Is this homework? (This list doesn't do homework for people!)
> (Animals maybe, but not people! :-) )
>
> (2) Your question isn't reall
(1) Is this homework? (This list doesn't do homework for people!)
(Animals maybe, but not people! :-) )
(2) Your question isn't really an R question but rather a
statistics/linear modelling
question. It is possible that you might get some insight from Frank
Harrel's book
"Regression Modelli
Hi,
I'm trying to fit regression model, but there is something wrong with it.
The dataset contains 85 observations for 85 students.Those observations are
counts of several actions, and dependent variable is final score. More
precisely, I have 5 IV and one DV. I'm trying to build regression model t
ct: Re: [R] Regression model for predicting ranks of the dependent variable
From: 538...@gmail.com
To: saumya.gu...@outlook.com
CC: r-help@r-project.org
What question (or questions) are you trying to answer? Any advice we may give
will depend on what you are trying to accomplish.
On Sat, Sep 14,
n 2010 were used. Calculating their scores is not
> necessary and even finding out the formula is not the objective. The
> objective is just to predict their ranks. But, finding the exact formula
> for calculating scores will be a bonus.
>
> ----------
>
t if
> the formula used in 2010 were used. Calculating their scores is not necessary
> and even finding out the formula is not the objective. The objective is just
> to predict their ranks. But, finding the exact formula for calculating scores
> will be a bonus.
> Date: Mon,
What question (or questions) are you trying to answer? Any advice we may
give will depend on what you are trying to accomplish.
On Sat, Sep 14, 2013 at 2:12 PM, Saumya Gupta wrote:
> I have a dataset which has several predictor variables and a dependent
> variable, "score" (which is numeric). T
require(rms)
?orm# ordinal regression model
For a case study see Handouts in
http://biostat.mc.vanderbilt.edu/CourseBios330
Since you have lost the original values, one part of the case study will
not apply: the use of Mean().
Frank
-
I have a dataset which has sever
I have a dataset which has several predictor variables and a dependent
variable, "score" (which is numeric). The score for each row is calculated
using a formula which uses some of the predictor variables. But, the "score"
figures are not explicitly given in the dataset. The scores are only arra
d from a given body of data.
~ John Tukey
-Oorspronkelijk bericht-
Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Namens
Michael Haenlein
Verzonden: dinsdag 6 december 2011 14:09
Aan: r-help@r-project.org
Onderwerp: [R] Regression model when dependent variable can o
Dear all,
I would like to run a regression of the form lm(y ~ x1+x2) where the
dependent variable y can only take positive values. Assume, for example,
that y is the height of a person (measured in cm), x1 is the gender
(measured as a binary indicator with 0=male and 1=female) and x2 is the age
of
On Tue, 12 Apr 2011, peter dalgaard wrote:
On Apr 12, 2011, at 08:45 , Achim Zeileis wrote:
On Mon, 11 Apr 2011, ty ty wrote:
Hello, dear experts. I don't have much experience in building
regression models, so sorry if this is too simple and not very
interesting question.
Currently I'm work
On Apr 12, 2011, at 08:45 , Achim Zeileis wrote:
> On Mon, 11 Apr 2011, ty ty wrote:
>
>> Hello, dear experts. I don't have much experience in building
>> regression models, so sorry if this is too simple and not very
>> interesting question.
>> Currently I'm working on the model that have to pr
On Mon, 11 Apr 2011, ty ty wrote:
Hello, dear experts. I don't have much experience in building
regression models, so sorry if this is too simple and not very
interesting question.
Currently I'm working on the model that have to predict proportion of
the debt returned by the debtor in some perio
Hello, dear experts. I don't have much experience in building
regression models, so sorry if this is too simple and not very
interesting question.
Currently I'm working on the model that have to predict proportion of
the debt returned by the debtor in some period of time. So the
dependent variable
John,
Thanks for the tip on that document; after some searching I found a PDF copy
of it on the 'Net. It may take me a little while to work through it, but it
looks to be full of good info and worth the effort.
I'll also take a look at leaps as I get time.
Thanks,
Monte
[[alternative
Monte Milanuk wrote:
>> I have a reference book that discusses regression model selection
>> using several methods - what they call 'Forward Model Selection' i.e.
>> add one variable at a time and examining R, R^2, Mallow's C-p value,
>> etc., Backward Model Selection' i.e. starting out with all t
Hello,
Newbie here, be gentle ;)
I have a reference book that discusses regression model selection using
several methods - what they call 'Forward Model Selection' i.e. add one
variable at a time and examining R, R^2, Mallow's C-p value, etc., 'Backward
Model Selection' i.e. starting out with all
Dear all:
I am searching for a regression model (e.g. y=Xb+e) in which dummy-coded events
(to time point t) on different regressors in X exhibit an effect on subsequent
responses in the vector y (to time-points t+1, t+2,… t+n). My aim is to
estimate how long the memory effect is and how strong
Dear all:
I am searching for a regression model (e.g. y=Xb+e) in which dummy-coded events
(to time point t) on different regressors in X exhibit an effect on subsequent
responses in the vector y (to time-points t+1, t+2,… t+n). My aim is to
estimate how long the memory effect is and how strong
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