Re: [R] linear programming in R | limits to what it can do, or my mistake?

On 2024/1/30 20:00, Martin Becker wrote: Apart from the fact that the statement "such that t1+t2+t3+t4=2970 (as it must)" is not correct, the LP can be implemented as follows: I was confused by "such that t1+t2+t3+t4=2970 (as it must)", otherwise, I also get the same solution.

Re: [R] linear programming in R | limits to what it can do, or my mistake?

Apart from the fact that the statement "such that t1+t2+t3+t4=2970 (as it must)" is not correct, the LP can be implemented as follows: library(lpSolve) LHS <- rbind( c(0,0,0,0, 1, 0, 0,0), c(1,0,0,0,-1, 1, 0,0), c(0,1,0,0, 0,-1, 1,0), c(0,0,1,0, 0, 0,-1,1), cbind(-diag(4),diag(4)),

[R] linear programming in R | limits to what it can do, or my mistake?

Question for 'experts' in LP using R (using the lpSolve package, say) -- which does not apply to me for the sort of problem I describe below. I've run any number of LP's using lpSolve in R, but all of them to date have objective and constraint functions that both contain the same variables.

Re: [R] Linear model and approx function

1. You should regress Elevation on Volume, no? 2. You are calling lm incorrectly for prediction. Please read ?lm and related links carefully and/or consult a tutorial. R-Help is really not the first place you should look for this sort of detailed info. 3. I think this is what you want: lm1 <-

[R] Linear model and approx function

Dear all; I have a dataframe with several columns. The columns are the elevation, volume and the area of the cells (which were placed inside a polygon). I have extracted them from DEM raster to calculate the volume under polygon and the elevation for a specific volume of the reservoir. >

Re: [R] Linear discriminant analysis

On 12.10.2023 16:25, Fernando Archuby wrote: Hi. I have successfully performed the discriminant analysis with the lda function, I can classify new individuals with the predict function, but I cannot figure out how the lda results translate into the classification decision. That is, I don't

Re: [R] Linear discriminant analysis

It's possible that neither of these will help, but (1) you can look at the source code of the predict method (MASS:::predict.lda) (2) you can look at the source reference ("Modern Applied Statistics in S", Venables and Ripley) to see if it gives more information (although it might not);

[R] Linear discriminant analysis

Hi. I have successfully performed the discriminant analysis with the lda function, I can classify new individuals with the predict function, but I cannot figure out how the lda results translate into the classification decision. That is, I don't realize how the classification equation for new

Re: [R] linear mixed model using lmer

There is no difference when running anova or t-test. So you shouldn't expect positive variance between batches. On Fri, Mar 4, 2022 at 7:06 PM array chip via R-help wrote: > Thanks Jeff for reminding me that the attachment is removed. I put it in > my google drive if anyone wants to test the

Re: [R] linear mixed model using lmer

Do you really think a variance from a sample size of 2 makes any sense? 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 Fri, Mar 4, 2022 at 5:06 PM array chip

Re: [R] linear mixed model using lmer

Thanks Jeff for reminding me that the attachment is removed. I put it in my google drive if anyone wants to test the data (https://drive.google.com/file/d/1lgVZVLHeecp9a_sFxEPeg6353O-qXZhM/view?usp=sharing) I'll try the mixed model mailing list as well. John On Friday, March 4, 2022,

Re: [R] Linear

str(dat2) data.frame': 37654 obs. ...: $ Yld: int $ A : int $ B : chr $ C : chr On Wed, Jan 26, 2022 at 10:49 AM Bert Gunter wrote: > What does str(dat2) give? > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along > and sticking

Re: [R] Linear

What does str(dat2) give? 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 Wed, Jan 26, 2022 at 7:37 AM Val wrote: > > Hi all, > > I am trying to get the

[R] Linear

Hi all, I am trying to get the lsmeans for one of the factors fitted in the following model Model1 = lm(Yld ~ A + B + C, data = dat2) M_lsm = as.data.frame(lsmeans(Model1, "C")), My problem is, I am getting this error message. "Error: The rows of your requested reference grid would be 81412,

Re: [R] linear mixed model required for the U.S. FDA

Dear Helmut, The mixed models list is more suitable for this kind of question. I'm forwarding it to that list. Please send any follow-up to that list instead of the general R help list. If I understand correctly, you'll need a different variance term for both treatments (the within subject for T

[R] linear mixed model required for the U.S. FDA

Dear all, I’m struggling to set up a model required for the FDA (haha, and the Chinese agency). The closest I could get given at the end (which matches the one preferred by other regulatory agencies worldwide). The FDA is happy with R but "close" is not close /enough/. Don't hit me. I'm

Re: [R] linear model contrast in R

I think you might be looking for ?contrasts to form the contrast matrix. Rich On Mon, May 13, 2019 at 7:31 AM Witold E Wolski wrote: > > I am looking for a function to compute contrasts with a interface > similar to that of > > lmerTest::contest > multcomp::glht > > i.e. taking the model and a

[R] linear model contrast in R

I am looking for a function to compute contrasts with a interface similar to that of lmerTest::contest multcomp::glht i.e. taking the model and a contrast vector or matrix as an argument, but for linear models, and without the multiple testing adjusted made by multcomp::glht. Thank you --

Re: [R] linear regression

Hi Kenneth, My guess is that you have tried to send screenshots of your output and these were blocked. Try to cut and paste the output into your message. Jim On Tue, Aug 7, 2018 at 6:38 PM, John wrote: > On Mon, 6 Aug 2018 20:18:38 +0200 > kenneth Barnhoorn wrote: > > Your examples did not

Re: [R] linear regression

On Mon, 6 Aug 2018 20:18:38 +0200 kenneth Barnhoorn wrote: Your examples did not appear. Remember to use plain text rather than html. JWDougherty > I have a problem with a linear regression output. > > In January I made an analysis of some data and received an certain > output, if I run the

[R] linear regression

I have a problem with a linear regression output. In January I made an analysis of some data and received an certain output, if I run the same code now I don’t receive the same output and I don’t see why. It is important to know the country, so I would like to see the country names behind the

Re: [R] linear regression with repeated measures

Generally, statistics questions are off topic here, although they do sometimes intersect R programming issues, as perhaps here. Nevertheless, I believe your post would fit better on the r-sig-mixed-models list, where repeated measures and other mixed effects (/variance components) models are

[R] linear regression with repeated measures

Dear list, this seemed to me like a very trivial question, but finally I haven't found any similar postings with suitable solutions on the net ... Basically, instead of regressing two simple series of measures 'a' and 'b' (like b ~ a), I would like to use independent replicate measurements for

Re: [R] Linear regression with tranformed dependant variable

Step back a minute: normality is NOT required for predictors in a multiple regression model, though the sqrt(x) transformation may also make the relationship more nearly linear, and linearity IS assumed when you fit a simple model such as y ~ x + w + z. (Normality is only required for the

Re: [R] Linear regression with tranformed dependant variable

Before going to stackexchange you should consider if a square root transformation is appropriate for the model that you are trying to estimate. If you do so, you may be able to interpret the coefficients yourself. If no explanation is obvious you probably should not be using a square root

Re: [R] Linear regression with tranformed dependant variable

Hello, R-Help answers questions on R code, your question is about statistics. You should try posting the question to https://stats.stackexchange.com/ Hope this helps, Rui Barradas Em 23-10-2017 18:54, kende jan via R-help escreveu: Dear all, I am trying to fit a multiple linear regression

[R] Linear regression with tranformed dependant variable

Dear all, I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...).  I have realised a sqrt(variable) transformation... The results are great, but I don't know how to interprete the beta coefficients... Is it

[R] linear trend JJAS spatial data (1979-2005)

Hello, I have a netcdf file for summer monsoon rainfall gridded data over Indian region. How can I find the linear trend in R? regards Sourabh Bal Dr. Sourabh Bal Assistant Professor Department of Physics Swami Vivekananda Institute of Science and Technology Kolkata 700145

[R] ?????? Linear Mixed-Effects Model

Thank You -- ԭʼ�ʼ� -- ��: "Bert Gunter"<bgunter.4...@gmail.com>; ʱ��: 2017��3��11��(��) 11:44 �ռ���: "��˳"<liushu...@qq.com>; : "r-help"<r-help@r-project.org>; : Re: [R] Linear Mixed-Effec

Re: [R] Linear Mixed-Effects Model

Have you read the docs? Is this some kind of homework? -- this list does not do homework. We expect minimal efforts at least on the part of posters. We do not do tutorials here. I think you need to do some reading on your own before posting further. Try posting on the r-sig-mixed-models list to

[R] Linear Mixed-Effects Model

Dear R Help: What does "lmer(responce ~ factor1*factor2 + (factor1*factor2 | group1) + (factor1*factor2| group2), data)" mean? And "lmer(responce ~ factor1*factor2 + (factor1*factor2 | group1) + (factor1*factor2| group2), data)" vs. "lmer(responce ~ factor1*factor2 + (factor1+factor2 | group1)

Re: [R] Linear optimization with quadratic constraints

Small example code to set up the problem? JN On 2017-01-07 06:26 AM, Preetam Pal wrote: > Hi Guys, > Any help with this,please? > Regards, > Preetam > > On Thu, Jan 5, 2017 at 4:09 AM, Preetam Pal wrote: > >> Hello guys, >> >> The context is ordinary multivariate

Re: [R] Linear optimization with quadratic constraints

, you should cite it and describe why not specifically so we don't find it and think "you should have found this yourself". The second hit that came up when I typed 'R linear optimization "quadratic constraint"' into Google was https://cran.r-project.org/web/packages/ROI/

Re: [R] Linear optimization with quadratic constraints

Hi Guys, Any help with this,please? Regards, Preetam On Thu, Jan 5, 2017 at 4:09 AM, Preetam Pal wrote: > Hello guys, > > The context is ordinary multivariate regression with k (>1) regressors, > i.e. *Y = XB + Error*, where > Y = n X 1 vector of predicted variable, > X =

[R] Linear optimization with quadratic constraints

Hello guys, The context is ordinary multivariate regression with k (>1) regressors, i.e. *Y = XB + Error*, where Y = n X 1 vector of predicted variable, X = n X (k + 1) matrix of regressor variables(including ones in the first column) B = (k+1) vector of coefficients, including intercept. Say, I

Re: [R] Linear Regressions with constraint coefficients

Grothendieck Cc: r-help@r-project.org Subject: Re: [R] Linear Regressions with constraint coefficients Thx a lot Gabor! Aljosa Aleksandrovic, FRM, CAIA Quantitative Analyst - Convertibles aljosa.aleksandro...@man.com Tel +41 55 417 76 03 Man Investments (CH) AG Huobstrasse 3 | 8808 Pfäffikon SZ

[R] Linear Dependance of Model Matrix and How Fitted/ Sums of Squares Follow

Below is the covariates for a model ~x1+x2+x3+x4+x5+x6. I noticed that when fitting this model that the coefficient x6 is unestimable.*Is this merely a case that adding more columns to my model matrix will eventually lead to linear dependance so the more terms I have in the model formulae the more

Re: [R] Linear model vs Mixed model

Thanks Brian for all your kind help. "didn't mean to imply that the different parameterization of the contrasts would make the lm estimates agree more with the lmer estimates, only that it might be easier to compare the regression summary output to see how similar/dissimilar they were ". Got it

Re: [R] Linear model vs Mixed model

Utkarsh: I think the differences between the lm and lmer estimates of the intercept are consistent with the regularization effect expected with mixed-effects models where the estimates shrink towards the mean slightly. I don't think there is any reason to expect exact agreement between the lm and

Re: [R] Linear model vs Mixed model

Hi Brian, This makes some sense to me theoretically, but doesn't pan out with my experiment. The contrasts default was the following as you said: > options("contrasts") $contrasts unordered ordered "contr.treatment" "contr.poly" I changed it as follows: >

Re: [R] Linear model vs Mixed model

Your lm() estimates are using the default contrasts of contr.treatment, providing an intercept corresponding to your subject 308 and the other subject* estimates are differences from subject 308 intercept. You could have specified this with contrasts as contr.sum and the estimates would be more

Re: [R] Linear model vs Mixed model

Hello Thierry, Thank you for your quick response. Sorry, but I am not sure if I follow what you said. I get the following outputs from the two models: > coef(lmer(Reaction ~ Days + (1| Subject), sleepstudy)) Subject(Intercept) Days 308292.1888 10.46729 309173.5556 10.46729 310

Re: [R] Linear model vs Mixed model

The parametrisation is different. The intercept in model 1 is the effect of the "average" subject at days == 0. The intercept in model 2 is the effect of the first subject at days == 0. ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team

[R] Linear model vs Mixed model

Hi experts, While the slope is coming out to be identical in the two methods below, the intercepts are not. As far as I understand, both are formulations are identical in the sense that these are asking for a slope corresponding to 'Days' and a separate intercept term for each Subject. # Model-1

[R] Linear Regressions with non-negativity constraint

Hi, Take a look at the package "ic.infer" by Ulrike Gromping. https://www.jstatsoft.org/article/view/v033i10 Best, Ravi Ravi Varadhan, Ph.D. (Biostatistics), Ph.D. (Environmental Engg) Associate Professor, Department of Oncology Division of Biostatistics & Bionformatics Sidney Kimmel

[R] Linear Regressions with non-negativity constraint

Hi all, I hope you are doing well? I'm currently using lm() to estimate a linear multi-factor (5 factors without intercept) model as follows ... factor.lm <- lm(y~x1+x2+x3+x4+x5-1, data = data.frame.rbind) Using nnls(A,b) I estimated the same model, extended by a non-negativity constraint on

Re: [R] Linear Regressions with non-negativity constraint

:06 To: Gabor Grothendieck Cc: r-help@r-project.org Subject: Re: [R] Linear Regressions with constraint coefficients Thx a lot Gabor! Aljosa Aleksandrovic, FRM, CAIA Quantitative Analyst - Convertibles aljosa.aleksandro...@man.com Tel +41 55 417 76 03 Man Investments (CH) AG Huobstrasse 3 | 8

Re: [R] Linear Regressions with constraint coefficients

] Sent: Donnerstag, 28. April 2016 14:48 To: Aleksandrovic, Aljosa (Pfaeffikon) Cc: r-help@r-project.org Subject: Re: [R] Linear Regressions with constraint coefficients The nls2 package can be used to get starting values. On Thu, Apr 28, 2016 at 8:42 AM, Aleksandrovic, Aljosa (Pfaeffikon

Re: [R] Linear Regressions with constraint coefficients

...@gmail.com] Sent: Dienstag, 26. April 2016 17:59 To: Aleksandrovic, Aljosa (Pfaeffikon) Cc: r-help@r-project.org Subject: Re: [R] Linear Regressions with constraint coefficients This is a quadratic programming problem that you can solve using either a quadratic programming solver with constraints

Re: [R] Linear Regressions with constraint coefficients

ail.com] > Sent: Dienstag, 26. April 2016 17:59 > To: Aleksandrovic, Aljosa (Pfaeffikon) > Cc: r-help@r-project.org > Subject: Re: [R] Linear Regressions with constraint coefficients > > This is a quadratic programming problem that you can solve using either a > quadratic prog

Re: [R] Linear Regressions with constraint coefficients

Any help with exporting anova output in R to csv or xlsx? From: "Aleksandrovic, Aljosa (Pfaeffikon)" <aljosa.aleksandro...@man.com> To: Bert Gunter <bgunter.4...@gmail.com> Cc: "r-help@r-project.org" <r-help@r-project.org> Sent: Tuesday, April 26,

Re: [R] Linear Regressions with constraint coefficients

Grothendieck [mailto:ggrothendi...@gmail.com] Sent: Dienstag, 26. April 2016 17:59 To: Aleksandrovic, Aljosa (Pfaeffikon) Cc: r-help@r-project.org Subject: Re: [R] Linear Regressions with constraint coefficients This is a quadratic programming problem that you can solve using either a quadratic

Re: [R] Linear Regressions with constraint coefficients

This is a quadratic programming problem that you can solve using either a quadratic programming solver with constraints or a general nonlinear solver with constraints. See https://cran.r-project.org/web/views/Optimization.html for more info on what is available. Here is an example using a

Re: [R] Linear Regressions with constraint coefficients

[mailto:bgunter.4...@gmail.com] Sent: Dienstag, 26. April 2016 17:49 To: Aleksandrovic, Aljosa (Pfaeffikon) Cc: r-help@r-project.org Subject: Re: [R] Linear Regressions with constraint coefficients Have you tried web searching on " R constrained linear regression" or similar. There seemed to be

Re: [R] Linear Regressions with constraint coefficients

unter [mailto:bgunter.4...@gmail.com] > Sent: Dienstag, 26. April 2016 16:51 > To: Aleksandrovic, Aljosa (Pfaeffikon) > Cc: r-help@r-project.org > Subject: Re: [R] Linear Regressions with constraint coefficients > > If the slope coefficients sum to a constant, the regressors are d

Re: [R] Linear Regressions with constraint coefficients

Subject: Re: [R] Linear Regressions with constraint coefficients If the slope coefficients sum to a constant, the regressors are dependent and so a unique solution is impossible (an infinity of solutions would result). So I think you have something going on that you don't understand and should

Re: [R] Linear Regressions with constraint coefficients

If the slope coefficients sum to a constant, the regressors are dependent and so a unique solution is impossible (an infinity of solutions would result). So I think you have something going on that you don't understand and should consult a local statistician to help you formulate your problem

Re: [R] Linear Regressions with constraint coefficients

Hi all, I hope you are doing well? I’m currently using the lm() function from the package stats to fit linear multifactor regressions. Unfortunately, I didn’t yet find a way to fit linear multifactor regressions with constraint coefficients? I would like the slope coefficients to be all

Re: [R] Linear Model and Missing Data in Predictors

One technique for dealing with this is called 'multiple imputation'. Google for 'multiple imputation in R' to find R packages that implement it (e.g., the 'mi' package). Bill Dunlap TIBCO Software wdunlap tibco.com On Tue, Mar 15, 2016 at 8:14 AM, Lorenzo Isella wrote:

Re: [R] Linear Model and Missing Data in Predictors

IMHO this is not a question about R... it is a question about statistics whether R is involved or not. As such, a forum like stats.stackexchange.com would be better suited to address this. FWIW I happen to think that expecting R to solve this for you is unreasonable. -- Sent from my phone.

[R] Linear Model and Missing Data in Predictors

Dear All, A situation that for sure happens very often: suppose you are in the following situation set.seed(1235) x1 <- seq(30) x2 <- c(rep(NA, 9), rnorm(19)+9, c(NA, NA)) x3 <- c(rnorm(17)-2, rep(NA, 13)) y <- exp(seq(1,5, length=30)) mm<-lm(y~x1+x2+x3) i.e. you try a simple linear

Re: [R] linear model solving

On 16/11/15 20:49, Ragia Ibrahim wrote: Dear group IF I had an objective function and some constrains formed in linear model form. is there a way,..library in R that helps me to solve such amodel and find the unknown variable in it? This is a very ill-posed question and is unlikely to provoke

[R] linear model solving

Dear group IF I had an objective function and some constrains formed in linear model form. is there a way,..library in R that helps me to solve such amodel and find the unknown variable in it? thanks in advance Ragia

Re: [R] Linear regression with a rounded response variable

Hi Ravi, And remember that the vanilla rounding procedure is biased upward. That is, an observation of 5 actually may have ranged from 4.5 to 5.4. Jim On Thu, Oct 22, 2015 at 7:15 AM, peter salzman wrote: > here is one thought: > > if you plug in your numbers into

Re: [R] Linear regression with a rounded response variable

:11 PM To: Charles C. Berry Cc: Ravi Varadhan; r-help@r-project.org Subject: Re: [R] Linear regression with a rounded response variable > On 21 Oct 2015, at 19:57 , Charles C. Berry <ccbe...@ucsd.edu> wrote: > > On Wed, 21 Oct 2015, Ravi Varadhan wrote: > >> [snippage] > &g

Re: [R] Linear regression with a rounded response variable

> Yes, and I think that the suggestion in another post to look at censored > regression is more in the right direction. I think this is right and perhaps the best (or at least better) pathway to pursue than considering this within the framework of measurement error (ME). Of course there *is*

[R] Linear regression with a rounded response variable

Hi, I am dealing with a regression problem where the response variable, time (second) to walk 15 ft, is rounded to the nearest integer. I do not care for the regression coefficients per se, but my main interest is in getting the prediction equation for walking speed, given the predictors (age,

Re: [R] Linear regression with a rounded response variable

On Wed, 21 Oct 2015, Ravi Varadhan wrote: Hi, I am dealing with a regression problem where the response variable, time (second) to walk 15 ft, is rounded to the nearest integer. I do not care for the regression coefficients per se, but my main interest is in getting the prediction equation

Re: [R] Linear regression with a rounded response variable

Hi Ravi, Thanks for this interesting question. My thoughts are given below. If you believe the rounding is indeed uniformly distributed, then the problem is equivalent with adding a uniform random error between (-0.5, 0.5) for every observation in addition to the standard normal error, which

Re: [R] Linear regression with a rounded response variable

here is one thought: if you plug in your numbers into any kind of regression you will get prediction that are real numbers and not necessarily integers, it may be that you predictions are good enough with this approximate value of Y. you could test this by randomly shuffling your data by +- 0.5

Re: [R] Linear regression with a rounded response variable

This could be modeled directly using Bayesian techniques. Consider the Bayesian version of the following model where we only observe y and X. y0 is not observed. y0 <- X b + error y <- round(y0) The following code is based on modifying the code in the README of the CRAN rcppbugs R

Re: [R] Linear regression with a rounded response variable

> On 21 Oct 2015, at 19:57 , Charles C. Berry wrote: > > On Wed, 21 Oct 2015, Ravi Varadhan wrote: > >> [snippage] > > If half the subjects have a value of 5 seconds and the rest are split between > 4 and 6, your assertion that rounding induces an error of >

Re: [R] Linear regression of 0/1 response ElemStatLearn (Fig. 2.1 the elements of statistical learning)

-project.org Subject: [R] Linear regression of 0/1 response ElemStatLearn (Fig. 2.1 the elements of statistical learning) Hello In chapter 2 ESL book authors write: Let's look at example of linear model in a classification context They fit a simple linear model g = 0.3290614 -0.0226360x1 + 0.2495983x2 + e

Re: [R] Linear relative rate / excess relative risk models

On 07/30/2014 05:00 AM, r-help-requ...@r-project.org wrote: A while ago, I inquired about fitting excess relative risk models in R. This is a follow-up about what I ended up doing in case the question pops up again. While I was not successful in using standard tools, switching to Bayesian

Re: [R] Linear relative rate / excess relative risk models

(stanFit) stanFit -Original Message- From: Wollschlaeger, Daniel Sent: Thursday, January 9, 2014 10:44 AM To: David Winsemius Cc: r-help@r-project.org Subject: RE: AW: [R] Linear relative rate / excess relative risk models Thanks for your suggestions! Here are links to simulated data

[R] Linear Regression Warning after plot: NaNs produced in

Hello, I'm a R beginner and I want to make a Multiple Regression about birds. My data is stord in a .csv file. I tried to do this with the following code: reg.data - read.table(file.choose(),header=T, sep=;,dec=,) attach(reg.data) names(reg.data) model - lm(Flights ~ Age + Gender + weight +

Re: [R] Linear Regression Warning after plot: NaNs produced in

One way to see where the first warning comes from is to turn warnings into errors with options(warn=2) and when the error happens call traceback(). Bill Dunlap TIBCO Software wdunlap tibco.com On Sun, Jun 29, 2014 at 4:12 AM, wat tele watt...@hotmail.de wrote: Hello, I'm a R beginner and I

[R] linear mixed model for non-normal negative and continous data

Dear all I try to fit a linear mixed model to my data. In short, my dependent variable reflects changes of the bone level (Knmn, in mm), thus this variable is continous and provides negative values. I have two different groups (factor Group) that were measured 3 times each (thus repeated

Re: [R] linear mixed model for non-normal negative and continous data

-project.org Onderwerp: [R] linear mixed model for non-normal negative and continous data Dear all I try to fit a linear mixed model to my data. In short, my dependent variable reflects changes of the bone level (Knmn, in mm), thus this variable is continous and provides negative values. I have two

[R] Linear line on pairs plot

Hi, Im trying to plot a linear line on the scatter plot using the pairs() function. At the moment the line is non linear. However, I want a linear line and the associated R value. Here is my current code: panel.cor.scale - function(x, y, digits=2, prefix=, cex.cor) { usr - par(usr);

Re: [R] Linear line on pairs plot

-help@r-project.org Subject: [R] Linear line on pairs plot Hi, Im trying to plot a linear line on the scatter plot using the pairs() function. At the moment the line is non linear. However, I want a linear line and the associated R value. Here is my current code: panel.cor.scale

Re: [R] Linear line on pairs plot

Sent: 25. april 2014 12:26 To: r-help@r-project.org Subject: [R] Linear line on pairs plot Hi, Im trying to plot a linear line on the scatter plot using the pairs() function. At the moment the line is non linear. However, I want a linear line and the associated R value. Here

Re: [R] Linear line on pairs plot

received this e-mail in error please contact the sender. From: Shane Carey [mailto:careys...@gmail.com] Sent: 25. april 2014 13:14 To: Frede Aakmann Tøgersen Cc: r-help@r-project.org Subject: Re: [R] Linear line on pairs plot Great, thanks Frede, This works perfectly. Ive tested these correlations

Re: [R] Linear line on pairs plot

:* 25. april 2014 13:14 *To:* Frede Aakmann Tøgersen *Cc:* r-help@r-project.org *Subject:* Re: [R] Linear line on pairs plot Great, thanks Frede, This works perfectly. Ive tested these correlations with ones in sigma plot and excel and for some reason the r squared value is different

Re: [R] Linear line on pairs plot

to www.vestas.com/legal/notice If you have received this e-mail in error please contact the sender. *From:* Shane Carey [mailto:careys...@gmail.com] *Sent:* 25. april 2014 13:14 *To:* Frede Aakmann Tøgersen *Cc:* r-help@r-project.org *Subject:* Re: [R] Linear line on pairs plot Great, thanks Frede

Re: [R] Linear line on pairs plot

[mailto:careys...@gmail.com] *Sent:* 25. april 2014 13:14 *To:* Frede Aakmann Tøgersen *Cc:* r-help@r-project.org *Subject:* Re: [R] Linear line on pairs plot Great, thanks Frede, This works perfectly. Ive tested these correlations with ones in sigma plot and excel and for some reason the r

[R] linear programming

I have this problem with this form: min (A*X) under some constraints. the unknown is X that is a Matrix. I can't use the function linp because in it X is a vector.. How can I do??? Can you help me [[alternative HTML version deleted]] __

Re: [R] linear programming

On 03/17/2014 07:57 AM, Barbara Rogo wrote: I have this problem with this form: min (A*X) under some constraints. the unknown is X that is a Matrix. I can't use the function linp because in it X is a vector.. How can I do??? Can you help me If X is a matrix, then A*X could be a matrix or

Re: [R] Linear relative rate / excess relative risk models

event and offset pyears. Many thanks, D -Original Message- From: David Winsemius [mailto:dwinsem...@comcast.net] Sent: Thursday, January 09, 2014 4:33 AM To: Wollschlaeger, Daniel Cc: r-help@r-project.org Subject: Re: AW: [R] Linear relative rate / excess relative risk models

[R] Linear relative rate / excess relative risk models

My question is how I can fit linear relative rate models (= excess relative risk models, ERR) using R. In radiation epidemiology, ERR models are used to analyze dose-response relationships for event rate data and have the following form [1]: lambda = lambda0(z, alpha) * (1 + ERR(x, beta)) *

Re: [R] Linear relative rate / excess relative risk models

I would fit a Poisson model to the dose-response data with offsets for the baseline expecteds. Sent from my iPhone On Jan 8, 2014, at 10:49 AM, Wollschlaeger, Daniel wollschlae...@uni-mainz.de wrote: My question is how I can fit linear relative rate models (= excess relative risk

Re: [R] Linear relative rate / excess relative risk models

Von: David Winsemius [dwinsem...@comcast.net] Gesendet: Mittwoch, 8. Januar 2014 19:06 An: Wollschlaeger, Daniel Cc: r-help@r-project.org Betreff: Re: [R] Linear relative rate / excess relative risk models I would fit a Poisson model to the dose-response data with offsets

Re: [R] Linear relative rate / excess relative risk models

Von: David Winsemius [dwinsem...@comcast.net] Gesendet: Mittwoch, 8. Januar 2014 19:06 An: Wollschlaeger, Daniel Cc: r-help@r-project.org Betreff: Re: [R] Linear relative rate / excess relative risk models I would fit a Poisson model to the dose-response

[R] Linear transformation of multivariate normal

Hello, Colleges! I'm having a problem with illustration of linear transformation of multivariate gaussian RV using R. According to the theory, if X ~ N(0, I), then CX ~ N(0,CC'). But the code below doesn't illustrate this. Please could you help me to find a mistake require(tmvtnorm) C =

[R] linear line in x, y plot

Hi I have a question about drawing a linear line in x, y plot. I usually use the following code, but for this time the x values are to small (-0.08 to -0.02) I wrote the following code, but r does not draw the line. However, it does not give an error when it takes the code. reg1- lm(CWSI~NWI,

Re: [R] linear line in x, y plot

A few problems ... This statement doesn't make sense. seq(-0.08, -0.02, len = -0.02) Perhaps you meant seq(-0.08, -0.02, by = 0.02) The xlim= and ylim= are arguments to higher level plot functions, like plot(), and won't work for functions lines() or abline(). Are you trying to limit the range

Re: [R] linear line in x, y plot

On Oct 28, 2013, at 8:30 AM, Ahmed Attia wrote: Hi I have a question about drawing a linear line in x, y plot. I usually use the following code, but for this time the x values are to small (-0.08 to -0.02) That is not the problem. I wrote the following code, but r does not draw the line.

Re: [R] linear line in x, y plot

On 10/29/2013 02:30 AM, Ahmed Attia wrote: Hi I have a question about drawing a linear line in x, y plot. I usually use the following code, but for this time the x values are to small (-0.08 to -0.02) I wrote the following code, but r does not draw the line. However, it does not give an error

[R] Linear Regression line equation

First of I am new to using R. I have a dataset that I plotted using R, I created a scatter plot and used abline to create the line, what I need is to find the equation of the line. Below is the script I have used up until this point. young400_1-read.csv(Z:\\SOFTEL\\North Key Largo

Re: [R] Linear Regression line equation

summary(lm(Canopy_Height~Ground_Elevation, data=young400_1)) #use data= instead of attach! Or even mylm - lm(Canopy_Height~Ground_Elevation, data=young400_1) mylm summary(mylm) coefficients(mylm) Most intro to R guides cover the basics of modeling; you might benefit from reading one of them.

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