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

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

[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 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.

[R] Linear regression repeat for each column

Hi everyone I need to calculate abnormal returns for different events applying event study methodology. I must create a market model in order to perform the analysis. I apply regression analysis to get OLS estimators. I have a problem to create a linear regression which I could repeat for each

Re: [R] Linear regression repeat for each column

Sure. Read the Posting Guide, and provide a reproducible example with sample data. I suspect the basic idea will be to merge the data frames and then setup the model to refer to the desired columns. --- Jeff Newmiller

Re: [R] Linear regression repeat for each column

To: r-help@r-project.org Cc: Sent: Saturday, July 20, 2013 7:55 PM Subject: [R] Linear regression repeat for each column Hi everyone I need to calculate abnormal returns for different events applying event study methodology. I must create a market model in order to perform the analysis. I apply

Re: [R] Linear regression on dataframe's sample

I fount out a solution ... check it out random_sample = function (dt, sample_size) { # determine the number of records in the data frame length_dt = length (dt[,1]) # extract the sample dt [sample (1:length_dt, size = sample_size),] } -- View this message in context:

[R] Linear regression on dataframe's sample

Hi guys :) I have a dataframe a a - read.dta(imprese.dta) 15867 obs. and 23 varaible I want to make a linear regression on a sample of 300 units how can i do? Thank you -- View this message in context: http://r.789695.n4.nabble.com/Linear-regression-on-dataframe-s-sample-tp4648288.html

[R] Linear regression

Hello there, I am new to using regression in R. I wanted to solve a simple regression problem where I have 2 equations and 2 unknowns. So lets say - y1 = alpha1*A + beta1*B y2 = alpha2*A + beta2*B y1 - runif(10, 0,1) y2 - runif(10,0,1) alpha1 - 0.6 alpha2 - 0.75 beta1 - 1-alpha1 beta2

Re: [R] Linear regression

1. Homework assignment? We don't do homework here. 2. If not, a mixture model of some sort? I suggest you state the context of the problem more fully. R has several packages to do mixture modeling, if that's what you're trying to do. 3. In any case, this cannot be done with lm() (at least

Re: [R] Linear regression

Hello Bert, This is definitely not for a homework problem. I am trying to estimate frequencies of mutations in different groups. The mutation frequencies can be modeled as a linear relation in cases of mixtures. So I have a lot of populations that follow the relationship - y = alpha*A + beta*B

Re: [R] Linear regression

...@gene.com To: Diviya Smith diviya.sm...@gmail.com Cc: r-help@r-project.org Sent: Monday, March 19, 2012 11:32 PM Subject: Re: [R] Linear regression 1. Homework assignment? We don't do homework here. 2. If not, a mixture model of some sort?  I suggest you state the context of the problem more

Re: [R] Linear regression

Note that your equations can be written: y = alpha*A + (1-alpha)*B, which is equivalent to y = (A-B) * alpha + B , i.e. of form y = C*alpha + B a simple linear equation in alpha You have two different values of alpha at which y was measured, so just stack up all your results into a single

Re: [R] linear regression by column

Thank everyone for your help. Problem solved. I'm getting more used with vectorization with your help, Regards, Phil -- View this message in context: http://r.789695.n4.nabble.com/linear-regression-by-column-tp4432564p4434937.html Sent from the R help mailing list archive at Nabble.com.

[R] linear regression by column

Hi everyone. I have a DF with the first column being my independant variable and all other columns the dependent variables. Something like: x y1 y2 y3 ... ... ... ... ... ... ... ... What I'm trying to do is to perform a linear model for each of my y. It

Re: [R] linear regression by column

On Feb 29, 2012, at 1:53 PM, Filoche wrote: Hi everyone. I have a DF with the first column being my independant variable and all other columns the dependent variables. Something like: x y1 y2 y3 ... ... ... ... ... ... ... ... What I'm trying to

Re: [R] linear regression by column

On Feb 29, 2012, at 6:39 PM, David Winsemius wrote: On Feb 29, 2012, at 1:53 PM, Filoche wrote: Hi everyone. I have a DF with the first column being my independant variable and all other columns the dependent variables. Something like: x y1 y2 y3 ... ... ...

Re: [R] linear regression by column

On 2012-02-29 15:45, David Winsemius wrote: On Feb 29, 2012, at 6:39 PM, David Winsemius wrote: On Feb 29, 2012, at 1:53 PM, Filoche wrote: Hi everyone. I have a DF with the first column being my independant variable and all other columns the dependent variables. Something like: x

[R] Linear regression using matrices

I'm new to R and I'm not a Statistician I'm an Accountant, but I'm finding it an excellent tool for the business analysis work I do. I need to run LM() where both response and predictor are held in matrices. The model follows the form:- regression1 = matrix1.col1 - matrix2.col1 regression2 =

Re: [R] Linear regression using matrices

I've done a lot of research on this very topic and found a few solutions. But all the ways I've discovered involve loops. Applying it to what you want, the best way I've found is to do (stolen from an experienced R user, of course): y-array(rnorm(100),dim=c(10,10))

Re: [R] Linear regression using matrices

OK thanks. In my case I think it might be possible to work around this by reshaping my data and then using lmlist() to run separate regressions for each data group. lmlist() is new to me but it looks like it will do the job. -- View this message in context:

[R] Linear Regression with Linear Equality Constraint

Please advice on the package I should use to run a linear regression model (weighted least squared) with linear equality constraint. I initially tried constrOptim but it turned out that it only supported inequality linear constraint. Thank you very much in advance. Cheers, Jon -- View this

Re: [R] Linear Regression with Linear Equality Constraint

I believe the package systemfit can help you with that. Haven't tried it myself, but give it a go. Regards, Kristian 2011/10/31 JW j...@phatrasecurities.com Please advice on the package I should use to run a linear regression model (weighted least squared) with linear equality constraint. I

Re: [R] Linear Regression with Linear Equality Constraint

Well, if I understand the question correctly (following the posting guide would have spared guessing, as usual), forget packages -- nothing more than elementary algebra is needed. e.g. lm(y ~ x1 + x2 + x3) subject to the constraint beta_2 = beta_3, the coefficients of x2 and x3, is the same as

Re: [R] Linear Regression with Linear Equality Constraint

On Oct 31, 2011, at 3:31 PM, Bert Gunter gunter.ber...@gene.com wrote: Well, if I understand the question correctly (following the posting guide would have spared guessing, as usual), forget packages -- nothing more than elementary algebra is needed. e.g. lm(y ~ x1 + x2 + x3) subject

Re: [R] Linear Regression with Linear Equality Constraint

On Oct 31, 2011, at 5:12 PM, Comcast dwinsem...@comcast.net wrote: On Oct 31, 2011, at 3:31 PM, Bert Gunter gunter.ber...@gene.com wrote: Well, if I understand the question correctly (following the posting guide would have spared guessing, as usual), forget packages -- nothing more

Re: [R] Linear Regression with Linear Equality Constraint

I think Alabama package on CRAN can do this; http://cran.r-project.org/web/packages/alabama/index.html -Original Message- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of JW Sent: 31 October 2011 17:57 To: r-help@r-project.org Subject: [R] Linear

Re: [R] Linear regression interaction terms

Andrey A avakoa at gmail.com writes: Hello I performed a linear regression, my equation is Y = âo+ â1A + â2B + â3AB. Is there a way to separate interaction terms, say â3AB and plot it against a certain variable? Thanks, Andrew Not quite sure what you mean here. Possibly something like

[R] Linear regression interaction terms

Hello I performed a linear regression, my equation is Y = âo+ â1A + â2B + â3AB. Is there a way to separate interaction terms, say â3AB and plot it against a certain variable? Thanks, Andrew [[alternative HTML version deleted]] __

[R] linear regression, log-transformation and plotting

Hello, I've some questions concerning log-transformations and plotting of the regression lines. So far as I know is it a problem to log-transform values smaller than 1 (0-1). In my statistics lecture I was told to do a log(x+1) transformation in such cases. So I provide here a small example to

Re: [R] linear regression, log-transformation and plotting

I think that you have not understood your lecturer. You can log transform any positive number. You can not log transform a negative number. Adding a constant to a negative number to make it constant before log transformation is sometimes suggested by those who do not understand what they are

Re: [R] Linear Regression with 2 grouping variables

At 02:15 23/08/2011, Nathan Miller wrote: Hi all, See comment in-line I have a data set that looks a bit like this. feed1 RFU Site Vial Time lnRFU 1 811 10 10.702075 2 4752111 20 10.768927 3 4290511 30 10.66674 4 4686711 40

Re: [R] Linear Regression with 2 grouping variables

Thanks Dennis! Worked perfectly. I keep forgetting that plyr can split data based on multiple subsetting variables. Thanks so much, Nate On Mon, Aug 22, 2011 at 10:12 PM, Dennis Murphy djmu...@gmail.com wrote: Hi: You're kind of on the right track, but there is no conditioning formula in

[R] Linear Regression with 2 grouping variables

Hi all, I have a data set that looks a bit like this. feed1 RFU Site Vial Time lnRFU 1 811 10 10.702075 2 4752111 20 10.768927 3 4290511 30 10.66674 4 4686711 40 10.755069 5 4299511 50 10.668839 6 4307411

Re: [R] Linear Regression with 2 grouping variables

You can do something like this sp-split(dat, list(dat$Vial,dat$Site)) seq.model-lapply(sp, function(x) lm(x$InRFU~x$Time)) Then, extract whatever you want from seq.model Weidong Gu On Mon, Aug 22, 2011 at 9:15 PM, Nathan Miller natemille...@gmail.com wrote: Hi all, I have a data set

Re: [R] Linear Regression with 2 grouping variables

Hi: You're kind of on the right track, but there is no conditioning formula in lm(); it's not lattice :) This is relatively easy to do with the plyr package, though: library('plyr') # Generate a list of models - the subsetting variables (Site, Vial) are # used to generate the data splits and

[R] linear regression

dear R users, my data looks like this PM10 Ref UZ JZ WT RH FT WR 1 10.973195 4.338874 nein Winter Dienstag ja nein West 26.381684 2.250446 nein SommerSonntag nein ja Süd 3 62.586512 66.304869 ja SommerSonntag nein nein Ost 4

Re: [R] linear regression

your dataframe needs to be called Nord. If it is not, then replace Nord with the actual name of your dataframe On Sat, Aug 13, 2011 at 10:43 PM, maggy yan kiot...@googlemail.com wrote: dear R users, my data looks like this         PM10       Ref   UZ     JZ         WT   RH   FT   WR 1  

Re: [R] linear regression

Hi: Try something like this, using dat as the name of your data frame: xyplot(PM10 ~ Ref | WR, data = dat, type = c('p', 'r')) The plot looks silly with the data snippet you provided, but should hopefully look more sensible with the complete data. The code creates a four panel plot, one per

Re: [R] linear regression

Don't forget to load `lattice` package. `latticeExtra` with `panel.ablineeq` can be also helpful. This was however for plotting. For subset regression by each WR without plotting you'd use something like `lapply` or `sapply`. ans - sapply(unique(data$WR), function(dir) { out -

[R] linear regression with replication

Hi, we want to calculate a linear regression that results from three test series. (The Average linear regression over 3 replication.) Each test serie consists of the values ​​for 3 times (0min, 25min, 50min) and is repeated three times. We need such a regression for each combination of subject

[R] Linear regression - several response variables vs few ind variables

Hi all, I need to run several simple linear regressions at once, using the following data. Response variables: Bird species (sp 1, sp2, sp3...spn). Independent variable: Natprop - proportion of natural area. covarate: Effort = hours). One single linear regression would be: lmSp1 - lm(sp1~ natprop

[R] Linear regression - several response variables vs few ind variables

Hi Everyone, I need to run several simple linear regressions at once, using the following data. Response variables: Bird species (sp 1, sp2, sp3...spn). Independent variable: Natprop - proportion of natural area. covarate: Effort = hours). One single linear regression would be: lmSp1 - lm(sp1~

Re: [R] linear regression in a ragged array

To: r-help@r-project.org Subject: [R] linear regression in a ragged array Hello, I have a large dataset of the form subj var1 var2 001100200 001120226 001130238 001140245 001150300 002110205 002125209 003101233 003

[R] linear regression in a ragged array

Hello, I have a large dataset of the form subj var1 var2 001100200 001120226 001130238 001140245 001150300 002110205 002125209 003101233 003115254 I would like to perform linear regression of var2 on var1 for each

Re: [R] linear regression in a ragged array

On Mar 21, 2011, at 5:25 AM, Marcel Curlin wrote: Hello, I have a large dataset of the form subj var1 var2 001100200 001120226 001130238 001140245 001150300 002110205 002125209 003101233 003115254 I would like to

[R] linear regression in a data.frame using recast

I have a very large dataset with columns of id number, actual value, predicted value. This used to be a time series but I have dropped the time component. So I now have a data.frame where the id number is repeated but each value in the actual and predicted columns are unique. I assume I need to

Re: [R] linear regression in a data.frame using recast

On Mar 16, 2011, at 3:19 PM, Justin Haynes wrote: I have a very large dataset with columns of id number, actual value, predicted value. This used to be a time series but I have dropped the time component. So I now have a data.frame where the id number is repeated but each value in the actual

Re: [R] linear regression in a data.frame using recast -- A fortunes candidate??

Ha! -- A fortunes candidate? -- Bert If this is really a time series, then you will have serious validity problems due to auto-correlation among non-independent units. (But if you are just searching for a way to pull the wool over the eyes of the statistically uninformed, then I guess

Re: [R] linear regression in a data.frame using recast -- A fortunes candidate??

Seconded On 03/16/2011 05:37 PM, Bert Gunter wrote: Ha! -- A fortunes candidate? -- Bert If this is really a time series, then you will have serious validity problems due to auto-correlation among non-independent units. (But if you are just searching for a way to pull the wool over the eyes

[R] Linear Regression on data from FRED

library(zoo) library(tseries) library(quantmod) #for access to FRED require(quantmod) require(TTR) secA - getSymbols(DEXUSEU,src=FRED) secB - getSymbols(DEXUSUK,src=FRED) secA - zoo(DEXUSEU[,1]) secB - zoo(DEXUSUK[,1]) t.zoo - merge(secA, secB, all=FALSE) t - as.data.frame(t.zoo) cat(Date

Re: [R] linear regression and t-distribution

did you try RSiteSearch(t-regression) ? That seems to give some usefull hits. On Mon, Feb 21, 2011 at 7:42 AM, Rosario Garcia Gil m.rosario.gar...@slu.se wrote: Hello I have a data set with outlier and it is not normally distributed. I would instead like to use a more robust distribution

Re: [R] linear regression and t-distribution

On 2011-02-21 02:42, Rosario Garcia Gil wrote: Hello I have a data set with outlier and it is not normally distributed. I would instead like to use a more robust distribution like t-distribution. My question is if the coefficients of the regression are different from zero, but assuming a

[R] linear regression and t-distribution

Hello I have a data set with outlier and it is not normally distributed. I would instead like to use a more robust distribution like t-distribution. My question is if the coefficients of the regression are different from zero, but assuming a t-distribution. Could someone hint me what package

Re: [R] linear regression for grouped data

At 02:23 29/12/2010, Entropi ntrp wrote: Hi, I have been examining large data and need to do simple linear regression with the data which is grouped based on the values of a particular attribute. For instance, consider three columns : ID, x, y, and I need to regress x on y for each distinct

Re: [R] linear regression for grouped data

Thanks alot for the quick responses. I have some additional questions related to this topic. In fact, my intention was to be able to answer questions like what percent of the regressions have p_values less than a certain threshold, how do residuals look like, how do the plots of y vs. x look like,

[R] linear regression for grouped data

Hi, I have been examining large data and need to do simple linear regression with the data which is grouped based on the values of a particular attribute. For instance, consider three columns : ID, x, y, and I need to regress x on y for each distinct value of ID. Specifically, for the set of

Re: [R] linear regression for grouped data

On Dec 28, 2010, at 9:23 PM, Entropi ntrp wrote: Hi, I have been examining large data and need to do simple linear regression with the data which is grouped based on the values of a particular attribute. For instance, consider three columns : ID, x, y, and I need to regress x on y for

Re: [R] linear regression for grouped data

: [R] linear regression for grouped data Hi, I have been examining large data and need to do simple linear regression with the data which is grouped based on the values of a particular attribute. For instance, consider three columns : ID, x, y, and I need to regress x on y for each distinct value

Re: [R] linear regression for grouped data

Hi: There are some advantages to taking a plyr approach to this type of problem. The basic idea is to fit a linear model to each subgroup and save the results in a list, from which you can extract what you want piece by piece. library(plyr) # One of those SAS style data sets... df -

[R] linear regression with dates

Hi, I am trying to do simple linear regression using dates in R but receiving error messages. With the data shown below, I would like to regress x on y. x y 11/12/1999 56.8 11/29/1999 17.9 01/04/2000 27.4 1/14/2000 96.8 1/31/2000 49.5 R gives the following

Re: [R] linear regression with dates

-Original Message- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Entropi ntrp Sent: Monday, December 27, 2010 3:05 PM To: r-help@r-project.org Subject: [R] linear regression with dates Hi, I am trying to do simple linear regression using

Re: [R] linear regression with dates

Thanks for the response. I proivded the necessary details below, and also have a general question for how to deal with dates in R. Is there a way to make R read dates as numbers? Here is the details of the R code: egfr - read.csv(file.choose(), header=TRUE, sep=,) #egfr is a matrix read from

Re: [R] linear regression with dates

On Dec 27, 2010, at 8:56 PM, Entropi ntrp wrote: Thanks for the response. I proivded the necessary details below, and also have a general question for how to deal with dates in R. Is there a way to make R read dates as numbers? Here is the details of the R code: egfr -

[R] Linear Regression

Dear R-group, We have begun to use it for teaching Statistics. In this context we have run into a problem with linear regression where we found the results of are confusing. Specifically, considering the data: x=c(4,5,6,3,7,8,10,14,13,15,6,7,8,10,11,4,5,17,12,11) y=c(rep(7,20)) and

Re: [R] Linear Regression

-Oorspronkelijk bericht- Van: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] Namens Vittorio Colagrande Verzonden: dinsdag 12 oktober 2010 15:01 Aan: r-help@r-project.org Onderwerp: [R] Linear Regression Dear R-group, We have begun to use it for teaching Statistics

Re: [R] Linear Regression

On Oct 12, 2010, at 9:01 AM, Vittorio Colagrande wrote: Dear R-group, We have begun to use it for teaching Statistics. In this context we have run into a problem with linear regression where we found the results of are confusing. Specifically, considering the data:

Re: [R] Linear regression equation and coefficient matrix

Dear Greg, Thanks for the tip. As I am new in R can you please provide me a script how do to so. It will help my learning process. Thanks, Asher -- View this message in context: http://r.789695.n4.nabble.com/Linear-regression-equation-and-coefficient-matrix-tp2329804p2330867.html Sent from

Re: [R] Linear regression equation and coefficient matrix

] On Behalf Of ashz Sent: Thursday, August 19, 2010 3:02 AM To: r-help@r-project.org Subject: Re: [R] Linear regression equation and coefficient matrix Dear Greg, Thanks for the tip. As I am new in R can you please provide me a script how do to so. It will help my learning process. Thanks

[R] Linear regression equation and coefficient matrix

Hi, I have 20*60 data matrix (with some NAs) and I wish to perfom a Pearson correlation coefficient matrix as well as simple linear regression equation and coefficient of determination (R2) for every possible combination. Any tip/idea/library/script how do to so. Thanks, As hz -- View

Re: [R] Linear regression equation and coefficient matrix

On Wed, Aug 18, 2010 at 6:09 AM, ashz a...@walla.co.il wrote: Hi, I have 20*60 data matrix (with some NAs) and I wish to perfom a  Pearson correlation coefficient matrix as well as simple linear regression equation The correlation matrix can be readily obtained by calling cor() on the entire

Re: [R] Linear regression equation and coefficient matrix

Hmm, after reading one of your other posts, I am thinking you may *just* want all pairwise combinations. This worked for me: # Create a sample data frame with 60 named columns x - data.frame(matrix(rnorm(1200), ncol = 60, dimnames = list(NULL, paste(Col, 1:60, sep='' # calculate the

Re: [R] Linear regression equation and coefficient matrix

Hi, Thanks, the cor() works. Regarding the simple linear regression equation (mainly, the slope parameter) and r2. I think I was not writing it well. I need to do it just for the columns. If I have a, b, c, d columns I wish to compute the relation of there data, e.g., between a-b, a-c, a-d,

Re: [R] Linear regression equation and coefficient matrix

: Wednesday, August 18, 2010 8:43 AM To: r-help@r-project.org Subject: Re: [R] Linear regression equation and coefficient matrix Hi, Thanks, the cor() works. Regarding the simple linear regression equation (mainly, the slope parameter) and r2. I think I was not writing it well. I need to do

Re: [R] Linear regression on several groups

Please read the posting guide and include a standalone example. Maybe you want something like the results from lm(weight ~ Time, data = ChickWeight, subset = Diet==1) lm(weight ~ Time, data = ChickWeight, subset = Diet==2) ## ... etc ... Then you could do (m - lm(weight ~ Time*Diet, data =

Re: [R] Linear regression on several groups

hi, maybe an ANCOVA is what you want, which is also done by lm in R lm(y~x*z) Am 12.08.2010 17:11, schrieb JesperHybel: I have a simple dataset of a numerical dependent Y, a numerical independent X and a categorial variable Z with three levels. I want to do linear regression Y~X for each

Re: [R] Linear regression on several groups

Example is spot on - sr for not providing one myself. The results you calculate are what I'm looking for. Would like a function F where I could type: F(weight ~ Time, data = ChickWeight, SOME ARGUMENT = Diet)) Resulting in for (i in 1:4){ print( lm(weight ~ Time, data = ChickWeight, subset =

Re: [R] Linear regression on several groups

Have a look at lmList() in the nlme package. -Original Message- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r- project.org] On Behalf Of JesperHybel Sent: Friday, August 13, 2010 7:56 AM To: r-help@r-project.org Subject: Re: [R] Linear regression on several groups

[R] Linear regression on several groups

I have a simple dataset of a numerical dependent Y, a numerical independent X and a categorial variable Z with three levels. I want to do linear regression Y~X for each level of Z. How can I do this in a single command that is without using lm() applied three isolated times? -- View this message

[R] Linear regression - R^2

Hello, I was doing a linear regression with the following formula: *lm(y~x+0)*, so it passes through the origin. But when I called the summary of the regression i saw that R squared is abnormally high (it's a lot lower in other programs such as SigmaPlot and MS Excel).The manual explained the

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