similar to: lm -- significance of x coefficient when I(x^2) is used

Hi, How would one go about determining if the slope terms from an analysis of covariance model are different from eachother? Based on the example from MASS: library(MASS) # parallel slope model l.para <- lm(Temp ~ Gas + Insul, data=whiteside) # multiple slope model l.mult <- lm(Temp ~ Insul/Gas -1, data=whiteside) # compare nested models: anova(l.para, l.mult) Analysis of Variance

Dear List-members, Hopefully someone will help through my confusion: In order to get the same coefficients as we get from the following ## require (MASS) summary ( lm(Gas ~ Insul/Temp - 1, data = whiteside) ) ...................... we need to do the following (if we use model.matrix to specify the model) ## summary ( lm(Gas ~ model.matrix(~ Insul/Temp - 1) - 1, data = whiteside) )

Page 140 of MASS uses the function xyplot. But I don't find it in R. Is there a package that I should load to use xyplot. Or there is a function with a different name in R that does the same thing as xyplot in S. xyplot(Gas ~ Temp | Insul, whiteside, panel = function(x, y, ...) { panel.xyplot(x, y, ...) panel.lmline(x, y, ...) }, xlab = "Average external temperature (deg. C)", ylab

so i am having this question what should i do if the give data file (.txt) has 4 columns, but different lengths? how can i read them in R? any idea for the following problem? Gas consumption (1000 cubic feet) was measured before and after insulation was put into a house. We are interested in looking at the effect of insulation on gas consumption. The average outside temperature (degrees celcius)

R-helpers; I tried to get a superscripted 3 in the following xyplot example but failed: >data(whiteside) >xyplot(Gas ~ Temp | Insul, whiteside, panel = function(x, y, ...) { panel.xyplot(x, y, ...) panel.lmline(x, y, ...) }, xlab = "Average external temperature (deg. C)", ylab = paste(paste("Gas consumption (1000", expression(ft^3),")"), aspect

A pragmatic solution could be to create a simple linear regression example with variables in the global environment and then another example with a data.frame. The latter might be somewhat more complex, e.g., with several regressors and/or mixed categorical and numeric covariates to illustrate how regression and analysis of (co-)variance can be combined. I like to use MASS's whiteside

On Sat, 15 Dec 2018, frederik at ofb.net wrote: > I agree with Steve and Achim that we should keep some examples with no > data frame. That's Objectively Simpler, whether or not it leads to > clutter in the wrong hands. As Steve points out, we have attach() > which is an excellent language feature - not to mention with(). Just for the record: Personally, I wouldn't recommend

Hello all, This is something that I am sure has a really suave solution in R, but I can't quite figure out the best (or even a basic) way to do it. I have a simple linear regression that is fit with lm for which I would like to estimate the x intercept with some measure of error around it (confidence interval). In biology, there is the concept of a developmental zero - a temperature under

Hello, I want to know if two quadratic regressions are significantly different. I was advised to make the test using step 1 bootstrapping both quadratic regressions and get their slope coefficients. (Let's call the slope coefficient *â*^1 and *â*^2) step 2 use the slope difference *â*^1-*â*^2 and bootstrap the slope coefficent step 3 find out the sampling distribution above and

I have ussed lm to generate a basic line correlation: fit = lm(hours.of.sleep ~ ToSleep) Note: From "Bayesian Computation with R", Jim Albert, p. 7 I understand the simple y = mx + b line that this fits the data to. Now apparently I don't understand formulas. The documentation indicates that there is an implied "intercept" in the formula so now I want to try and fit the

R folks, As an R novice, I struggle with the mystery of subsetting. Textbook and online examples of this seem quite straightforward yet I cannot get my mind around it. For practice, I'm using the code in MASS Ch. 6, "whiteside data" to analyze a different data set with similar variables and structure. Here is my data frame: ###subset one of three cases for the variable

Dear list, I am currently writing up some of my R models in a more formal sense for a paper, and I am having trouble with the notation. Although this isn't really an 'R' question, it should help me to understand a bit better what I am actually doing when fitting my models! Using the analysis of co-variance example from MASS (fourth edition, p 142), what is the correct notation for the

gday R gurus, I have a multivariate regression for which I want to constrain the coefficients to be > 0. Is this possible? I've check the doco and searched CRAN but can't find anything. thanks, John Strumila -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send

I have a data frame with two variables that are factors. One is actually a TRUE/FALSE factor, and I have coded it as 1/0, a continuous variable, but I could turn it back into a factor. The second is an ordered factor and consists of five timepoints. There are several continuous variables as well. Now I want to fit a linear model to my data, using lm (or another R procedure if recommended).

Hi, I would like to get the coefficient of polynomial expansion. For example, (1+ x)^2 = 1 + 2x + x^2, and the coefficients are 1, 2 and 1. (1 + x + x^2)^3 = 1 + 3*x + 6*x^2 + 7*x^3 + 6*x^4 + 3*x^5 + x^6, and the coefficients are 1, 3, 6, 7, 6, 3, and 1. I know that we can use polynom library. Is there any other way to do it without loading a library. Thanks a lot for your help. Peter

Dear Mr. and Mrs. I'm very grateful for these software and this list. My question is: when a use linear multiple regression (lm()) for my data, abundance ichthyoplankton ~ salinity + temperature + month of the year(f1 is a factor: 1 for january, 2 for february, ..., 12 for december), the summary() of results is ... Coefficients: Estimate Std. Error t value Pr(>|t|)

Hi guys... I have problem with this excersise... Consider the pressure data frame again. (a) Plot pressure against temperature, and use the following command to pass a curve through these data: > curve((0.168 + 0.007*x)?(20/3), from=0, to=400, add=TRUE) (b) Now, apply the power transformation y3/20 to the pressure data values. Plot these transformed values against temperature. Is a linear

Hi for some data I working on I am merely plotting time against temperature for a variable named filmclip. So for example, I have volunteers who watched various film clips and have used infared camera to monitor the temperature on their face at every second of the clip. The variable names I have used are Normalised ( for the temperature) and Frame (for the time in seconds). So I have fitted a

Hello R community, I have a question regarding correlation and regression analysis. I have two variables, x and y. Both have a standard deviation of 1; thus, correlation and slope from the linear regression (which also must have an intercept of zero) are equal. I want to probe two particular questions: 1) Is the slope significantly different from zero? This should be easy with the lm

Hi Everyone, I am uncertain that I am writing the contrast statements correctly. Basically, I'm unsure when to use a -1 and a 1 when writing the contrasts. Specifically I am interested in comparing the slopes between different temperature regimes. Temperature is therefore a factor. Time and percent are numerical. Using the gmodels package I made the following model: