similar to: Syntax for fit.contrast

David, Thank you for responding to my post. Please consider the following output (typeregional is a factor having two levels, "regional" vs. "general"): Call: glm(formula = events ~ type, family = poisson(link = log), data = data, offset = log(SS)) Deviance Residuals: Min 1Q Median 3Q Max -43.606 -17.295 -4.651 4.204 38.421 Coefficients:

David, Again you have my thanks!. You are correct. What I want is not technically a contrast. What I want is the estimate for "regional" and its SE. I don't mind if I get these on the log scale; I can get the anti-log. Can you suggest how I can get the point estimate and its SE for "regional"? The predict function will give the point estimate, but not (to my knowledge)

> On Oct 22, 2017, at 6:04 AM, Sorkin, John <jsorkin at som.umaryland.edu> wrote: > > I have a model (run with glm) that has a factor, type. Type has two levels, "general" and "regional". I am trying to get estimates (and SEs) for the model with type="general" and type ="regional" using fit.contrast ?fit.contrast No documentation for

> On Oct 22, 2017, at 3:56 PM, Sorkin, John <jsorkin at som.umaryland.edu> wrote: > > David, > Thank you for responding to my post. > > Please consider the following output (typeregional is a factor having two levels, "regional" vs. "general"): > Call: > glm(formula = events ~ type, family = poisson(link = log), data = data, > offset =

> On Oct 22, 2017, at 5:01 PM, Sorkin, John <jsorkin at som.umaryland.edu> wrote: > > David, > Again you have my thanks!. > You are correct. What I want is not technically a contrast. What I want is the estimate for "regional" and its SE. There needs to be a reference value for the contrast. Contrasts are differences. I gave you the choice of two references

David, predict.glm and se.fit were exactly what I was looking for. Many thanks! John John David Sorkin M.D., Ph.D. Professor of Medicine Chief, Biostatistics and Informatics University of Maryland School of Medicine Division of Gerontology and Geriatric Medicine Baltimore VA Medical Center 10 North Greene Street GRECC (BT/18/GR) Baltimore, MD 21201-1524 (Phone) 410-605-7119 (Fax)

I've been playing around with contrasts in lm by specifying the contrasts argument. So, I want to specify a specific contrast to be tested Say: > y _ rnorm(100) > x _ cut(rnorm(100, mean=y, sd=0.25),c(-3,-1.5,0,1.5,3)) > reg _ lm(y ~ x, contrasts=list(x=c(1,0,0,-1))) > coef(reg)[2] x1 -1.814101 I was surprised to see that I get a different estimate for the

Hi, we need some help to define planned comparisons. I've based my understanding of the problem on reading Tabachnick and Fidell (2006), ie: http://www.ablongman.com/catalog/academic/product/0,1144,0205459382,00.html I don't understand how to specify planned comparisons in R. I've not found explanations for this in MASS or elsewhere. There is only discussion of the contrast

I have some doubts about the validity of my procedure to estimeate linear contrasts ina a factorial design. For sake of semplicity, let's imagine a one way ANOVA with three levels. I am interested to test the significance of the difference between the first and third level (called here contrast C1) and between the first and the seconda level (called here contrast C2). I used the following

Dear R-users, I want to fit a linear model with Y as response variable and X a categorical variable (with 4 categories), with the aim of comparing the basal category of X (category=1) with category 4. Unfortunately, there is another categorical variable with 2 categories which interact with x and I have to include it, so my model is s "reg3: Y=x*x3". Using fit.contrast to make the

Dear lme and lmer -ers, I have some problems using "home-made" contrast matrix in lme and lmer. I did an experiment to investigate the relationship between the response of an animal and some factors, namely the light wavelength (WA), the light intensity to which this animal was exposed and the sex of the animal tested. - The response can be a variable LA (normal distribution) or

Windows XP R2.8.1 I am running an balanced aov, in which Time has 4 levels, Group has 2 levels: fit2<-aov(Post ~ as.factor(Time) +as.factor(Group)+as.factor(Group*Time)+Error(SS), alldata) I would like to get a p value for each level of time, each level of Group and each level of Group*Time. How can I do this? I get this, which appears to be omnibus p values for the factors: Error: SS

R2.1.1 Win 2k I have a function, B, within a function, A. I would like to have B print the name of the argument passed to it (not the value of the arguments). i.e., A<-function() { B<-function(x,y) { fit1<-lm(y~x,data=jo) print(summary(fit1) I want B to print the string "age" and the string "height". } B(age,height) }

Forgive my resending this post. To data I have received only one response (thank you Bert Gunter), and I still do not have an answer to my question. Respectfully, John Windows XP R 2.12.1 contrast package. I am trying to understand how to create contrasts for a model that contatains an interaction. I can get contrasts to work for a model without interaction, but not after adding the

Dear All: I am using lmer() {lme4} to analyze results from a reciprocal transplant experiment where the response variable is modeled as a function of two fixed effects and their interaction. Example data follow: #library(lme4) #library(gmodels)

Hi, I want to test for a contrast from a regression where I am regressing the columns of a matrix. In short, the following. X <- matrix(rnorm(50),10,5) Y <- matrix(rnorm(50),10,5) lm(Y~X) Call: lm(formula = Y ~ X) Coefficients: [,1] [,2] [,3] [,4] [,5] (Intercept) 0.3350 -0.1989 -0.1932 0.7528 0.0727 X1 0.2007 -0.8505 0.0520

Hi, mod <- lme(A ~ -1 + B+C+D+E+F+G, random = ~1 | ...) results in summary(mod)$coeff B C D E F G (Intercept) b c d e f g i Now I'm interested in the function f <- function(B,C,D,E,F,G) <- { return(i + b*B + c*C + d*D + e*E + f*F + g*G) } Is there a easier way to create such function with flexible number of coefficient, than do it by hand? thx Christof

Hi Folks, This should have been simple to answer, but despite much chasing I don't seem able to catch this particular mouse! Basically (somewhat simplified): I have a binary response variable Y (0/1) and a 2-level factor A (0/1). I want to assign a contrast to A such that, when I run summary(glm(Y~A, family=binomial))$coef the Intercept coefficient is the result that I would get from

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:

#I am trying to understand how R fits models for contrasts in a #simple one-way anova. This is an example, I am not stupid enough to want #to simultaneously apply all of these contrasts to real data. With a few #exceptions, the tests that I would compute by hand (or by other software) #will give the same t or F statistics. It is the contrast estimates that R produces #that I can't seem to