search for: constrasts

Hello, I've noticed that all contrast functions, like contr.treatment, contr.poly, etc., take a logical argument called 'contrasts'. The default is TRUE, in which case they do their normal thing of returning a n x n-1 matrix whose columns are linearly-independent of the intercept. If contrasts=FALSE, they instead return an n x n matrix with full rank (usually the identity matrix,

....factor(x$new) contrasts(x$new) <- cbind(" my constrast" = c( .5, -.5, 0, .5, 0, -.5)) summary(lm(y ~ new, x)) I have two questions concerning this: 1) if I take a look at the constrast matrix derived from the one contrast I specified, I assume that R automatically adds the missing constrasts so they are orthogonal. round(cor(attributes(x$new)$contrast), 1) Is that always the case? 2) Can I get this in a simpler way? I find it a bit tedious to define the constrast like above. Is there something simpler, like: lm(y ~ f1:f2, + "user defined contrast definition inside lm th...

Hello, I have a problem with creating an identity matrix for glmnet by using the contrasts function. I have a factor with 4 levels. When I create dummy variables I think there should be n-1 variables (in this case 3) - so that the contrasts would be against the baseline level. This is also what is written in the help file for 'contrasts'. The problem is that the function

Hello, R users, I have two factors (treat, section) anova design experiment where there are 3 replicates. The objective of the experiment is to test if there is significant difference of yield between top (section 9 to 11) and bottom (section 9 to 11) of the fruit tree under treatment. I found that there are interaction between two factors. I wonder if I can contrast means from levels of

Hi, Somebody knows how to make contrasts if i'm using the function adonis? Thanks.

Dear All, The std. error of the estimated coefficients obtained by the summary.lm function can be calculated as: y=rnorm(20) x=y+rnorm(20) fit <- lm(y ~ x) summary(fit) sqrt( sum(fit$resid**2)/fit$df.resid * solve(t(model.matrix(fit))%*%model.matrix(fit)) ) Is posible calculate Std. Error for glm as lm, using cov(hat beta) = phi * solve(t(X) %*% hat W %*% X)^-1 on R? Who is hat W and

...ts. Now I want to test if Heart is significant higher than Liver and Brain, or Liver is significant higher than Heart an Brain, ... I read about the pairwise.t.test, but I dont't want to test Heart against Liver and Heart against Brain, ... Is there any possibility to do that? ANOVA? Duncan? Constrasts? Please help -- View this message in context: http://www.nabble.com/Tissue-specific-genes-by-ANOVA--tp18441289p18441289.html Sent from the R help mailing list archive at Nabble.com.

Hello, I'm posting this to receive some comments/hints about a rather statistical than R-technical question ... . In an anova of a lme factor SSPos11 shows up non-significant, but in the t-test of the summay 2 of the 4 levels (one for constrast) are significant. See below for some truncated output. I realize that the two test are different (F-test/t-test), but I'm looking for for a

Dear R experts, I'm beginner. My question about ANOVA for unequal sample sized data should be obsolete but I can not clarify it. I have a dataset from 23 males and 18 females. I measured one condition('cond') with 4 levels. So I'd like to see main effect of gender, cond and gender by cond interaction and also postHoc test. (In fact, I have to do anova 90 times) * 1. Question

...say time 3 is the base. This works fine. However, when I next use the subset argument to remove the last two time values, the output indicates that the specified contrast is not maintained (see below). I can solve this by creating a new dataframe for the subset of interest and redefining the constrasts, but I was wondering if there is a direct method that allows me to continue w/ the subset argument? (perhaps via supplying a contrast argument to lmer directly, but this doesn't seem possible based on the defintion of this argument in model.matrix.default). Thanks, Dave z <- rnorm(24...

...example variable might be a directional component of angular momentum for a particular joint measured at a large number (101) of equally spaced timepoints. Each bowler generates a (fairly smooth) curve for each variable measured. I decided to represent each curve by a few orthogonal polynomial constrasts. There are 4 groups of bowlers corresponding to various speeds of delivery. I want to use canonical variant analysis to find linear combinations of my transformed variables discriminating well between the groups of bowlers. I used lda() from the MASS library to do this, but examining the outpu...

Hello, R experts, Sorry for asking this question again again since I really want a help! I have a two-factor experiment data and like to calculate estimates of interation contrasts say factor A has levels of a1, a2, and B has levels of b1, b2, b3, b4, and b5 with 3 replicates. I am not sure the constrast estimate I got is right using the script below:

I am looking for help to analyze an unbalanced AB/BA cross-over design by requesting the type III SS ! # Example 3.1 from S. Senn (1993). Cross-over Trials in Clinical Research outcome<-c(310,310,370,410,250,380,330,270,260,300,390,210,350,365,370,310,380,290,260,90,385,400,410,320,340,220) subject<-as.factor(c(1,4,6,7,10,11,14,1,4,6,7,10,11,14,2,3,5,9,12,13,2,3,5,9,12,13))

The expressions > x<-factor(x) and > class(x)<-"factor" behave differently when x is already an ordered factor. It may not be a bug but it caught me out when I was trying to remove the "orderedness" from a factor variable. The following R code illustrates the difference. Is this difference between the 2 commands desirable? > x<-1:3 > class(x) NULL

...a2 vs (a1, a3) 1 ? ? ? ? a1 vs a3 1 ? ? ? ? -------------------------------------------------------- Residuals 18 23.090 1.283 -------------------------------------------------------- Or, In this case, how can I to test these constrasts? Thanks for any help, -- Jose Claudio Faria Brasil/Bahia/UESC/DCET Estatistica Experimental/Prof. Adjunto mails: joseclaudio.faria at terra.com.br joseclaudio.faria at oi.com.br jc_faria at uesc.br jc_faria at uol.com.br tel: 73-3634.2779

Hello, R experts, Sorry for asking this question again since I really want a help! I have a two-factor experiment data and like to calculate estimates of interation contrasts say factor A has levels of a1, a2, and B has levels of b1, b2, b3, b4, and b5 with 3 replicates. I am not sure the constrast estimate I got is right using the script below:

Dear R-helpers, In the case of two categorical factors, say a and b, once I have fixed the constrasts, the model matrix is set according to these contrasts with "lm", and the t-tests for the significance of the parameters provided by "summary" indeed concern the comparison of the model with each submodel obtained by removing the corresponding column of the model matrix. So :...

Hello, R experts, I have a two-factor experiment data and like to calculate estimates of interation contrasts say factor A has levels of a1, a2, and B has levels of b1, b2, b3, b4, and b5 with 3 replicates. I am not sure the constrast estimate I got is right using the script below: score<-c(7.2,6.5,6.9,6.4,6.9,6.1,6.9,5.3,7.2,5.7,5.1,5.9,7.6,6.9,6.8,

Hello, R experts, Sorry for asking this question again since I really want a help! I have a two-factor experiment data and like to calculate estimates of interation contrasts say factor A has levels of a1, a2, and B has levels of b1, b2, b3, b4, and b5 with 3 replicates. I am not sure the constrast estimate I got is right using the script below:

Dear R-listers I'm a new R-user who needs some help with a test that I want to do. I have done a field experiment: four treatments (cont, x, y and xy) at three sites (A, B and C), the response is count data (0 - 15). I've done a Poisson regression: >glm(response~as.factor(treatment)*as.factor(site), family=quasipoisson, offset(max.response), data=dat) The "offset" is the