search for: nonsignificance

hello i have a mixed effect model which gives slope and intercept terms for 6 groups (diagnosis (3 levels) by risk group(2 levels)). the fixed part of the model is -- brain volume ~ Diagnosis + Risk Group + (Risk Group * age : Diagnosis) - 1 thus allowing risk group age/slope terms to vary within diagnosis and omitting a nonsignificant diagnosis by risk group intercept (age was centered)

Consider the following example (based on an example in Pat Altham's GLM notes) pyears <- scan() 18793 52407 10673 43248 5710 28612 2585 12663 1462 5317 deaths <- scan() 2 32 12 104 28 206 28 186 31 102 Smoke <- gl(2,1,10,labels=c("No","Yes")) Age <- gl(5,2,10,labels=c("35-44","45-54","55-64","65-74","75-84"),

hello! i don't want to test my sample data for normality, but exponential- lognormal- or gammadistribution. as i've learnt the anderson-darling-test in R is only for normality and i am not supposed to use the kolmogorov-smirnov test of R for parameter estimates from sample data, is that true? can you help me, how to do this anyway! thank you very much! nadja

Hi! I'm having some issues on both conceptual and technical levels for selecting the right combination of variables for this model I'm working on. The basic, all inclusive form looks like lm(mic ~ B * D * S * U * V * ICU) Where mic, U, V, and ICU are numeric values and B D and S are factors with about 16, 16 and 2 levels respectively. In short, there's a ton of actual explanatory

...intercept. This is in many cases not what was initially intended. Instead, what one wants is perhaps to determine values of the covariate at which the groups differ. I??ve seen a description of the Johnson-Neyman procedure (in Huitema (1980)), which allows to determine the so called regions of nonsignificance, which sounds a lot like what I want. The problem is I have very seldom seen it used (at least in my field of work), but unequal slopes is a common problem. (Searching the R-help archives, however, didn??t give me a single match.) My first question is therefore if the Johnson-Neyman procedure i...

Hello, I've been making a decent amount of use of the lme function recently, and I am aware that I can extract the variance and correlation components with the VarCorr(model) function. However, I am interested in testing these components as well...is there a module or function available for testing these? I understand there's some debate as to which test is best for a given situation

Hi all, I hope that there might be some statistician out there to help me for a possible explanation for the following simple question. Y1~ lm(y~ t1 + t2 + t3 + t4 + t5,data=temp) # oridnary linear model library(gam) Y2~ gam(y~ lo(t1) +lo(t2) +lo(t3) +lo(t4) +lo(t5),data=temp) # additive model In the first model t1, t2 and t3 found to be significant,. However, in the second model (using

Hello, Sorry to be a bit longwinded, but I've struggled quite a bit with the following over the last few days. I've read all entries related to spatial autocorrelation in R help and haven't found what I'm after. If it's okay, I'm going to first describe my general understanding of the process by which a mixed model can account for correlated errors. If possible, please

Hi all, As you can see from below, the result is strange... I would imagined that the bb result should be much higher and close to 1, any way to improve the fit? Any other classification methods? Thank you! data=data.frame(y=rep(c(0, 1), times=100), x=1:200) aa=glm(y~x, data=data, family=binomial(link="logit")) newdata=data.frame(x=6, y=100) bb=predict(aa, newdata=newdata,

Bother! This cold has made me accident-prone. I meant to hit Reply-all. Clarification below. -------- Original Message -------- Subject: Re: [R] Parameterization puzzle Date: Fri, 21 Jul 2006 19:10:03 +1200 From: Murray Jorgensen <maj at waikato.ac.nz> To: Prof Brian Ripley <ripley at stats.ox.ac.uk> References: <44C063E5.3020703 at waikato.ac.nz>

I'm fitting generalized linear mixed models to using several fixed effects (main effects and a couple of interactions) and a grouping factor (site) to explain the variation in a dichotomous response variable (family=binomial). I wanted to compare the output I obtained using PROC GLIMMIX in SAS with that obtained using lmer in R (version 2.6.1 in Windows). When using lmer I'm specifying

Dear R people, I notice that the confidence intervals of a very small sample (e.g. n=6) derived from the one-sample wilcox.test are just the maximum and minimum values of the sample. This only occurs when the required confidence level is higher than 0.93. Example: > sample <- c(1.22, 0.89, 1.14, 0.98, 1.37, 1.06) > summary(sample) Min. 1st Qu. Median Mean 3rd Qu. Max.

-------- Original Message -------- Subject: Re: [R] Simulation - Natrual Selection Date: Wed, 05 Jan 2011 17:24:05 +0000 From: Ben Ward <benjamin.ward@bathspa.org> To: Bert Gunter <gunter.berton@gene.com> CC: Mike Marchywka <marchywka@hotmail.com> On 05/01/2011 17:08, Bert Gunter wrote: > Couple of brief comments inline below. -- Bert > > On Wed, Jan 5, 2011 at

Hi guys, I have a data-set that fits well into a Weibull model y = a-b*exp(-c*x^d). I want to estimate the parameters of the coefficients a, b, c and d, given x and y. Can you guys help me? Just as an example, I fit the data y <- c(1,2,3,4,10,20) and x <- c(1,7,14,25,29,30) According to this model above, and using the software CurveExpert, and I got the estimates of a (2.95),b (2.90),c

Hi, I've been modelling some data over the past few days, of my work, repeatedly challenging microbes to a certain concentration of cleaner, until the required concentration to inhibit or kill them increaces, at which point they are challenged to a slightly higher concentration each day. I'm doing ths for two different cleaners and I'm collecting the required concentration to

Dear R experts, I am trying to analyze data from an article, the data looks like this Patient Age Sex Aura preCSM preFreq preIntensity postFreq postIntensity postOutcome 1 47 F A 4 6 9 2 8 SD 2 40 F A/N 5 8 9 0 0 E 3 49 M N 5 8 9 2 6 SD 4 40 F A 5 3 10 0 0 E 5 42 F N 5 4 9 0 0 E 6 35 F N 5 8 9 12 7 NR 7 38 F A 5 NA 10 2 9 SD 8 44 M A 4 4 10 0 0 E 9 47 M A 4 5 8 2 7 SD 10 53 F A 5 3 10 0 0 E 11

Hi All I am really very interested in starting to use R within our company. I particularly like the open source nature of the product. My company is a medical research company which is part of the University of London. We conduct contract virology research for large pharma companies. My question is how do we validate this software? I wonder if anyone else has had the problem and might be able to