search for: liver

Hello list, I'm trying to figure out how exactly the specification of nested random effects works in the lmer function of lme4. To give a concrete example, consider the rat-liver dataset from the R book (rats.txt from: http://www.bio.ic.ac.uk/research/mjcraw/therbook/data/ ). Crawley suggests to analyze this data in the following way: library(lme4) attach(rats) Treatment <- factor(Treatment) Rat <-factor(Rat) Liver<-factor(Liver) m1<-lmer(Glycogen~Treatm...

...rying to understand the lme output and procedure. I''m using the Crawley''s book. I''m try to analyse the rats example take from Sokal and Rohlf (1995). I make a nested analysis using aov following the book. > summary(rats) Glycogen Treatment Rat Liver Min. :125.0 Min. :1 Min. :1.0 Min. :1 1st Qu.:135.8 1st Qu.:1 1st Qu.:1.0 1st Qu.:1 Median :141.0 Median :2 Median :1.5 Median :2 Mean :142.2 Mean :2 Mean :1.5 Mean :2 3rd Qu.:150.0 3rd Qu.:3 3rd Qu.:2.0 3rd Qu.:3 Max. :162.0 Max....

...er analysis output of your aov nested model example as given.The Glycogen treatment has to be a Fixed Effect.If a 'treatment' isn't a Fixed Effect what is ? If Douglas Bates' lmer model is modified to treat Glycogen Treatment as a purely Fixed Effect,with Rat and the interaction Rat:Liver as random effects then-- > model.lmer<-lmer(Glycogen~Treatment+(1|Rat)+(1|Rat:Liver)) > summary(model.lmer) Linear mixed-effects model fit by REML Formula: Glycogen ~ Treatment + (1 | Rat) + (1 | Rat:Liver) AIC BIC logLik MLdeviance REMLdeviance 239.095 248.5961 -113.5475...

...ataset "rats" : http://www.bio.ic.ac.uk/research/mjcraw/statcomp/data/ The advantage is that most work is already done in Crawley's book (page 361 onwards) so I can check what I am doing. I am tryg to reproduce the nested analysis on page 368: model<-aov(Glycogen~Treatment/Rat/Liver + Error(Treatment/Rat/Liver), rats) using lme. The code: model1<- lme(Glycogen~Treatment, random = ~1|Rat/Liver, data=rats) VarCorr(model1) Variance StdDev Rat = pdLogChol(1) (Intercept) 20.6019981 4.538942 Liver = pdLogChol(1) (Intercept) 0.0...

...also using lmer and to see how the different approaches and outputs differs - from the more or less manual old-school (?) approach in Sokal, aov in Crawley, and to mixed models by lmer. In the example, three treatments (Treatment) with two rats (Rat) each (i.e six unique rats in total). Three liver preparations (Liver) are taken from each rat (i.e 18 unique liver preparations), and two glycogen readings (Glycogen) are taken from each liver preparation (36 readings). We want to test if treatments has affected the glycogen levels. The readings are nested in preparation and the preparations...

...you ? Let me know if there is as this server is not live yet... All the best, Noel NB ATTACHMENTS REMOVED FOR LIST POSTING Domain/network info: Domain = UK Win2000 DC (192.168.5.4) = BRAIN Live Samba server 2.2.3a (192.168.5.5) = BELLY New Samba server 2.2.3a (192.168.5.6) = LIVER ################ > Feb 24 11:01:01 belly logger: Restarting winbind > Feb 24 11:01:01 belly logger: Killing winbindd.... > Feb 24 11:01:03 belly logger: Reloading winbindd... > Feb 24 11:01:03 belly logger: Winbind loaded!.... > Feb 24 11:01:03 belly logger: root 20044 0.0...

Hello all! I am once again analyzing patient survival data with chronic liver disease. The severity of the liver disease is given by a number which is continuously variable. I have referred to this number as "meld"--model for end stage liver disease--which is the result of a mathematical calculation on underlying laboratory values. So, for example, I can generate...

...al "Biostatistics" and trying to run a cox test using a dataset from: http://faculty.washington.edu/~heagerty/Books/Biostatistics/chapter16.html (Primary Biliary Cirrhosis data link at top of the page), I'm using the following code: --------------- start of code library(survival) liver <- scan("liver2.txt",list(age=0,albumin=0,alkphos=0,ascites=0,bili=0, cholest=0,edema=0,edmadj=0,hepmeg=0,obstime=0,platelet=0,protime=0, sex=0,sgot=0,spiders=0,stage=0,status=0,treatmnt=0, triglyc=0,urinecu=0)) fit<-coxph(Surv(obstime,status)~bili+edmadj+alb...

...t al. (Use of the Probability Integral Transformation to Fit Nonlinear Mixed-Models with Nonnormal Random Effects - 2006). Firstly the authors fit a Poisson model with canonical link and a single normal random effect bi ~ N(0;Sigma^2).The SAS routine was: log_s =log(SURVT) cens=1 proc nlmixed data=liver; parms logsig2 = 0 b0 = -2.8 btrt = -0.54 bhrt =0.18; xb= log_s + b0 + btrt * treat + bhrt * heart+ bi; lambda=exp(xb); model cens ~ poisson(lambda); random bi ~ normal(0,exp(logsig2)) subject=INST; run; I obtained the same results for parameters estimates and standarderrors, by using: glmer(cens...

...r. `No, it's not,' she said. `I've gone very near with papa.' -----Original Message----- From: Guadalupe Simpson [mailto:hmeg@xtamkm.com] To: saul acton; minh fears; garry mcquilkin; patrick leavitt Sent: Sunday, July, 2004 1:33 AM Subject: good news - regenerating of the heart, liver, kidneys and other organs Tea over and the tray rem-oved, she again summoned us to the fire; we sat one on each side of her, and now a conversation followed between her and Helen, which it was indeed a privilege to be admitted to hear. I put her cool hand to my ho~t forehead; 'No, Die, not o...

...different distributions of data like a glm so my data does not have to be strictly normal or have equal variance. Another concern of mine is whether I should be using lme as above, or as a book I read states, re-coding factor levels, and using lmer, for example: Treatment<-factor(Treatment) Liver<-factor(Liver) Rat<-factor(Rat) rat<-Treatment:Rat liver<-Treatment:Rat:Liver model<-lmer(Glycogen~Treatment+(1|rat)+(1|liver) so with me it might be: Group<-factor(Group) Lineage<-factor(Lineage) Dish<-factor(Dish) Disk<-factor(Disk) lineage<-Group:Lineage dish<-...

Hello, I am just a beginner of R 1.9.0. I try to construct a predictive score for the development of liver cancer in cirrhotic patients. So dependant variable is binanry (cancer yes or no). Independant variables are biological data. The aim is to find out a cut-off value which differentiate (theoratically) from normal to pathological state for each biological data. How can I step in procedue to get a...

Hi, I make this model using lme m.lme <- lme(Glycogen~Treatment,random=~1|rTrt/Liver) How to make this using lmer? I try > m.lmer <- lmer(Glycogen~Treatment+(1|rTrt/Liver)) Erro em lmer(Glycogen ~ Treatment + (1 | rTrt/Liver)) : entry 0 in matrix[0,0] has row 2147483647 and column 2147483647 Al??m disso: Mensagem de aviso: / not meaningful for factors in: Ops.factor(rTrt...

Hello, unfortunately I have I big problem I can't solve. I have to analyse if a gene is tissue specific. For example for the gene xyz I have following expression values: Heart Liver Brain 8.998497 10.013561 12.277407 9.743556 10.137574 11.033957 For every tissue I have two values from two different experiments. 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 pa...

Hi, I have a question about how to do covariate adjustment. I have two sets of 'gene expression' data. They are from two different tissue types, 'liver' and 'brain', respectively. The purpose of my analysis is to compare the pattern of the whole genome 'gene expression' between the two tissue types. I have 'age' and 'sex' as covariates. Since 'age' and 'sex' definitely have influence on gene ex...

...4,5,6. The other labs has one tissue type each. The 'sample' data is as below: ------------------------------------------------------------------------------------------------ Sample.ID Gene tissue.type batch(lab) expression.level id1 gene1 liver batch1 0.67 id1 gene2 liver batch1 0.89 id2 gene1 kidney batch1 0.52 id2 gene2 kidney batch1 0.45 . . id...

...gl(5, 1, 15) dd <- data.frame(y = y, cond = cond, obs = obs, subj = subj) l1 <- lmer(y~cond + (1|cond:obs), dd) l2 <- lmer(y~cond + (1|cond:subj), dd) l3 <- lmer(y~cond + (1|obs), dd) Douglas Bates a ??crit: The difference between models like lmer(Glycogen~Treatment+(1|Rat)+(1|Rat:Liver)) and lmer(Glycogen~Treatment+(1|Treatment:Rat)+(1|Treatment:Rat:Liver)) is more about the meaning of the levels of "Rat" than about the meaning of "Treatment". As I understood it there are three different rats labelled 1. There is a rat 1 on treatment 1 and a rat 1 on trea...

Hi! How to calculate the correct SE of mean in a nested or spliplot anova? Nested example: --------------------- m <- aov(Glycogen~Treatment+Error(Treatment/Rat/Liver)) > m Call: aov(formula = Glycogen ~ Treatment + Error(Treatment/Rat/Liver)) Grand Mean: 142.2222 Stratum 1: Treatment Terms: Treatment Sum of Squares 1557.556 Deg. of Freedom 2 Estimated effects may be unbalanced Stratum 2: Treatment:Rat Terms:...

Hi, I have two doubts about the nested aov output. 1) I have this: > anova.ratos <- aov(Glicogenio~Tratamento+Error(Tratamento/Rato/Figado)) > summary(anova.ratos) Error: Tratamento Df Sum Sq Mean Sq Tratamento 2 1557.56 778.78 Error: Tratamento:Rato Df Sum Sq Mean Sq F value Pr(>F) Residuals 3 797.67 265.89 Error: Tratamento:Rato:Figado

Hi Dear all, I have some gene expression data samples from different tissue types ----------------------------------------------- - 120 samples from blood (B) - 20 samples from Liver (L) - 15 samples from Kidney (K) - 6 samples from heart (H) ----------------------------------------------- All the samples are from different individuals, so there are in total 161 individuals from which the DNA was collected. For each sample DNA, the expression level of 200 genes were obtained,...