search for: glycogen

...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 nested in rats. The data can be found here (or on p. 289 in Sokal): http://www.bio.ic.ac.uk/resear...

Ronaldo, Further to my previous posting on your Glycogen nested aov model. Having read Douglas Bates' response and Reflected on his lmer 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...

...r 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~Treatment+(1|Treatment/Rat/Liver)) The problem that I have with this analysis is that Treatment gets both a fixed and a random effect [1]. So I would like to remove the fixed effect from Treatment, but I'm not sure how to do this. Is the following correct? m2<-lmer(Glycogen~Treatment+(1|T...

Hi, I''m trying 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.:...

...g around Mick Crawley dataset "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)...

...15) obs <- gl(15, 1) subj <- 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...

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...

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: Tr...

...e data of the box 10.5 in the Biometry from Sokal and Rohlf (2001) using R. This is a three-level nested anova with equal sample size : 3 different treatments are compared ; 2 rats (coded 1 or 2) / treatment are studied ; 3 preparations (coded 1, 2 or 3) / rats are available ; 2 readings of the glycogen content / preparations are realised. Treatment is fixed whereas Rats (nested in Treatment) and Prep (nested in Rats) are random effects. According to a previous discussion found in the R-help archives (January 2007), I have tried the following formula : > box105.lme<-lme(content~treatm...

...also_BCAA)", "Fatty_Acid_Synthesis", "Fibrinogen_Cleavage_Peptide", "Fructose,_Mannose_and_Galactose", "Gamma-glutamyl_Amino_Acid", "Glutamate", "Glutathione", "Glycerolipid", "Glycine,_Serine_and_Threonine", "Glycogen", "Glycolysis,_Gluconeogenesis,_and_Pyruvate", "Guanidino_and_Acetamido", "Hemoglobin_and_Porphyrin", "Histidine", "Inositol", "Ketone_Bodies", "Leucine,_Isoleucine_and_Valine", "Long_Chain_Fatty_Acid", "Lysin...

...t; "Fatty_Acid_Synthesis", "Fibrinogen_Cleavage_Peptide", > "Fructose,_Mannose_and_Galactose", > "Gamma-glutamyl_Amino_Acid", "Glutamate", "Glutathione", "Glycerolipid", > "Glycine,_Serine_and_Threonine", "Glycogen", > "Glycolysis,_Gluconeogenesis,_and_Pyruvate", > "Guanidino_and_Acetamido", "Hemoglobin_and_Porphyrin", "Histidine", > "Inositol", "Ketone_Bodies", "Leucine,_Isoleucine_and_Valine", > "Long_Chain_Fatty_Aci...

...nce. 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<-Group:Lineage:Dish disk<-Group:Lineage:Dish:Disk model<-lmer(Diameter~Group+(1|lineage)+(1|dish)+(1|disk) I...

On 19/09/2017 9:47 AM, greg holly wrote: > Hi all; > > I have data at 734*22 dimensions with rows and columns names are > non-numeric.When I convert this data into matrix then all values show up > with quotes. Then when I use > x1= noquotes(x) to remove the quotes from the matrix then non-numeric row > names remain all other values in matrix disappear. > > Your help is

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 all; I have data at 734*22 dimensions with rows and columns names are non-numeric.When I convert this data into matrix then all values show up with quotes. Then when I use x1= noquotes(x) to remove the quotes from the matrix then non-numeric row names remain all other values in matrix disappear. Your help is greatly appreciated. Greg [[alternative HTML version deleted]]