search for: fert

Dear R sages, I used the function rbind to combine a matrix (M) and a vector (Fert) to get a new matrix (A). This was fine. The issue is however, that the new matrix A has as its row names the name of the vector Fert, even though I set teh new vector A to have dimnames=NULL. See short code below fyi. *Fert<-c(0,1,5) * *M <- matrix(0, 2, 3) diag(M) <- c(0.3,0.5) * *A&lt...

...t;alerted" me that I have done something wrong. I designed an experiment with all combinations of all levels of each treatment, but lost a little data (3 out of 192 plots). With the following data, I run the following model: > collim[c(1:6,187:192),c(1,3:6,9)] plot Litter Fert Seed S density X192.4 192 R 16 C 7 12 X192.3 192 C 16 C 14 26 X192.2 192 C 16 A 8 3 X192.1 192 R 16 A 9 12 X191.4 191 R 32 A 3 0 X191.3 191 C 32 A 10 11 ..... X146.2 146 R 32 C 7...

...rast::contrast(), gmodels::estimable(), multcomp::glht() and all these fail when model has NA estimates. This is becouse vcov() and coef() has different dimensions. Is possible set lm() to omit NA? Below same toy data and code. > # toy data > adi <- expand.grid(cult=gl(1,3,la=LETTERS[1]), fert=101) > fat <- expand.grid(cult=gl(2,3,la=LETTERS[2:3]), fert=seq(50,150,50)) > da <- rbind(adi, fat) > da$y <- da$fert+rnorm(nrow(da),0,10) > > # plot > require(lattice) > xyplot(y~fert|cult, da) > > # adjust > m0 <- lm(y~cult*fert, da) > summary(m0) .....

...; is intervals marginal or approximate? Which should I trust as more accurate or, alternaitvely, more conservative? Partial output follows. Thanks for the insight. Hank > anova(fm0) numDF denDF F-value p-value (Intercept) 1 69 6331.902 <.0001 Fert 3 44 42.176 <.0001 Seed 1 44 0.488 0.4886 Litter 1 69 1.830 0.1805 Density 1 69 68.714 <.0001 Fert:Seed 3 44 0.061 0.9799 Fert:Litter...

...s and I need correcting for multiplicity. So, I can use multcomp::glht() for this. However, glht() return an error message that is not compatible with my expectations. Someone know I or has a suggestion for? Below some reproducible code. # toy data adi <- expand.grid(cult=gl(1,3,la=LETTERS[1]), fert=101) fat <- expand.grid(cult=gl(2,3,la=LETTERS[2:3]), fert=seq(50,150,50)) da <- rbind(adi, fat) da$y <- da$fert+rnorm(nrow(da),0,10) # plot require(lattice) xyplot(y~fert|cult, da) # adjust complete factorial m0 <- aov(y~cult*fert, subset(da, cult!="A")) summary(m0) coef(m0...

...the method described in Mixed-Effects Methods and Classes for S and S-PLUS, version 3.0, but when I add the covariates, I get the error " starting values for the fixed component are not the correct length" My data has the following structure "Subject" "Species" "Fert" "Photo" "PARi" , where several "Photo" measurements where taken on the same subject by changing "PARi", 4 Species levels and 2 Fert levels, there are 31 Subjects (one missing value), and 323 observations DATA extract Subject Species Fert Photo PARi 1...

...same 4 functional plant groups (funktGr), each represented by four species (the species are nested in the functional plant groups, because if I take species as a single factor I get 16 species, which is not true. in each chamber I have four replicates (pseudoreplicates) half of the plants got fertilisation (Fert), that means each plant got it's own fertilisation the response variable is biomass (g) Randomisation was done to plot and subplot level. I tried this: aov(log(g)~CO2*funktGr/Species*Fert+Error(Gruppe/CO2),data=biomass) the output is: Error: Gruppe Df Sum Sq...

Hi R-Helpers: I?m trying to use the function GLMM from lme4 package, (R-1.8.1, Windows 98),and I get the following error: > pd5 = GLMM(nplant~sitio+ + fert+ + remo+ + sitio:fert+ + remo:sitio+ + remo:fert+ + remo:fert:sitio + data=datos, + family=binomial, + random=~repe:sitio) Error in parse(file, n, text, prompt) : parse erro...

...effectively, and am wondering if it does not handle unbalanced data (3 out of 192 missing). I include below the model and an xyplot that almost does the job. I would happily send anyone the data if they would be willing to help. Many Thanks in advance. Hank The model: fm <- lme( log(S,2) ~ Fert*Litter*Seed*Density, data = collimd, random = ~1|block/plot/Seed ) # The following is close, but fits lines within each panel rather than giving me the fitted values generated by the model. xyplot( log(S,2) ~ log10(Nper0.5m) | Fert*Seed, data = collimd, groups=Litter, scales = lis...

...) ). Their subsets were listed as Crop1$CContent for example and there was quite a few of them. There is a run file that gets the list file going in the following code: #--Initialising the lists to store variable values tmp <- init_lists() Param <- tmp[[1]] Crop1 <- tmp[[2]] Fert <- tmp[[3]] Meteo <- tmp[[4]] Soil <- tmp[[5]] RainPattern <- tmp[[6]] Crop2 <- tmp[[7]] Cropres <- tmp[[8]] rm(tmp) The problem here is that the lists get confused with each other; that is elements of Soil appear in Crop2. In the run file, I change...

...alues for this model but I got a message that "using F test with a binomial family is inappropriate in: anova.glm(model, test = "F")". Should I not report F statistics on this kind of analysis? I would appreciate any comment on this. This is my output: > model=glm(y~agua*fert+bloco, family=binomial) > anova.glm(model,test="F") Analysis of Deviance Table Model: binomial, link: logit Response: y Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL 43 85.018...

Hi, I'm trying to fit a logit model to a set of data that was collected under a split plot scheme. The structure of the data is Whole plot factors: Watering Frequency (2 levels: Hi/Lo) and Fertilizer type (3 factors A/B/C) Subplot factor: slope type (2 factors up/down) Response: Proportion of infected leaves(Infected leaves/Total leaves) of the plant (2 plants recorded from each plot) Dataset (Partial) Water Fert Slope Plant Total Infect Hi A up 1 24 2...

Hi I have a file saved in R, named agrexp.Rdata, shown below > agrdata fert yield 1 25 84 2 50 80 3 75 90 4 100 154 5 125 148 If I double clicked on this file, the data is displayed without problem. However if I tried to import using: > agrdata<-read.table("agrexp.Rdata") or > agrdata<-read.table("agrexp.Rdata&q...