search for: variety

...When I use package lme4 for mixed model analysis, I can't distinguish the significant and insignificant variables from all random independent variables. Here is my data and result: Data: Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9), Variety=rep(rep(c("A1","A2","A3"),each=3),3), Stand=rep(c("B1","B2","B3"),9), Block=rep(1:3,each=9)) Rice.lmer<-lmer(Yield ~ (1|Variety) + (1|Stand) + (1|Block) + (1|Variety:Stand), data = Rice) Result: L...

Hi Gurus- I want to return the variety from the species table based on the active record link to inventory table class Species < ActiveRecord::Base has_many :inventories end class Inventory < ActiveRecord::Base belongs_to :species (this is the singular and the plural, so that''s OK) end My inventory_controller look...

...there are sometimes one or two values missing. I realized that if the data is slightly unbalanced, the effect of the subplot-treatment will also appear and be tested against the mainplot-error term. I replicated this with the Oats dataset from Yates (1935), contained in the nlme package, where Variety is on mainplot, and nitro on subplot. > # Oats dataset (Yates 1935) from the nlme package > require(nlme); data <- get(data(Oats)) > data$nitro <- factor(data$nitro);data$Block <- as.factor(as.character(data$Block)) > nrow(data) # 6 Blocks * 4 N-levels * 3 Varieties = 72...

...elp, Why can't lme cope with an incomplete whole plot when analysing a split-plot experiment? For example: R : Copyright 2006, The R Foundation for Statistical Computing Version 2.3.1 (2006-06-01) > library(nlme) > attach(Oats) > nitro <- ordered(nitro) > fit <- lme(yield ~ Variety*nitro, random=~1|Block/Variety) > anova(fit) numDF denDF F-value p-value (Intercept) 1 45 245.14333 <.0001 Variety 2 10 1.48534 0.2724 nitro 3 45 37.68560 <.0001 Variety:nitro 6 45 0.30282 0.9322 # Excellent! However ---...

1) I have the following data situation: 96 plots 12 varieties 2 time points 2 technical treatments the experiment is arranged as follows: a single plot has two varieties tested on it. if variety A on plot #1 has treatment T1 applied to it, then variety B on plot #1 has treatment T2 applied to it. across the whole experiment variety A is exposed to treatment T1 the same number of times as treatment T2. with respect to time points, plots come in 3 kinds. (1) varietyA, timepoint#1 vs. va...

I have the following dataset: 96 plots 12 varieties 2 time points The experiment is arranged as follows: A single plot has two varieties tested on it. With respect to time points, plots come in 3 kinds: (1) varietyA, timepoint#1 vs. variety B, timepoint#1 (2) varietyA timepoint #2 vs. varietyB timepoint #2 (3) varietyA timepoint #1 vs. variety A timepoint#2 - there are 36 of each kind of within timepoint comparison and 24 between timepoint comparisons. so it isn't a fully connected design. Plots a...

Hello R-users, I am currently trying to learn how to use the function gls of the nlme library. I fitted the following model: Generalized least squares fit by REML Model: response ~ array + dye + genes + variety + variety * genes + array * genes + dye * genes Data: data I have 11 arrays, 2 dyes, 2 varieties, 3200 genes, and 2 replications for each. Therefore I should have the corresponding degrees of freedom and number of coefficients, but instead I have the following: Coefficients: (Intercept)...

This is an example from chapter 11 of the 6th edition of Devore's engineering statistics text. It happens to be a balanced data set in two factors but the calculations will also work for unbalanced data. I create a factor called 'cell' from the text representation of the Variety level and the Density level using '/' as the separator character. The coefficients for the linear model 'Yield ~ cell - 1' are exactly the cell means. > library(Devore6) > data(xmp11.07) > str(xmp11.07) `data.frame': 36 obs. of 3 variables: $ Yield : num 10.5 9.2 7...

...ment / factor structure Year: level 1:3, the whole the experiment was repeated in three years, random factor village: level 1:2 # the level is much higher just three are shown as example, random factor Farm : level 1:9 # the level is much higher just three are shown as example, random factor Variety: 10 variety were grown (may or not be different at different years, farm, villages, some of them were repeated) (fixed effect) Thus layout of treatment structure would like the follows for each year - Year[1] Villlage[1] Farm[1] Variety: 1, 2, 8,...

Dear List, I have been using R to create an xyplot using the panel function within lattice libraries. This plot is based on the data supplied in R named 'Oats'. The graph represents oat yield by nitro level with an overlay of each variety of oats for each nitro level. I have three questions regarding this graph: 1) I cannot seem to specify the type of symbol used by the plot, even though it is included in the code below, it will change when the code is changed? 2) I have managed to include a legend on the graph using the key functi...

Hello, I'm trying to analyse the spatial organization of different fields planted with different varieties (each field has only one variety), but I have problems trying to understand the results of the test I did. To do this, I created different neighbourhood matrix. For example, for the first matrix, fields are considered as neighbours if they are distant from 0 to 1000 meters from each others (independently on the variety). In the se...

...gle random term, but if there are multiple terms or random interactions, the documentation available just doesn't hold up. Here's an example: a split block (strip plot) design evaluated in SAS with PROC MIXED (an excerpt of the model and random statements): model DryMatter = Compacting|Variety / outp = residuals ddfm = satterthwaite; random Rep Rep*Compacting Rep*Variety; Now the fixed part of that model is easy enough in lme: "DryMatter~Compacting*Variety" But I can't find anything that adequately explains how to simply add the random terms to the model, ie "rep +...

...quot;jul","may","sep","nov") condition<-c(rep("A",15),rep("B",15),rep("C",15)) species<-c(rep("sp1",6),rep("sp2",9),rep("sp1",6),rep("sp2",9),rep("sp1",6),rep("sp2",9)) variety<-c(rep("var1",3),rep("var2",3),rep("var3",3),rep("var4",3),rep("var5",3),rep("var1",3),rep("var2",3),rep("var3",3),rep("var4",3),rep("var5",3),rep("var1",3),rep("var2",3),rep(&...

Hi All, I'm trying to plot boxplot graph. I tried barchart with "groups=" option and it worked fine. But when I try to generate same kind of graph using bwplot(), "groups=" option doesn't seem to work. Though this works, yield ~ variety | site * year I'm thinking why "groups=" doesn't work in this case, can anyone help please... #Code: library(lattice) barchart(yield ~ variety | site, data = barley, groups = year, layout = c(1,6), auto.key = list(points = FALSE, rectangles = TRUE, space = "right&...

Hi, i''ve got the following problem. I have a check_box witch shows a list of varieties. Now i want to call a action when the user changes his selection. (the method to be called, will store the selection from the user). This is my code: <%= select(''variety'', ''variety_id'', Variety.find_all.collect {|c| [c.description, c.id]}, {}, {"onchange" => url_for(:controller => ''account'', :action => ''add_to_varietytmp...

...library(nlme) Soybean[1:3, ] (fm1Soy.lis <- nlsList(weight ~ SSlogis(Time, Asym, xmid, scal),                        data = Soybean)) (fm1Soy.nlme <- nlme(fm1Soy.lis)) fm2Soy.nlme <- update(fm1Soy.nlme, weights = varPower()) plot(ranef(fm2Soy.nlme, augFrame = TRUE),      form = ~ Year * Variety, layout = c(3,1)) soyFix <- fixef(fm2Soy.nlme) options(contrasts = c("contr.treatment", "contr.poly")) (fm3Soy.nlme <-  update(fm2Soy.nlme,         fixed = Asym + xmid + scal ~ Year,         start = c(soyFix[1], 0, 0, soyFix[2], 0, 0, soyFix[3], 0, 0))) # here I wouldn&...

...ks by giving you the panels for the corresponding levels of the conditioning variable(s). Note that, if there is more than one conditioning variable, you will need more than one subsetting index. For example, taking this plot with two conditioning variables and 12 panels in total ... dotplot(variety ~ yield | year * site, data=barley) ... this produces three pages ... dotplot(variety ~ yield | year * site, data=barley, layout=c(2,2)) ... and this produces the second page (both panels for the first conditioning variable and the third and fourth panels for the second conditioning v...

Dear R users, this is a follow up of this message http://tolstoy.newcastle.edu.au/R/e6/help/09/05/13897.html I'm reproducing the core of it for convenience. > // > / data(Oats, package = "MEMSS") / > / tp1.oats <- xyplot(yield ~ nitro | Variety + Block, / > / data = Oats, / > / panel = function(x, y, subscripts, ...) { / > / # How to normalize my heatmap metric > with / > / # the value of the panel that has > maximum...

...; corresponding levels of the conditioning variable(s).? Note that, if > there is more than one conditioning variable, you will need more than > one subsetting index. > > For example, taking this plot with two conditioning variables and 12 > panels in total ... > > dotplot(variety ~ yield | year * site, data=barley) > > ... this produces three pages ... > > dotplot(variety ~ yield | year * site, data=barley, > ??????? layout=c(2,2)) > > ... and this produces the second page (both panels for the first > conditioning variable and the third and four...

x.fun <- function( formula, data ) dotplot( formula, data ) x.grp <- function( formula, groups, data ) dotplot( formula, groups, data ) data( barley ) > x.fun( variety ~ yield | site, data=barley ) # no problem > dotplot( variety ~ yield | site, groups=year, data=barley ) # no problem > x.grp( variety ~ yield | site, groups=year, data=barley ) object "year" not found # that's my error, so I do: > x.grp( variety ~ yield | site, groups=ba...