search for: varieties

Hi R Users, 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),

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 looks like: def show @inventory =

..., 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 obs -> orthogonal and balanced [1] 72 > # split-plot anova > summary(aov(yield ~ Block+Variety*nitro + Error(Block/Variety),data)) Error: Block ????? Df Sum Sq Mean Sq Block? 5? 15875??? 3175 Error: Block:Variety ????????? Df Sum Sq Mean Sq F value Pr(>F) Variety??? 2?? 1786...

Dear R-help, 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

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 treat...

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. variet...

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) array dye genes variety 5.955503e+00 2.695750e-02 4.120987e-01 -2.49957...

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

Hi R experts: I am new to mixed model commodity. I am tryping to specify a model using lmer in lme4 package. I am not sure if I am doing right, so I need your help....please...... Treatment / 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

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

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 (indepen...

Hi All, I'm taking an Experimental Design course this semester, and have spent many long hours trying to coax the professor's SAS examples into something that will work in R (I'd prefer that the things I learn not be tied to a license). It's been a long semester in that regard. One thing that has really frustrated me is that lme has an extremely counterintuitive way for

...e to be sure that I’m not doing something stupid. Here is my data: I’ve measured some variable responses (Y, for example leaf size) for different plants grown on three differing conditions (A, B and C). These plants come from two different species (sp1 and sp2) and within each species, two to three varieties were tested (var1 and var2 within sp1 and var3 to var5 within sp2). Finally I’ve got of course three replicates for each condition*plant combinations and I’ve got another factor (month) that corresponds to the “block” effect. Thus, my data looks like that:   month<-c("jun","aug&qu...

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:

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]},...

How to set the nlme() function to return the answer without the intercept parametrization? #========================================================================================= 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,

Hi I think the subsetting works 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

...ed in the panel itself. After all, this is what "panel = function (x,y ....." is meant for, right ? But what, if I want to highlight some panels ? Arbitrarily or conditioned by another variable. Say I want to shade in gray only the upper right panels (Block VI, Victory and Marvellous varieties ) Given a data frame like this, with a variable intended to set the colour of the panel background / data(Oats, package = "MEMSS") /Oats1 <- cbind.data.frame(Oats, Highlight = ifelse(Oats$Block == "VI" &...

On 24/04/18 15:17, Paul Murrell wrote: > Hi > > I think the subsetting works 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

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