similar to: Help needed: Split-split plot analysis

Hi everyone, I have a question about calculating r-squared in R. I have tried searching the archives and couldn't find what I was looking for - but apologies if there is somewhere I can find this... I carried out a droughting experiment to test plant competition under limited water. I had: - 7 different levels of watering treatment (1 -7 - from most watered to least watered/) - 15

Dear list users, I have trouble with covariance analysis. I measured nitrate concentrations in the soil (NO3) and the percentage of legumes (LEG, continuous), affected by 2 different CO2 concentrations (CO2, discrete). I suspect that CO2 has an effect on LEG and NO3, but also that LEG has an effect on NO3, so this is the formula I wrote to test this: NO3 ~ CO2 + LEG + CO2:LEG Will LEG be

How do I set up my model equation in aov to analyze a split-plot design? I have two factors (CO2 and NITROGEN), each with two levels (high and ambient). CO2 is my whole-plot factor with three replicates for each level (i.e., 6 rooms total). Is this syntax below correct? summary(aov(response ~ ROOM + CO2*NITROGEN + Error(ROOM/CO2)))

Hello all R listers, I'm struggling to select an appropriate statistical method for my data set. I have collected soil moisture measurements every hour for 2 years. There are 75 sensors taking these automated measurements, spread evenly across 4 treatments and a control. I'm not interested in being able to predict soil future soil moisture trends, but rather in knowing whether the

Hi All, I am trying to lable the y-axis on my scatterplot with the following: "Soil moisture content (m3m-3)" I am using the following coding for plotting the graph: plot(soilmoisture~gradientlevel, xlab="Levels of droughting gradient", ylab="Soil moisture content (m3m-3)", bty="l", font.main="2", pch=16, las=1, cex.lab="1.13") I have

I am trying to it a particular nonlinear model common in Soil Science to moisture release data from soil. I have written the function as shown below according to the logist example in Ch8 of Pinheiro & Bates. I am getting the following error (R version 2.1.1) *Error in qr(attr(rhs, "gradient")) : NA/NaN/Inf in foreign function call (arg 1)* Below is the function and data. /#

Hi, I'm a bit new to R and I would like to know how can I compare simple main effects when using the aov function. I'm doing a mixed model ANOVA with two between subjects variables and one within. When I get an interaction of two of the variables I don't know how to check for simple main effect of that interaction (A at B1 and A at B2 for example). The aov function is very simple but

HI, I am trying to perform ANOVA with 2 factors: material (3), temperature(3). The interaction is significant. I tried something like, ( summary(avt, split=list("temp:mat"=list("15"=1, "70"=2, "125"=3))) is not correct). Thanks, av <- aov(time ~ mat*temp, data=dados) avt <- aov(time ~ temp/mat) summary(avt,

Dear all I'm not sure if I did the right analysis for my specific split splot design. We are studying biomass increase with different CO2 concentrations with four different functional plant groups (e.g. grasses, herbs, broad-leafed trees and conifers). Of each functional plant group we have four species. The design is orthogonal. The design is: Blocks: 2 (climate chambers, called

I am working with a RCB 2x2x3 ANCOVA, and I have noticed a difference in the calculation of sum of squares in a Type III calculation. Anova output is a follows: > Anova(aov(MSOIL~Forest+Burn*Thin*Moisture+ROCK,data=env3l),type=3) Anova Table (Type III tests) Response: MSOIL Sum Sq Df F value Pr(>F) (Intercept) 22.3682 1 53.2141 3.499e-07 *** Forest

Hello, For an experiment, I selected plots of land within a forest either with honeysuckle or without honeysuckle. Thus, my main factor is fixed, with 2 levels: "honeysuckle present"(n=11) and "honeysuckle absent"(n=8). Within each plot of land, I have a "trenched" subplot and an "untrenched" subplot. Within each subplot of every plot, I measured soil

Dear R user, I am new with split-plot designs and I have problems with multiple comparisons. This data correspond to an split-plot experiment with two replications (bloque).(Hoshmand, 2006 pp 138). Briefly, the whole-plot factor is Nitrogen concentration ("nitrogeno") and the subplot factor is the variety of corn ("hibrido"). The aim is to determine if major differences

Dear R community, I am trying to do a split-plot analysis as follows. I have a data set (?morf?) with plant data from 6 ?blocks? at different latitudes, each divided in 3 plots. The full-plot ?treatment? is ?soil type? and has three levels. Within each plot I have two levels of radiation, coded as ?SUN? and ?SHADE?. I have data for several response traits for 30 plants within each subplot,

Dear R community, I am trying to do a split-plot analysis as follows. I have a data set (?morf?) with plant data from 6 ?blocks? at different latitudes, each divided in 3 plots. The full-plot ?treatment? is ?soil type? and has three levels. Within each plot I have two levels of radiation, coded as ?SUN? and ?SHADE?. I have data for several response traits for 30 plants within each subplot,

I have difficulties in interpreting high order interaction terms in high-way ANOVA. According to Introductory Statistics with R by Peter Dalgaard (Section 12.5), "The exact definition of the interaction terms and the interpretation of their associated regression coefficients can be elusive. Some peculiar things happen if an interaction term is present but one or more of the main effects are

Hi, For a n-level factor, I'd like to specify the first contrast and have the remaining n-2 constructed automatically so that the set is orthogonal. I then test the contrasts with summary.lm(anova-object). In S-Plus, the following works: >y.anova <- aov( y ~ C(CO2,c(1,0,-1)) ) >summary.lm(y.anova) In R, it fails with the following error: >levels(CO2) [1]

I have a data.frame paint (below-mentioned). In SPlus I used the command raov( MOISTURE ~ BATCH / PROBE ) (you could do raov( MOISTURE ~ BATCH + PROBE%in%BATCH) as well) so that the factors are taken as random. In R this function raov doesn't exist. How can I calculate the same? Maybe with lme, but how? str(paint) ---------- `data.frame': 60 obs. of 5 variables: $ BATCH : Factor

Dear list, i have a quick and (hopefully) straightforward question regarding the plot-function after running aov. if i plot an equation like this: plot(dataSubjects~factorA, data=mydata) R gives me the boxplots for this particular factor A. my model, however contains several factors. is there a straightforward way to plot barplots for a specific factor with the constraint that those values

Hi, For my master thesis I have 24 micro-plots on which I did measurements during 3 months. The measurements were: - Rainfall and runoff events throughout 3monts (runoff being dependant on the rainfall, a coefficient (%) has been made per rainfall event and per 3 months) - Soil texture (3 different textures were differentiated) - Slope (3 classes of slopes) - Stoniness (one time measurement)

All, I'm trying to analyze the results of a reciprocal transplant experiment using lme(). While I get the error-term right in aov(), in lme() it appears impossible to get as expected. I would be greatful for any help. My experiment aimed to identify whether two fixed factors (habitat type and soil type) affect the development of plants. I took soil from six random sites each of two types