similar to: Bugs/issues with model.tables() (PR#8275)

This concerns whether p-values from lmer can be trusted. From simulations, it seems that lmer can produce very small, and probably spurious, p-values. I realize that lmer is not yet a finished product. Is it likely that the problem will be fixed in a future release of the lme4 package? Using simulated data for a quite standard mixed-model anova (a balanced two-way design; see code for the

I've been experimenting with drop1 for my biostatistics class, to obtain the so-called Type III sums of squares. I am fully aware of the deficiencies of this method, however I feel that the students should be familiar with it. What I find baffling is that when applied to a fully balanced design, you obtain different sums of squares. I've used this for several years in Splus and R and never

My apologies to the list for sending this without adequate research. I have found my answer; please ignore! Thanks. I've been experimenting with drop1 for my biostatistics class, to obtain the so-called Type III sums of squares. I am fully aware of the deficiencies of this method, however I feel that the students should be familiar with it. What I find baffling is that when applied to a fully

I have been trying for hours now to perform an orthogonal contrast through an ANOVA in R. I have done a two-factor factorial experiment, each factor having three levels. I converted this dataset to a dataframe with one factor with nine treatments, as I couldn't work out what else to do. I have set up a matrix with the eight orthogonal contrasts that I wish to perform, but despite

Dear all ReadeRs I was finding a quick method to improve test readability adding or constructing (with your help....) one or more function that allow what follow. Please consider Trt<-c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,4,4,5,5,5,5) Block<-c(1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4,1,2,3,4) AD2DAT<-c(1.44,2.32,1.68,1.28,0.12,0.08,0.24,0.52,0.12,0.08,0.16,0.28,0.16,0.08,0.12,0.12,0.16,0.08,0.32,0.76)

Hi there, The following data is obtained from a long-term experiments. > mydata <- read.table(textConnection(" + y year Trt + 9.37 1993 A + 8.21 1995 A + 8.11 1999 A + 7.22 2007 A + 7.81 2010 A + 10.85 1993 B + 12.83 1995 B + 13.21 1999 B + 13.70 2007 B + 15.15 2010 B + 5.69 1993 C + 5.76 1995 C + 6.39 1999

Dear R People: I'm using R2HTML but having a strange result. Here is the original data: resp trt block 90.3 A I 89.2 A II 98.2 A III 93.9 A IV 87.4 A V 97.9 A VI 92.5 B I 89.5 B II 90.6 B III 94.7 B IV 87.0 B V 95.8 B VI 85.5 C I 90.8 C II 89.6 C III 86.2 C IV 88.0 C V 93.4 C VI 82.5 D I 89.5 D II 85.6 D III 87.4 D IV 78.9 D V 90.7 D VI And here are the commands: > resin1.df <-

Dear all, I was trying to understand how "multcomp" package works by running the examples given in the documentation. However I still don't understand when it comes to multiple comparison set by user (please refer to "Ksub" in the code). Therefore I run 2 other cases along with the original example (case 1), with the expectation I'll get the point from the output. The

Dear list members, I'm running some tests looking at differences between means for various levels of a factor, using Tukey's HSD method. I would like to plot the data as boxplots or dotplots, with horizontal significance lines indicating which groups are statistically significantly different, according to Tukey HSD. Here's a nice image showing an example of such a graphical

Hello, R experts, I have a list called dataHP which has 30 elements (m1, m2, ..., m30). Each element is a 7x6 matrix holding yield data from two factors experimental design, with treatment in column, position in row. For instance, the element 20 is: dataHP[[20]] col1 col2 col3 trt1 trt2 trt3 [1,] 22.0 20.3 29.7 63.3 78.5 76.4 [2,]

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My question concerns the logic behind hypothesis tests for fixed-effect terms in models fitted with lme. Suppose the levels of Subj indicate a grouping structure (k subjects) and Trt is a two-level factor (two treatments) for which there are several (n) responses y from each treatment and subject combination. If one suspects a subject by treatment interaction, either of the following models seem

Dear R gurus, I have the following model: appcov.aov <- aov(yield ~ prevyield + trt + block) where prevyield is a continuous numeric covariate and trt and block are factors (yes, I did factor()!) Now, when I do a TukeyHSD, my diff's are all screwed up! For instance: treatment mean for treatmen "E" is 277.25 and for treatment "O" is 279.5, so I figure the diff O-E

I'm running an ANOVA on some data for respiration in a forest. I am having a problem with my degrees of freedom. For one of my variables I get one fewer degrees of freedom than I should. I have 12 plots and I therefore expected 11 degrees of freedom, but instead I got 10. Any ideas? I have some code and output below: > class(Combined.Plot) [1] "character" >

I get the following error trying to connect to a MySQL database: > library(RMySQL) Loading required package: DBI > drv<-dbDriver("MySQL") > con<-dbConnect(drv, user="hothand", password=xxx, host="localhost", dbname="hh03"); Error in mysqlNewConnection(drv, ...) : RS-DBI driver: (could not connect hothand at localhost on dbname

Hello! I have read somewhere (somehow, I can't seem to find it again, it's been a couple of months) that when analyzing factorial block design, the position where you put the block factor is important, even more when there are missing values. I understand that when using anova.lm, the order is sequential, so that if I want to check for a treatment effect, I should put my blocking factor

Dear r-helpers, I can't get lsa to run because: > library(lsa) Loading required package: Rstem Error in library(pkg, character.only = TRUE, logical = TRUE, lib.loc = lib.loc) : 'Rstem' is not a valid package -- installed < 2.0.0? In addition: Warning message: cannot create HTML package index in: make.packages.html() > install.packages('Rstem') Warning in

I am transitioning from SAS to R and am struggling with a relatively simple analysis. Have tried Venables and Ripley and other guides but can't find a solution. I have an experiment with 12 tanks. Each tank holds 10 fish. The 12 tanks have randomly assigned one of 4 food treatments - S(tarve), L(ow), M(edium) and H(igh). There are 3 reps of each treatment. I collect data on size of each

Is it possible for S4 to (continue) dispatch to a class created during dispatching? The code below doesn't work; is this not possible or have I ommitted something? Concept was to create a SEXP with R_AllocatePtr, give it a class attribute, and continue dispatch. Example code below omits multiple parameters that can be different types. Essentially, any parameter would be converted to an