similar to: Repost: Estimation when interaction is present: How do I get get the parameters from nlme?

G'day, My problem is I'm not sure how to extract effect sizes from a nonlinear regression model with a significant interaction term. My data sets are multiple measurements of force response to an agonist with two superimposed treatments each having two levels. This is very similar to the Ludbrook example in Venables and Ripley. The experiment is that a muscle is exposed to an agonist

Dear All I would like to produce interaction boxplots and this seems to work: par(mfrow=c(2,2)) A=sample(rnorm(50,50,10)) B=sample(rnorm(50,100,10)) Test=merge(A,B,by=0)#by=0 where 0 is the row.names TreatA=(gl(2,50,100,labels=c("High","Low"))) TreatB=rep(gl(2,25,50,labels=c("High","Low")),2) Newdata=data.frame(TreatA,TreatB,Test)

Dear list! > gregexpr("a+(b+)", "abcdaabbc") [[1]] [1] 1 5 attr(,"match.length") [1] 2 4 What I want is the offsets of the matches for the group (b+), i.e. 2 and 7, not the offsets of the complete matches. Is there a way in R to get that? I know about gsubgn and strapply, but they only give me the strings matched by groups not their offsets. I could write

I have grades data. I read them from a csv in letter-grade format. I then converted them to levels levels(grades$grade)=c('A+','A','A-','B+','B','B-','C+','C','C-','D+','D','D-') And then to numbers grades$gp=grades$grade levels(grades$gp)=c(4.3,4.0,3.7, 3.3,3.0,2.7, 2.3,2.0,1.7, 1.3,1.0,0.7)

Dear R Community, I am using the package DRC ( to fit a boltzman model to my data. I can fit the model and extract the lower limit, upper limit, and ED50 (aka V50), but I cannot figure out how to get the slope of the curve at ED50. Is there a simple way to do this? I've searched the mailing list and looked through the package documentation, but could not find anything. I am new to r, and

I am using semiparametric Model  library(mgcv) sm1=gam(y~x1+s(x2),family=binomial, f) How should I  find out standard error for ed50 for the above model ED50 =( -sm1$coef[1]-f(x2)) / sm1$coef [2]   f(x2) is estimated value for non parametric term.   Thanks [[alternative HTML version deleted]]

Hi, I am trying to use drc package to calculate IC50 value. The ED50 calculated in some models (LL4 for example) as a response half-way between the upper and lower limit, which is the definition of the relative IC50 value. Does that mean the ED50 in drc package is IC50? How the ED function in drc package distinguish to estimate ED or IC values? Thanks a lot [[alternative HTML version

Hi R-Users, I have plotted a region whose polygon coordinates are given in shp format ED50 UTM (zone=30) ) using "readShapePoly" in library(maptools). Now I need to plot a set of points in that region (my.dataframe, with X and Y geographic coordinates), which have been read using GPS in Longitud-Latitud form (using WGS84 system), so I first need to convert these Longitud-Latitud data

I am working with Probit regression (I cannot switch to logit) can anybody help me in finding out how to obtain with R Finney's fiducial confidence intervals for the levels of the predictor (Dose) needed to produce a proportion of 50% of responses(LD50, ED50 etc.)? If the Pearson chi-square goodness-of-fit test is significant (by default), a heterogeneity factor should be used to calculate

Dear R People: I have the following in a file: resp factA factB 39.5 low B- 38.6 high B- 27.2 low B+ 24.6 high B+ 43.1 low B- 39.5 high B- 23.2 low B+ 24.2 high B+ 45.2 low B- 33.0 high B- 24.8 low B+ 22.2 high B+ and I construct the data frame: > collard.df <- read.table("collard.txt",header=TRUE) > collard.aov <- aov(resp~factA*factB,data=collard.df) >

All, I have just returned to R after a decade of absence, and it is good to see that R has become such a great success! I'm trying to bring Design of Experiments into some aspects of software performance evaluation, and to teach myself that, I picked up "Experiments: Planning, Analysis and Optimization" by Wu and Hamada. I try to reproduce an analysis in the book using lm, but

Hello, We use drc to fit dose-response curves, recently we discovered that there are quite different standard error values returned for the same dataset depending on the drc-version / R-version that was used (not clear which factor is important) On R 2.9.0 using drc_1.6-3 we get an IC50 of 1.27447 and a standard error on the IC50 of 0.43540 Whereas on R 2.7.0 using drc_1.4-2 the IC50 is

Hi, I'm making a multipanel lattice densityplot figure with 2 factors (3 and 20 classes in each factor) with the following statement (the type="percent" is there to prevent plotting the actual points which detract from the figure - is there another way of doing this?): densityplot(~End-Begin | Type * Chromosome, data=Mon, layout=c(5,12), xlab="Element

Hello, I am trying to fit my Elisa results (absorbance readings) to a standard curve. To create the standard curve model, I performed a 4-parameter logistic fit using the 'drc' package (ExpectedConc~Absorbance). This gave me the following: > FourP A 'drc' model. Call: drm(formula = Response ~ Expected, data = SC, fct = LL.4()) Coefficients: b:(Intercept) c:(Intercept)

Hi! I want to use the DRC package in order to calculate the IC50 value of an enzyme inhibition assay. The problem is that the estimated ED50, is always out of the fitted curve. In the example below, I had a ED50 value of 2.2896, But when I predict the response level for this concentration I get a value of 45.71 instead of the expected value of 50. This is my data: #Dose unit is concentration

According to the documentation for as.vector, it removes all attributes from its argument ("the attributes of x are removed"). This does not seem to be the case for a list with a dim attribute. Consider the following code: numarray <- vector("numeric",4) dim(numarray) <- c(2,2) dimnames(numarray) <- list(c("A","B"),c("C","D"))

Hi, can anyone tell me how to nest two fixed factors using glmer in lme4? I have a split-plot design with two fixed factors - A (whole plot factor) and B (subplot factor), both with two levels. I want to do GLMM as I also want to include different plots as a random factor. But I am interested on the effect of A a B and their interaction on the response variable. I tried

Classification: UNCLASSIFIED Caveats: NONE A similar question has been posted in the past but never answered. My question is this: for probit analysis, how do you program a 95% confidence interval for the LD50 (or LC50, ec50, etc.), including a heterogeneity factor as written about in "Probit Analysis" by Finney(1971)? The heterogeneity factor comes into play through the chi-squared

thanks a lot Renaud. but i was interested in Finney's fiducial confidence intervals of LD50 so to obtain comparable results with SPSS. But your reply leads me to the next question: does anybody know what is the best method (asymptotic, bootstrap etc.) for calculating confidence intervals of LD50? i could "get rid" of Finney's fiducial confidence intervals but

Please be patient with my ignorance if what I am asking is meaningless in any way. I am not too technically knowledgeable about filesystem internals but I am willing to learn. (I thought of posting to linux-ext4 but did not want to intrude within the technical threads with my layman thread.) From Wikipedia > ReiserFS article > Design section: [quote]ext2 and other Berkeley FFS-like