similar to: Conditional model in R

Hi, I'm trying to work through the examples and code in Loader's LOCAL REGRESSION AND LIKELIHOOD, and have run into a problem with the code for one sided smoothing and change point analysis (p. 110-112). The code, after loading locfit: midp<-(1945:1988)+0.5 fitl<-locfit(thickness~left(year), data=penny, alpha=c(0,10), deg=1, ev=midp)

hey fletcher, thanks for your input here. :+) and -- quite obviously -- your program will be whatever it is that _you_ think that it should be. of course. the thing is, i am certain that i have been clear that the feature that i believe will be "killer" is on-the-fly formatted display. that's my stand. and i'd say my reasoning has been equally clear, namely that this

I have created a list of tables with the same columns but different number of row. Example (actual list has ~200 elements): > temp1<- data.frame(ID=c("Herb","Shrub"),stat=c(4,5),pvalue=c(.03,.04)) > temp2<- data.frame(ID=c("Herb","Shrub", > "Tree"),stat=c(12,15,13),pvalue=c(.2,0.4,.3)) > L<-list(a=temp1,b=temp2) > L $a

I have a function (see below). This function has one object, ID. If I run the loops by themselves using a character value (ie,"VFFF1-7") instead of the function object, then the loops work fine. However, when I try to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop

Dear R experts, I want to plot a line chart with another secondary axis placed right to the standard secondary axis which one can access with the axis command, so that the data lines are seen in the same plot. Is there any way to do this in R? Many thanks, Kirsten.

I have a function (see below). This function has one object, ID. If I run the loops by itself using a character value (ie,"VFFF1-7"), then the loops work fine. However, when I try to insert the character value via the function call, it doesn't work. I don't get an error, but the TotalCover.df dataframe does not update according to the loop criteria. Any obvious problems that

Hi, I am having problems trying to assess the significance of random terms in a generalized linear mixed model using lme4 package. The model describes bird species richness R along roads (offset by log length of road log_length) as a function of fixed effects Shrub (%shrub cover) and Width (width of road), and random effect Site (nested within Site Cluster). >From reading answers to previous

Hi, we have a split-plot experiment in which we measured the yield of crop fields. The factors we studied were: B : 3 blocks I : 2 main plots for presence of Irrigation V : 2 plots for Varieties N : 3 levels of Nitrogen Each block contains two plots (irrigated or not) . Each plot is divided into two secondary parcels for the two varieties. Each of these parcels is divided into three subplots

I am basically trying to append a value(vector) to one dataframe using a relational value from another dataframe. Obviously, I can use a loop to accomplish this. However, is there a way to vectorize it? Example: > data <- data.frame(c(1,1,1,2,2,2,3,3,3),rep(2,9)); names(data) <- > c("Sample","Score") > meta <-

Hello everyone, I have a .csv file with the following format: uniqueID SubjectID Distance_miles Tag 1 1001 5.5 3 2 1001 7 1 3 1001 6.5 1 4 1001 5 1 5 1002

R 2.9.1 Windows XP I am fitting a random effects ANOVA with two factors Group which has two levels and Time which has three levels: fita<-lme(Post~Time+factor(Group)+factor(Group)*Time, random=~1|SS,data=blah$alldata) I want to get the omnibus significance tests for each factor and the interaction. I believe I can get the omnibus test for the interaction by running the model:

Hello, I have tried to find this out some other way, but unsuccessful I have to try this list. I assume this should be quite simple. I have a dataset with 4 columns, "Sample_no", "Species", "Nitrogen", "Carbon" in csv format. In the species column I have many different species with varying number of obs per species Eg "Sample_no"

I have a list of ICD9 (disease) codes with various formats - 3 digit, 4 digit, 5 digit. The first three digits of these codes are what I am most interested in. I would like to either add zeros to the 3 and 4 digit codes to make them 5 digit codes or add decimal points to put them all in the format ###.##. I did not see a function that allows me to do this in the formatting command. This seems

Hello, I have a problem with using replace() to convert a vector of dates from yyyy-mm-dd to julian date. For example, I type replace(x,2004-05-14,134) and I receive an error: Error in `[<-.data.frame`(`*tmp*`, list, value = 134) : new columns would leave holes after existing columns If I can successfully convert, I have a script that will convert all of the dates in

Dear All I have a problem with the height of the boxes in the key in the following. (The text is over 2 lines to accentuate the problem of no space between the rectangles.) Is there an easy way to put a space between the rectangles; size controls the width but there appears to be nothing for the height? xyplot(1~1, key = list(corner = c(0.8,0.8),

I have a researcher who is consistently get the warning message: In max(i) : no non-missing arguments to max; returning -Inf Best as I can tell the code is working properly and the output is as expected. I would like some help in understanding why he is getting this error message and what its implications are. I have his code. Sincerely, Kirsten Miles Support Specialist Research Computing Lab

What test do I use to determine if there is a correlation between a discrete variable and a continuous variable? For example - I have water quality ratings for streams (excellent, good, fair, poor) and a corresponding nitrogen concentration for each rating. I want to know if the the ratings correlate with the concentration of nitrogen in the stream. Help?

Hi everyone, I'm having some trouble with my ifelse statements. I'm trying to put 12 conditions within 3 groups. Here is the code I have so far: dat$cond <- ifelse(test = dat$cond == "cond1" | dat$cond == "cond2" | dat$cond == "cond3" dat$cond == "cond4" yes = "Uniform" no = ifelse(test =

Hi Jim, Thanks for your help, I really appreciate it. Perhaps I'm misunderstanding, but does this formula run different ajustment values for this function? logit(p = doc$value, adjust = 0.025) I'm looking to plot the p-values of different adjustment values. Thanks so much, Kirsten On Wed, Jul 12, 2017 at 8:49 PM, Jim Lemon <drjimlemon at gmail.com> wrote: > Hi Kirsten,

Dear R users, Could somebody tell me why the grey color ramp is repeated in this graph, eventhough the ramp values go from 0 to 1? I must be missing something obvious, but I can not see it! z <- c(0.064329041,0.117243316,0.161565116,0.19923015,0.231642175,0.259835539,0.284571226, 0.038507288,0.094184749,0.140959431,0.180803984,0.215159105,0.245096084,0.271412845,