similar to: How to update an old unsupported package

I have a class with 732 members, so using rpart.plot is giving me a tiny plot in the middle of the window. Is there a good way to modify the plot, or replace the long list with something like "group1"? -- View this message in context: http://r.789695.n4.nabble.com/Plotting-rpart-trees-with-long-list-of-class-members-tp4635671.html Sent from the R help mailing list archive at

Hi, I am using ccf but I could not figure out how to calculate the actual lag in number of periods from the returned results. The documentation for ccf says:"The lag is returned and plotted in units of time". What does "units of time" mean? For example: > x=ldeaths > x1=lag(ldeaths,1) > results=ccf(x,x1) > results Autocorrelations of series 'X', by lag

Hi Ronnie, if your printer do work, this message is useless for you, trash it. I cannot confirm Tim's hypothesis, but i can say i presently have the same problem as you: in the printers folder there's that red message alerting me. My PC is running Win2K pro. I did not mind it, right-clicked on the printer's icon (my specific case is: lpq@router.x.y) and chose 'connect...'

Hi list, I have the following code to compute the acf of a time series acfresid <- acf(residfit), where residfit is the series when I type acfresid at the prompt the follwoing is displayed Autocorrelations of series ?residfit?, by lag 0.0000 0.0833 0.1667 0.2500 0.3333 0.4167 0.5000 0.5833 0.6667 0.7500 0.8333 1.000 -0.015 0.010 0.099 0.048 -0.014 -0.039 -0.019 0.040 0.018

Currently, for scalable vectors, only splat shuffles are allowed; we're considering allowing more different kinds of shuffles. The issue is, essentially, that a shuffle mask is a simple list of integers, and that isn't enough to express a scalable operation. For example, concatenating two fixed-length vectors currently looks like this: shufflevector <2 x i32> %v1, <2 x i32>

Hello, My name is Rodrigo, I am using R program and I have a trouble. I am trying to do a dendrogram with genetics information. Let me explain... The Similarity Matrix was already did, and with this matrix I want to construct a dendrogram. So, the distance is done. I need to transform this matrix (that I have) in a dendrogram, I woud be very grateful if someone could help me. PS: I am sending

I have the joint distribution of three discrete random variables z1, z2 and z3 which is captured by "z" and "prob" as described below. For example, the probability for z1=0.46667, z2=-1 and z3=-1 is 2.752e-13. Also, the probability adds up to 1. > head(z) z1 z2 z3 [1,] -0.46667 -1.0000 -1.0000 [2,] -0.33333 -0.9333 -0.9333 [3,] -0.20000 -0.8667 -0.8667

Hi all, I noticed something strange when I ran aov and anova. vtot=c(7.29917, 7.29917, 7.29917) #identical values fac=as.factor(c(1,1,2)) #group 1 has first two elements, group 2 has the 3rd element When I run: > anova(lm(vtot~fac)) Analysis of Variance Table Response: vtot Df Sum Sq Mean Sq F value Pr(>F) fac 1 1.6818e-30 1.6818e-30 0.3333 0.6667 Residuals 1

Hi All, Just hoping some one can give me a hand with a problem... I have a dataframe (DF) with about 5 million entries that looks something like the following: >DF ID Cl Co Brd Ind A AB AB 1 S-3 IND A BR_F BR_F01 1 0 0 2 S-3 IND A BR_F BR_F01 1 0 0 3 S-3 IND A BR_F BR_F01 1 0 0 4 S-3 IND A BR_F BR_F01 1 0 0 5 S-3 IND A BR_F BR_F01 1 0 0 6 S-3 IND A BR_F

Hi All, I've been searching the help archives but haven't found a workable solution to this problem. I'm running an lme model with the following call: >lme.fnl <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID) > anova(lme.fnl) numDF denDF F-value p-value (Intercept) 1 168 19255.389 <.0001 S 1 168 5.912 0.0161 Tr

I have a data frame with one column "x": > str(data) `data.frame': 20 obs. of 1 variable: $ x: num 0.0495 0.0986 0.9662 0.7501 0.8621 ... Normally, I know that the notation dataframe[indexes,] gives you a new data frame which is the specified set of rows. But I find: > str(data[1:10,]) num [1:10] 0.0495 0.0986 0.9662 0.7501 0.8621 ... Here, it looks like the operation

Most likely, previous computations have ended up giving slightly different values of say 0.13333. A pragmatic way out is to round to, say, 5 digits before applying unique. In this particular case, it seems like all numbers are multiples of 1/30, so another idea could be to multiply by 30, round, and divide by 30. -pd > On 28 Jul 2017, at 17:17 , li li <hannah.hlx at gmail.com> wrote:

I have data as below.Please let me know how the ACF and Pacf used to determine the order od arima model. Is there any rules need to be followed to determine order.Please advise > turkey.price.ts Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2001 1.58 1.75 1.63 1.45 1.56 2.07 1.81 1.74 1.54 1.45 0.57 1.15 2002 1.50 1.66 1.34 1.67 1.81 1.60 1.70 1.87 1.47 1.59 0.74 0.82

I've used Anova() from the car package to get marginal (aka type II) sum-of-squares and tests for linear models with categorical variables. Is it possible to get marginal SSs also for continuous variables, when the model includes powers of the continuous variables? For instance, if A and B are categorical ("factor"s) and x is continuous ("numeric"), Anova (lm (y ~ A*B +

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Hi folks, My wife and I are taking a long weekend to celibrate our anniversary. I won't be around again until Wednesday to answer email or do anything else on Vorbis. Yeah, I know, I usually answer my email once a month anyway (so likely no one would notice me being gone), but just so folks know if anything really juicy comes up :-) I'll be merging my latest branch with mainline

>2) Under MacOS 10.2.2. Given the following dataset: > > > summary(missing.data) > Hosp..No. Category Offset Side > r060093: 4 F Post op 0.5 year :62 Min. :-5.8333 L: 63 > r023250: 3 E Pre op 0.5 year :54 1st Qu.:-0.4167 R:141 > r026316: 3 H Post op 1.5 years :44 Median : 7.7083 > r032583: 3 G Post op 1 year

Hello, I am trying to use the projection.matrix( ) function and am following the example given. I have my data formatted very similar to the test.census example. > str(AsMi05mat) `data.frame': 1854 obs. of 6 variables: $ Tag : num 501 502 503 504 505 506 507 508 509 510 ... $ Year : int 1995 1995 1995 1995 1995 1995 1995 1995 1995 1995 ... $ Length : num 34

Trying to use contrast to look at differences within an lme lme.fnl.REML <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID, method = "REML") I have three levels of Tr I'm trying to contrast among different years (R, T97, T98), years = 1997-1999, so I'm interested in contrasts of the interaction term. > anova(lme.fnl.REML) numDF denDF F-value

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