similar to: Is my data set too large

I have a data set like this: if I want to less than 200000 obs from this data set. How can I do these? > str(p1982) 'data.frame': 465979 obs. of 6 variables: $ p : Factor w/ 1982 levels "154l_aa","1A0P_aa",..: 1 1 1 1 1 1 1 1 1 1 ... $ aa : Factor w/ 19 levels "ALA","ARG","ASN",..: 2 16 4 5 18 3 19 3 2 9 ... $ as : num 152.0

I have a data set p1982, its structure is the following Then I take 20 observations from this dataset, and assign to pr. in p1982, p has 1982 levels, in dataset pr, p should have 1 levels. But I do str(pr), it shows that p still has 1982 levels. also for these > pr$aa [1] ARG THR ASP CYS TYR ASN VAL ASN ARG ILE ASP THR THR ALA SER CYS LYS THR ALA LYS Levels: ALA ARG ASN ASP CYS GLN

I have a data set like this I want to assign "outward" to Y if sc <90 and assign "inward" to Y if sc>=90. then cbind(p1982,Y) to get like these p aa as ms cur sc Y 1 154l_aa ARG 152.04 108.83 -0.1020140 92.10410 inward 2 154l_aa THR 15.86 28.32 0.2563560 103.67100 inward 3 154l_aa ASP 65.13 59.16 0.0312137 7.27311 outward 4 154l_aa CYS 57.20 49.85

Hi everyone, I determined the presence of three types parasites in a passerine bird over two years. I would like to create a bar chart that shows the proportion infected on the y and year/parasite on the x such that each type of parasite is grouped together (single label) and a bar for each year . This would show if there have been changes in the prevalence of a the parasite over two years.

Dear list, I would like to simulate individual survival times from a model that has been fitted using the survreg procedure (library survival). Output shown below. My plan is to extract the shape and scale arguments for use with rweibull() since my error terms are assumed to be Weibull, but it does not make any sense. The mean survival time is easy to predict, but I would like to simulate

I have a question in R. In directory H:/Delta_angle I have 19 files like this: ALA.delta ASN.delta ASP.delta CYS.delta GLN.delta GLU.delta HIS.delta ILE.delta LEU.delta LYS.delta MET.delta PHE.delta PRO.delta SER.delta THR.delta TRP.delta TYR.delta VAL.delta I want to read these files to 19 data sets in R. All these data sets have "P","AA",index","delta"

I have a data(ABC) like this: x y A 3 4 A 1 3 B 2 6 B 4 8 C 5 4 C 6 7 I want to split this data into A: x y A 3 4 A 1 3 B B 2 6 B 4 8 C C 5 4 C 6 7 anyone knows how to do that? thanks, Aimin Yan

I try to type this in my R-winEdt. but I got these. Do you know? Aimin > install.packages('http://rh-mirror.linux.iastate.edu/CRAN/bin/windows/contrib/2.4/plotrix_2.1-6.zip') Warning in download.packages(pkgs, destdir = tmpd, available = available, : no package 'http://rh-mirror.linux.iastate.edu/CRAN/bin/windows/contrib/2.4/plotrix_2.1-6.zip' at the

I try Sweave and get Sweave-test-1.tex but hot to run LaTeX on 'Sweave-test-1.tex'? I am using WinEdt. thanks, Aimin > Sweave(testfile) Writing to file Sweave-test-1.tex Processing code chunks ... 1 : print term verbatim 2 : term hide 3 : echo print term verbatim 4 : term verbatim 5 : echo term verbatim 6 : echo term verbatim eps pdf 7 : echo term verbatim eps pdf

I have a full data set like this: aa bas aas bms ams bcu acu omega y 1 ALA 0 127.71 0 69.99 0 -0.2498560 79.91470 outward 2 PRO 0 68.55 0 55.44 0 -0.0949008 76.60380 outward 3 ALA 0 52.72 0 47.82 0 -0.0396550 52.19970 outward 4 PHE 0 22.62 0 31.21 0 0.1270330 169.52500 inward 5 SER 0 71.32 0 52.84 0 -0.1312380 7.47528 outward 6

consider p as random effect with 5 levels, what is difference between these two models? > p5.random.p <- lmer(Y ~p+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) > p5.random.p1 <- lmer(Y ~1+(1|p),data=p5,family=binomial,control=list(usePQL=FALSE,msV=1)) in addtion, I try these two models, it seems they are same. what is the difference between these two model. Is

I have a dataset like this: p aa index x y z sdx sdy sdz delta as ms cur sc 1 821p MET 1 -5.09688 32.8830 -5.857620 1.478200 1.73998 0.825778 13.7883 126.91 92.37 -0.1320180 111.0990 2 821p THR 2 -4.07357 28.6881 -4.838430 0.597674 1.37860 1.165780 13.7207 64.09 50.72 -0.0977129 98.5319 3 821p GLU 3 -5.86733 30.4759

Hi all, I have this figure: http://docs.google.com/Doc?id=df5zfsj4_103rjt2v4d5 created with the following steps: > x [1] 90.4 57.8 77.0 103.7 55.4 217.5 68.1 85.3 152.0 113.0 97.1 89.9 [13] 68.1 83.7 77.4 34.5 104.9 170.3 88.6 88.1 108.8 77.4 85.6 82.7 [25] 81.3 108.0 49.5 71.0 85.7 99.3 203.5 275.9 51.1 84.8 16.5 72.6 [37] 160.5 158.3 136.7 140.0 98.4 116.1

I use knncat to make a predictive model and get misclass rate > knncat.m<-knncat(training.new,k=c(10,20),classcol=5) > knncat.m Training set misclass rate: 36.88% then I try to calculate prediction accuracy by the following: > pr.knncat.train <- predict (knncat.m,training.new,training.new,train.classcol=5,newdata.classcol=5) > tb.knncat.train <-table (pr.knncat.train,

I am trying to use scatterplot function in CAR like the following: scatterplot(X~Y) I want to label X points and Y ponits using the different color. Any idea for this? Aimin

How to use "contour" function? I type "contour(th1,th2,SumofSquares,levels=c(seq(1000,4000,200)))" But I didn't see contour in the plot, I guess it is because my data is so big that contour is out of range. Does anybody know how to fix this problem? here th1 is a 1x101 array(158741.8-298529.6) th2 is another 1X101 array(-0.0058352269-0.2093140935) SumofSquares is a

Hello, I use nnet to do prediction for a continuous variable. after that, I calculate correlation coefficient between predicted value and real observation. I run my code(see following) several time, but I get different correlation coefficient each time. Anyone know why? In addition, How to calculate prediction accuracy for prediction of continuous variable? Aimin thanks, > m.nn.omega

Hello, I am new to R, How to delete a matrix column. Thanks,

if I want to put fig1plot to the left, figYPplot to the right figYAaplot on the bottom. How to modify the following cod to do these? \begin{figure}[H] \centering \begin{minipage}[t]{0.5\textwidth} \centering <<label=fig1plot,fig=TRUE,echo=FALSE>>= <<fig1plot>> @ %\caption{Caption 1} \end{minipage} \begin{minipage}[t]{0.5\textwidth} \centering

I want to integrate R code to my WinEdt file. Can someone tell me how to do this? I copy R code to my WinEdt file, but it does't work. Aimin Yan