similar to: How to "bin"/average" time points?

Hi I have a panel dataset with large number of groups and differing number of observations for each group. I want to randomly select say, 20% of the groups or 200 groups, but along with all observations from the selcted groups (with the corresponding data). I guess it is possible to generate a random sample from the groups ids and then match that with the entire dataset to have the intended

This is my first post to R-help and I look forward receiving some advice for a novice like me... I?ve got a simple repeated (4 periods so far) 10-question survey data that is very easy to work on Excel. However, I?d like to move the compilation to R but I?m having some trouble operating on count list data in a neat way. The data C > str(C) 'data.frame': 551 obs. of 13

This command cdmoutcome<- glm(log(value)~factor(year) > +log(gdppcpppconst)+log(gdppcpppconstAII) > +log(co2eemisspc)+log(co2eemisspcAII) > +log(dist) > +fdiboth > +odapartnertohost > +corrupt > +log(infraindex) > +litrate > +africa >

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

Dear all -- I want to compute weighted.mean() for grouped rows. Data frame extract is just below. For each Key, I want the mean of IAC weighted by Wt. DP0[1:20,] Key IAC Wt 2 C3-PD030020050.PD030020050.3.12.3.0 0.765 0.8590000 3 C3-PD030020050.PD030020050.3.12.3.0 0.764 0.8449651 4 C3-PD030020050.PD030020050.3.12.3.0

Hi all, I am trying to run a glm with mixed effects. My response variable is number of seedlings emerging; my fixed effects are the tree species and distance from the tree (in two classes - near and far).; my random effect is the individual tree itself (here called Plot). The command I've used is: mod <- glmer(number ~ Species + distance + offset(area) + (1|Plot), family = poisson)

Greetings, I'd be grateful if a good Samaritan helps me to approach this problem.... with my data, I've created the following model lm(formula = OUTCOME ~ VAR1 + VAR2) summary(model) Call: lm(formula = OUTCOME ~ VAR1 + VAR2) Residuals: Min 1Q Median 3Q Max -1.4341 -0.3621 0.1879 0.4994 0.7696 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.89020

Hi R-users,   I would like to search for the values of seq that match my rand values.  In excel I will use =VLOOKUP(G2,$E$2:$F$32,2).  For example, for rand=.262 it will give me approximately seq=120 and rand=0.964293344, seq=460 and etc.   E           F     G cdf         seq   rand 0.00E+00    0     0.262123478 1.56E-03    20    0.964293344 1.55E-02    40    0.494827113 5.30E-02    60   

Hi, When I run the survfit function, I want to get the restricted mean value and the standard error also. I found out using the "print" function to do so, as shown below, print(km.fit,print.rmean=TRUE) Call: survfit(formula = Surv(diff, status) ~ 1, type = "kaplan-meier") records n.max n.start events *rmean *se(rmean) median 200.000

Dear UseRs,Extremely sorry for a basic question. I have a matrix of 19 rows and 365 columns. what i want to do is the following...First i want to leave out column number 1 and want to calculate the row wise mean of the remaining columns, which will obviously give me 365 values in one column, and then subtracting these values from the column i left out i.e. col=1 then i want to leave out column 2

Hello I am using R 2.2.0 with Windows XP. I've got a five element list object, each element containing two dataframes of equivalent size. > str(mylist) List of 1 $ data1:List of 2 ..$ data1a :`data.frame': 77 obs. of 63 variables: .. ..$ var1 : num [1:77] 0.41375 0.00056 1.43040 1.43528 0.61730 ... .. ..$ var2 : num [1:77] 1.154 1.686 0.673 0.800 0.760 ... ..

I am installing R 2.15.1 onto RHEL 6, using gcc 4.7.0 with Intel MKL 10.3.7 and the following environment: export BLAS_LIBS="-Wl,--start-group /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_gf_lp64.a /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_sequential.a /usr/caen/intel-12.1/mkl/lib/intel64/libmkl_core.a -Wl,--end-group -lpthread" export LAPACK_LIBS="-Wl,--start-group

Hi, a while ago I posted a question regarding the use of alternative models, including a quasibinomial mixed-effects model (see Results 1). I rerun the exact same model yesterday using R 2.7.2 and lme4_0.999375-26 (see Results 2) and today using R 2.7.2 and lme4_0.999375-27 (see Results 3). While the coefficient estimates are basically the same in all three regressions, the estimated standard

Hi everyone, Running the vif() function from the car package like ---------------------------------------------------- > reg2 <- lm(CARsPur~Delay_max10+LawChange+MarketTrend_20d+MultiTrade, data=data.frame(VarVecPur)) > vif(reg2) Delay_max10 LawChange MarketTrend_20d MultiTrade 1.010572 1.009874 1.004278 1.003351

Hello: In the example below (or for a censored data) using survfit.coxph, can anyone point me to a link or a pdf as to how the probabilities appearing in bold under "summary(pred$surv)" are calculated? Do these represent acumulative probability distribution in time (not including censored time)? Thanks very much, parmee *fit <- coxph(Surv(futime, fustat) ~ age, data = ovarian)*

Hi R users, I need some help in the followings: I'm doing factor analysis and I need to extract the loading values and the Proportion Var and Cumulative Var values one by one. Here is what I am doing: > fact <- factanal(na.omit(gnome_freq_r2),factors=5); > fact$loadings Loadings: Factor1 Factor2 Factor3 Factor4 Factor5 b1freqr2 0.246 0.486 0.145

Hi, I would consider the calculation of r-squared in the following to be a bug, but then, I've been wrong before. It seems that R looks to see if the model contains an intercept term, and if it does not, computes r-squared in a way I don't understand. To my mind, the following are two alternative parametrizations of the same model, and should yield the same r-squared. Any insight much

Hi, I am using netem to add loss and then adding another qdisc within netem according to the wiki. Then i want to change the netem drop probability without having to delete the qdisc and recreate it. I try it but I get invalid argument: thorium-ini hedpe # tc qdisc add dev ath0 root handle 1:0 netem drop 1% thorium-ini hedpe # tc qdisc add dev ath0 parent 1:1 handle 10: xcp capacity 54Mbit

Good Morning. Below I like statement like j<-grep(".r\\b",colnames(mydata),value=TRUE); j with the \\b option which I read long time ago which Ive found useful. Are there more or these options, other than ? grep? Thanks. dstat is just my own descriptive routine. > x ?[1] "age"????????? "sleep"??????? "primary"????? "middle" ?[5]

Hello, my test.R file contains two huge arrays (>3000 entries), from which R needs to calculate the Pearson Correlation, if I look at the file the numbers look correct. if I run R R < test.R --no-save I see things like this: 0.723, 0.838, 1.002, 0.364, 0.357, 0.227, 0.982+ , 0.963, 0.535, 1.214, 1.270, 0.832, 1.033, 0.632, 2.482, 1.239, 0.743, 1.077, 0.962, 1.052, 1.075, 1.427, 1.395,