similar to: table function

Hello all, I want to analyze data that looks like this: Id var1 var2 var3.. 1 0 1 0 1 0 1 1 2 2 2 2 Not all id's have the same no. of observations. At the first stage I want to count how many people in the survey, how many have 1 in var1, etc. How do I do that? Thank you, Sigalit. [[alternative HTML version deleted]]

Hello, I want to create a new variable which includes 4 age categories in this way: if (age>=12 && age<32) age1==1 if (age>=32 && age<52) age1==2 if (age>=52 && age<72) age1==3 if (age>=72 && age<100) age1==4 but I get the results only for the first observation. how can I apply this condition to all observations? Thanks in advance,

Hello, How do I sample observations with replacement from a normal distribution with a specific mean and s.d? (I want to see the sample, not only the statistic.) Thank you, Sigalit. [[alternative HTML version deleted]]

hello, i want to compute the mean of a variable ("aps") for every class (1,2, and 3). every id have a few obs., "aps" and class are constant over id. like this: id aps class 1 11 2 1 11 2 1 11 2 1 11 2 1 11 2 2 8 3 2 8 3 2 8 3 3 12 2 3 12 2 . . i tried:

hello, I have a simple question: I want to compute the average of 6 variables, my data looks like this: t0 t1 t2 t3 t4 t5 1 32 34 36 40 38 40 2 35 37 38 36 33 39 . . . I want the mean of t0-t5 for each row (observation). thank you, sigalit. [[alternative HTML version deleted]]

Hello, I have a simple (?) simulation problem. I'm doing a simulation with logistic model and I want to reapet it 600 times. The simulation looks like this: z <- 0 x <- 0 y <- 0 aps <- 0 tiss <- 0 for (i in 1:500){ z[i] <- rbinom(1, 1, .6) x[i] <- rbinom(1, 1, .95) y[i] <- z[i]*x[i] if (y[i]==1) aps[i] <- rnorm(1,mean=13.4, sd=7.09) else aps[i] <-

Hello, I have a loop with probability computed from a logistic model like this: for (i in 1:300){ p[i]<-exp(-0.834+0.002*x[i]+0.023*z[i])/(1+exp(-0.834+0.002*x[i]+0.023 +z[i])) x and z generated from normal distribution. I get 300 different probabilities And I want to generate variables from bernulli distribution with P for every observation: T[i]<-rbinom(1,1,p[i]) But i get missing

Hello, I have a question about glm: if i have a binary covariate (unit=1,0) the reference group would be 0? (prediction for unit=1) example: dat1<-data.frame(y,unit,x1,x2) log_u <- glm(y~.,family=binomial,data=dat1) summary(log_u) Estimate Std. Error z value Pr(>|z|) (Intercept) -0.54247 0.24658 -2.200 0.0278 * unit1 -0.13052 0.22861 -0.571 0.5680 aps

hello, I asked about this simulation a few days ago, but still i can't get what i need. I have 2 units: icu and regular. from icu I want to take 200 observations from binomial distribution, when probability for disease is: p=0.6. from regular I want to take 300 observation with the same probability: p=0.6 . the distribution to detect disease when disease occurred- *for someone from icu* - is:

hello, I have a longitudinal data: idn mort30 newinfec 1 0 1 1 0 1 1 0 1 1 0 1 2 1 1 2 1 1 2 1 1 3 0 0 3 0 0 3 0 0 3 0 0 3

hello, i want to do a binomial simulation, by taking 200 var. from one group (x) and 300 from another (y). the prob. for disease=.6 in both groups. x <- rbinom(200, 1, .6) y <- rbinom(300, 1, .6) if the person is from group x - the probability to find the disease, assuming the person is sick, is .95, if he is from group Y its .80. i want to know the joint probability: p(the person has the

hello, I want to do a single Imputation method on binary data set. Is it possible to use imp.cat from CAT package? I have a problem defining "theta" when data is binary. Do you know any references on the subject? Thank you, Sigalit. [[alternative HTML version deleted]]

hello, I have a problem in how to generate data in a simulation study. I have a logistic model to evaluate p by 3 covariates. I need to generate 4 variables: the binary outcome Y and 3 covariates: gender (binary) and aps and tiss (continuous variables). I have the logistic model which is the expected model: log(p(y=1)/(1-p(y=1))=-1.659-0.05*sex+0.063*aps+0.04*tiss0) I generate the outcome y like

Hello, I have a question about "qplot": How can I add another line to the same plot ? (like function "lines" in "plot"). Thank You, Sigalit. [[alternative HTML version deleted]]

Hi, I'm trying to do a double for loop like this: for (k in 1:1000){ for (i in 1:200){ y[i]<-rbinom(1,1,0.8) x1[i]<-ifelse(y[i]==1,rnorm(1,mean=20, sd=2),rnorm(1,mean=16, sd=2.2)) .... } for (j in 1:300){ .... } } Does anyone know a good reference about double loops? Thank you, Sigalit [[alternative HTML version deleted]]

hello, I want to simulate 200 times the mean of a joint probability (y1) and 200 times the mean of another joint distribution (y2), that is I'm expecting to get 200 means of y1 and 200 means of y2. y1 and y2 are probabilities that I calculate from the marginal prob. (z1 and z2 respectively) multiple by the conditional prob. (x1 and x2 respectively), which I generaterd from the binomial

Hi all, i'm trying to run a two-sample t-test. i have a longitudinal data that looks like this: idn age class 1 22 1 1 22 1 1 22 1 1 22 1 2 63 3 2 63 3 2 63 3 3 43 2 3 43 2 3 43 2 3 43 2 3 43 2 3 43 2 4 37 1 4 37

Hello, This is the first time i'm trying to plot in R. I want to plot estimates of OR and their confidence limits, like a scatter plot: the vertical axis should be the estimated OR (with upper and lower conf. limits), and the horizontal exis should be fixed values: (1,0.8,0.7,0.6,0.5,0.4) Here is a part of my code: ...ests=matrix(ncol=3,nrow=6)

Hello, I have a loop with 1000 repetitions which includes OR computation of an exposure factor and outcome. I compute OR like this: t<-table(exposure,outcome) oddsratio(t)$measure["estimate"] This gives me the estimates for exposure=0 and exposure=1 but exposure=0 is the reference group and i need only the estimate for exposure=1. I specified a matrix OR with 3 columns (for OR

Hi, I make an analysis and depending of the order of the variables, the significance change, look. m1 <- glm((infec/ntot)~idade+sexo+peso,family=binomial,weights=ntot) > anova(m1,test="F") Analysis of Deviance Table Model: binomial, link: logit Response: (infec/ntot) Terms added sequentially (first to last) Df Deviance Resid. Df Resid. Dev F Pr(>F)