search for: var6

...er donde hay discontinuidades. No me interesa llenar los vacíos > en las variables, sólo las fechas y dejar las variables en blanco ¿es > posible hacerlo en R?. Mi serie de tiempo es del tipo (con fecha en formato > dd-mm-yyyy hh:mm): > > 01-01-2011 00:00 var1 var2 var3 var4 var5 var6 var7 var8 > 01-01-2011 00:01 var1 var2 var3 var4 var5 var6 var7 var8 > 01-01-2011 00:02 var1 var2 var3 var4 var5 var6 var7 var8 > 01-01-2011 00:03 var1 var2 var3 var4 var5 var6 var7 var8 > 01-01-2011 00:04 var1 var2 var3 var4 var5 var6 var7 var8 > 01-01-2011 00:07 var1 var2 var3 var4...

Dear all, I am trying (!!!) to generate pdfs that have 8 plots on one page: df = data.frame( day = c(1,2,3,4), var1 = c(1,2,3,4), var2 = c(100,200,300,4000), var3 = c(10,20,300,40000), var4 = c(100000,20000,30000,4000), var5 = c(10,20,30,40), var6 = c(0.001,0.002,0.003,0.004), var7 = c(123,223,123,412), var8 = c(213,123,234,435), all = as.factor(c(1,1,1,1))) pdf("test1.pdf", width=20, heigh=27, paper="a4") print(plot(groupedData(var1 ~ day | all, data = df), main = "var1", xlab="", ylab="...

...a dataframe called "data" with 5 records (in rows) each of which has been scored on each of many variables (in columns). Five of the variables are named var1, var2, var3, var4, var5 using headers. The other variables are named using other conventions. I can create a new variable called var6 with the value 15 for each record with this code: > var6=var1+var2+var3+var4+var5 but this is tedious for my real dataset with dozens of variables. I would rather use a wildcard to add up all the variables that begin with "Var" like this pseudocode: > Var6=sum(var*) Any suggesti...

Hi, I am trying to merge data similar to the example data below > dat0 id var1 var2 var3 2 1 0 1 3 1 0 1 4 0 1 1 5 0 1 1 > dat1 id var4 var5 var6 2 1 0 1 3 1 0 1 6 0 1 1 7 0 1 1 > dat2 id var7 var8 var9 2 1 0 1 5 1 0 1 6 0 1 1 8 0 1 1 Basically what I...

Hi, Suppose I have a data.frame like this Lines <- "var1 var2 var3 var4 var5 var6 0 2 1 2 0 0 2 3 7 6 0 1 1.5 4 9 9 6 0 1.0 6 10 22 3 3 " DF <- read.table(textConnection(Lines), skip=1) names(DF) <- scan(textConnection(Lines), what = "", nlines = 1) How do I f...

Dear all, I have a dataset that looks like id var1 var2 var4 var7 var8 1 0.0 0.1 0.3 0.9 0.0 2 0.4 0.6 0.0 0.0 0.2 3 0.0 0.0 0.0 0.8 0.7 Some columns are missed, for example, here the fourth (var3), sixth(var5) and seventh (var6) columns. I want to first determine which columns are missed in a huge dataset and then add the missed columns with the values of zeros that looks like id var1 var2 var3 var4 var5 var6 var7 var8 1 0.0 0.1 0.0 0.3 0.0 0.0 0.9 0.0 2 0.4 0.6 0.0 0.0 0...

Dear all, I have a dataset that looks like this: x <- read.table(textConnection("col1 col2 3 1 2 2 4 7 8 6 5 10"), header=TRUE) I want to rewrite it as below: var1 var2 var3 var4 var5 var6 var7 var8 var9 var10 1 0 1 0 0 0 0 0 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0 0 0...

Let say, I have a matrix with 8 rows and 6 columns:  >  df1  <- matrix(NA, 8, 4)  > df1       [,1] [,2] [,3] [,4]  [1,]   NA   NA   NA   NA  [2,]   NA   NA   NA   NA  [3,]   NA   NA   NA   NA  [4,]   NA   NA   NA   NA  [5,]   NA   NA   NA   NA  [6,]   NA   NA   NA   NA  [7,]   NA   NA   NA   NA  [8,]   NA   NA   NA   NA  Now I want to get **all possible** ways to fetch 6 cells at a

...ear list, after fitting an lrm with the Design package (stored as "mymodel") I try running a summary, but I get the following error: dim(mydata) [1] 235 9 names(mydata) [1] "id" "VAR1" "VAR2" "VAR3" "VAR4" "VAR5" "VAR6" "VAR7" "VAR8" summary(mymodel) Error in `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels but, VAR1 is my dependent > nlevels(factor(VAR1)) [1] 2 VAR2 is continuous > summary(psa)...

Dear R Users! I have a dataframe with duplicate cases. Var1 duplicated by var2. var1 var2 var3 var4 var5 var6 1 4 500 1 2 a 1 3 200 2 5 b 1 8 125 1 9 b 2 2 120 2 52 b...

...ange_prob[[7]]  <- c(0.44,0.07,0.49) range_prob[[8]]  <- c(0.54,0.06,0.40) range_prob[[9]]  <- c(0.26,0.62,0.12) range_prob[[10]] <- c(0.65,0.19,0.16)   pdf <- expand.grid(range_prob)   data_lab$probs <- apply(pdf, 1, prod)   joint_probs = xtabs(probs ~ Var1 + Var2+Var3+Var4+Var5+Var6+Var7+Var8+Var9+Var10, data = data_lab)   write.csv(data.frame(joint_probs), 'joint_probs.csv', row.names = FALSE) ONS = read.csv('joint_probs.csv')   Names = NULL   for (i in 1:length(joint_probs))  {        Names[i] = paste(ONS$Var1[i], ONS$Var2[i], ONS$Var3[i], ONS$Var4[i], ONS$...

...coxph; in my example below- sim.fr is the generated data). However, sometimes i get warning messages like "Ran out of iterations and did not converge" or "Error in var(x, na.rm = na.rm) : missing observations in cov/cor" because in some cases one of my covariate (say, var5 or var6 or both who are binary variables) becomes all 0's! How do I exclude the unfit data (that does not fit/converge: that produces warning messages) that may be generated in any iteration, and still continue by replacing it by the next iteration data (untill it generates acceptable data that does n...

Hello, I have many hundred variables in my longitudinal dataset and lots of missings. In order to plot data I need to remove missings. If I do > data <- na.omit(data) that will reduce my dataset to 2% of its original size ;) So I only need to listwise delete missings on 3 variables (the ones I am plotting). data$variable1 <-na.omit(data$variable1) does not work. Thank you

...ks like this: <Transaction> <ID value="0044"/> <Var1 value="XYZ159"/> <Var2 value="_"/> <Var3 value="AMR1.0-INT-1005"/> <Var4 value="2010-05-25 10:44:16:673"/> <Var5 value="1"/> <Var6 value="0"/> </Transaction> <Transaction> <ID value="0046"/> <Var1 value="XBC254"/> <Var2 value="GLOBAL"/> <Var3 value="AMR2.0-INT-9997"/> <Var4 value="2010-05-25 11:22:50:803"/> <...

...inal data with repeated measures (measurements are equally spaced in time, subjects are measured several times a year). I need to allow slope and intercept vary. SAS codes are: proc mixed data = survey method=reml; class subject var1 var3 var2 time; model score = var2 score_base var4 var5 var3 var6 var7 var1 time/ noint solution; random intercept timecontinious / subject=subject type=un g gcorr v vcorr; run; Thank you a lot! [[alternative HTML version deleted]]

Let's say I have a program that returns variables whose names may be any string within the vector NAMES=c("varA","varB","varC","varD","varE","varF"..."varZ"), but I do not ever know which ones have actually been created. So in one example output, "varA", "varC", and "varD" could exist, but

Hi, what is the recommended way to vertically concatenate 2 data frames with the same column names but different number of rows? My problem is something along these lines: df1 <- data.frame(var1=var1,var2=var2,var3=var3) # nrow(df1)=1000 df2 <- data.frame(var1=var4,var2=var5,var3=var6) # nrow(df2)=2000 I tried df <- c(df1,df2), no success. stack does not seem to be appropriate either for my problem. Thanks Zava -------------------------------------------------------- This is not an offer (or solicitation of an offer) to buy/se...{{dropped}}

...ent(NULL)' is deprecated" Why does it occur? How can I hide such warning message, especially when coxme is under a loop? Mohammad Ehsanul Karim (Institute of Statistical Research and Training, University of Dhaka) > fit.me1<-coxme(Surv(times,censored) ~ var1+var2+var3+var4+var5+var6, data=sim.fr1, random= ~1|id) Warning messages: 1: using 'as.environment(NULL)' is deprecated 2: using 'as.environment(NULL)' is deprecated 3: using 'as.environment(NULL)' is deprecated > sim.fr1 id var1 var2 var3 1 4 50.04399 239.8418 56.00348 2 2...

...I wanted to print a table where the odd-numbered rows contain the variable names and the even-numbered rows contain the correlations. So if VarA is correlated with all the variables in mydata.df, then it would look like var1 var2 var3 corr1 corr2 corr3 var4 var5 var6 corr4 corr5 corr6 . . etc. I tried using a matrix for the correlations and another one for the variable names. I built the correlation matrix using x = matrix(format(cor(mydata.df[,1],mydata.df[,c(2:79)]),digits=4),nc=3) and the variable names matrix using y = matrix(ls(mydata.df[c(2:79)...

...function svyvar gives the estimated population variance from a given sampling scheme. I am working with a data having more than four continuous variables. In order to have have population total for all those cont. variables I have written in the following format svyvar(~var1+var2+var3+var4+var5+var6,data) ; var1,var2,...,var6 are 6 cont. variables Now the problem is that,this function works properly when the number of cont. variables are less than or equal to three. But when the number of variables goes beyond three it gives a wrong answer,where we have two estimates for the 1st variable (her...