similar to: Strange predicted values ?

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)))

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

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

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 find the

Dear 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*`,

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

Dear R helpers   Following is a part of R code.   data_lab <- expand.grid(c("R11", "R12", "R13"), c("R21", "R22", "R23"), c("R31", "R32", "R33"), c("R41", "R42", "R43"), c("R51", "R52", "R53"), c("R61", "R62", "R63"),

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

I have 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

Hola.. yo lo haría de la siguiente manera ... En excel:genero una columna con la serie de fechas continuas ... con la función buscarv, agrego las variables que tienen dato a esta serie.. en base a la fecha muy largo y mecánico para mi gusto.... En R ..de igual manera genero el vector de fechas require(chron)# crear el vector continuo de fechasfch01 <- data.frame(''fch'' =

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

Dear R users, I need help with translating these SAS codes into R with lme()? I have a longitudinal 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

Dear list, I have been trying to use coxme in R 2.3.1. When I use coxme in the following data sim.fr1, i get "Warning messages: using 'as.environment(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) >

Hi, I am using R 1.9.1 on on i686 PC with SuSE Linux 9.0. I have a data.frame, e.g.: > myData <- data.frame( var1 = c( 1:4 ), var2 = c (5:8 ) ) If I add a new column by > myData$var3 <- myData[ , "var1" ] + myData[ , "var2" ] everything is fine, but if I omit the commas: > myData$var4 <- myData[ "var1" ] + myData[ "var2" ] the name

I am trying to Sweave the output of calculating correlations between one variable and several others. 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

Hi, I am running a logistic regression model using lrm library and I get the following error when I run the command: mod1 <- lrm(death ~ factor(score), x=T, y=T, data = env1) Unable to fit model using ?lrm.fit? where score is a numeric variable from 0 to 6. LRM executes fine for the following commands: mod1 <- lrm(death ~ score, x=T, y=T, data = env1) mod1<- lrm(death ~

Hello I was wondering if it was possible to enter R commands from an external text file. If it is possible, it will be easy for repetetive tasks. Does anyone have an idea ? thanks in advance Ahmet Temiz TURKEY ______________________________________ ______________________________________ The views and opinions expressed in this e-mail message are ...{{dropped}}

Hi R-experts I have many text files to read and combined them into one into R that are output from other programs. My textfile have unbalanced number of rows for example: ;this is example ; r help Var1 Var2 Var3 Var4 Var5 0 0.05 0.01 12 1 0.04 0.06 18 A 2 0.05 0.08 14 3 0.01 0.06

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

Hello, I am struggling to produce an MDS plot using the randomForest package with a moderately large data set. My data set has one categorical response variables, 7 predictor variables and just under 19000 observations. That means my proximity matrix is approximately 133000 by 133000 which is quite large. To train a random forest on this large a dataset I have to use my institutions high