model.matrix

问题 3 days old to R and can't figure out what I'm doing wrong. I'm trying to send some columns with two way interactions into a glmnet cox model. I have some data.frame() called dtable Edit to make the code reproducible xs<-c("Col1", "Col2", "Col3") v<-c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, NA, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, NA, 25, 26, 27, 28, 29, 30) df<-data.frame(matrix(v,ncol=3)) dm<-as.matrix(df) dm<-matrix(dm[complete.cases(dm)], ncol=3) colnames(dm)<-xs dfdata<-data.frame(dm) f

问题 I am trying to create a sparse matrix with numerical and categorical data which will be used as an input to cv.glmnet. When only numerical data is involved, I can create a sparseMatrix using the following syntax sparseMatrix(i=c(1,3,5,2), j=c(1,1,1,2), x=c(1,2,4,3), dims=c(5,2)) For categorical variables, the following approach seems to work: sparse.model.matrix(~-1+automobile, data.frame(automobile=c("sedan","suv","minivan","truck","sedan"))) My VERY sparse instance has 1,000,000

问题 I am using the dummies package to generate dummy variables for categorical variables, some with more than two categories. testdf<- data.frame( "A" = as.factor(c(1,2,2,3,3,1)), "B" = c('A','B','A','B','C','C'), "C"= c('D','D','E','D','D','E')) # #Generate dummy variables: # testdf<- cbind(testdf, dummy(testdf$C, sep='_')) testdf<- cbind(testdf, dummy(testdf$B, sep='_')) For both commands I get: Warning message: In model.matrix.default(~x - 1, model.frame(~x - 1), contrasts = FALSE) : non-list

问题 I would like to create a matrix of indicator variables. My initial thought was to use model.matrix, which was also suggested here: Automatically expanding an R factor into a collection of 1/0 indicator variables for every factor level However, model.matrix does not seem to work if a factor has only one level. Here is an example data set with three levels to the factor 'region': dat = read.table(text = " reg1 reg2 reg3 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 0

问题 I have a quite big data frame in R with two columns. I am trying to make out of the Code column ( factor type with 858 levels) the dummy variables. The problem is that the R Studio always crashed when I am trying to do that. > str(d) 'data.frame': 649226 obs. of 2 variables: $ User: int 210 210 210 210 269 317 317 317 317 326 ... $ Code : Factor w/ 858 levels "AA02","AA03",..: 164 494 538 626 464 496 435 464 475 163 ... The User column is not unique, meaning that there can be several rows

问题 This question already has an answer here : Creating dummy variables in R data.table (1 answer) Closed 3 years ago . I have a data table and I want to create a new column for each unique day, and then assign a 1 in each row where the day matches the column name I have done this using a for loop but I was wondering if there was any way to optimise it using data.table and set? Here is an example dt <- data.table(Week_Day = c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday",

问题 I've trained an elastic net model in R using glmnet and would like to use it to make predictions off of a new data set. But I'm having trouble producing the matrix to use as an argument in the predict() method because some of my factor variables (dummy variables indicating the presence of comorbidities) in the new data set only have one level (the comorbidities were never observed), which means I can't use model.matrix(RESPONSE ~ ., new_data) because it gives me the (expected) Error in

问题 This question arose as a result of another question posted here: non-conformable arguments error from lmer when trying to extract information from the model matrix When trying to obtain predicted means from an lmer model containing a factor variable, the output varies depending on how the factor variable is specified. I have a variable agegroup, which can be specified using the groups "Children <15 years", "Adults 15-49 years", "Elderly 50+ years" or "0-15y", "15-49y", "50+y". My choice

问题 I have a set of basketball lineup data with five columns, each sharing the same factor, like so: head(dat) V1 V2 V3 V4 V5 1 MILES,KEATON KINGSLEY,MOSES BELL,ANTHLON HANNAHS,DUSTY DURHAM,JABRIL 2 MILES,KEATON KINGSLEY,MOSES BELL,ANTHLON HANNAHS,DUSTY DURHAM,JABRIL 3 KINGSLEY,MOSES BELL,ANTHLON HANNAHS,DUSTY DURHAM,JABRIL THOMPSON,TREY 4 KINGSLEY,MOSES BELL,ANTHLON HANNAHS,DUSTY THOMPSON,TREY BEARD,ANTON 5 THOMPSON,TREY BEARD,ANTON KOUASSI,WILLY WHITT,JIMMY WATKINS,MANUALE 6 THOMPSON,TREY BEARD

问题 I have a multi-variant data frame and want to convert the categorical data inside to dummy variables, I used model.matrix but it does not quite work. Please refer to the example below: age = c(1:15) #numeric sex = c(rep(0,7),rep(1,8)); sex = as.factor(sex) #factor bloodtype = c(rep('A',2),rep('B',8),rep('O',1),rep('AB',4));bloodtype = as.factor(bloodtype) #factor bodyweight = c(11:25) #numeric wholedata = data.frame(cbind(age,sex,bloodtype,bodyweight)) model.matrix(~.,data=wholedata)[,-1] The