search for: predictor

Hello I am using the step function in order to do backward selection for a linear model of 52 variables with the following commands: object<-lm(vars[,1] ~ (vars[,2:(ncol(predictors)+1)]-1)) BackS<-step(object,direction="backward") but it isn't dropping any if the variables in the model, but there are lots of not significant variables as you can see here > object<-lm(vars[,1] ~ (vars[,2:(ncol(predictors)+1)]-1)) > summary(object) Call: lm(formula =...

Hi, everyone. I need to run lm with the same response vector but with varying predictor vectors. (i.e. 1 response vector on each individual 6,000 predictor vectors) After looking through the R archive, I found roughly 3 methods that has been suggested. Unfortunately, I need to run this task multiple times(~ 5,000 times) and would like to find a faster way than the existing methods. Al...

...UME <- read_excel(excel_file_path) # Anzeigen des gesamten Dataframes print(INVESTMENTVOLUME) # Make sure BFS is numeric right before dataprep INVESTMENTVOLUME$BFS <- as.numeric(INVESTMENTVOLUME$BFS) # running dataprep dataprep_out <- dataprep( foo = INVESTMENTVOLUME, predictors = c("Predictor 1", " Predictor 2", " Predictor 3" Predictor 4", " Predictor 5", " Predictor 6"), special.predictors = list(list("Special Predictor 1", seq(1, 12, by = 1))), dependent = "INVESTMENTVOLUME_12_MONTH_AVERAGE&qu...

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Hi, I would like to simulate data with a binary outcome and a set of predictors that are correlated. I want to be able to fix the number of event (Y=1) vs. non-event (Y=0). Thus, I fix this and then simulate the predictors. I have 2 questions: 1. When the predictors are continuous, I can use mvrnorm(). However, if I have continuous, ordinal and binary predictors, I'm not...

I'm fitting a lme growth curve model with two predictors and their interaction as predictors. The multilevel model is nested so that level 1 is time within the individual, and level 2 is the individual. I would like to plot the mean group-level trajectories at plus and minus 1 SD from the mean of the main effects composing the interaction term. Thus,...

Hello! I think I am relatively clear on how predictor importance (the first one) is calculated by Random Forests for a Classification tree: Importance of predictor P1 when the response variable is categorical: 1. For out-of-bag (oob) cases, randomly permute their values on predictor P1 and then put them down the tree 2. For a given tree, subtract th...

Hello, i am a newby on R and i am trying to make a backward selection on a binomial-logit glm on a large dataset (69000 lines for 145 predictors). After 3 days working, the stepAIC function did not terminate. I do not know if that is normal but i would like to try computing a "homemade" backward with a repeated glm ; at each step, the predictor with the max pvalue would be excluded until reaching a set of 20 predictors for...

Dear R-ers, I have a data frame data with predictors x1 through x5 and the response variable y. I am running a simple regression: reg<-lm(y~x1, data=data) I would like to loop through all predictors. Something like: predictors<-c("x1","x2",... "x10) for(i in predictors){ reg<-lm(y~i) etc. } But it's not wo...

...unning into problems. I have imported the data set into "task" (data frame). The data frame is made of factors with variable names in the first row. I am running a loop to set a variable equal to a column in the data frame. Here is an example of my problem: for (i in 1:dim(task)[2]){ predictor.loop <- c(task[i]) predictor.loop.mc <- predictor.loop - mean(predictor.loop, na.rm=T) } I get the following error: Error in predictor.loop - mean(predictor.loop, na.rm = T) : non-numeric argument to binary operator In addition: Warning message: In mean.default(predictor.loop, na.rm = T)...

Dear R-devel list members, I've discovered an inconsistency in how lm() and similar functions handle logical predictors as opposed to factor or character predictors. An "lm" object for a model that includes factor or character predictors includes the levels of a factor or unique values of a character predictor in the $xlevels component of the object, but not the FALSE/TRUE values for a logical predictor e...

Hi, folks, Happy work in weekends >_< My question is how to plot the dependent variable against one of the predictors with other predictors as constant. Not for the original data, but after prediction. It means y is the predicted value of the dependent variables. The constane value of the other predictors may be the average or some fixed value. ####### y=1:10 x=10:1 z=2:11 lin_model=lm(z~x+y) x_new=11:2 #######...

...s for each level" is wrong: look at the anova() function. Bob On 29 June 2017 at 11:13, Beno?t PELE <benoit.pele at acoss.fr> wrote: > Hello, > > i am a newby on R and i am trying to make a backward selection on a > binomial-logit glm on a large dataset (69000 lines for 145 predictors). > > After 3 days working, the stepAIC function did not terminate. I do not > know if that is normal but i would like to try computing a "homemade" > backward with a repeated glm ; at each step, the predictor with the max > pvalue would be excluded until reaching a set of...

Hi All, I'm dealing with binary response data for the first time, and I'm confused about what kind of graphics I could explore in order to pick relevant predictors and their relation with response variable. I have 8-10 continuous predictors and 4-5 categorical predictors. Can anyone suggest what kind of graphics I can explore to see how predictors behave w.r.t. response variable... Any help would be greatly appreciated, thanks, Kim [[alternative HTML ver...

Dear Abby, > On Aug 30, 2019, at 8:20 PM, Abby Spurdle <spurdle.a at gmail.com> wrote: > >> I think that it would be better to handle factors, character predictors, and logical predictors consistently. > > "logical predictors" can be regarded as categorical or continuous (i.e. 0 or 1). > And the model matrix should be the same, either way. I think that you're mistaking a coincidence for a principle. The coincidence is that FALSE/TRUE...

Dear all, I have a question about using categorical predictors for SVM, using "svm" from library(e1071). If I have multiple categorical predictors, should they just be included as factors? Take a simple artificial data example: x1<-rnorm(500) x2<-rnorm(500) #Categorical Predictor 1, with 5 levels x3<-as.factor(rep(c(1,2,3,4,5),c(50,150,13...

...ng lmer() to model my data, and using anova() to compare the fit of various models. When I run two models, the output of each model is generated correctly as far as I can tell (e.g. summary(f1) and summary(f2) for the multilevel model output look perfectly reasonable), and in this case (see below) predictor.1 explains vastly more variance in outcome than predictor.2 (R2 = 15% vs. 5% in OLS regression, with very large N). What I am utterly puzzled by is that when I run an anova comparing the two multilevel model fits, the Chisq comes back as 0, with p = 1. I am pretty sure that fit #1 (f1) is a much...

Hi, I appologise if this is a rudimentary question and long winded but I just wanted to let ye know where I'm comming from. I'm new to R and I'm trying to use the 'randomForest' package to classify and predict. The Error message that is troubling me is: > pr<-predict(predictors,rf1, ext=ext) Error in x[...] <- m : NAs are not allowed in subscripted assignments In addition: Warning message: 'newdata' had 153595 rows but variable(s) found have 109 rows My steps are outlinded below which hopefully will give you insight into where I'm going horribly wrong....

Hi, I'm using package "caret" to rank predictors using random forest model and draw predictors importance plot. I used below commands: rf.fit<-randomForest(x,y,ntree=500,importance=TRUE) ## "x" is matrix whose columns are predictors, "y" is a binary resonse vector ## Then I got the ranked predictors by ranking "rf1$...

...p the tick labels. However, when I try all of these, the x-axis scale of the first panel is used for all three. How do I keep different scales for the different panels? Here is an example: library(lattice) library(latticeExtra) response <- c(76, 14, 15, 44, 26, 19, 74, 123, 49, 8, 56, 17, 18) predictor1 <- c(107, 7, 25, 501, 64, 88, 344, 367, 379, 10, 66, 31, 32) predictor2 <- c(10, 9, 8, 10, 29, 27, 55, 48, 2, 6, 14, 10, 5) predictor3 <- c(67, 22, 66, 41, 72, 64, 69, 63, 64, 70, 60, 75, 78) pred1_plot <- xyplot(response ~ predictor1, scales = list(log = TRUE, equispaced.log = FALSE)...