I am trying to build a dynamic regression model and so far I did it with the dynlm package. Basically the model looks like this y_t = a*x1_t + b*x2_t + ... + c*y_(t-1). y_t shall be predicted, x1_t and x2_t will be given and so is y_(t-1). Building the...

I apologize for the novice question, but am new to lme4. I am using lme4 to model the survival of bee colonies among six sites composed of varying types of land use over three years and have produced the following model after already eliminating other competing models using REML: land1=lmer(asin(sqrt(prop_survival))~log(area_forage_uncult)...

I've searched all over stackoverflow and google for these kind predicitons but found nothing for IBk or KStar or LWL. I would need one instance predictions from any of these three clasifiers.I am doing this in Android studio. I've found ways of getting predictions from other classifiers like these: for...

I'm trying to predict related videos to a video, is that possible in weka? the database takes the following structrue: video_id,catygory,keywords,related_videos_ids a single keywords field might have many values (for ex: stackoverflow, predict, videos),so the related_videos (the video could have more than one related video). The related videos depend on...

Using R 3.2.0 with caret 6.0-41 and randomForest 4.6-10 on a 64-bit Linux machine. When trying to use the predict() method on a randomForest object trained with the train() function from the caret package using a formula, the function returns an error. When training via randomForest() and/or using x= and...

I am trying to create prediction based on time series data. My data frame call dat looks like this: dput(head(dat)) dat <- structure(list(out = c(5, 0, 0, 0, 0, 0), Date = c(1423825200000, 1423825500000, 1423825800000, 1423826100000, 1423826400000, 1423826700000 )), .Names = c("out", "Date"), row.names = c(NA, 6L), class = "data.frame")...

I have to run regressions by group_id and then generate the predictions. It doesn't seem like predict allows the "by" option. Is there a way I can predict after running regressions by group_id? The data are stacked by group_id. The regression command I am thinking of using is as follows:...

This is my first post on StackOverflow and I could use a little help... Please forgive me if I am not following the correct posting protocols. There is another example in the StackOverflow for which I am heavily basing my work off of but I cant quite figure out how...

I tried to predict several models using a previous function, but but I'm getting "Error in eval(expr, envir, enclos) : object 'var.1' not found". That's is weird because var.1 is not in the dataframe. The code is: library(randomForest) library(ada) library(class) library(e1071) library(rpart) library(car) library(nnet) library(kknn) Consenso <- function(DF,VAR.DEP){ #entries are...

This is my data (EXAMPLE): EXAMPLE<-data.frame( X=c(99.6, 98.02, 96.43, 94.44, 92.06, 90.08, 87.3, 84.92, 82.14, 79.76, 76.98, 74.21, 71.03, 67.86, 65.08, 62.3, 59.92, 56.35, 52.38, 45.63, 41.67, 35.71, 30.95, 24.6, 17.86, 98.44, 96.48, 94.14, 92.19, 89.84, 87.5, 84.38, 82.42, 78.52, 76.17, 73.83, 70.7, 65.63, 62.89, 60.16, 58.2, 54.69, 52.73, 49.61,...

I have a linear model: mod=lm(weight~age, data=f2) I would like to input an age value and have returned the corresponding weight from this model. This is probably simple, but I have not found a simple way to do this....

New to R. Looking to limit the range of values that can be predicted. df.Train <- data.frame(S=c(1,2,2,2,1),L=c(1,2,3,3,1),M=c(400,450,400,700,795),V=c(423,400,555,600,800),G=c(4,3.2,2,2.7,3.4), stringsAsFactors=FALSE) m.Train <- lm(G~S+L+M+V,data=df.Train) df.Test <- data.frame(S=c(1,2,1,2,1),L=c(1,2,3,1,1),M=c(400,450,500,800,795),V=c(423,475,555,600,555), stringsAsFactors=FALSE) round(predict(m.Train, df.Test, type="response"),digits=1) #seq(0,4,.1) #Predicted values should fall in this range I've experimented with the...

Using "stackloss" data in R, I created a regression model as seen below: stackloss.lm = lm(stack.loss ~ Air.Flow + Water.Temp + Acid.Conc.,data=stackloss) stackloss.lm newdata = data.frame(Air.Flow=stackloss$Air.Flow, Water.Temp= stackloss$Water.Temp, Acid.Conc.=stackloss$Acid.Conc.) Suppose I get a new data set and would need predict its "stack.loss" based on the previous model as seen below:...

new.y = predict(model, newx = new.x), the length of new.y is different from the row length of new.x Code is here: install.packages('ISLR') library(ISLR) fix(Hitters) # load data Hitters = na.omit(Hitters) # remove NA x = model.matrix(Salary ~ ., Hitters)[ , -1] y = Hitters$Salary set.seed(1) train = sample(1:nrow(x), nrow(x)/2) #...

How should I predict missing values NA based on other values in R? Mean value is not enough. All values are dependable - columns values are tree scope rate, rows are three height in meters. My excel file is here. Is there any possible way to do that? I've been...