I'm currently working on preditive models with the 'randomForest' package.
Fitting my model as follow
rf <- foreach(ntree=rep(10, 3), .combine= combine, .packages='randomForest') %dopar% {
randomForest(bou~.,data=train, trees=50, importance=TRUE)}
When using 'confusionMatrix' from the 'caret' package, I've got the results below :
I'd like to know if it's possible to set the positive class to 1 in the model. I searched in the package description but couldn't find anything about it.
Thank you very much.
Edit : I've found it. It's an option in the 'confusionMatrix' command from the 'caret' package. I was lokking at the wrong place. Here an example if needed.
confusionMatrix(predicted,true_values,positive='1')
Should I leave my post or delete it ?
I've found it. It's an option in the confusionMatrix command from the caret package. I was looking at the wrong place. Here an example if needed:
confusionMatrix(predicted,true_values,positive='1')
Related
I'm using R Studio based on R 3.4.3. However, when I tried to call the forecast.HoltWinters function, R told me that "could not find function "forecast.HoltWinters"". Inspect the installed package (v8.2) told me that it's true, there is no forecast.HoltWinters. But the manual in https://cran.r-project.org/web/packages/forecast/ clearly stated that forecast.HoltWinters is still available.
I have also tried stats::HoldWinters, but it's working wrong. The code run fine on another computer, but it couldn't run at all on mine. Is there any solution?
Here is the code. Book2.csv has enough data to last more than 3 periods.
dltt <- read.csv("book2.csv", header = TRUE)
dltt.ts <- ts(dltt$Total, frequency=12, start=c(2014,4))
dltt.ts.hw <- HoltWinters(dltt.ts)
library(forecast)
dltt.ts.hw.fc <- forecast.HoltWinters(dltt.ts.hw) //Error as soon as I run this line
Fit a HoltWinters model using the HoltWinters function and then use forecast. Its all in the help for HoltWinters and forecast, namely "The function invokes particular _methods_ which depend on the class of the first argument". I'll copy the guts of it here:
m <- HoltWinters(co2)
forecast(m)
Note this will call the non-exported forecast.HoltWinters function, which you should never call directly using triple-colon notation as some may suggest.
I'm using h2o package and trying to create a learner using the below given code
install.packages("h2o")
library("h2o")
h2o.learner <- makeLearner("regr.h2o.deeplearning",predict.type = "response")
But I'm getting this error
> h2o.learner <- makeLearner("regr.h2o.deeplearning",predict.type = "response")
Error: could not find function "makeLearner"
Note: Few months back I used this code without any problem.
Any idea what could be possible thing for this error?
The correct code for this is simply
library(mlr)
h2o.learner = makeLearner("regr.h2o.deeplearning")
The makeLearner() is not part of H2O. It appears to be part of the mlr package. It also seems that mlr does have h2o support, so it might be as simple as adding a library(mlr) to the top of your script? (Making sure that the mlr package has been installed, already, of course.)
I am using R version 3.3.2 and the package copula version 0.999-15 to evaluate the fitting of the normal copula to my data. My data and code are:
Data: https://www.dropbox.com/s/tdg8bfzmy4nd1dd/jumps.dat?dl=0
library(copula)
data <- read.csv(file="jumps.dat", head=F, sep="")
cop_model <- ellipCopula("normal", dim = 2)
m <- pobs(as.matrix(data))
fitCopula(cop_model, m, method = 'mpl')
After I run the code I receive the following error:
Error in `freeParam<-`(`*tmp*`, value = estimate) : the length of 'value' is not equal to the number of free parameters
Calls: fitCopula ... fitCopula.ml -> fitCopStart -> fitCopula.icor -> freeParam<-
Execution halted
I have no idea what is happening here. The fitting for Clayton and Gumbel is pretty fine. Searching for similar errors in the web, I have found nothing. Reading the documentation (https://www.rdocumentation.org/packages/copula/versions/0.999-15/topics/fitCopula?) for some specificity for ellipCopula, I have found an specific option for posDef, but it did not returns any solution at all.
Old question, but I found this, so will share my solution.
Try to run the following, this is a minimum working example:
library(copula)
print("-----------")
mycop <- ellipCopula("normal", dim=4)
data <- matrix(runif(400), nrow=4)
fitCopula(mycop, t(data))
print("-----------")
For me, this works fine if I open R and type in the lines one by one, but fails if I run as a script with Rscript. The solution is that you need library(methods) as well.
For some reason this worked with copula v0.999-v14, but was broken by v0.999-v16. Alas.
library(mice)
md.pattern(dat1)
temp<-mice(dat1, m = 5, seed = 101)
dat1 <- complete(temp, 2)
Error in UseMethod("complete_") :
no applicable method for 'complete_' applied to an object of class "mids"
Hi, I'm trying to impute missing values using mice package.
But I got the above error message.
The first time I imputed missing data it worked, but when I tried again it didn't. I've tried a lot with different options (changing seed, deleting existing data or "temp" variable)
Sometimes it worked but other times it didn't.
What is the problem and solution?
Thanks in advance.
I think the problem here is that you should rather be using some other libraries in your program which have a function named "complete". Just typing "complete" in help menu gave me 2 other libraries (tidyr,RCurl) which have the function in the same name. As simon suggested, I tried using "mice::complete". It works for me.
Try this:
dat1<-mice::complete(temp,2)
mice 3.7.5 redefines the complete() function as the S3 complete.mids() method for the generic tidyr::complete().
Assuming that mice is attached, you should no longer see no applicable method for 'complete_' applied to an object of class "mids".
I use R version 2.15.1 (2012-06-22) and mgcv version 1.7-22
I load the following set of packages in R:
library(sqldf)
library(timeDate)
library(forecast)
library(xts)
library(tseries)
library(MASS)
library(mgcv)
It happens that I can not run a simple model (I omit the code). Even the sample code taken from the help pages:
dat = gamSim(1,n=400,dist="normal",scale=2)
b = gam(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
gives an error:
Error in qr.qty(qrc, sm$S[[l]]) :
NA/NaN/Inf in foreign function call (arg 5)
In addition: Warning message:
In smoothCon(split$smooth.spec[[i]], data, knots, absorb.cons, scale.penalty = scale.penalty, :
number of items to replace is not a multiple of replacement length
Note that everything works fine, if I just load the package mgcv and then use the sample code right away. It also works if I just load all the packages and run the sample code. It just does not work if I
load all packages
do some file reading, sqldf statements, ts operations and some models from package forecast.
if I then apply GAM, it does not work anymore.
Apparently the variable definitions in the general environment mess up the functioning of the package.
Are there any known issues? Are there general rules that I have to obey if I load various packages? Can I write code that "disturbed" the package mgcv?
# Richard there are 2 GAM related packages: gam and mgcv. Loading both libraries at the same time usually causes a conflict.
Loading mgcv as the first package solved my problem ... strange but true.