Firstly, I constructed a model by
cf1 <- cforest(y~., data = DATA, strata = DATA$y,
ntree = 200L, mtry = 10)
Here considering the dataset is very imbalanced (y=1 takes 7% of the whole observations), so I add strata here to make sure observations with y=1 are not ignored in bagging. cf1 works normally, in terms of the confusion matrix. However, when I tried to implement feature selection by
cf1.imp_cond <- varimp(cf1, conditional = TRUE)
It returns
Error in x[strata == s] <- .resample(x[strata == s]) :
NAs are not allowed in subscripted assignments
I can't figure out what does this error mean. Someone met this before?
----update
Here is an manipulated test data from the original dataset I am using. Here is the code
cf2 <- cforest(X5_years_survival~., data = test, strata = X5_years_survival,
ntree = 200L, mtry = 6)
cf2.imp_cond <- varimp(cf2, conditional = TRUE)
Still, I have the error:
Error in x[strata == s] <- .resample(x[strata == s]) :
NAs are not allowed in subscripted assignments
---update
The error occurs when kidids_node function is applied.
The truth is, if I keep all integer type covariate, instead of converting them by as.factor, applying varimp makes no error.
Related
New to stackoverflow. I'm working on a project with NHIS data, but I cannot get the svyglm function to work even for a simple, unadjusted logistic regression with a binary predictor and binary outcome variable (ultimately I'd like to use multiple categorical predictors, but one step at a time).
El_under_glm<-svyglm(ElUnder~SO2, design=SAMPdesign, subset=NULL, family=binomial(link="logit"), rescale=FALSE, correlation=TRUE)
Error in eval(extras, data, env) :
object '.survey.prob.weights' not found
I changed the variables to 0 and 1 instead:
Under_narm$SO2REG<-ifelse(Under_narm$SO2=="Heterosexual", 0, 1)
Under_narm$ElUnderREG<-ifelse(Under_narm$ElUnder=="No", 0, 1)
But then get a different issue:
El_under_glm<-svyglm(ElUnderREG~SO2REG, design=SAMPdesign, subset=NULL, family=binomial(link="logit"), rescale=FALSE, correlation=TRUE)
Error in svyglm.survey.design(ElUnderREG ~ SO2REG, design = SAMPdesign, :
all variables must be in design= argument
This is the design I'm using to account for the weights -- I'm pretty sure it's correct:
SAMPdesign=svydesign(data=Under_narm, id= ~NHISPID, weight= ~SAMPWEIGHT)
Any and all assistance appreciated! I've got a good grasp of stats but am a slow coder. Let me know if I can provide any other information.
Using some make-believe sample data I was able to get your model to run by setting rescale = TRUE. The documentation states
Rescaling of weights, to improve numerical stability. The default
rescales weights to sum to the sample size. Use FALSE to not rescale
weights.
So, one solution maybe is just to set rescale = TRUE.
library(survey)
# sample data
Under_narm <- data.frame(SO2 = factor(rep(1:2, 1000)),
ElUnder = sample(0:1, 1000, replace = TRUE),
NHISPID = paste0("id", 1:1000),
SAMPWEIGHT = sample(c(0.5, 2), 1000, replace = TRUE))
# with 'rescale' = TRUE
SAMPdesign=svydesign(ids = ~NHISPID,
data=Under_narm,
weights = ~SAMPWEIGHT)
El_under_glm<-svyglm(formula = ElUnder~SO2,
design=SAMPdesign,
family=quasibinomial(), # this family avoids warnings
rescale=TRUE) # Weights rescaled to the sum of the sample size.
summary(El_under_glm, correlation = TRUE) # use correlation with summary()
Otherwise, looking code for this function's method with 'survey:::svyglm.survey.design', it seems like there may be a bug. I could be wrong, but by my read when 'rescale' is FALSE, .survey.prob.weights does not appear to get assigned a value.
if (is.null(g$weights))
g$weights <- quote(.survey.prob.weights)
else g$weights <- bquote(.survey.prob.weights * .(g$weights)) # bug?
g$data <- quote(data)
g[[1]] <- quote(glm)
if (rescale)
data$.survey.prob.weights <- (1/design$prob)/mean(1/design$prob)
There may be a work around if you assign a vector of numeric values to .survey.prob.weights in the global environment. No idea what these values should be, but your error goes away if you do something like the following. (.survey.prob.weights needs to be double the length of the data.)
SAMPdesign=svydesign(ids = ~NHISPID,
data=Under_narm,
weights = ~SAMPWEIGHT)
.survey.prob.weights <- rep(1, 2000)
El_under_glm<-svyglm(formula = ElUnder~SO2,
design=SAMPdesign,
family=quasibinomial(),
rescale=FALSE)
summary(El_under_glm, correlation = TRUE)
I have written some code in R. This code takes some data and splits it into a training set and a test set. Then, I fit a "survival random forest" model on the training set. After, I use the model to predict observations within the test set.
Due to the type of problem I am dealing with ("survival analysis"), a confusion matrix has to be made for each "unique time" (inside the file "unique.death.time"). For each confusion matrix made for each unique time, I am interested in the corresponding "sensitivity" value (e.g. sensitivity_1001, sensitivity_2005, etc.). I am trying to get all these sensitivity values : I would like to make a plot with them (vs unique death times) and determine the average sensitivity value.
In order to do this, I need to repeatedly calculate the sensitivity for each time point in "unique.death.times". I tried doing this manually and it is taking a long time.
Could someone please show me how to do this with a "loop"?
I have posted my code below:
#load libraries
library(survival)
library(data.table)
library(pec)
library(ranger)
library(caret)
#load data
data(cost)
#split data into train and test
ind <- sample(1:nrow(cost),round(nrow(cost) * 0.7,0))
cost_train <- cost[ind,]
cost_test <- cost[-ind,]
#fit survival random forest model
ranger_fit <- ranger(Surv(time, status) ~ .,
data = cost_train,
mtry = 3,
verbose = TRUE,
write.forest=TRUE,
num.trees= 1000,
importance = 'permutation')
#optional: plot training results
plot(ranger_fit$unique.death.times, ranger_fit$survival[1,], type = 'l', col = 'red') # for first observation
lines(ranger_fit$unique.death.times, ranger_fit$survival[21,], type = 'l', col = 'blue') # for twenty first observation
#predict observations test set using the survival random forest model
ranger_preds <- predict(ranger_fit, cost_test, type = 'response')$survival
ranger_preds <- data.table(ranger_preds)
colnames(ranger_preds) <- as.character(ranger_fit$unique.death.times)
From here, another user (Justin Singh) from a previous post (R: how to repeatedly "loop" the results from a function?) suggested how to create a loop:
sensitivity <- list()
for (time in names(ranger_preds)) {
prediction <- ranger_preds[which(names(ranger_preds) == time)] > 0.5
real <- cost_test$time >= as.numeric(time)
confusion <- confusionMatrix(as.factor(prediction), as.factor(real), positive = 'TRUE')
sensitivity[as.character(i)] <- confusion$byclass[1]
}
But due to some of the observations used in this loop, I get the following error:
Error in confusionMatrix.default(as.factor(prediction), as.factor(real), :
The data must contain some levels that overlap the reference.
Does anyone know how to fix this?
Thanks
Certain values in prediction and/or real have only 1 unique value in them. Make sure the levels of the factors are the same.
sapply(names(ranger_preds), function(x) {
prediction <- factor(ranger_preds[[x]] > 0.5, levels = c(TRUE, FALSE))
real <- factor(cost_test$time >= as.numeric(x), levels = c(TRUE, FALSE))
confusion <- caret::confusionMatrix(prediction, real, positive = 'TRUE')
confusion$byClass[1]
}, USE.NAMES = FALSE) -> result
result
I am building a predictive model with caret/R and I am running into the following problems:
When trying to execute the training/tuning, I get this error:
Error in if (tmps < .Machine$double.eps^0.5) 0 else tmpm/tmps :
missing value where TRUE/FALSE needed
After some research it appears that this error occurs when there missing values in the data, which is not the case in this example (I confirmed that the data set has no NAs). However, I also read somewhere that the missing values may be introduced during the re-sampling routine in caret, which I suspect is what's happening.
In an attempt to solve problem 1, I tried "pre-processing" the data during the re-sampling in caret by removing zero-variance and near-zero-variance predictors, and automatically inputting missing values using a carets knn automatic imputing method preProcess(c('zv','nzv','knnImpute')), , but now I get the following error:
Error: Matrices or data frames are required for preprocessing
Needless to say I checked and confirmed that the input data set are indeed matrices, so I dont understand why I get this second error.
The code follows:
x.train <- predict(dummyVars(class ~ ., data = train.transformed),train.transformed)
y.train <- as.matrix(select(train.transformed,class))
vbmp.grid <- expand.grid(estimateTheta = c(TRUE,FALSE))
adaptive_trctrl <- trainControl(method = 'adaptive_cv',
number = 10,
repeats = 3,
search = 'random',
adaptive = list(min = 5, alpha = 0.05,
method = "gls", complete = TRUE),
allowParallel = TRUE)
fit.vbmp.01 <- train(
x = (x.train),
y = (y.train),
method = 'vbmpRadial',
trControl = adaptive_trctrl,
preProcess(c('zv','nzv','knnImpute')),
tuneGrid = vbmp.grid)
The only difference between the code for problem (1) and (2) is that in (1), the pre-processing line in the train statement is commented out.
In summary,
-There are no missing values in the data
-Both x.train and y.train are definitely matrices
-I tried using a standard 'repeatedcv' method in instead of 'adaptive_cv' in trainControl with the same exact outcome
-Forgot to mention that the outcome class has 3 levels
Anyone has any suggestions as to what may be going wrong?
As always, thanks in advance
reyemarr
I had the same problem with my data, after some digging i found that I had some Inf (infinite) values in one of the columns.
After taking them out (df <- df %>% filter(!is.infinite(variable))) the computation ran without error.
I have constructed a decision tree using rpart for a dataset.
I have then divided the data into 2 parts - a training dataset and a test dataset. A tree has been constructed for the dataset using the training data. I want to calculate the accuracy of the predictions based on the model that was created.
My code is shown below:
library(rpart)
#reading the data
data = read.table("source")
names(data) <- c("a", "b", "c", "d", "class")
#generating test and train data - Data selected randomly with a 80/20 split
trainIndex <- sample(1:nrow(x), 0.8 * nrow(x))
train <- data[trainIndex,]
test <- data[-trainIndex,]
#tree construction based on information gain
tree = rpart(class ~ a + b + c + d, data = train, method = 'class', parms = list(split = "information"))
I now want to calculate the accuracy of the predictions generated by the model by comparing the results with the actual values train and test data however I am facing an error while doing so.
My code is shown below:
t_pred = predict(tree,test,type="class")
t = test['class']
accuracy = sum(t_pred == t)/length(t)
print(accuracy)
I get an error message that states -
Error in t_pred == t : comparison of these types is not implemented In
addition: Warning message: Incompatible methods ("Ops.factor",
"Ops.data.frame") for "=="
On checking the type of t_pred, I found out that it is of type integer however the documentation
(https://stat.ethz.ch/R-manual/R-devel/library/rpart/html/predict.rpart.html)
states that the predict() method must return a vector.
I am unable to understand why is the type of the variable is an integer and not a list. Where have I made the mistake and how can I fix it?
Try calculating the confusion matrix first:
confMat <- table(test$class,t_pred)
Now you can calculate the accuracy by dividing the sum diagonal of the matrix - which are the correct predictions - by the total sum of the matrix:
accuracy <- sum(diag(confMat))/sum(confMat)
My response is very similar to #mtoto's one but a bit more simply... I hope it also helps.
mean(test$class == t_pred)
I have a problems with spdep(). Starting with a matrix of non-missing distances produced by a function
dist_m <- geoDistMatrix(data1, group = 'fips_dist')
dist_m[upper.tri(dist_m)] <- t(dist_m)[upper.tri(dist_m)]
we then turn into weights with linear inverse
max_dist <- max(dist_m)
w1 <- (max_dist + 1 - dist_m)/(max_dist + 1)
and now
lw <- mat2listw(w1, row.names = rownames(w1), style = 'M')
I check to make sure no missing weights:
any(is.na(lw$weights))
and since there aren't, go ahead with:
errorsarlm(cvote ~ inc, data = data1, lw, method = 'eigen', quiet = F, zero.policy = TRUE)
leads to the following error:
Error in subset.listw(listw, subset, zero.policy = zero.policy) :
Not yet able to subset general weights lists
This is because at least one observation in data1 is not complete, i.e. has missing values. Hence, errorsarlm wants to subset the data, i.e. restrict to complete cases. But it can't do it now - that's what the error message says.
Best is to subset the data manually or correct the incomplete cases.
This is because the spdep function created a listw object only for non-general weights by default. Set zero.polcy=TRUE beform you perform mat2listw or nb2listw function so that it consider non-neighbors that have zero value.