I am working on a tree regression. Everything works fine with my code but I don't get the predicted values at all. Instead I get all values for my y variable (response variable). Here's the code:
Separating in train and test set for data
`sample = sample.split(Data, SplitRatio = .80)
train = subset(Data, sample == TRUE)
test = subset(Data, sample == FALSE)
varYTrain <- train[c(3)]
varYTest <- test[c(3)]
varXTrain <- train[c(5:27)]
varXTest <- test[c(5:27)]`
Model
`x <- cbind(varXTrain,varYTrain)
fit <- rpart(as.matrix(varYTrain) ~ ., data = x, method="class")
summary(fit)`
This one doesn't work as I don't get predictions based on test data set for an unknown reason
`predicted <- predict(fit, data=varXTest)
summary(predicted)`
I would also like in the end for the output to compare predicted values to real values in my dataset, can I do that?
Thank you very much and don't hesitate to ask me a question if I am not clear enough it's my first time posting.
Cheers
Related
When trying to create a table with the conditional random effects in r using the gtsummary function tbl_regression from a glmmTMB mixed effects negative-binomial zero-inflated model, I get duplicate random effects rows.
Example (using Mollie Brooks' Zero-Inflated GLMMs on Salamanders Dataset):
data(Salamanders)
head(Salamanders)
library(glmmTMB)
zinbm2 = glmmTMB(count~spp + mined +(1|site), zi=~spp + mined + (1|site), Salamanders, family=nbinom2)
zinbm2_table_cond <- tbl_regression(
zinbm2,
tidy_fun = function(...) broom.mixed::tidy(..., component = "cond"),
exponentiate = TRUE,
estimate_fun = purrr::partial(style_ratio, digits = 3),
pvalue_fun = purrr::partial(style_sigfig, digits = 3))
zinbm2_table_cond
Output:
Random Effects Output (cond)
When extracting the random effects from de zero-inflated part of the model I get the same problem.
Example:
zinbm2_table_zi <- tbl_regression(
zinbm2,
tidy_fun = function(...) broom.mixed::tidy(..., component = "zi"),
exponentiate = TRUE,
estimate_fun = purrr::partial(style_ratio, digits = 3),
pvalue_fun = purrr::partial(style_sigfig, digits = 3))
zinbm2_table_zi
Output:
Random Effects Output (zi)
The problem persists if I specify the effects argument in broom.mixed.
tidy_fun = function(...) broom.mixed::tidy(..., effects = "ran_pars", component = "cond"),
Looking at confidence intervals in both outputs it seems that somehow it is extracting random effects from both parts of the model and changing the estimate of the zero-inflated random effects (in 1st image; opposite in the 2nd image) to match the conditional part estimate while keeping the CI.
I am not knowledgeable enough to understand why this is happening. Since both rows have the same label I am having difficulty removing the wrong one.
Any tips on how to avoid this problem or a workaround to remove the undesired rows?
If you need more info, let me know.
Thank you in advance.
PS: Output images were changed to link due to insufficient reputation.
I'm getting the following error when calling NbClust():
Error in NbClust(data = ds[, sapply(ds, is.numeric)], diss = NULL, distance = "euclidean", : The TSS matrix is indefinite. There must be too many missing values. The index cannot be calculated.
I've called ds <- ds[complete.cases(ds),] just before running NbClust so there's no missing values.
Any idea what's behind this error?
Thanks
I had same issue in my research.
So, I had mailed to Nadia Ghazzali, who is the package maintainer, and got an answer.
I'll attached my mail and her reply.
my e-mail:
Dear Nadia Ghazzali. Hello Nadia. I have some questions about
NbClust function in R library. I have tried googling but could not
find satisfying answers. First, I’m so grateful for you to making
this awsome R library. It is very helpful for my reasearch. I tested
NbClust function in NbClust library with my own data like below.
> clust <- NbClust(data, distance = “euclidean”,
min.nc = 2, max.nc = 10, method = ‘kmeans’, index =”all”)
But soon, an error has occurred. Error: division by zero! Error in
Indices.WBT(x = jeu, cl = cl1, P = TT, s = ss, vv = vv) : object
'scott' not found So, I tried NbClust function line by line and
found that some indices, like CCC, Scott, marriot, tracecovw,
tracew, friedman, and rubin, were not calculated because of object
vv = 0. I’m not very familiar with argebra so I don’t know meaning
of eigen value. But it seems to me that object ss(which is squart of
eigenValues) should not be 0 after prodected.
So, here is my questions.
I assume that my data is so sparse(a lot of zero values) that sqrt(eigenValues) becomes too small, is that right? I’m sorry I
can’t attach my data but I can attach some part of eigenValues and
squarted eigenValues.
> head(eigenValues)
[1] 0.039769880 0.017179826 0.007011972 0.005698736 0.005164871 0.004567238
> head(sqrt(eigenValues))
[1] 0.19942387 0.13107184 0.08373752 0.07548997 0.07186704 0.06758134
And if my assume is right, what can I do for this problems? Only one
way to drop out 7 indices?
Thank you for reading and I’ll waiting your reply. Best regards!
and her reply:
Dear Hansol,
Thank you for your interest. Yes, your understanding is good.
Unfortunately, the seven indices could not be applied.
Best regards,
Nadia Ghazzali
#seni The cause of this error is data related. If you look at the source code of this function,
NbClust <- function(data, diss="NULL", distance = "euclidean", min.nc=2, max.nc=15, method = "ward", index = "all", alphaBeale = 0.1)
{
x<-0
min_nc <- min.nc
max_nc <- max.nc
jeu1 <- as.matrix(data)
numberObsBefore <- dim(jeu1)[1]
jeu <- na.omit(jeu1) # returns the object with incomplete cases removed
nn <- numberObsAfter <- dim(jeu)[1]
pp <- dim(jeu)[2]
TT <- t(jeu)%*%jeu
sizeEigenTT <- length(eigen(TT)$value)
eigenValues <- eigen(TT/(nn-1))$value
for (i in 1:sizeEigenTT)
{
if (eigenValues[i] < 0) {
print(paste("There are only", numberObsAfter,"nonmissing observations out of a possible", numberObsBefore ,"observations."))
stop("The TSS matrix is indefinite. There must be too many missing values. The index cannot be calculated.")
}
}
And I think the root cause of this error is the negative eigenvalues that seep in when the number of clusters is very high, i.e. the max.nc is high. So to solve the problem, you must look at your data. See if it got more columns then rows. Remove missing values, check for issues like collinearity & multicollinearity, variance, covariance etc.
For the other error, invalid clustering method, look at the source code of the method here. Look at line number 168, 169 in the given link. You are getting this error message because the clustering method is empty. if (is.na(method))
stop("invalid clustering method")
I was using glm with dredge in MuMIn package. But now since my data is large I am using bigglm from biglm package. Now how do I do model selection now since dredge does not work with bigglm? Is there another package I can use to achieve this?
On applying the dredge on bigglm I am receiving the following error:
Error in nobs.default(global.model) : no 'nobs' method is available
dredge relies on availability of logLik method for the the given model class. big[g]lm object does not provide such value, and there seems to be a long known bug in the AIC method for big[g]lm-class that makes it impossible to calculate LL from it (it uses deviance rather than LL to calculate AIC, so AIC-values are not comparable to other model types, see here: AIC different between biglm and lm).
You could try adding the missing methods (using deviance instead of LL, which may be slippery):
# incorrect if any prior weights are 0
nobs.biglm <- function (object, ...) object$n
logLik.bigglm <- function(object, ...) {
dev <- deviance(object, ...)
df <- object$n - object$df.resid
structure(dev, df = df, nobs = object$n)
}
coefTable.biglm <- function (model, data, ...) {
ct <- summary(model)$mat[, c(1L,4L,5L), drop = FALSE]
.makeCoefTable(ct[, 1L], se = ct[, 2L], df = model$df.resid, coefNames = rownames(ct))
}
environment(coefTable.biglm) <- asNamespace("MuMIn")
#from example(bigglm)
fm <- bigglm(log(Volume)~log(Girth)+log(Height),data=trees, chunksize=10, sandwich=TRUE)
dredge(fm, rank = AIC)
I am having problems doing a prediction with decision trees (CART).
I have this code:
training <- read.csv("pml-training.csv", header=TRUE)
set.seed(1972)
inTrain <- createDataPartition(y=training2$classe, p=0.6, list=FALSE)
wk_training <- training2[inTrain,]
wk_testing <- training2[-inTrain,]
wk_trainng dataset has 11776 vars and wk_testing 7846.
set.seed(1972)
model_dt <- train(wk_training$classe ~ ., data = wk_training, method="rpart")
print(model_dt, digits=3)
Run against wk_testing
predictions_dt <- predict(model_dt, newdata=wk_testing)
Then I expect predictions_dt to have 7846 rows as it has wk_testing,
but predictions_dt has only 165 rows ????
I don't know what I am doing wrong...
Can anybody help me?
Thanks in advance
If you have missing values, the predict function defaults to na.action = na.omit. You can test to see if this is the issue using na.action = na.fail. If this is the case, you might want to impute. See the preProcess option in train.
I'm using RandomForest for classification, and I got an unbalanced dataset, as: 5830-no, 1006-yes. I try to balance my dataset with class_weight and sample_weight, but I can`t.
My code is:
X_train,X_test,y_train,y_test = train_test_split(arrX,y,test_size=0.25)
cw='auto'
clf=RandomForestClassifier(class_weight=cw)
param_grid = { 'n_estimators': [10,50,100,200,300],'max_features': ['auto', 'sqrt', 'log2']}
sw = np.array([1 if i == 0 else 8 for i in y_train])
CV_clf = GridSearchCV(estimator=clf, param_grid=param_grid, cv= 10,fit_params={'sample_weight': sw})
But I don't get any improvement on my ratios TPR, FPR, ROC when using class_weight and sample_weight.
Why? Am I doing anything wrong?
Nevertheless, if I use the function called balanced_subsample, my ratios obtain a great improvement:
def balanced_subsample(x,y,subsample_size):
class_xs = []
min_elems = None
for yi in np.unique(y):
elems = x[(y == yi)]
class_xs.append((yi, elems))
if min_elems == None or elems.shape[0] < min_elems:
min_elems = elems.shape[0]
use_elems = min_elems
if subsample_size < 1:
use_elems = int(min_elems*subsample_size)
xs = []
ys = []
for ci,this_xs in class_xs:
if len(this_xs) > use_elems:
np.random.shuffle(this_xs)
x_ = this_xs[:use_elems]
y_ = np.empty(use_elems)
y_.fill(ci)
xs.append(x_)
ys.append(y_)
xs = np.concatenate(xs)
ys = np.concatenate(ys)
return xs,ys
My new code is:
X_train_subsampled,y_train_subsampled=balanced_subsample(arrX,y,0.5)
X_train,X_test,y_train,y_test = train_test_split(X_train_subsampled,y_train_subsampled,test_size=0.25)
cw='auto'
clf=RandomForestClassifier(class_weight=cw)
param_grid = { 'n_estimators': [10,50,100,200,300],'max_features': ['auto', 'sqrt', 'log2']}
sw = np.array([1 if i == 0 else 8 for i in y_train])
CV_clf = GridSearchCV(estimator=clf, param_grid=param_grid, cv= 10,fit_params={'sample_weight': sw})
This is not a full answer yet, but hopefully it'll help get there.
First some general remarks:
To debug this kind of issue it is often useful to have a deterministic behavior. You can pass the random_state attribute to RandomForestClassifier and various scikit-learn objects that have inherent randomness to get the same result on every run. You'll also need:
import numpy as np
np.random.seed()
import random
random.seed()
for your balanced_subsample function to behave the same way on every run.
Don't grid search on n_estimators: more trees is always better in a random forest.
Note that sample_weight and class_weight have a similar objective: actual sample weights will be sample_weight * weights inferred from class_weight.
Could you try:
Using subsample=1 in your balanced_subsample function. Unless there's a particular reason not to do so we're better off comparing the results on similar number of samples.
Using your subsampling strategy with class_weight and sample_weight both set to None.
EDIT: Reading your comment again I realize your results are not so surprising!
You get a better (higher) TPR but a worse (higher) FPR.
It just means your classifier tries hard to get the samples from class 1 right, and thus makes more false positives (while also getting more of those right of course!).
You will see this trend continue if you keep increasing the class/sample weights in the same direction.
There is a imbalanced-learn API that helps with oversampling/undersampling data that might be useful in this situation. You can pass your training set into one of the methods and it will output the oversampled data for you. See simple example below
from imblearn.over_sampling import RandomOverSampler
ros = RandomOverSampler(random_state=1)
x_oversampled, y_oversampled = ros.fit_sample(orig_x_data, orig_y_data)
Here it the link to the API: http://contrib.scikit-learn.org/imbalanced-learn/api.html
Hope this helps!