I'm working the npreg example in the R np package documentation (by T. Hayfield, J. Racine), section 3.1 Univariate Regression.
library("np")
data("cps71")
model.par = lm(logwage~age + I(age^2),data=cps71)
summary(model.par)
#
attach(cps71)
bw = npregbw(logwage~age) # thislne not in example 3.1
model.np = npreg(logwage~age,regtype="ll", bwmethod="cv.aic",gradients="TRUE",
+ data=cps71)
This copied directly from the example, but the npreg call results in error message
*Rerun with Debug
Error in npreg.rbandwidth(txdat = txdat, tydat = tydat, bws = bws, ...) :
NAs in foreign function call (arg 15)
In addition: Warning message:
In npreg.rbandwidth(txdat = txdat, tydat = tydat, bws = bws, ...) :
NAs introduced by coercion*
The npreg R documentation indicates the first argument should be BW specificaion. I tried setting bws=1
model.np = npreg(bws=1,logwage~age,regtype="ll",
+ bwmethod="cv.aic",gradients="TRUE", data=cps71)
which gives the following error
*Error in toFrame(xdat) :
xdat must be a data frame, matrix, vector, or factor*
First time working with density estimation in R. Please suggest how to resolve these errors.
Related
Can anyone figure out why I get the error below after running the following code?
library(haven)
library(survival)
library(dplyr)
library(readr)
library(glmmLasso)
par(mar=c(1,1,1,1))
library(discSurv)
HH<- as.data.frame(read_dta("https://www.stata.com/data/jwooldridge/eacsap/recid.dta") )
HHC <- contToDisc(dataShort = HH, timeColumn = "durat", intervalLimits = 20,equi = TRUE)
dtLong<-dataLong(dataShort = HHC, timeColumn = "timeDisc",
eventColumn = "cens",timeAsFactor = FALSE)
formula.1<-y~factor(black)+factor(alcohol)
family<-binomial(link = "logit")
lambda <- 20
penal.vec<-20
next.try<-TRUE
BIC_vec<-rep(Inf,length(lambda))
Deltama.glm2<-as.matrix(t(rep(0,3)))#coefficients + Intercept
Smooth.glm2<-as.matrix(t(rep(0,20)))
j<-1;test.step<-1;
glm2 <- glmmLasso(formula.1,
rnd = NULL,family = family, data = dtLong, lambda=lambda[j],final.re=T,switch.NR=F,
control = list(smooth=list(formula=~- 1+as.numeric(timeInt),nbasis=20,spline.degree=3,
diff.ord=2,penal=penal.vec[test.step],start=Smooth.glm2[j,]),
method.final="EM", print.iter=T,print.iter.final=T,
eps.final=1e-4,epsilon=1e-4,complexity="non.zero",
start=Deltama.glm2[j,]))
Iteration 41
Final Re-estimation Iteration 9Error in Z_aktuell * D : non-conformable arrays
when I change Deltama.glm2<-as.matrix(t(rep(0,3))) to Deltama.glm2<-as.matrix(t(rep(0,2)))
I get the error
Iteration 1Error in grad.lasso[b.is.0] <- score.beta[b.is.0] - lambda.b * sign(score.beta[b.is.0]) :
NAs are not allowed in subscripted assignments
I have tried to remove the starting values as suggested on glmmLasso error and warning without success
Switching from R version 4.2.2 to 3.6.0 solved the issue. Seems there is a compatibility issue between glmmlasso and the newer versions of R.
I'm trying to use the knn function in r but I keep getting this error message when I try to compute it.
> knn(Taxi_train,Taxi_test,cl,k=100)
Error in knn(Taxi_train, Taxi_test, cl, k = 100) :
NA/NaN/Inf in foreign function call (arg 6)
In addition: Warning messages:
1: In knn(Taxi_train, Taxi_test, cl, k = 100) : NAs introduced by coercion
2: In knn(Taxi_train, Taxi_test, cl, k = 100) : NAs introduced by coercion
I don't know what exactly is wrong with my code so I need some help to get it working.
I tried making sure that all the variables are numeric but that didn't change anything. It may also be an issue with my cl factor in the knn equation.
Here is what my code is currently:
date<-chicago_taxi$date
class(date)
Date <- as.Date(date)
class(Date)
Julian <- yday(Date)
class(Julian)
head(Julian)
chicago_taxi <- cbind(chicago_taxi,Julian)
chicago_taxi$seconds <- as.numeric(chicago_taxi$seconds)
set.seed(7777)
train_set <- sample(1:13081,10400,replace = FALSE)
Taxi_train <- chicago_taxi[train_set,]
Taxi_test <- chicago_taxi[-train_set,]
cl <- Taxi_train$payment_type
scale(chicago_taxi$miles)
scale(chicago_taxi$seconds)
scale(chicago_taxi$Julian)
knn(Taxi_train,Taxi_test,cl,k=100)
I had a similar problem to what posted here. To resolve the issue, followed the answer by #Jack Gisby there. Now a new error showed up:
Working on TCGA data , I am getting the same error (first error):
Error in `.rowNamesDF<-`(x, value = value) :
duplicate 'row.names' are not allowed
running duplicated() on each relevant field returned FALSE.
Her is the second error (just after trimming identifiers to not start with a common string like "TCGA-"):
Error in `[.data.frame`(df, neworder2) : undefined columns selected
> traceback()
5: stop("undefined columns selected")
4: `[.data.frame`(df, neworder2)
3: df[neworder2]
2: M3Creal(as.matrix(mydata), maxK = maxK, reps = repsreal, pItem = pItem,
pFeature = 1, clusterAlg = clusteralg, distance = distance,
title = "/home/christopher/Desktop/", des = des, lthick = lthick,
dotsize = dotsize, x1 = pacx1, x2 = pacx2, seed = seed, removeplots = removeplots,
silent = silent, fsize = fsize, method = method, objective = objective)
1: M3C(pro.vst, des = clin, removeplots = FALSE, iters = 25, objective = "PAC",
fsize = 8, lthick = 1, dotsize = 1.25)
I've added to an opened issue on the M3C GitHub.
I got the same error as Hamid Ghaedi while running M3C. I managed to track it down to the following line of code (line 476 on the M3C.R file):
df <- data.frame(m_matrix)
Many of my sample names (column names) started with a number and the data.frame() function added an "X" to the beginning of each name that started with a number ("1" becomes "X1"). This caused a mismatch with the names listed in neworder2.
To get around this problem, I changed all of my sample names to start with a letter and M3C is now running correctly.
Edit: This workaround can be easily applied by using the data.frame() function on your input dataset before running M3C.
Lately when I run my code that uses coxph in the survival package
coxph(frml,data = data), I am now getting warning messages of the following type
1: In model.matrix.default(Terms, mf, contrasts = contrast.arg) :
partial argument match of 'contrasts' to 'contrasts.arg'
2: In seq.default(along = temp) :
partial argument match of 'along' to 'along.with'"
I'm not exactly sure why all of a sudden these partial argument match warnings started popping up, but I don't think they effect me.
However, when I get the following warning message, I want coxph(frml,data = data) = NA
3: In fitter(X, Y, strats, offset, init, control, weights = weights, :
Loglik converged before variable 2 ; beta may be infinite.
6: In coxph(frml, data = data) :
X matrix deemed to be singular; variable 1 3 4
I used tryCatch when I wasn't getting the partial argument match warning using this code where if the nested tryCatch got either a warning or error message it would return NA
coxphfit = tryCatch(tryCatch(coxph(frml,data = data), error=function(w) return(NA)), warning=function(w) return(NA))
However, now that I am getting the partial argument match warnings, I need to only return an NA if there is an error or if I get the above warning messages 3 and 4 . Any idea about how to capture these particular warning messages and return an NA in those instances?
It's actually interesting question, if you are looking for quick and dirty way of capturing warnings you could simply do:
withCallingHandlers({
warning("hello")
1 + 2
}, warning = function(w) {
w ->> w
}) -> res
In this example the object w created in parent environment would be:
>> w
<simpleWarning in withCallingHandlers({ warning("hello") 1 + 2}, warning = function(w) { w <<- w}): hello>
You could then interrogate it:
grepl(x = w$message, pattern = "hello")
# [1] TRUE
as
>> w$message
# [1] "hello"
Object res would contain your desired results:
>> res
[1] 3
It's not the super tidy way but I reckon you could always reference object w and check if the warning message has the phrase you are interested in.
library(nnet)
set.seed(9850)
train1<- sample(1:155,110)
test1 <- setdiff(1:110,train1)
ideal <- class.ind(hepatitis$class)
hepatitisANN = nnet(hepatitis[train1,-20], ideal[train1,], size=10, softmax=TRUE)
j <- predict(hepatitisANN, hepatitis[test1,-20], type="class")
hepatitis[test1,]$class
table(predict(hepatitisANN, hepatitis[test1,-20], type="class"),hepatitis[test1,]$class)
confusionMatrix(hepatitis[test1,]$class, j)
Error:
Error in nnet.default(hepatitis[train1, -20], ideal[train1, ], size = 10, :
NA/NaN/Inf in foreign function call (arg 2)
In addition: Warning message:
In nnet.default(hepatitis[train1, -20], ideal[train1, ], size = 10, :
NAs introduced by coercion
hepatitis variable consists of the hepatitis dataset available on UCI.
This error message is because you have character values in your data.
Try reading the hepatitis dataset with na.strings = "?". This is defined in the description of the dataset on the uci page.
headers <- c("Class","AGE","SEX","STEROID","ANTIVIRALS","FATIGUE","MALAISE","ANOREXIA","LIVER BIG","LIVER FIRM","SPLEEN PALPABLE","SPIDERS","ASCITES","VARICES","BILIRUBIN","ALK PHOSPHATE","SGOT","ALBUMIN","PROTIME","HISTOLOGY")
hepatitis <- read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/hepatitis/hepatitis.data", header = FALSE, na.strings = "?")
names(hepatitis) <- headers
library(nnet)
set.seed(9850)
train1<- sample(1:155,110)
test1 <- setdiff(1:110,train1)
ideal <- class.ind(hepatitis$Class)
# will give error due to missing values
# 1st column of hepatitis dataset is the class variable
hepatitisANN <- nnet(hepatitis[train1,-1], ideal[train1,], size=10, softmax=TRUE)
This code will not give your error, but it will give an error on missing values. You will need to do address those before you can continue.
Also be aware that the class variable is the first variable in the dataset straight from the UCI data repository
Edit based on comments:
The na.action only works if you use the formula notation of nnet.
So in your case:
hepatitisANN <- nnet(class.ind(Class)~., hepatitis[train1,], size=10, softmax=TRUE, na.action = na.omit)