I am trying to use nnetar for some time series forecasting, and running into an issue when the data has repeating values (i.e. the same counts observed in a time period). To reproduce the error I have created a list of values and replaced the first 10 values with a 0:
dummy.ls <- runif(n=80)
for(i in 1:10)
dummy.ls[i] <- 0
fit <- nnetar(dummy.ls, lambda=0)
When running the nnetar function I receive the following error:
Error in approx(idx, x[idx], tt, rule = 2) :
need at least two non-NA values to interpolate
In addition: Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
I see similar errors in other questions, but unsure how to avoid the error?
Related
Trying to plot some data in R - I am a basic user and teaching myself. However, whenever I try to plot, it fails, and I am not sure why.
> View(Pokemon_BST)
> Pokemon_BST <- read.csv("~/Documents/Pokemon/Pokemon_BST.csv")
> View(Pokemon_BST)
> plot("Type_ID", "Gender_ID")
Error in plot.window(...) : need finite 'xlim' values
In addition: Warning messages:
1: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
2: In xy.coords(x, y, xlabel, ylabel, log) : NAs introduced by coercion
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf
5: In min(x) : no non-missing arguments to min; returning Inf
6: In max(x) : no non-missing arguments to max; returning -Inf
This is my code, but I thought it might be an issue with my .csv file? I have attributed numbers to the "Type_ID" and "Gender_ID" columns. Type_ID has values between 1-20; Gender_ID has 1 for male, 2 for female, and 3 for both. I should state that both ID columns are just made of numeric values. Nothing more.
I then tried using barplot function. This error occurred:
> barplot("Gender_ID", "Type_ID")
Error in width/2 : non-numeric argument to binary operator
In addition: Warning message:
In mean.default(width) : argument is not numeric or logical: returning NA
There are no missing values, no characters within these columns, nothing that SHOULD cause an error according to my basic knowledge. I am just not sure what is going wrong.
To me it seems as you are giving the plot function the wrong inputs.
For the x and y axis plot expects numeric values and you are only providing a single string. The function does not know that the "Type_ID" and "Gender_ID" come from the Pokemon_BST data frame.
To reach your data you must tell R where the object comes from. You do this by opening square brackets behind the object you want to access and write the names of the objects to be accessed into it.
View(Pokemon_BST)
Pokemon_BST <- read.csv("~/Documents/Pokemon/Pokemon_BST.csv")
# Refer to the object
plot(Pokemon_BST["Type_ID"], Pokemon_BST["Gender_ID"])
# Sould also work now
barplot(Pokemon_BST["Gender_ID"], Pokemon_BST["Type_ID"])
See also here for a introduction on subsetting in R
The problem is how you're passing the values to the plot function. In your code above, "Gender_ID" is just some string and the plot function doesn't know what to do with that. One way to plot your values is to pass the vectors Pokemon_BST$Gender_ID and Pokemon_BST$Type_ID to the function.
Here's a sample dataframe with the plot you were intending.
Pokemon_BST <- data.frame(
Type_ID = sample(1:20, 10, replace = TRUE),
Gender_ID = sample(1:3, 10, replace = TRUE))
plot(Pokemon_BST$Gender_ID, Pokemon_BST$Type_ID)
I have a dataframe named as kef, consisted of 512 rows, and the fields x, y (referring to coordinates) and v (refering to a certain numeric value for each cell).
I also have a map layer named as grecia.map, loaded in R through the readOGR command consisted of a polygon which represents a certain area.
While running the following command:
kriged <- kriging(kef$x, kef$y, kef$v, polygons = grecia.map, pixels=30000)
I receive the following error messages:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) :
0 (non-NA) cases
In addition: Warning messages:
1: In max(x) : no non-missing arguments to max; returning -Inf
2: In min(x) : no non-missing arguments to min; returning Inf
3: In max(y) : no non-missing arguments to max; returning -Inf
4: In min(y) : no non-missing arguments to min; returning Inf
Well, I managed to correct the issue. The problem had to do with the data. the coordinates along with the sample values were messed and apparently there was not any spatial logic with the data.
After importing a correct dataset the function worked properly so I guess that this question could be deleted.
Thank you
I have computed a PCA with the principal function in the psych package in R. I would like to build a screeplot from the eigenvalues, but both scree(PCA) and screeplot(PCA) give me errors and no plot. Is there a function within this package that I'm not aware of (I have very, very little R experience)??
NOTE: I've been simply working in the command line.
Error for scree(PCA):
Error in if (nvar != dim(rx)[1]) { : argument is of length zero
Error for screeplot(PCA):
Error in plot.window(xlim, ylim, log = log, ...) :
need finite 'xlim' values
In addition: Warning messages:
1: In min(w.l) : no non-missing arguments to min; returning Inf
2: In max(w.r) : no non-missing arguments to max; returning -Inf
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf
Without data it is hard for us to check this. The error message looks like the data is empty.
Here are some tips for R beginners.
Try get help on scree function. Are you missing a parameter? Type in command line.
help(scree)
Look at your variable PCA
head(PCA) - shows first few rows of your data
str(PCA) - shows structure of the variable. Is it what scree function is expecting?
Do you have missing values or text values in your data? The function may be thrown out by these. You can drop missing data - take a look at complete.cases. is.na() is how you check for NA values (i.e. if I wanted to check for NAs in variable mydata, sum(is.na(mydata)) would tell me how many I have. Drop those rows and see if that gets your scree function working okay.
Take a look at the vignette for the package:
https://cran.r-project.org/web/packages/psych/vignettes/overview.pdf
Hope this gets you on track.
Did you enter a correlation matrix as your input to the scree( ) function?
Using my own data, I was able to generate a scree plot with the following two lines of code:
humor_cor <- cor(humor, use = "pairwise.complete.obs")
scree(humor_cor, factors = FALSE)
I have a data which contain some NA value in their elements.
What I want to do is to perform clustering without removing rows
where the NA is present.
I understand that gower distance measure in daisy allow such situation.
But why my code below doesn't work?
I welcome other alternatives than 'daisy'.
# plot heat map with dendogram together.
library("gplots")
library("cluster")
# Arbitrarily assigning NA to some elements
mtcars[2,2] <- "NA"
mtcars[6,7] <- "NA"
mydata <- mtcars
hclustfunc <- function(x) hclust(x, method="complete")
# Initially I wanted to use this but it didn't take NA
#distfunc <- function(x) dist(x,method="euclidean")
# Try using daisy GOWER function
# which suppose to work with NA value
distfunc <- function(x) daisy(x,metric="gower")
d <- distfunc(mydata)
fit <- hclustfunc(d)
# Perform clustering heatmap
heatmap.2(as.matrix(mydata),dendrogram="row",trace="none", margin=c(8,9), hclust=hclustfunc,distfun=distfunc);
The error message I got is this:
Error in which(is.na) : argument to 'which' is not logical
Calls: distfunc.g -> daisy
In addition: Warning messages:
1: In data.matrix(x) : NAs introduced by coercion
2: In data.matrix(x) : NAs introduced by coercion
3: In daisy(x, metric = "gower") :
binary variable(s) 8, 9 treated as interval scaled
Execution halted
At the end of the day, I'd like to perform hierarchical clustering with the NA allowed data.
Update
Converting with as.numeric work with example above.
But why this code failed when read from text file?
library("gplots")
library("cluster")
# This time read from file
mtcars <- read.table("http://dpaste.com/1496666/plain/",na.strings="NA",sep="\t")
# Following suggestion convert to numeric
mydata <- apply( mtcars, 2, as.numeric )
hclustfunc <- function(x) hclust(x, method="complete")
#distfunc <- function(x) dist(x,method="euclidean")
# Try using daisy GOWER function
distfunc <- function(x) daisy(x,metric="gower")
d <- distfunc(mydata)
fit <- hclustfunc(d)
heatmap.2(as.matrix(mydata),dendrogram="row",trace="none", margin=c(8,9), hclust=hclustfunc,distfun=distfunc);
The error I get is this:
Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf
Error in hclust(x, method = "complete") :
NA/NaN/Inf in foreign function call (arg 11)
Calls: hclustfunc -> hclust
Execution halted
~
The error is due to the presence of non-numeric variables in the data (numbers encoded as strings).
You can convert them to numbers:
mydata <- apply( mtcars, 2, as.numeric )
d <- distfunc(mydata)
Using as.numeric may help in this case, but I do think that the original question points to a bug in the daisy function. Specifically, it has the following code:
if (any(ina <- is.na(type3)))
stop(gettextf("invalid type %s for column numbers %s",
type2[ina], pColl(which(is.na))))
The intended error message is not printed, because which(is.na) is wrong. It should be which(ina).
I guess I should find out where / how to submit this bug now.
I have results from a survey. I am trying to create a graphic displaying the relationship of two variables: "Q1" and "Q9.1". "Q1" is the independent and "Q9.1" is the dependent. Both variables have responses from like scale questions: -2,-1,0,1,2. A typical plot places the answers on top of each other - not very interesting or informative. I was thinking that hexbin would be the way to go. The data is in lpp.
I have not been able to use "Q1" and "Q9.1" for x and y. However:
> is.numeric("Q1")
[1] FALSE
q1.num <- as.numeric("Q1")
Warning message:
NAs introduced by coercion
The values for Q1 are (hundreds of instances of): -2,-1,0,1,2
How can I make a hexbin graph with this data?
Is there another graph I should consider?
Error messages so far:
Warning messages:
1: In xy.coords(x, y, xl, yl) : NAs introduced by coercion
2: In xy.coords(x, y, xl, yl) : NAs introduced by coercion
3: In min(x) : no non-missing arguments to min; returning Inf
4: In max(x) : no non-missing arguments to max; returning -Inf
5: In min(x) : no non-missing arguments to min; returning Inf
6: In max(x) : no non-missing arguments to max; returning -Inf
How about taking a slightly different approach? How about thinking of your responses as factors rather than numbers? You could use something like this, then, to get a potentially useful representation of your data:
# Simulate data for testing purposes
q1 = sample(c(-2,-1,0,1,2),100,replace=TRUE)
q9 = sample(c(-2,-1,0,1,2),100,replace=TRUE)
dat = data.frame(q1=factor(q1),q9=factor(q9))
library(ggplot2)
# generate stacked barchart
ggplot(dat,aes(q1,fill=q9)) + geom_bar()
You may want to switch q1 and q9 above, depending on the view of the data that you want.
Perhaps ggplot2's stat_binhex could sort that one for you?
Also, I find scale_alpha useful for dealing with overplotting.