I ran a simple function cocor for the difference in correlation, but I got the error message: one of the variables (temporality) must be numeric. So I checked the data type of the variable and it is double/numeric. I do not have the issue to calculate partial correlation or confidence interval using the same database.
cocor(~temporality+expectability|temporality+positive,data =data2)
is.numeric(data2$temporality) # True
Data2 is a database with 5 variables (gender and 4 numeric measures).
So what is the real reason behind the issue? Thank you
I had the same problem with "The variable 'x' must be numeric." for the cocor function. I found somewhere that cocor does not seem to work with tibbles, but when the data is converted to data.frame it works.
Your script would go like this:
cocor(~temporality+expectability|temporality+positive, data = as.data.frame(data2))
Finally, I used cocor.indep.groups and cocor.dep.groups.overlap to deal with numeric issues.
Related
I am trying to fit a model with the SuperLearner package. However, I can't even get past the stage of playing with the package to get comfortable with it....
I use the following code:
superlearner<-SuperLearner::SuperLearner(Y=y, X=as.data.frame(data_train[1:30]), family =binomial(), SL.library = list("SL.glmnet"), obsWeights = weights)
y is a numeric vector of the same length as my dataframe "data_train", containing the correct labels with 9 different classes. The dataframe "data_train" contains 30 columns with numeric data.
When i run this, i get the Error:
Error in get(library$screenAlgorithm[s], envir = env) :
Objekt 'All' not found
I don't really know what the problem could be and i can't really wrap my head around the source code. Please note that the variable obsWeights in the function contains a numeric vector of the same length as my data with weights i calculated for the model. This shouldn't be the problem, as it doesn't work either way.
Unfortunately i can't really share my data on here, but maybe someone had this error before...
Thanks!
this seems to happen if you do not attach SuperLearner, you can fix via library(SuperLearner)
I am working with a dataframe from NYC opendata. On the information page it claims that a column, ACRES, is numeric, but when I download it is chr. I've tried the following:
parks$ACRES <- as.numeric(as.character(parks$ACRES))
which turned the column info type into dbl, but I was unable to take the mean, so I tried:
parks$ACRES <- as.integer(as.numeric(parks$ACRES))
I've also tried sapply() and I get an error message with NAs introduced by coercion. I tried convert() to but R didn't recognize it though it is supposed to be part of dplyr.
Either way I get NA as a result for the mean.
I've tried taking the mean a few different ways:
mean(parks[["ACRES"]])
mean(parks$ACRES)
Which also didn't work? Is it the dataframe? I'm wondering since it is from the government there are limits?
I'd appreciate any help.
You have NAs in your data. Either they were there before you converted or some of the data can't be converted to numeric directly (do you have comma separators for the 1000s in your input? Those need to be removed before converting to numeric).
Identifying why you have NAs and fixing if necessary is the first step you'll need to do. If the NAs are valid then what you want to do is to add the na.rm = TRUE parameter to the mean function which ignores NAs while calculating the mean.
Check to see how ACRES is being loaded in (i.e., what data type is it?). If it's being loaded in as a factor, you will have trouble changing a factor to a numerical value. The way to solve this is to use the 'stringsAsFactors = FALSE' argument in your read.csv or whatever function you're using to read in the data.
I am conducting a sensitivity study using the Sensitivity package. When trying to calculate the sensitivity indices with the output data of the external model I get the error specified in the titel.
The output is a three column table stored in a csv file which I read in as follows:
day1 <- read.csv("day_1_outputs.csv",header=FALSE)
Now when I try to calculate sensitivity indices with the ouput of the first column:
tell(sob.pars,day1[,1])
I get:
Error in `row.names<-.data.frame`(`*tmp*`, value = value) :
invalid 'row.names' length
At first I thought I should use a matrix like object because in another study I conducted I generated the ouput from a raster image read in as a matrix which worked fine, but that didn't help.
The help page for tell states using a vector for the model results but even if I store the column of the dataframe before using tell the problem persists.
I guess my main problem is that I don't understand the error message in conjunction with the tell function, sob.pars is a list returned by sensitivity analyses objects constructors from the same package so I don't know to which rownames of that object the message is refering.
Any hint is appreciated.
Finally found out what the problem was. The error is kind of missleading.
The problem was not the row names since these were identical, that's what irritated me in the first place. There was obviously nothing wrong with them.
The actual problem was the column names in sob.pars. These were missing. Once I added these everything worked fine. Thanks rawr anyways (I just only now noticed someone had commented on the question, I thought I would be notified when this happens, but I guess not).
I have a dataset in stata and I want to take it to R, but there are some missing values in state and they are represented using a period. I want to get the data into R which I do by loading the foreign package and then I use read.table() function. How do I convert the periods in state which are genuinely missing to NA in R?
If i understand you correctly, you first load the Foreign-Package for loading a .dta-File, correct?
library("foreign")
Then you would read in your Data by using:
myRFile <- read.dta(file="someStataFile.dta")
You are asking for a way that the missing operator from Stata, often denoted by a dot ., is converted to the missing operator in R, NA, also correct?
One thing to know here is, that Stata handles missing values "behind the scenes" in multiple ways. There are actually about 27 different missing operators in Stata, which are usually not distinguishable for the user. You do not need to know them for you problem though, because read.dta() handles them itself.
To learn how you can tackle a simple problem like this yourself in the future, you always need to check the help file for your function first:
help(read.dta)
Here you see, that the function handles the extensive missing-data types from Stata automatically and correctly.
If you want to have information about which type of missing operator was recognized, you can set the argument missing.type=TRUE, by using:
myRFile <- read.dta(file="someStataFile.dta", missing.type=TRUE)
Then, according to the help file, the following will happen:
If missing.type is TRUE a separate list is created with the same
variable names as the loaded data. For string variables the list value
is NULL. For other variables the value is NA where the observation is
not missing and 0–26 when the observation is missing. This is attached
as the "missing" attribute of the returned value.
I am a relatively novice r user and am attempting to use the partimat() function within the klaR package to plot decision boundaries for a linear discriminant analysis but I keep encountering the same error. I have tried inputing the arguments multiple different ways according to the manual, but keep getting the following error:
Error in partimat.default(x, grouping, ...) :
at least two classes required
Here is an example of the input I've given:
partimat(sources1[,c(3:19)],grouping=sources1[,2],method="lda",prec=100)
where my data table is loaded in under the name "sources1" with columns 3 through 19 containing the explanatory variables and column 2 containing the classes. I have also tried doing it by entering the formula like so:
partimat(sources1$group~sources1$tio2+sources1$v+sources1$cr+sources1$co+sources1$ni+sources1$rb+sources1$sr+sources1$y+sources1$zr+sources1$nb+sources1$la+sources1$gd+sources1$yb+sources1$hf+sources1$ta+sources1$th+sources1$u,data=sources1)
with these being the column heading.
I have successfully run an LDA on this same data set without issue so I'm not quite sure what is wrong.
From the source code of the partimat.default function getAnywhere(partimat.default) it states
if (nlevels(grouping) < 2)
stop("at least two classes required")
Therefore maybe you haven't defined your grouping column as a factor variable. If you try summary(sources1[,2]) what do you get? If it's not a factor, try
sources1[,2] <- as.factor(sources1[,2])
Or in method 2 try removing the "sources1$"on each of your variable names in the formula as you specify the data frame in which to look for these variable names in the data argument. I think you are effectively specifying the dataframe twice and it might be looking, for instance, for
"sources1$sources1$groups"
Rather than
"sources1$groups"
Without further error messages or a reproducible example (i.e. include some data in your post) it's hard to say really.
HTH