I have merged two data sets (data1.csv and data2.csv) and created a data file to run the analyses on. But, when I do the analysis, R cannot recognize some of my columns (variables). My analysis is a multinational logit one.
The code for analysis is:
library(nnet)
mlogit<- multinom(groupadmit~mid_pop, tot_male10, tot_female10, male16,
tot_female16, agemean10, agemean16, IMD_score, IMD_score2, data =
admitqof10_16)
and the error is:
Error in model.frame.default(formula = groupadmit ~ mid_pop, data =
admitqof10_16, :
object 'male16' not found
I tried a couple of ways to solve the problem. For example, I use ls() to see whether the created data.table is in the R or not and it was there. I changed the name of the variable, but again it does not work. I exported the created data and imported it again. But, I have failed.
May someone please help me know why this problem happens and what I can do to resolve it?
Thanks in advance
Related
I want to create a CytoTree CYT object in R to analyse my .FCS files. When I am using the Quick start code in the package description, I will always get an error when running the createCYT() function that says:
Error in createCYT(raw.data = fcs.data, normalization.method = "log") :
2022-09-26 15:46:26 meta.data must be a data.frame
As the function should not rely on any meta data and that object is optional, I do not know how to solve the error.
Here is the description:
https://ytdai.github.io/CytoTree/quick-start.html#quick-start-code
I thank you very much in advance for your help!
BR
I have encountered same problem previously.
In the end it worked only when the data.frame was added (yes, even though it should not depend on it).
Here is how:
meta.data <- data.frame(cell = rownames(fcs.data), stage = gsub(".fcs.+", "", rownames(fcs.data)))
meta.data$stage <- factor(as.character(meta.data$stage))
You may as well have a look at the Cytotree PDF in case of more issues.
I was trying to do a QCA analysis of sufficeny in RStudio 4.1.3. using truthTable command. By running the code i get Error: wrong outcome specification. The outcome is written correctly. When I do an analysis of necessity with superSubset code everything works fine. Everything is coded numeric but it does not work with bivariate callibration either.
> ttCO21 <- truthTable( data = mydata, outcome = "CO21",
+ conditions = "GENDG1, FDI1, GDP1",
+ sort.by="incl, n", show.cases = TRUE, complete = TRUE)
**Error: Incorrect outcome specification.**
I had the same issue today and the solution was to delete any unnecessary columns (or create a new dataframe). I left only the calibrated conditions and outcome, because when I found this error, the raw data was in the same df, even if was not using it in the truthTable() command.
Because of the 3 hours i wasted on this problem i'll share my situation and solution. I had the exact same problem. creating a new df did not help, nor did removing unnecessary colums (wouldn't make sense anyway). Completely reinstalling en reloading the QCA package did work for me. Hope this helps someone.
you can try "comma" instead of "dot" (in “decimal") when you import file
I'm getting a strange error when I run an lmer function in r.
I've tried changing the variable types (all of them are numeric or factor) and removing the NA before analysis, but nothing seems to work.
model_1 <- lmer(Q14 ~ gender * time + (1|OMID), data=data)
summary(model_1)
Specifically, my error message reads:
Error in as(value, fieldClass, strict = FALSE) :
internal problem in as(): “labelled” is(object, "numeric") is TRUE, but the metadata asserts that the 'is' relation is FALSE
Not sure why this is happening, but I can't seem to find any answers for it. Any help would be appreciated.
Thanks!
I think lmer has a problem with 'labelled' data. If you un-label the predictors it should work fine.
I had the same error: the code for the lme-formula worked perfectly fine and one day I encountered that error. The solution in my case was simply to restart the R session, reload the data - in my case from SPSS via library("haven")::read.sps and after that load library("lme4") and execute the lme-formula.
So, if the formula worked before without any error, maybe just clean the project environment and re-run the most crucial code without any additional packages loaded. Maybe it's just some "cross-contamination" between packages or an unwanted effect of any package on the dataframe.
We have always been an SPSS shop but we're trying to learn R. Been doing some basics. I am trying to do a simple t-test, just to learn that code.
Here's my code, and what's happening:
Code screenshot
I don't get why it says "score" isn't found. It's in the table, and the read.csv code defaults to assuming the first row contains headers. I can't see why it's not "finding" the column for score. I'm sure it's something simple, but would appreciate any help.
You didn't store your imported csv file in a variable. It printed to the console because it had nowhere else to go - it just gets printed and then thrown away. You need to assign it for it to be saved in memory:
my_data_frame <- read.csv("ttest.csv")
Now your data exists in the variable my_data_frame and you can use it by supplying it as the data argument:
t.test(score ~ class, mu=0, alt="two.sided", conf=0.95, var.eg=F, paired=F, data=my_data_frame)
Also, in general, I would recommend using read_csv from the package readr over the default read.csv - it's faster.
Finally, when you ask questions, please provide your code as text, not a picture. You should also provide your data or a toy dataset - you can use the function dput to print code that will create your data, or just provide the csv file or some code that creates toy data.
Yesterday I posted this question on Stats Exchange and based on the response I got, I decided to do some analysis using R's src() function. It's part of the "sensitivity" package.
I installed the package with no trouble, and then tried the following command:
sens <- src(seminars, REV, rank=TRUE, nboot=100)
sens is a new variable to store the results of the test
seminars is a data frame that I imported from a CSV file using the read.csv() command
REV is the name of a variable/column in seminars and my desired response variable
When I ran the command, I got the following error:
Error in data.frame(Y = y, X) : object 'REV' not found
Any thoughts?
From the documentation of src
y: a vector containing the responses corresponding to the design
of experiments (model output variables).
The input needs to be a vector (apparently) and you're attempting to pass in a name (and not even quoting the name at that). Since REV isn't defined (I'm guessing due to the error message) in the global environment it doesn't know what to do.
From reading the documentation it sounds like what you want to do is pass sensitivity[,-which(colnames(sensitivity) == "REV")] (just the design matrix - you don't want to include the responses) in as x and sensitivity[,"REV"] in as y.
This error is linked to the fact that the data.frame X=seminars include factors with 0 value, which produce an error while constructing the regression coefficient. You can first remove them as they don't contribute to the variance of the output.