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
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 have been getting the error Error in default + theme : non-numeric argument to binary operator. I have been using R and teaching R for a long time but I can't find this problem. I have included a reproducible example that fails this way below:
library(tibble)
library(ggplot2)
brains <- as_tibble(brains)
brains <- brains[1:10, ]
brains
ggplot(brains, aes(x = BodyWt, y = BrainWt)) +
geom_point()
The error occurs when executing the ggplot() statement.
My hardware is an HP Laptop 15-ef0xxx. I am running Windows 10 Home version 2004. I am running RStudio community edition "Water Lily" and R version R x64** 4.0.2.
I know this is a simple error and it is driving me crazy.
So I finally solved this problem. On the github issue I had opened Hiroaki commented that "One possibility is that you might set a invalid default theme in your .Rprofile, but I'm not sure..." (see link to issue below).
I'm not sure if your R file is part of a project but mine is.
So I went back and deleted the theme_set() line in my R file, went in and double checked all my project options and selected the option "Disable .Rprofile execution on session start/resume" and "Quit child processes on exit". And then I restarted the R session and now everything works. Including on the default R editor console.
I'm not sure if all those steps are necessary but that seemed to do the trick for me! Hope it helps.
I thought this was an RStudio issue but it seems it's possibly a ggplot2 > problem. I have verified using two different datasets that the same >error comes up when I try using ggplot2 in RStudio or using the default R >console. I get the exact same error with code that's been working fine >but now suddenly won't. I have opened an issue on Github (ggplot2) with a >reprex. Might be worth checking there: >https://github.com/tidyverse/ggplot2/issues/4177
I know this is not an answer per se but I don't have enough reputation >points to add a comment to the previous answer but I thought linking to >the issue on Github might help.
I think you have numbers quoted somewhere and you are trying to perform mathematical operation on character values. Consider
x <- c("5","6")
y <- x/1
> y <- x/1
Error in x/1 : non-numeric argument to binary operator
Now try converting x to numeric and perform the same operation.
y <- as.numeric(x)/1
> y
[1] 5 6
So, you need to use as.numeric on your variable.
The following should resove this issue
ggplot(brains, aes(x = as.numeric(BodyWt), y = as.numeric(BrainWt)))
Earlier, I had posted a question and was able to load in my data successfully and create a topGO object after some help. I'm trying to visualise GO terms that are significantly associated with the list of differentially expressed genes that I have from mouse RNA-seq data.
Now, I'd want to raise a concern about ViSEAGO's tutorial. The tutorial initially specifies loading two files: 'selection.txt' and 'background.txt'. The origin of these files is not clearly stated. However, after a lot of digging into topGO's documentation, I was able to find the datatypes for each of the files. But, even after following these, I have a problem running the following code. Does anyone have any insights to share?
WORKING CODE:
mysampleGOdata <- new("topGOdata",
description = "my Simple session",
ontology = "BP",
allGenes = geneList_new,
nodeSize = 1,
annot = annFUN.org,
mapping="org.Mm.eg.db",
ID = "SYMBOL")
resultFisher <- runTest(mysampleGOdata, algorithm = "classic", statistic = "fisher")
head(GenTable(mysampleGOdata,fisher=resultFisher),20)
myNewBP<-GenTable(mysampleGOdata,fisher=resultFisher)
PROBLEMS:
> head(myNewBP,2)
GO.ID Term Annotated Significant Expected fisher
1 GO:0006006 glucose metabolic process 194 12 0.19 1.0e-19
2 GO:0019318 hexose metabolic process 223 12 0.22 5.7e-19
> ###################
> # merge results
> myBP_sResults<-ViSEAGO::merge_enrich_terms(
+ Input=list(
+ condition=c("mysampleGOdata","resultFisher")
+ )
+ )
Error in setnames(x, value) :
Can't assign 3 names to a 2 column data.table
> myNewBP<-GenTable(mysampleGOdata,fisher=resultFisher)
> ###################
> # display the merged table
> ViSEAGO::show_table(myNewBP)
Error in ViSEAGO::show_table(myNewBP) :
object must be enrich_GO_terms, GO_SS, or GO_clusters class objects
According to the tutorial, the printed table contains for each enriched GO terms, additional columns including the list of significant genes and frequency (ratio of the number of significant genes to the number of background genes) evaluated by comparison. I think I have that, but it's definitely not working.
Can someone see why? I'm not very clear on this.
Thanks!
I think you try to circumvent an error you made at the beginning. You receive the error due to the fact that you did not use the wrapper function from the ViSEAGO package. As you stated in your last question, you had initial problems formatting your data.
Here are some tips:
The "selection" file is a character vector with your DEGs names or IDs. I recommend using EntrezID's.
The "Background" file is a character vector with known genes. I recommend using EntrezID's as well. You can easily generate this character vector with:
background=keys(org.Hs.eg.db, keytype ='ENTREZID').
With these two files, you can easily proceed to the next steps of the package as described in the vignette.
# connect to EntrezGene
EntrezGene<-ViSEAGO::EntrezGene2GO()
# load GO annotations from EntrezGene
# with the add of GO annotations from orthologs genes (see above)
#id = "9606" = homo sapiens
myGENE2GO<-ViSEAGO::annotate(id="9606", EntrezGene)
BP<-ViSEAGO::create_topGOdata(
geneSel = selection, #your DEG vector
allGenes = background, #your created background vector
gene2GO=myGENE2GO,
ont="BP",
nodeSize=5
)
classic<-topGO::runTest(
BP,
algorithm ="classic",
statistic = "fisher"
)
# merge results
BP_sResults<-ViSEAGO::merge_enrich_terms(
Input=list(
condition=c("BP","classic")
)
)
You should get a merged list of your enriched GO terms with the corresponding statistical tests you prefer.
I have faced this problem recently, it was very frustrating. In my case the whole issue seemed to be related to the package version I was using.
I used conda to install ViSEAGO. Nevertheless, R's version in my conda environment was a bit old (i.e. 3.6.1 to be specific). Therefore, when installing ViSEAGO with conda, the version 1.0.0 of the package was installed. Please note that the most recent version of ViSEAGO is 1.4.0.
Therefore, I created a conda environment with R version 4.0.3, and repeated the procedure to install ViSEAGO by using conda. When doing this, ViSEAGO's 1.4.0 version was installed, and everything went fine.
I've tried to backtrack the error, and only find one thing: in the older ViSEAGO version, the function Custom2GO loaded tables with 4 columns; in the most recent version it admits 5 columns (the new one being 'gene_symbol'). I think this disagreement might be part of the issue, as the source code of the function merge_enrich_terms seems to deal with the columns 'gene_id' and 'gene_symbol' at some point, but I'm not sure.
Hope you find my comment helpful!
Cheers,
Mauricio
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.
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