I have a sample dataset:
Score <- c(1,2,3,4,5,6,7,8)
Condition <- c(rep(1,each=5),rep(2,each=3))
Test <- data.frame(Condition,Score)
I tried running cohen.d from the effsize package using the following code:
cohen.d(Test,group="Condition")
but I obtained this error:
Error in cohen.d.default(Test, group = "Condition") : First parameter must be a numeric type
even though both column are numeric (I checked both the workspace and used as.numeric).
May I know what did I do wrong? I am aware that someone has solved this issue before (here's the link) but I fail to understand what she did.
Thank you
Change Condition column to factor.
Test$Condition <- factor(Test$Condition)
There are two ways in which you can apply the function.
Using values.
library(effsize)
cohen.d(Test$Score, Test$Condition)
Using formula syntax.
cohen.d(Score~Condition, Test)
Related
I have a dataset "res.sav" that I read in via haven. It contains 20 columns, called "Genes1_Acc4", "Genes2_Acc4" etc. I am trying to find a correlation coefficient between those and another column called "Condition". I want to separately list all coefficients.
I created two functions, cor.condition.cols and cor.func to do that. The first iterates through the filenames and works just fine. The second was supposed to give me my correlations which didn't work at all. I also created a new "cor.condition.Genes" which I would like to fill with the correlations, ideally as a matrix or dataframe.
I have tried to iterate through the columns with two functions. However, when I try to pass it, I get the error: "NAs introduced by conversion". This wouldn't be the end of the world (I tried also suppressWarning()). But the bigger problem I have that it seems like my function does not convert said columns into the numeric type I need for my cor() function. I receive the "y must be numeric" error when trying to run the cor() function. I tried to put several arguments within and without '' or "" without success.
When I ran str(cor.condition.cols) I only receive character strings, which makes me think that my function somehow messes up with the as.numeric function. Any suggestions of how else I could iter through these columns and transfer them?
Thanks guys :)
cor.condition.cols <- lapply(1:20, function(x){paste0("res$Genes", x, "_Acc4")})
#save acc_4 columns as numeric columns and calculate correlations
res <- (as.numeric("cor.condition.cols"))
cor.func <- function(x){
cor(res$Condition, x, use="complete.obs", method="pearson")
}
cor.condition.Genes <- cor.func(cor.condition.cols)
You can do:
cor.condition.cols <- paste0("Genes", 1:20, "_Acc4")
res2 <- as.numeric(as.matrix(res[cor.condition.cols]))
cor.condition.Genes <- cor(res2, res$Condition, use="complete.obs", method="pearson")
eventually the short variant:
cor.condition.cols <- paste0("Genes", 1:20, "_Acc4")
cor.condition.Genes <- cor(res[cor.condition.cols], res$Condition, use="complete.obs")
Here is an example with other data:
cor(iris[-(4:5)], iris[[4]])
I have a large dataframe with many factor class features that I am attempting to one hot encode. I'm attempting to use the dummyVars function from the caret package to do this. My issue is that since I have a large dataframe, I cannot OHE all of them at once. Here is the solution that I have come up with:
fac <- data.frame()
for (i in names(train.fact)) {
dmy <- dummyVars( ~ i , data = train.fact)
trsf <- data.frame(predict(dmy, newdata = train.fact))
fac <- cbind(fac, trsf)
}
My hope is that this for loop would OHE the first feature, store that in fac, then move onto the next feature, OHE it, and cbind that information to fac and so on.
When attempting to run this, I get this error:
Error in `[.data.frame`(data, , vars, drop = FALSE) : undefined columns selected
I believe this is due to the way the names of each feature are being passed into 'i'.
I also thought this may be done through an apply function but cannot come up with the appropriate syntax.
Any help is appreciated!
I am a second year M.Sc student and I am running into a bit of a snag running my statistics.
I am trying to run a contingency table and Fishers test and I keep getting an error.
Error in fisher.test(GAL4UAS) : if 'x' is not a matrix, 'y' must be given
If anyone can see what I have done wrong/may be missing I would really appreciate it?
This is the code:
setwd("/Users/Pria/Desktop/Data Analysis/")
GAL4UAS <-- data.frame(Yes=c(20,21,19),No=c(10,9,11))
GAL4UAS <- lapply(GAL4UAS, abs)
fisher.test(GAL4UAS)
fisher.test(GAL4UAS[c(1,2)])
fisher.test(GAL4UAS[c(1,3)])
fisher.test() is anticipating a matrix as an input and not a data frame. Try putting your data into a matrix. One option among several would be:
m <- matrix(c(20,21,19,10,9,11),nrow = 3,ncol=2,byrow=FALSE)
fisher.test(m)
When you apply the abs() using lapply the output is a list and not a data.frame. The apply function returns the output in a matrix format which is expected in the fisher.test(). So maybe you can try this:
GAL4UAS <- data.frame(Yes=c(20,21,19),No=c(10,9,11))
GAL4UAS <- apply(GAL4UAS, abs, MARGIN=c(1,2))
fisher.test(GAL4UAS)
I am Having a little problem doing a Levene test in R. I does not get any output value, only NaN. Anyone know what the problem might be?
Have used the code:
with(Test,levene.test(Sample1,Sample2,location="median"))
The problem
Best regards
The levene.test function assumes the data are in a single vector. The second argument is a grouping variable.
Concatenate your data using the c() function: data=c(Sample1, Sample2). Construct a vector of group names like gp = rep('Gp1','Gp2', each=240). Then, call the function as follows: levene.test(data, gp, location='median').
This can also be done directly:
levene.test(c(Sample1, Sample2), rep('Gp1', 'Gp2', each=240)), location='median')
I have the following block of code. I am a complete beginner in R (a few days old) so I am not sure how much of the code will I need to share to counter my problem. So here is all of it I have written.
mdata <- read.csv("outcome-of-care-measures.csv",colClasses = "character")
allstate <- unique(mdata$State)
allstate <- allstate[order(allstate)]
spldata <- split(mdata,mdata$State)
if (num=="best") num <- 1
ranklist <- data.frame("hospital" = character(),"state" = character())
for (i in seq_len(length(allstate))) {
if (outcome=="heart attack"){
pdata <- spldata[[i]]
pdata[,11] <- as.numeric(pdata[,11])
bestof <- pdata[!is.na(as.numeric(pdata[,11])),][]
inorder <- order(bestof[,11],bestof[,2])
if (num=="worst") num <- nrow(bestof)
hospital <- bestof[inorder[num],2]
state <- allstate[i]
ranklist <- rbind(ranklist,c(hospital,state))
}
}
allstate is a character vector of states.
outcome can have values similar to "heart attack"
num will be numeric or "best" or "worst"
I want to create a data frame ranklist which will have hospital names and the state names which follow a certain criterion.
However I keep getting the error
invalid factor level, NA generated
I know it has something to do with rbind but I cannot figure out what is it. I have tried googling about this, and also tried troubleshooting using other similar queries on this site too. I have checked any of my vectors I am trying to bind are not factors. I also tried forcing the coercion by setting the hospital and state as.character() during assignment, but didn't work.
I would be grateful for any help.
Thanks in advance!
Since this is apparently from a Coursera assignment I am not going to give you a solution but I am going to hint at it: Have a look at the help pages for read.csv and data.frame. Both have the argument stringsAsFactors. What is the default, true or false? Do you want to keep the default setting? Is colClasses = "character" in line 1 necessary? Use the str function to check what the classes of the columns in mdata and ranklist are. read.csv additionally has an na.strings argument. If you use it correctly, also the NAs introduced by coercion warning will disappear and line 16 won't be necessary.
Finally, don't grow a matrix or data frame inside a loop if you know the final size beforehand. Initialize it with the correct dimensions (here 52 x 2) and assign e.g. the i-th hospital to the i-th row and first column of the data frame. That way rbind is not necessary.
By the way you did not get an error but a warning. R didn't interrupt the loop it just let you know that some values have been coerced to NA. You can also simplify the seq_len statement by using seq_along instead.