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I am trying to make a histogram of grades. Here are my variables.
> grade <- factor(c("A","A","A","B","A","A","A","A","B","A","C","B","B","B"))
> numberBook <- c(53,42,40,40,39,34,34,30,28,24,22,21,20,16)
But when I plot it, I get an error message.
> hist(numberBook~grade)
Error in hist.default(numberBook ~ grade) : 'x' must be numeric
What can I do?
I'm not sure why you've got multiple letters so I've guessed that you want a total of all the A, B and Cs. This may not be quite right. I've recreated your data like this using rep and summing the counts of grades (could be wrong)
data <-c(rep("A",(53+42+40+34+34+30+28+22)), rep("B",(39+24+20+16+22)),rep("C",22))
Then I can plot the data using barplot:
barplot(prop.table(table(data)))
Barplot is probably what you want here.
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How do I get the VIF value without Gvif and GVIF^(1/(2*Df)). I have tried the command vif(model) and need just the vif value but I get the output as gvif
If you want the VIF value from a regression model, the simplest solution is using the car package:
library(car)
vif(model)
Which returns the vif value:
gdp labour_participation m_per1000f
1.100277 1.457567 1.667722
time_prison
1.247356
If you want to calculate the VIF value manually (the harder way to doing it but without using any library), you can do so to verify that the results you got from the above is indeed correct:
vif_lp <- 1/(1-(summary(lm(labour_participation ~ gdp + m_per1000f + time_prison, crime))$r.squared))
vif_lp
# returns [1] 1.457567
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How can I scale(x) only certain columns of a dataframe? I have a dataframe with 7 columns and I want to scale only column 3 and 6. The rest should stay as it is.
We can do this with lapply. Subset the columns of interest, loop through them with lapply, assign the output back to the subset of data. Here, we are using c because the outpuf of scale is a matrix with a single column. Using c or as.vector, it gets converted to vector
df[c(3,6)] <- lapply(df[c(3, 6), function(x) c(scale(x)))
Or another option is mutate_at from dplyr
library(dplyr)
df %>%
mutate_at(c(3,6), funs(c(scale(.))))
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dat = runif(10,1,10)
dat2 = runif(10,1,10)
dat3 = runif(10,1,10)
data = rbind(dat,dat2,dat3)
In the case of above data, I am wondering how I can filter out rows as long as there is one element in that row exceeding 5.
I know that I can use loop to achieve this, but I am wondering if there is more succinct way to do this.
try this:
data[apply(data>5, 1, sum)>0,]
This says "for condition having more than zero number exceeding 5 in each row, filter data".
data[do.call(pmax,data.frame(data))<=5,]
Cheers,
Bert
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When I submit this code to R:
x <- c(1,2,4)
z <- c(7,6,3)
a <- x * z
I get:
a
[1] 7 12 12
So R just multiples element by element. But the two vectors are not compatible for multiplication because the first one has three columns and the second one does not have three rows.
What is happening internally?
Please note that these are vectors; not tables.
This means they can of course be multiplied with each other and would give the expected result through their inner product.
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Does anybody know the examples on how to run paired ttest in Matlab/R/SAS or Python/Java on many columns (I have 1139 variables) in all combinations or selected respective columns in a loop.
thank you
MATLAB Solution:
If I understand correctly, you're just looking for a way to feed ttest with two different columns from your input matrix everytime. You can get all possible combinations of column pairs using nchoosek:
pairs = nchoosek(1:size(X, 2), 2);
Now you can iterate over these indices, each time invoking ttest with a different pair:
for idx = transpose(pairs)
h = ttest(X(:, idx(1)), X(:, idx(2)));
%// Do something with the result...
end