Hi Steven,
Neat solution. With a lot more values on the time axis, you will be
better off with something like:
yrticks<-as.Date(as.character(seq(min(sydf$year1),max(sydf$year1),by=2)),
"%Y")
axis(1,at=yrticks,labels=format(yrticks,"%Y"))
You probably won't need staxlab for that.
Jim
On Fri,
Thanks Jim, that worked.
> I expected that the axis labels would be crowded so I used the plotrix
> library to stagger the x-axis labels. Hope this solves your problem.
I liked how that showed, not overlapping on each other. I wasn't aware of the
plotrix library.
In my code I was using plot_ly
Hi Steven,
It is pretty easy, but there are one or two things to watch for.
First, don't use a hyphen in a field name unless you enclose it in
single quotes when extracting it. I've just removed the hyphen in this
example:
sydf<-read.table(text="year1 monthday rate
1993 05-01 0.608
1994 06-01
I don't understand your question.
> I am simulating multi-dimensional and categorical items
What do you mean by "and"?
Reading this sentence alone, I would assume you want one simulated dataset
that's categorical and multivariate.
Which is easy to do...
Because (in general) you can simulate
Hi. Again.
I still cannot understand that why the simulated categories have opposite of
normal distribution. Theta is set as normal function. Then, more obs. should
have more proportion of middle categories. But, all items of simulated data
have the most probabilities at the least ability
Thank you for answering.
I know mirt is not related the distribution of categories.
My study is factor analysis results difference of limited information methods
and full information methods. I need a simulation data to compare those. And
limited information factor analysis need an assumption of
?axis
-- And note the examples!
*Please* go through one or more of the web tutorials on plotting in R. I
feel that it is unfair of you to ask for such basic tutorials here when
resources are already available to you (others may disagree, of course). If
you have questions *after* reading, then
There's a typo in the first column name of your data - "year1" should
be "year". Sometimes R will do partial matching and find it, but not
always. Is this closer to what you're looking for?
plot(rate~year, sydf, type="b", xaxt="n", xlab="Month-Day")
axis(1, sydf$year, sydf$month.day)
This will
Sorry Jim, I saw that you answered to the other Steven.
I had a question and nobody responded to that yet, I thought you responded
to me.
I searched for mine and your email came up, but I realize the subject line
is different. My question was:
"How to change x axes labels in plot_ly?"
Hi Jim,
Thanks for your email.
My question was: how to change the x axis labels without changing the chart.
Or is that not possible?
Using your example, I added another column:
sydf<-read.table(text="year1 month-day rate
1993 05-01 0.608
1994 06-01 0.622
1996 07-01 0.623
1998 08-01 0.647
You're asking a question unrelated to R programming and so you won't get a
useful response here. However, your question also suggests a misunderstanding
of IRT. Generating multi-dimensional data involves generating ability estimates
with additional nuisance dimensions and that has no
Hi. again.
Still the same problem, but I made a new code to see better of my question.
Like the first email, I still want each item's categories to have a normal
distribution. it doesn't have to statistically fit. I made 2 different code.
And, I found if the histogram is the opposite, they will
Hi. I always thank you all of the R program worker and researchers.
I am using R for my thesis, and I have a question.
I am simulating multi-dimensional and categorical items (polytomous) with
mirt-simdata.
However, I wish each items' categories are normal distribution. I checked a lot
of
The help for cov() clearly notes that pairwise only works with pearson. I'm
curious why as it is surely not computational. Certainly an analyst knows the
nature of the missingness in their data and if it's not MCAR then applying
pairwise to spearman would have the same inferential consequence
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