Using the mice package, I have created multiple imputed datasets to deal
with missing data. I am looking for an example of the R code to use in
order to analyze the set of imputed datasets using tetrachoric correlations
in such a way that after pooling, I will have a combined tetrachoric
covariance
Hi,
I am making some plots with plot() which have a fair number of points
(thousands) and I would like to be able to interactively select a
region of the plot and zoom in on it. I tried the zoom package which
has the function zm() but I found that it was unworkably slow to
refresh the display. I g
According to this handy vignette, the object contains the predicted
values in $pred
https://cran.r-project.org/web/packages/incidence/vignettes/overview.html
early.fit <- fit(i.7[1:20])
early.fit
#>
#>
#> $model: regression of log-incidence over time
#>
#> $info: list containing the following it
Hello
How can I make a prediction with object type incidence_fit.
incidence_fit object is returned by the function fit in library(incidence).
Best regard
maicel
--
Este mensaje le ha llegado mediante el servicio de correo ele
I suspect much if not all of your trouble would be eliminated by using
file.path() instead of paste0().
https://www.rdocumentation.org/packages/base/versions/3.6.2/topics/file.path
(Also check your file name - you probably want a . between name and
csv, so using paste(name, "csv", sep = ".") woul
What package is "read.dbf" from? What error message/behavior did you see?
Should it be:
path<-setwd(paste0("inpath/",folder)) ## did you forget the "/" ?
Bert Gunter
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathe
Hi Everyone,
I am trying to loop through the folders in the major working directory.
Read the dbf file into the data frame then save the data frame as CSV file
in another folder.
For this, I have written this code, But not able to figure out where it is
going wrong. Any ideas will be of great suppo
Or using matplot:
matplot(cbind(g1, g2, g3), type = "l",
col = c("red", "black", "blue"), lty = 1, lwd = 2)
Best,
luke
On Fri, 24 Apr 2020, Jim Lemon wrote:
Hi Luigi,
This is pretty easy using "plot" and "lines":
# assume your example data
plot(g1,col ="red",type = "l", lwd = 2,
Hello,
This is probably off-topic since you have chosen base graphics but this
is much easier with ggplot.
library(ggplot2)
ggplot(df, aes(Index, Incidence, colour = Country)) +
geom_line() +
scale_colour_manual(values = c("red", "black", "blue")) +
theme_minimal()
Hope this helps,
Hi Luigi,
This is pretty easy using "plot" and "lines":
# assume your example data
plot(g1,col ="red",type = "l", lwd = 2,
xlab = "Days since start", ylab = "Count of infections",
main = "Daily cases of COVID-19")
lines(g2,col="black", lwd = 2)
lines(g3,col="blue", lwd = 2)
text(c(15,80,
Hi Luigi,
the problem is not the first graph vs the second graph. The first
graph would also show the same effect if you added type='l' to the
plot command.
There are various ways to approach this. A quick search turned up the
following which gives you different options.
https://stackoverflow.com/q
Hello,
I am trying to make an epidemic plot of the COVID pandemic using the core
plot function. I am looking at three countries and the countries are as
factors. The idea is to colour the entry by country, following this scheme:
```
df = data.frame(index = 1:10,
value = c(rnorm(10),
The build system rolled up R-4.0.0.tar.gz (codename "Arbor Day") this morning.
The list below details the changes in this release.
You can get the source code from
http://cran.r-project.org/src/base/R-4/R-4.0.0.tar.gz
or wait for it to be mirrored at a CRAN site nearer to you.
Binaries for var
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