I working with the ACDm package in Rstudio, which is a very helpful package for my topic. I came across the author's manual and noticed that the LACD1 and LACD2 functions deviates slightly from the original paper:
The conditional duration ( in the picture as mu_i) enters as a logarithmic value in the equation, which is not according to the original paper assumption.
They should enter in the equation like this:
The conditional duration (here as psi) enters not as a logarithmic value. The conditional duration is then used to calculate the expected durations as x_i = exp(psi_i)*randomErrorTerm
If the conditional duration enters already as a logarithmic value (picture 1) in the formula and it is then used to calculate the expected durations, it looks like this x_i = exp(log(psi_i)))*randomErrorTerm, which is not according to the origin paper.
To correct this, I basically have remove a "log" in the source code of the ACDm Package.
The problem is, that that the source code of the LACD1/LACD12 is written in c.
I was wondering if RStudio provides a method to change source code written in c, and use the package with the changed source code for at least the duration of the session?
Related
Working with code that describes a poisson cluster process in spatstat. Breaking down each line of code one at a time to understand. Easy to begin.
library(spatstat)
lambda<-100
win<-owin(c(0,1),c(0,1))
n.seeds<-lambda*win$xrange[2]*win$yrange[2]
Once the window is defined I then generate my points using a random generation function
x=runif(min=win$xrange[1],max=win$xrange[2],n=pmax(1,n.seeds))
y=runif(min=win$yrange[1],max=win$yrange[2],n=pmax(1,n.seeds))
This can be plotted straight away I know using the ppp function
seeds<-ppp(x=x,
y=y,
window=win)
plot(seeds)
The next line I add marks to the ppp object, it is apparently describing the angle of rotation of the points, I don't understand how this works right now but that is okay, I will figure out later.
marks<-data.frame(angles=runif(n=pmax(1,n.seeds),min=0,max=2*pi))
seeds1<-ppp(x=x,
y=y,
window=win,
marks=marks)
The first problem I encounter is that an objects called pops, describing the populations of the window, is added to the ppp object. I understand how the values are derived, it is a poisson distribution given the input value mu, which can be any value and the total number of observations equal to points in the window.
seeds2<-ppp(x=x,
y=y,
window=win,
marks=marks,
pops=rpois(lambda=5,n=pmax(1,n.seeds)))
My first question is, how is it possible to add a variable that has no classification in the ppp object? I checked the ppp documentation and there is no mention of pops.
The second question I have is about using double variables, the next line requires an sapply function to define dimensions.
dim1<-pmax(1,sapply(seeds1$marks$pops, FUN=function(x)rpois(n=1,sqrt(x))))
I have never seen the $ function being used twice, and seeds2$marks$pop returns $ operator is invalid for atomic vectors. Could you explain what is going on here?
Many thanks.
That's several questions - please ask one question at a time.
From your post it is not clear whether you are trying to understand someone else's code, or developing code yourself. This makes a difference to the answer.
Just to clarify, this code does not come from inside the spatstat package; it is someone's code using the spatstat package to generate data. There is code in the spatstat package to generate simulated realisations of a Poisson cluster process (which is I think what you want to do), and you could look at the spatstat code for rPoissonCluster to see how it can be done correctly and efficiently.
The code you have shown here has numerous errors. But I will start by answering the two questions in your title.
The rules for creating ppp objects are set out in the help file for ppp. The help says that if the argument window is given, then unmatched arguments ... are ignored. This means that in the line seeds2<-ppp(x=x,y=y,window=win,marks=marks,pops=rpois(lambda=5,n=pmax(1,n.seeds)))
the argument pops will be ignored.
The idiom sapply(seeds1$marks$pops, FUN=f) is perfectly valid syntax in R. If the object seeds1 is a structure or list which has a component named marks, which in turn is a structure or list which has a component named pops, then the idiom seeds1$marks$pops would extract it. This has nothing particularly to do with sapply.
Now turning to errors in the code,
The line n.seeds<-lambda*win$xrange[2]*win$yrange[2] is presumably meant to calculate the expected number of cluster parents (cluster seeds) in the window. This would only work if the window is a rectangle with bottom left corner at the origin (0,0). It would be safer to write n.seeds <- lambda * area(win).
However, the variable n.seeds is used later as it it were the number of cluster parents (cluster seeds). The author has forgotten that the number of seeds is random with a Poisson distribution. So, the more correct calculation would be n.seeds <- rpois(1, lambda * area(win))
However this is still not correct because cluster parents (seed points) outside the window can also generate offspring points inside the window. So, seed points must actually be generated in a larger window obtained by expanding win. The appropriate command used inside spatstat to generate the cluster parents is bigwin <- grow.rectangle(Frame(win), cluster_diameter) ; Parents <- rpoispp(lambda, bigwin)
The author apparently wants to assign two mark values to each parent point: a random angle and a random number pops. The correct way to do this is to make the marks a data frame with two columns, for example marks(seeds1) <- data.frame(angles=runif(n.seeds, max=2*pi), pops=rpois(n.seeds, 5))
I am trying to create a person item map that organizes the questions from a dataset in order of difficulty. I am using the eRm package and the output should looks like follows:
[person-item map] (https://hansjoerg.me/post/2018-04-23-rasch-in-r-tutorial_files/figure-html/unnamed-chunk-3-1.png)
So one of the previous steps, before running the function that outputs the map, I have to fit the data set to have a matrix which is the object that the plotting functions uses to create the actual map, but I am having an error when creating that matrix
I have already tried to follow and review some documentation that might be useful if you want to have some extra-information:
[Tutorial] https://hansjoerg.me/2018/04/23/rasch-in-r-tutorial/#plots
[Ploting function] https://rdrr.io/rforge/eRm/man/plotPImap.html
[Documentation] https://eeecon.uibk.ac.at/psychoco/2010/slides/Hatzinger.pdf
Now, this is the code that I am using. First, I install and load the respective libraries and the data:
> library(eRm)
> library(ltm)
Loading required package: MASS
Loading required package: msm
Loading required package: polycor
> library(difR)
Then I fit the PCM and generate the object of class Rm and here is the error:
*the PCM function here is specific for polytomous data, if I use a different one the output says that I am not using a dichotomous dataset
> res <- PCM(my.data)
>Warning:
The following items have no 0-responses:
AUT_10_04 AUN_07_01 AUN_07_02 AUN_09_01 AUN_10_01 AUT_11_01 AUT_17_01
AUT_20_03 CRE_05_02 CRE_07_04 CRE_10_01 CRE_16_02 EFEC_03_07 EFEC_05
EFEC_09_02 EFEC_16_03 EVA_02_01 EVA_07_01 EVA_12_02 EVA_15_06 FLX_04_01
... [rest of items]
>Responses are shifted such that lowest
category is 0.
Warning:
The following items do not have responses on
each category:
EFEC_03_07 LC_07_03 LC_11_05
Estimation may not be feasible. Please check
data matrix
I must clarify that all the dataset has a range from 1 to 5. Is a Likert polytomous dataset
Finally, I try to use the plot function and it does not have any output, the system just keep loading ad-infinitum with no answer
>plotPImap(res, sorted=TRUE)
I would like to add the description of that particular function and the arguments:
>PCM(X, W, se = TRUE, sum0 = TRUE, etaStart)
#X
Input data matrix or data frame with item responses (starting from 0);
rows represent individuals, columns represent items. Missing values are
inserted as NA.
#W
Design matrix for the PCM. If omitted, the function will compute W
automatically.
#se
If TRUE, the standard errors are computed.
#sum0
If TRUE, the parameters are normed to sum-0 by specifying an appropriate
W.
If FALSE, the first parameter is restricted to 0.
#etaStart
A vector of starting values for the eta parameters can be specified. If
missing, the 0-vector is used.
I do not understand why is necessary to have a score beginning from 0, I think that that what the error is trying to say but I don't understand quite well that output.
I highly appreciate any hint that you can provide me
Feel free to ask for any information that could be useful to reach the solution to this issue
The problem is not caused by the fact that there are no items with 0-responses. The model automatically corrects this by centering the response scale categories on zero. (You'll notice that the PI-map that you linked to is centered on zero. Also, I believe the map you linked to is of dichotomous data. Polytomous data should include the scale categories on the PI-map, I believe.)
Without being able to see your data, it is impossible to know the exact cause though.
It may be that the model is not converging. That may be what this error was alluding to: Estimation may not be feasible. Please check data matrix. You could check by entering > res at the prompt. If the model was able to converge you should see something like:
Conditional log-likelihood: -2.23709
Number of iterations: 27
Number of parameters: 8
...
Does your data contain answers with decimal numbers? I found the same error, I solved it by using dplyr::dense_rank() function:
df_ranked <- sapply(df_decimal_data, dense_rank)
Worked.
I am trying to (eventually) plot data by groups, using the prodlim function.
I'm adjusting and adapting code that someone else (not available for questions) has written, and I'm not very familiar with the prodlim library/function. There are definitely other ways to do what I'd like to, but I'm trying to keep it consistent with what the previous person did.
I have code that works, when dividing the data into 2 groups, but when I try to adjust for a 4 group situation, I get an error.
Of note, the data is coming over from SAS using StatTransfer, which has been working fine.
I am new to coding, but I have compared the dataframes I'm trying to work with. The second is just a subset of the first (where the code does work), with all the same variables, and both of the variables I'm trying to group by are integer values.
Hist(medpop$dz_time, medpop$dz_status) works just fine, so the problem must be with the prodlim function, and I haven't understood much of what I've looked up about it, sadly :/ But it the documentation seems to indicate it supports continuous or categorical variables, and doesn't seem limited to binary either. None of the options seem applicable as I understand them.
this works:
M <- prodlim(Hist(dz_time, dz_status)~med, data=pop)
where med is a binary value =1 when a member of this population is taking it, and dz is a disease that some portion develop.
this does not:
(either of these get the error as below)
N <- prodlim(Hist(dz_time, dz_status)~strength, data=medpop)
N <- prodlim(Hist(dz_time, dz_status)~strength, data=pop, subset=pop$med==1)
medpop = the subset of the original population taking the med,
strength = categorical variable ("1","2","3","4")
For the line that does work, the next step is just plot(M), giving a plot with two lines, med==0 and med==1 (showing cumulative incidence of dz_status by dz_time).
For the other line, I get an error saying
Error in KernSmooth::dpik(cumtabx/N, kernel = "box") :
scale estimate is zero for input data
I don't know what that means or how to fix it.. :/
I am using gnuplot and the function fitting facilities to perform least squares fitting to some of my data.
I have many data points (sometimes tens of millions) and hence fitting to all data points is impossible. (Or at least too slow to be practical.)
It is possible to plot data points with the keyword every (EDIT: Should be pointinterval not every!) followed by an integer, N, to plot only every other Nth point.
eg plot 'data.csv' using 1:2 pointinterval 1000 plots every thousandth data point. Useful for when plotting 10's of millions of points - you can't see anything useful otherwise.
Is there a similar way of doing this with fitting, ie, fit only every 1000'th point?
I tried fit 'data.csv' f(x) using 1:2 pointinterval 1000 via a,b where a and b are parameters of my f(x) - but I just get an error: ';' expected.
I also tried googling this and reading the documentation for gnuplot plotting but didn't find anything.
Alternatively, I could change my program code to only write every 1000th point to datafile, but then I will have to have 2 lots of datafiles - one with all the points and one with 1 in every 1000 data points... which seems kind of wasteful.
Edit: I am not sure why I thought every was the correct syntax for this. Turns out it should be pointinterval (pi short) followed by an integer.
However, this only works for plotting, not function fitting, so the question is still open.
Note for future: use every syntax
I am trying to plot a variable that is in a NetCDF file using GrADS and I would like to plot only the values that are smaller than -20 (could be any other number as an example). I can't find a way to do it though. I saw several examples of variable substitution using both maskoutand const (for example define ones = const(const(maskout(p,p-10),1),0,-u), which is here) but I couldn't make that work for my purposes.
I want the variable at a given point not plotted in case its value is below -20, and not this value change to another one in such point.
Solved it using the command maskout(p,p+20). The badly written documentation is what delayed me on this one. Answered here so that the community may benefit.
Thanks.