I am working with data where I need to create multiple scatter plots for different populations. I also recently upgraded from SPSS v26 to v28 and the code that I used for this worked in v26 but is no longer working correctly in v28. Instead of producing multiple scatter plots like it's supposed to, it is now producing 1 plot in v28, presumably the first subpopulation in the split. I tested the split file function with descriptives and it worked as intended.
I have scoured everything in the GUI menus to find any kind of setting that was ticked by default and came up with nothing. I also tried to use a filter based on the criterion variables in my split file function and ran a scatter plot but that gave me a graph of the whole population instead of the subpopulation I filtered. Any guidance on what could be going on with scatter plots and the split file function in SPSS v28 would be greatly appreciated.
Here is my code for reference:
SORT CASES BY exit_yr service sigscore.
SPLIT FILE SEPARATE BY exit_yr service sigscore.
*Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=wait.time[name="wait_time"]
score.change[name="score_change"] service.ideal[name="service_ideal"] MISSING=VARIABLEWISE
REPORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE
/FITLINE TOTAL=NO SUBGROUP=NO.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: wait_time=col(source(s), name("wait_time"))
DATA: score_change=col(source(s), name("score_change"))
DATA: service_ideal=col(source(s), name("service_ideal"), unit.category())
GUIDE: axis(dim(1), label("wait.time: Difference in days between referral submission ",
"and referral acceptance"))
GUIDE: axis(dim(2), label("score.change: score change from pre to post"))
GUIDE: legend(aesthetic(aesthetic.color.interior), label("service.ideal"))
GUIDE: text.title(label("Grouped Scatter of score.change: score change from pre to ",
"post by wait.time: Difference in days between referral submission and referral ",
"acceptance by service.ideal"))
ELEMENT: point(position(wait_time*score_change), color.interior(service_ideal))
END GPL.
SPLIT FILE OFF.
Related
I'm currently doing arima forecasting in R and i'm already on the last step of displaying the forecast result but I am having trouble in displaying the forecast on the graph.
Here is my code:
mydata.arima005 <- arima(d.y, order= c(0,0,5))
mydata.pred1 <- predict (mydata.arima005, n.head =100)
plot(d.y)
lines(mydata.pred1$pred, col="blue")
lines(mydata.pred1$pred+2*mydata.pred1$se, col="red")
lines(mydata.pred1$pred-2*mydata.pred1$se, col="red")
So as you can see, I want my graph to show the forecast values in color blue and the confidence interval on red. But this is what I am getting instead.
So as you can see it's not color coded at all. My code contains no error.
This is a sample of the output I am expecting(got it from youtube)I used the same codes used in this video I got thats why i was wondering why my graph doe not look like this.Hope you could help
The forecast package has some functions for displaying nice plots of the forecasts and prediction intervals.
You would need something like this:
library(forecast)
mydata.arima005 <- Arima(d.y, order= c(0,0,5))
mydata.pred1 <- forecast(mydata.arima005, h=100)
plot(mydata.pred1)
Or using ggplot2 graphics:
library(ggplot2)
autoplot(mydata.pred1)
does anyone use arima.rob() function described by Eric Zivot and Jiahui Wang in { Modelling Financial Time Series with S-PLUS } ?
I have a question about it:
I used a dataset of network traffic flows that has anomaly, and I tried to predict the last part of dataset by robust ARIMA method (Arima.rob() function) .I compare this model with arima.mle of S-PLUS. But Unexpectedly, arima.rob’s prediction did not better than that.
I’m not sure my codes are correct and may be the reason of fault is my codes.
Please, help me if I used Arima.rob inappropriately?
tmp.rr<-arima.rob((tmh75)~1,p=2,d=1,q=2,freq=24,maxiter=4,max.fcal=80000)
tmp.for<-predict(tmp.rr,n.predict=10,newdata=df1,se=T)
plot(tmp.for,tmh75)
summary(tmp.for)
my code for classic arima:
`model <- list(list(order=c(2,1,2)),list(order=c(3,1,2),period=24))
fith <- arima.mle(tmh75-mean(tmh75),model=model)
foreh <- arima.forecast(tmh75,n=25,model=fith$model)
tsplot(tmh75,foreh$mean,foreh$mean+foreh$std.err,foreh$mean-foreh$std.err)
`
Its quite a big task but ill try to explain.
I have an array with a list of 200 strings and I want to be able to randomly select one and add it to the stage using code. I have movieclips exported for actionscript with the same class name as the strings in the array. Also, if it is possible, would I be able to select the strings with predictability such as the first has a 0.7 chance the second a 0.1 etc. Here is what i have currently
var nameList:Array=["Jimmy","Bob","Fred"]
var instance:DisplayObject = createRandom(nameList);
addChild(instance);
function createRandom(typeArray:Array):*
{
// Select random String from typeArray.
var selection:String = typeArray[ int(Math.random() * typeArray.length) ];
// Create instance of relevant class.
var Type:Class = getDefinitionByName(selection) as Class;
// Return created instance.
return new Type();
}
All this throws me this error
ReferenceError: Error #1065: Variable [class Jimmy] is not defined.
Ive searched for other threads similar but none combine the three specific tasks of randomisation, predictability and addChild().
I think that you've got two problems: a language problem and a logic problem. In the .fla connected to your code above, in the Library find each symbol representing a name and write into the 'AS linkage' column for that symbol the associated name -- e.g., 'Bob,' 'Fred' -- just the name, no punctuation.
Now getDefinitionByName() will find your 'Class'
If you put a different graphic into each MovieClip -- say, a piece of fruit or a picture of Bob,Jim, Fred -- and run your program you'll get a random something on stage each time.
That should solve your language problem. But the logic problem is a little harder, no?
That's why I pointed you to Mr. Kelly's solution (the first one, which for me is easier to grasp).
I'm trying to figure out how to populate a table from a JSON array. So far, I can populate my table cells perfectly fine by using the following code:
self.countries = [[NSArray alloc]initWithObjects:#"Argentina",#"China",#"Russia",nil];
Concerning the JSON, I can successfully retrieve one line of text at a time and display it in a label. My goal is to populate an entire table view from a JSON array. I tried using the following code, but it still won't populate my table. Obviously I'm doing something wrong, but I searched everywhere and still can't figure it out:
NSURL *url = [NSURL URLWithString:#"http://BlahBlahBlah.com/CountryList"];
NSURLRequest *request = [NSURLRequest requestWithURL:url];
AFJSONRequestOperation *operation = [AFJSONRequestOperation JSONRequestOperationWithRequest:request success:^(NSURLRequest *request, NSHTTPURLResponse *response, id JSON)
{
NSLog(#"%#",[JSON objectForKey:#"COUNTRIES"]);
self.countries = [JSON objectForKey:#"COUNTRIES"];
}
failure:nil];
[operation start];
I am positive that the data is being retrieved, because the NSLog outputs the text perfectly fine. But when I try setting my array equal to the JSON array, nothing happens. I know the code is probably wrong, but I think I'm on the right track. Your help would be much appreciated.
EDIT:
This is the text in the JSON file I'm using:
{
"COUNTRIES": ["Argentina", "China", "Russia",]
}
-Miles
It seems that you need some basic JSON parsing. If you only target iOS 5.0 and above devices, then you should use NSJSONSerialization. If you need to support earlier iOS versions, then I really recommend the open source JSONKit framework.
Having recommended the above, I myself almost always use the Sensible TableView framework to fetch all data from my web service and automatically display it on a table view. Saves me a ton of manual labor and makes app maintenance a breeze, so it's probably something to consider too. Good luck!
I wish to search twitter for a word (let's say #google), and then be able to generate a tag cloud of the words used in twitts, but according to dates (for example, having a moving window of an hour, that moves by 10 minutes each time, and shows me how different words gotten more often used throughout the day).
I would appreciate any help on how to go about doing this regarding: resources for the information, code for the programming (R is the only language I am apt in using) and ideas on visualization. Questions:
How do I get the information?
In R, I found that the twitteR package has the searchTwitter command. But I don't know how big an "n" I can get from it. Also, It doesn't return the dates in which the twitt originated from.
I see here that I could get until 1500 twitts, but this requires me to do the parsing manually (which leads me to step 2). Also, for my purposes, I would need tens of thousands of twitts. Is it even possible to get them in retrospect?? (for example, asking older posts each time through the API URL ?) If not, there is the more general question of how to create a personal storage of twitts on your home computer? (a question which might be better left to another SO thread - although any insights from people here would be very interesting for me to read)
How to parse the information (in R)? I know that R has functions that could help from the rcurl and twitteR packages. But I don't know which, or how to use them. Any suggestions would be of help.
How to analyse? how to remove all the "not interesting" words? I found that the "tm" package in R has this example:
reuters <- tm_map(reuters, removeWords, stopwords("english"))
Would this do the trick? I should I do something else/more ?
Also, I imagine I would like to do that after cutting my dataset according to time (which will require some posix-like functions (which I am not exactly sure which would be needed here, or how to use it).
And lastly, there is the question of visualization. How do I create a tag cloud of the words? I found a solution for this here, any other suggestion/recommendations?
I believe I am asking a huge question here but I tried to break it to as many straightforward questions as possible. Any help will be welcomed!
Best,
Tal
Word/Tag cloud in R using "snippets" package
www.wordle.net
Using openNLP package you could pos-tag the tweets(pos=Part of speech) and then extract just the nouns, verbs or adjectives for visualization in a wordcloud.
Maybe you can query twitter and use the current system-time as a time-stamp, write to a local database and query again in increments of x secs/mins, etc.
There is historical data available at http://www.readwriteweb.com/archives/twitter_data_dump_infochimp_puts_1b_connections_up.php and http://www.wired.com/epicenter/2010/04/loc-google-twitter/
As for the plotting piece: I did a word cloud here: http://trends.techcrunch.com/2009/09/25/describe-yourself-in-3-or-4-words/ using the snippets package, my code is in there. I manually pulled out certain words. Check it out and let me know if you have more specific questions.
I note that this is an old question, and there are several solutions available via web search, but here's one answer (via http://blog.ouseful.info/2012/02/15/generating-twitter-wordclouds-in-r-prompted-by-an-open-learning-blogpost/):
require(twitteR)
searchTerm='#dev8d'
#Grab the tweets
rdmTweets <- searchTwitter(searchTerm, n=500)
#Use a handy helper function to put the tweets into a dataframe
tw.df=twListToDF(rdmTweets)
##Note: there are some handy, basic Twitter related functions here:
##https://github.com/matteoredaelli/twitter-r-utils
#For example:
RemoveAtPeople <- function(tweet) {
gsub("#\\w+", "", tweet)
}
#Then for example, remove #d names
tweets <- as.vector(sapply(tw.df$text, RemoveAtPeople))
##Wordcloud - scripts available from various sources; I used:
#http://rdatamining.wordpress.com/2011/11/09/using-text-mining-to-find-out-what-rdatamining-tweets-are-about/
#Call with eg: tw.c=generateCorpus(tw.df$text)
generateCorpus= function(df,my.stopwords=c()){
#Install the textmining library
require(tm)
#The following is cribbed and seems to do what it says on the can
tw.corpus= Corpus(VectorSource(df))
# remove punctuation
tw.corpus = tm_map(tw.corpus, removePunctuation)
#normalise case
tw.corpus = tm_map(tw.corpus, tolower)
# remove stopwords
tw.corpus = tm_map(tw.corpus, removeWords, stopwords('english'))
tw.corpus = tm_map(tw.corpus, removeWords, my.stopwords)
tw.corpus
}
wordcloud.generate=function(corpus,min.freq=3){
require(wordcloud)
doc.m = TermDocumentMatrix(corpus, control = list(minWordLength = 1))
dm = as.matrix(doc.m)
# calculate the frequency of words
v = sort(rowSums(dm), decreasing=TRUE)
d = data.frame(word=names(v), freq=v)
#Generate the wordcloud
wc=wordcloud(d$word, d$freq, min.freq=min.freq)
wc
}
print(wordcloud.generate(generateCorpus(tweets,'dev8d'),7))
##Generate an image file of the wordcloud
png('test.png', width=600,height=600)
wordcloud.generate(generateCorpus(tweets,'dev8d'),7)
dev.off()
#We could make it even easier if we hide away the tweet grabbing code. eg:
tweets.grabber=function(searchTerm,num=500){
require(twitteR)
rdmTweets = searchTwitter(searchTerm, n=num)
tw.df=twListToDF(rdmTweets)
as.vector(sapply(tw.df$text, RemoveAtPeople))
}
#Then we could do something like:
tweets=tweets.grabber('ukgc12')
wordcloud.generate(generateCorpus(tweets),3)
I would like to answer your question in making big word cloud.
What I did is
Use s0.tweet <- searchTwitter(KEYWORD,n=1500) for 7 days or more, such as THIS.
Combine them by this command :
rdmTweets = c(s0.tweet,s1.tweet,s2.tweet,s3.tweet,s4.tweet,s5.tweet,s6.tweet,s7.tweet)
The result:
This Square Cloud consists of about 9000 tweets.
Source: People voice about Lynas Malaysia through Twitter Analysis with R CloudStat
Hope it help!