Re: [R] using phia with glmmTMB
Should also make the example reproducible [1][2][3] when you do post there because some mismatch between the model and the data is frequently where the problem turns out to be, and without an example that triggers the problem it is very tough to figure that out. [1] http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example [2] http://adv-r.had.co.nz/Reproducibility.html [3] https://cran.r-project.org/web/packages/reprex/index.html (read the vignette) -- Sent from my phone. Please excuse my brevity. On September 14, 2017 3:45:38 PM PDT, Bert Gunter wrote: >Dunno ... > >But you might do better posting this on the r-sig-mixed-models list >where >it both should fit better and where you are more likely to find the >relevant expertise. > >Cheers, >Bert > > > >Bert Gunter > >"The trouble with having an open mind is that people keep coming along >and >sticking things into it." >-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) > >On Thu, Sep 14, 2017 at 2:26 PM, Joseph Ciarrochi > >wrote: > >> Hi folks, >> >> I love the Phia package andwant to use it with glmmTMB, but when i >try to >> use the interactionMeans command, i get the below error >> >> modelrepeatedmain2 <- glmmTMB(counts ~ >> cluster*nominated*nominator*junior_senior+Ltime+ >> (1|school)+(1|id), >> data=d_shortf, >>family=nbinom1) >> >> interactionMeans(modelrepeatedmain2) >> >> Error in array(x, c(length(x), 1L), if (!is.null(names(x))) >list(names(x), >> : >> 'data' must be of a vector type, was 'NULL' >> >> >> I can get this to work with GLMER of course, but I love the speed of >> glmmTMB. >> >> Is there any way to get interactionMeans to work with glmmTMB? >> >> best >> Joseph >> >> [[alternative HTML version deleted]] >> >> __ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/ >> posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > [[alternative HTML version deleted]] > >__ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] using phia with glmmTMB
Dunno ... But you might do better posting this on the r-sig-mixed-models list where it both should fit better and where you are more likely to find the relevant expertise. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Sep 14, 2017 at 2:26 PM, Joseph Ciarrochi wrote: > Hi folks, > > I love the Phia package andwant to use it with glmmTMB, but when i try to > use the interactionMeans command, i get the below error > > modelrepeatedmain2 <- glmmTMB(counts ~ > cluster*nominated*nominator*junior_senior+Ltime+ > (1|school)+(1|id), > data=d_shortf, >family=nbinom1) > > interactionMeans(modelrepeatedmain2) > > Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), > : > 'data' must be of a vector type, was 'NULL' > > > I can get this to work with GLMER of course, but I love the speed of > glmmTMB. > > Is there any way to get interactionMeans to work with glmmTMB? > > best > Joseph > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/ > posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] using phia with glmmTMB
Hi folks, I love the Phia package andwant to use it with glmmTMB, but when i try to use the interactionMeans command, i get the below error modelrepeatedmain2 <- glmmTMB(counts ~ cluster*nominated*nominator*junior_senior+Ltime+ (1|school)+(1|id), data=d_shortf, family=nbinom1) interactionMeans(modelrepeatedmain2) Error in array(x, c(length(x), 1L), if (!is.null(names(x))) list(names(x), : 'data' must be of a vector type, was 'NULL' I can get this to work with GLMER of course, but I love the speed of glmmTMB. Is there any way to get interactionMeans to work with glmmTMB? best Joseph [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Print All Warnings that Occurr in All Parallel Nodes
> How could I check that a CSV can be opened before applying the function, > and create an empty data.frame for those CSV. Use tryCatch(). E.g., instead of result <- read_csv2(file) use result <- tryCatch(read_csv2(file), error=function(e) makeEmptyDataFrame(conditionMessage(e))) where makeEmptyDataFrame(msg=NULL) is a function (which you write) that returns a data.frame with no rows but with the proper column names and types. I show it with a msg (message) argument, as you might want to attach the error message to it as an attribute so you can see what went wrong. Bill Dunlap TIBCO Software wdunlap tibco.com On Thu, Sep 14, 2017 at 12:48 AM, TELLERIA RUIZ DE AGUIRRE, JUAN < jtelle...@external.gamesacorp.com> wrote: > Dear R Users, > > I have developed the following code for importing a series of zipped CSV > by parallel computing. > > My problems are that: > > A) Some ZIP Files (Which contain CSVs inside) are corrupted, and cannot be > opened. > B) After executing parRapply I can only see the last.warning variable > error, for knowing which CSV have failed in each node, but I cannot see all > warnings, only 1 at a time. > > So: > > * For showing a list of all warnings in all nodes, I was thinking of using > the following function in the code: > > warnings(DISPOIN_CSV_List <- parRapply(c1, DISPOIN_DIR_REL, > parRaplly_Function)) > > Would this work? > > * And also, How could I check that a CSV can be opened before applying the > function, and create an empty data.frame for those CSV. > > Thank you, > Juan > > > CODE > > > ## DISPOIN Data Import Into MariaDB > > > > ## > - > ## Packages > ## > - > > # update.packages("RODBC") > # update.packages("tidyverse") > > ## > - > ## Libraries > ## > - > > suppressMessages(require(RODBC)) > suppressMessages(require(tidyverse)) > suppressMessages(require(parallel)) > > ## > - > ## CMD: Command for DISPOIN's Directory Acquisition > ## > - > > # shell(cmd = 'pushd "\\srvdiscsv\data" && dir *AL*.zip /b /s > > D:\DISPOIN_Data_Directories.csv && popd') > > ## > - > ## RODBC > ## > - > > ## A) MariaDB Connection String > > con <- odbcConnect("MariaDB_Tornado24") > > invisible(sqlQuery(con, "USE dispoin;")) > > # B) Import R Data Directories from MariaDB > > DISPOIN_DIR_REL <- as_tibble(sqlFetch(con, "dispoin.t_DISPOIN_DIR_REL")) > > odbcClose(con) > > # C) Import Zipped CSV data into List of Dataframes, which latter on are > compiled as a single dataframe by > #means of rbind > > # C.1) parRapply Function Initialization: > > parRaplly_Function <- function (DISPOIN_CSV_Row) > { > return(read_csv2( > file = DISPOIN_CSV_Row, > col_names = c( > "SCADA", > "TAG", > "ID_del_AEG", > "Descripcion", > "Time_ON", > "Time_OFF", > "Delta_Time", > "Comentario", > "Es_Alarma", > "Es_Ultima", > "Comentarios"), > col_types = cols( > "SCADA" = "c", > "TAG" = "c", > "ID_del_AEG" = "c", > "Descripcion" = "c", > "Time_ON" = "c", > "Time_OFF" = "c", > "Delta_Time" = "c", > "Comentario" = "c", > "Es_Alarma" = "c", > "Es_Ultima" = "c", > "Comentarios" = "c"), > locale = default_locale(), > na = c("", " "), > quoted_na = TRUE, > quote = "\"", > comment = "", > trim_ws = TRUE, > skip = 0, > n_max = Inf, > guess_max = min(1000, n_max), > progress = FALSE)) > } > > # C.2) parallel Package: Environment Settings > > no_cores <- detectCores() > > c1 <- makeCluster(no_cores) > > invisible(clusterEvalQ(c1, library(readr))) > > setDefaultCluster(c1) > > # C.3) parRapply Function Application: > > DISPOIN_CSV_List <- parRapply(c1, DISPOIN_DIR_REL, parRaplly_Function) > > suppressWarnings(stopCluster(c1)) > > # D) List's Tibbles Compilation into a single Tibble: > > DISPOIN_CSV <- do.call(rbind, DISPOIN_CSV_List) > > # E) Write Compiled Table into CSV: > > write_csv( > DISPOIN_CSV, > path = file.path("D:/MySQL/R", "DISPOIN_CSV.csv"), > na = "\\N", > append = FALSE, > col_names = TRUE) > > # F) Data Cleaning: Enviro
Re: [R] Help understanding why glm and lrm.fit runs with my data, but lrm does not
Fixed 'maxiter' in the help file. Thanks. Please give the original source of that dataset. That dataset is a tiny sample of GUSTO-I and not large enough to fit this model very reliably. A nomogram using the full dataset (not publicly available to my knowledge) is already available in http://biostat.mc.vanderbilt.edu/tmp/bbr.pdf Use lrm, not lrm.fit for this. Adding maxit=20 will probably make it work on the small dataset but still not clear on why you are using this dataset. Frank -- Frank E Harrell Jr Professor School of Medicine Department of *Biostatistics* *Vanderbilt University* On Thu, Sep 14, 2017 at 10:48 AM, David Winsemius wrote: > > > On Sep 14, 2017, at 12:30 AM, Bonnett, Laura < > l.j.bonn...@liverpool.ac.uk> wrote: > > > > Dear all, > > > > I am using the publically available GustoW dataset. The exact version I > am using is available here: https://na01.safelinks. > protection.outlook.com/?url=https%3A%2F%2Fdrive.google.com%2Fopen%3Fid% > 3D0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk&data=02%7C01%7Cf.harrell%40vanderbilt.edu% > 7Cadb58b13c3994f89209708d4fb8807f0%7Cba5a7f39e3be4ab3b45067fa80fa > ecad%7C0%7C0%7C636410009046132507&sdata=UZgX3%2Ba% > 2FU2Eeh8ybHMI6JnF0Npd2XJPXAzlmtEhDgOY%3D&reserved=0 > > > > I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, > HRT and ANT. I have successfully fitted a logistic regression model using > the "glm" function as shown below. > > > > library(rms) > > gusto <- spss.get("GustoW.sav") > > fit <- glm(DAY30~AGE+HYP+factor(KILLIP)+HRT+ANT,family= > binomial(link="logit"),data=gusto,x=TRUE,y=TRUE) > > > > However, my review of the literature and other websites suggest I need > to use "lrm" for the purposes of producing a nomogram. When I run the > command using "lrm" (see below) I get an error message saying: > > Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : > > Unable to fit model using "lrm.fit" > > > > My code is as follows: > > gusto2 <- gusto[,c(1,3,5,8,9,10)] > > gusto2$HYP <- factor(gusto2$HYP, labels=c("No","Yes")) > > gusto2$KILLIP <- factor(gusto2$KILLIP, labels=c("1","2","3","4")) > > gusto2$HRT <- factor(gusto2$HRT, labels=c("No","Yes")) > > gusto2$ANT <- factor(gusto2$ANT, labels=c("No","Yes")) > > var.labels=c(DAY30="30-day Mortality", AGE="Age in Years", > KILLIP="Killip Class", HYP="Hypertension", HRT="Tachycardia", ANT="Anterior > Infarct Location") > > label(gusto2)=lapply(names(var.labels),function(x) > label(gusto2[,x])=var.labels[x]) > > > > ddist = datadist(gusto2) > > options(datadist='ddist') > > > > fit1 <- lrm(DAY30~AGE+HYP+KILLIP+HRT+ANT,gusto2) > > > > Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : > > Unable to fit model using "lrm.fit" > > > > Online solutions to this problem involve checking whether any variables > are redundant. However, the results for my data suggest that none are. > > redun(~AGE+HYP+KILLIP+HRT+ANT,gusto2) > > > > Redundancy Analysis > > > > redun(formula = ~AGE + HYP + KILLIP + HRT + ANT, data = gusto2) > > > > n: 2188 p: 5nk: 3 > > > > Number of NAs: 0 > > > > Transformation of target variables forced to be linear > > > > R-squared cutoff: 0.9 Type: ordinary > > > > R^2 with which each variable can be predicted from all other variables: > > > > AGEHYP KILLIPHRTANT > > 0.028 0.032 0.053 0.046 0.040 > > > > No redundant variables > > > > I've also tried just considering "lrm.fit" and that code seems to run > without error too: > > lrm.fit(cbind(gusto2$AGE,gusto2$KILLIP,gusto2$HYP, > gusto2$HRT,gusto2$ANT),gusto2$DAY30) > > > > Logistic Regression Model > > > > lrm.fit(x = cbind(gusto2$AGE, gusto2$KILLIP, gusto2$HYP, gusto2$HRT, > > gusto2$ANT), y = gusto2$DAY30) > > > > Model Likelihood DiscriminationRank > Discrim. > > Ratio Test Indexes Indexes > > Obs 2188LR chi2 233.59R2 0.273C > 0.846 > > 0 2053d.f. 5g1.642Dxy > 0.691 > > 1135Pr(> chi2) <0.0001gr 5.165gamma > 0.696 > > max |deriv| 4e-09 gp 0.079tau-a > 0.080 > >Brier0.048 > > > > Coef S.E. Wald Z Pr(>|Z|) > > Intercept -13.8515 0.9694 -14.29 <0.0001 > > x[1]0.0989 0.0103 9.58 <0.0001 > > x[2]0.9030 0.1510 5.98 <0.0001 > > x[3]1.3576 0.2570 5.28 <0.0001 > > x[4]0.6884 0.2034 3.38 0.0007 > > x[5]0.6327 0.2003 3.16 0.0016 > > > > I was therefore hoping someone would explain why the "lrm" code is > producing an error message, while "lrm.fit" and "glm" do not. In > particular I would welcome a solution to ensure I can produce a nomogram. > > Try this: > > lrm # look at code, do a search on "fail" > ?lrm.fit # read the structure of the returned value of lrm.fit > > my.fit <- lrm.fit(x = cbind(
Re: [R] To implement OO or not in R package, and if so, how to structure it?
I think you should consider whether the advantages of making an object-aware collections class are worth the effort... lists are the standard tool for this task in R, and are normally handled using the functional programming paradigm. Just make sure a sufficiently-complete set of methods are available for the objects you plan to make lists of. -- Sent from my phone. Please excuse my brevity. On September 14, 2017 7:27:55 AM PDT, Alexander Shenkin wrote: Did you read this? https://cran.r-project.org/doc/contrib/Leisch-CreatingPackages.pdf Maybe it could give you some insight in how to create package. >>> >>> That resource is ~9 years old. There are more modern treatments >available. You >>> can read mine at http://r-pkgs.had.co.nz. >>> >>> Hadley >>> > >Thanks both. I'm reading through your new book now Hadley... thanks >for >that. I'll probably take a shot at building a class to hold one tree >per object, and search for objects of a class (per >https://stackoverflow.com/questions/5158830/identify-all-objects-of-given-clas-for-further-processing) > >to implement collections when necessary... > >It does seem like there might be niche out there for a resource for >folks deciding how to structure their package given what they're trying > >to provide; i.e. should they construct a collection of functions, or >class defs, or... Could well exist already, and I may just have missed > >it... > >Thanks, >Allie > >__ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide >http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Help understanding why glm and lrm.fit runs with my data, but lrm does not
> On Sep 14, 2017, at 12:30 AM, Bonnett, Laura > wrote: > > Dear all, > > I am using the publically available GustoW dataset. The exact version I am > using is available here: > https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk > > I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT > and ANT. I have successfully fitted a logistic regression model using the > "glm" function as shown below. > > library(rms) > gusto <- spss.get("GustoW.sav") > fit <- > glm(DAY30~AGE+HYP+factor(KILLIP)+HRT+ANT,family=binomial(link="logit"),data=gusto,x=TRUE,y=TRUE) > > However, my review of the literature and other websites suggest I need to use > "lrm" for the purposes of producing a nomogram. When I run the command using > "lrm" (see below) I get an error message saying: > Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : > Unable to fit model using "lrm.fit" > > My code is as follows: > gusto2 <- gusto[,c(1,3,5,8,9,10)] > gusto2$HYP <- factor(gusto2$HYP, labels=c("No","Yes")) > gusto2$KILLIP <- factor(gusto2$KILLIP, labels=c("1","2","3","4")) > gusto2$HRT <- factor(gusto2$HRT, labels=c("No","Yes")) > gusto2$ANT <- factor(gusto2$ANT, labels=c("No","Yes")) > var.labels=c(DAY30="30-day Mortality", AGE="Age in Years", KILLIP="Killip > Class", HYP="Hypertension", HRT="Tachycardia", ANT="Anterior Infarct > Location") > label(gusto2)=lapply(names(var.labels),function(x) > label(gusto2[,x])=var.labels[x]) > > ddist = datadist(gusto2) > options(datadist='ddist') > > fit1 <- lrm(DAY30~AGE+HYP+KILLIP+HRT+ANT,gusto2) > > Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : > Unable to fit model using "lrm.fit" > > Online solutions to this problem involve checking whether any variables are > redundant. However, the results for my data suggest that none are. > redun(~AGE+HYP+KILLIP+HRT+ANT,gusto2) > > Redundancy Analysis > > redun(formula = ~AGE + HYP + KILLIP + HRT + ANT, data = gusto2) > > n: 2188 p: 5nk: 3 > > Number of NAs: 0 > > Transformation of target variables forced to be linear > > R-squared cutoff: 0.9 Type: ordinary > > R^2 with which each variable can be predicted from all other variables: > > AGEHYP KILLIPHRTANT > 0.028 0.032 0.053 0.046 0.040 > > No redundant variables > > I've also tried just considering "lrm.fit" and that code seems to run without > error too: > lrm.fit(cbind(gusto2$AGE,gusto2$KILLIP,gusto2$HYP,gusto2$HRT,gusto2$ANT),gusto2$DAY30) > > Logistic Regression Model > > lrm.fit(x = cbind(gusto2$AGE, gusto2$KILLIP, gusto2$HYP, gusto2$HRT, > gusto2$ANT), y = gusto2$DAY30) > > Model Likelihood DiscriminationRank Discrim. > Ratio Test Indexes Indexes > Obs 2188LR chi2 233.59R2 0.273C 0.846 > 0 2053d.f. 5g1.642Dxy 0.691 > 1135Pr(> chi2) <0.0001gr 5.165gamma 0.696 > max |deriv| 4e-09 gp 0.079tau-a 0.080 >Brier0.048 > > Coef S.E. Wald Z Pr(>|Z|) > Intercept -13.8515 0.9694 -14.29 <0.0001 > x[1]0.0989 0.0103 9.58 <0.0001 > x[2]0.9030 0.1510 5.98 <0.0001 > x[3]1.3576 0.2570 5.28 <0.0001 > x[4]0.6884 0.2034 3.38 0.0007 > x[5]0.6327 0.2003 3.16 0.0016 > > I was therefore hoping someone would explain why the "lrm" code is producing > an error message, while "lrm.fit" and "glm" do not. In particular I would > welcome a solution to ensure I can produce a nomogram. Try this: lrm # look at code, do a search on "fail" ?lrm.fit # read the structure of the returned value of lrm.fit my.fit <- lrm.fit(x = cbind(gusto2$AGE, gusto2$KILLIP, gusto2$HYP, gusto2$HRT, gusto2$ANT), y = gusto2$DAY30) print(my.fit$fail) # the error message you got from the lrm call means convergence failed Documentation bug: The documentation of the cause of the 'fail'- value incorrectly gives the name of this parameter as 'maxiter' in the Value section. -- David. > > Kind regards, > Laura > > Dr Laura Bonnett > NIHR Post-Doctoral Fellow > > Department of Biostatistics, > Waterhouse Building, Block F, > 1-5 Brownlow Street, > University of Liverpool, > Liverpool, > L69 3GL > > 0151 795 9686 > l.j.bonn...@liverpool.ac.uk > > > > [[alternative HTML version deleted]] > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. David Winsemius Alameda, CA, USA 'Any technology distinguishable from magic is insufficiently advanced.' -Gehm's Corollary to Clarke's Third Law __
Re: [R] To implement OO or not in R package, and if so, how to structure it?
Did you read this? https://cran.r-project.org/doc/contrib/Leisch-CreatingPackages.pdf Maybe it could give you some insight in how to create package. That resource is ~9 years old. There are more modern treatments available. You can read mine at http://r-pkgs.had.co.nz. Hadley Thanks both. I'm reading through your new book now Hadley... thanks for that. I'll probably take a shot at building a class to hold one tree per object, and search for objects of a class (per https://stackoverflow.com/questions/5158830/identify-all-objects-of-given-clas-for-further-processing) to implement collections when necessary... It does seem like there might be niche out there for a resource for folks deciding how to structure their package given what they're trying to provide; i.e. should they construct a collection of functions, or class defs, or... Could well exist already, and I may just have missed it... Thanks, Allie __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] To implement OO or not in R package, and if so, how to structure it?
Hi Hadley Yes, I found it too quite easily by Google. Cheers Petr > -Original Message- > From: Hadley Wickham [mailto:h.wick...@gmail.com] > Sent: Thursday, September 14, 2017 3:33 PM > To: PIKAL Petr > Cc: Alexander Shenkin ; r-help > Subject: Re: [R] To implement OO or not in R package, and if so, how to > structure it? > > > Did you read this? > > https://cran.r-project.org/doc/contrib/Leisch-CreatingPackages.pdf > > > > Maybe it could give you some insight in how to create package. > > That resource is ~9 years old. There are more modern treatments available. You > can read mine at http://r-pkgs.had.co.nz. > > Hadley > > > -- > http://hadley.nz Tento e-mail a jakékoliv k němu připojené dokumenty jsou důvěrné a jsou určeny pouze jeho adresátům. Jestliže jste obdržel(a) tento e-mail omylem, informujte laskavě neprodleně jeho odesílatele. Obsah tohoto emailu i s přílohami a jeho kopie vymažte ze svého systému. Nejste-li zamýšleným adresátem tohoto emailu, nejste oprávněni tento email jakkoliv užívat, rozšiřovat, kopírovat či zveřejňovat. Odesílatel e-mailu neodpovídá za eventuální škodu způsobenou modifikacemi či zpožděním přenosu e-mailu. V případě, že je tento e-mail součástí obchodního jednání: - vyhrazuje si odesílatel právo ukončit kdykoliv jednání o uzavření smlouvy, a to z jakéhokoliv důvodu i bez uvedení důvodu. - a obsahuje-li nabídku, je adresát oprávněn nabídku bezodkladně přijmout; Odesílatel tohoto e-mailu (nabídky) vylučuje přijetí nabídky ze strany příjemce s dodatkem či odchylkou. - trvá odesílatel na tom, že příslušná smlouva je uzavřena teprve výslovným dosažením shody na všech jejích náležitostech. - odesílatel tohoto emailu informuje, že není oprávněn uzavírat za společnost žádné smlouvy s výjimkou případů, kdy k tomu byl písemně zmocněn nebo písemně pověřen a takové pověření nebo plná moc byly adresátovi tohoto emailu případně osobě, kterou adresát zastupuje, předloženy nebo jejich existence je adresátovi či osobě jím zastoupené známá. This e-mail and any documents attached to it may be confidential and are intended only for its intended recipients. If you received this e-mail by mistake, please immediately inform its sender. Delete the contents of this e-mail with all attachments and its copies from your system. If you are not the intended recipient of this e-mail, you are not authorized to use, disseminate, copy or disclose this e-mail in any manner. The sender of this e-mail shall not be liable for any possible damage caused by modifications of the e-mail or by delay with transfer of the email. In case that this e-mail forms part of business dealings: - the sender reserves the right to end negotiations about entering into a contract in any time, for any reason, and without stating any reasoning. - if the e-mail contains an offer, the recipient is entitled to immediately accept such offer; The sender of this e-mail (offer) excludes any acceptance of the offer on the part of the recipient containing any amendment or variation. - the sender insists on that the respective contract is concluded only upon an express mutual agreement on all its aspects. - the sender of this e-mail informs that he/she is not authorized to enter into any contracts on behalf of the company except for cases in which he/she is expressly authorized to do so in writing, and such authorization or power of attorney is submitted to the recipient or the person represented by the recipient, or the existence of such authorization is known to the recipient of the person represented by the recipient. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] To implement OO or not in R package, and if so, how to structure it?
> Did you read this? > https://cran.r-project.org/doc/contrib/Leisch-CreatingPackages.pdf > > Maybe it could give you some insight in how to create package. That resource is ~9 years old. There are more modern treatments available. You can read mine at http://r-pkgs.had.co.nz. Hadley -- http://hadley.nz __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] To implement OO or not in R package, and if so, how to structure it?
I just finished the first draft of the chapters on OO programming for the 2nd edition of "Advanced R": https://adv-r.hadley.nz - you might find them helpful. Hadley On Thu, Sep 14, 2017 at 7:58 AM, Alexander Shenkin wrote: > Hello all, > > I am trying to decide how to structure an R package. Specifically, do I use > OO classes, or just provide functions? If the former, how should I > structure the objects in relation to the type of data the package is > intended to manage? > > I have searched for, but haven't found, resources that guide one in the > *decision* about whether to implement OO frameworks or not in one's R > package. I suspect I should, but the utility of the package would be aided > by *collections* of objects. R, however, doesn't seem to implement > collections. > > Background: I am writing an R package that will provide a framework for > analyzing structural models of trees (as in trees made of wood, not > statistical trees). These models are generated from laser scanning > instruments and model fitting algorithms, and hence may have aspects that > are data-heavy. Furthermore, coputing metrics based on these structures can > be computationally heavy. Finally, as a result, each tree has a number of > metrics associated with it (which may be expensive to calculate), along with > the underlying data of that tree. It will be important as well to perform > calculations across many of these trees, as one would do in a dataframe. > > This last point is important: if one organizes data across potentially > thousands of objects, how easy or hard is it to massage properties of those > objects into a dataframe for analysis? > > Thank you in advance for thoughts and pointers. > > Allie > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. -- http://hadley.nz __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] To implement OO or not in R package, and if so, how to structure it?
Hi I do not consider myself as an expert in field of R package programming but if your data are rectangular, why not use data.frames. OTOH if they are structured in free form (something like lm result) you could use lists. Did you read this? https://cran.r-project.org/doc/contrib/Leisch-CreatingPackages.pdf Maybe it could give you some insight in how to create package. Cheers Petr > -Original Message- > From: R-help [mailto:r-help-boun...@r-project.org] On Behalf Of Alexander > Shenkin > Sent: Thursday, September 14, 2017 2:59 PM > To: r-help > Subject: [R] To implement OO or not in R package, and if so, how to structure > it? > > Hello all, > > I am trying to decide how to structure an R package. Specifically, do I use > OO > classes, or just provide functions? If the former, how should I structure the > objects in relation to the type of data the package is intended to manage? > > I have searched for, but haven't found, resources that guide one in the > *decision* about whether to implement OO frameworks or not in one's R > package. I suspect I should, but the utility of the package would be aided by > *collections* of objects. R, however, doesn't seem to implement collections. > > Background: I am writing an R package that will provide a framework for > analyzing structural models of trees (as in trees made of wood, not > statistical > trees). These models are generated from laser scanning instruments and model > fitting algorithms, and hence may have aspects that are data-heavy. > Furthermore, coputing metrics based on these structures can be > computationally heavy. Finally, as a result, each tree has a number of > metrics > associated with it (which may be expensive to calculate), along with the > underlying data of that tree. It will be important as well to perform > calculations across many of these trees, as one would do in a dataframe. > > This last point is important: if one organizes data across potentially > thousands > of objects, how easy or hard is it to massage properties of those objects > into a > dataframe for analysis? > > Thank you in advance for thoughts and pointers. > > Allie > > __ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. Tento e-mail a jakékoliv k němu připojené dokumenty jsou důvěrné a jsou určeny pouze jeho adresátům. Jestliže jste obdržel(a) tento e-mail omylem, informujte laskavě neprodleně jeho odesílatele. Obsah tohoto emailu i s přílohami a jeho kopie vymažte ze svého systému. Nejste-li zamýšleným adresátem tohoto emailu, nejste oprávněni tento email jakkoliv užívat, rozšiřovat, kopírovat či zveřejňovat. Odesílatel e-mailu neodpovídá za eventuální škodu způsobenou modifikacemi či zpožděním přenosu e-mailu. V případě, že je tento e-mail součástí obchodního jednání: - vyhrazuje si odesílatel právo ukončit kdykoliv jednání o uzavření smlouvy, a to z jakéhokoliv důvodu i bez uvedení důvodu. - a obsahuje-li nabídku, je adresát oprávněn nabídku bezodkladně přijmout; Odesílatel tohoto e-mailu (nabídky) vylučuje přijetí nabídky ze strany příjemce s dodatkem či odchylkou. - trvá odesílatel na tom, že příslušná smlouva je uzavřena teprve výslovným dosažením shody na všech jejích náležitostech. - odesílatel tohoto emailu informuje, že není oprávněn uzavírat za společnost žádné smlouvy s výjimkou případů, kdy k tomu byl písemně zmocněn nebo písemně pověřen a takové pověření nebo plná moc byly adresátovi tohoto emailu případně osobě, kterou adresát zastupuje, předloženy nebo jejich existence je adresátovi či osobě jím zastoupené známá. This e-mail and any documents attached to it may be confidential and are intended only for its intended recipients. If you received this e-mail by mistake, please immediately inform its sender. Delete the contents of this e-mail with all attachments and its copies from your system. If you are not the intended recipient of this e-mail, you are not authorized to use, disseminate, copy or disclose this e-mail in any manner. The sender of this e-mail shall not be liable for any possible damage caused by modifications of the e-mail or by delay with transfer of the email. In case that this e-mail forms part of business dealings: - the sender reserves the right to end negotiations about entering into a contract in any time, for any reason, and without stating any reasoning. - if the e-mail contains an offer, the recipient is entitled to immediately accept such offer; The sender of this e-mail (offer) excludes any acceptance of the offer on the part of the recipient containing any amendment or variation. - the sender insists on that the respective contract is concluded only upon an express mutual agreement on all i
[R] To implement OO or not in R package, and if so, how to structure it?
Hello all, I am trying to decide how to structure an R package. Specifically, do I use OO classes, or just provide functions? If the former, how should I structure the objects in relation to the type of data the package is intended to manage? I have searched for, but haven't found, resources that guide one in the *decision* about whether to implement OO frameworks or not in one's R package. I suspect I should, but the utility of the package would be aided by *collections* of objects. R, however, doesn't seem to implement collections. Background: I am writing an R package that will provide a framework for analyzing structural models of trees (as in trees made of wood, not statistical trees). These models are generated from laser scanning instruments and model fitting algorithms, and hence may have aspects that are data-heavy. Furthermore, coputing metrics based on these structures can be computationally heavy. Finally, as a result, each tree has a number of metrics associated with it (which may be expensive to calculate), along with the underlying data of that tree. It will be important as well to perform calculations across many of these trees, as one would do in a dataframe. This last point is important: if one organizes data across potentially thousands of objects, how easy or hard is it to massage properties of those objects into a dataframe for analysis? Thank you in advance for thoughts and pointers. Allie __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Help understanding why glm and lrm.fit runs with my data, but lrm does not
With lrm.fit you are fitting a completely different model. One of the things lrm does, is preparing the input for lrm.fit which in this case means that dummy variables are generated for categorical variables such as 'KILLIP'. The error message means that model did not converge after the maximum number of iterations. One possible solution is to try to increase the maximum number of iterations, e.g.: fit1 <- lrm(DAY30~AGE+HYP+KILLIP+HRT+ANT, data = gusto2, maxit = 100) HTH, Jan On 14-09-17 09:30, Bonnett, Laura wrote: Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <- spss.get("GustoW.sav") fit <- glm(DAY30~AGE+HYP+factor(KILLIP)+HRT+ANT,family=binomial(link="logit"),data=gusto,x=TRUE,y=TRUE) However, my review of the literature and other websites suggest I need to use "lrm" for the purposes of producing a nomogram. When I run the command using "lrm" (see below) I get an error message saying: Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : Unable to fit model using "lrm.fit" My code is as follows: gusto2 <- gusto[,c(1,3,5,8,9,10)] gusto2$HYP <- factor(gusto2$HYP, labels=c("No","Yes")) gusto2$KILLIP <- factor(gusto2$KILLIP, labels=c("1","2","3","4")) gusto2$HRT <- factor(gusto2$HRT, labels=c("No","Yes")) gusto2$ANT <- factor(gusto2$ANT, labels=c("No","Yes")) var.labels=c(DAY30="30-day Mortality", AGE="Age in Years", KILLIP="Killip Class", HYP="Hypertension", HRT="Tachycardia", ANT="Anterior Infarct Location") label(gusto2)=lapply(names(var.labels),function(x) label(gusto2[,x])=var.labels[x]) ddist = datadist(gusto2) options(datadist='ddist') fit1 <- lrm(DAY30~AGE+HYP+KILLIP+HRT+ANT,gusto2) Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : Unable to fit model using "lrm.fit" Online solutions to this problem involve checking whether any variables are redundant. However, the results for my data suggest that none are. redun(~AGE+HYP+KILLIP+HRT+ANT,gusto2) Redundancy Analysis redun(formula = ~AGE + HYP + KILLIP + HRT + ANT, data = gusto2) n: 2188 p: 5nk: 3 Number of NAs: 0 Transformation of target variables forced to be linear R-squared cutoff: 0.9 Type: ordinary R^2 with which each variable can be predicted from all other variables: AGEHYP KILLIPHRTANT 0.028 0.032 0.053 0.046 0.040 No redundant variables I've also tried just considering "lrm.fit" and that code seems to run without error too: lrm.fit(cbind(gusto2$AGE,gusto2$KILLIP,gusto2$HYP,gusto2$HRT,gusto2$ANT),gusto2$DAY30) Logistic Regression Model lrm.fit(x = cbind(gusto2$AGE, gusto2$KILLIP, gusto2$HYP, gusto2$HRT, gusto2$ANT), y = gusto2$DAY30) Model Likelihood DiscriminationRank Discrim. Ratio Test Indexes Indexes Obs 2188LR chi2 233.59R2 0.273C 0.846 0 2053d.f. 5g1.642Dxy 0.691 1135Pr(> chi2) <0.0001gr 5.165gamma 0.696 max |deriv| 4e-09 gp 0.079tau-a 0.080 Brier0.048 Coef S.E. Wald Z Pr(>|Z|) Intercept -13.8515 0.9694 -14.29 <0.0001 x[1]0.0989 0.0103 9.58 <0.0001 x[2]0.9030 0.1510 5.98 <0.0001 x[3]1.3576 0.2570 5.28 <0.0001 x[4]0.6884 0.2034 3.38 0.0007 x[5]0.6327 0.2003 3.16 0.0016 I was therefore hoping someone would explain why the "lrm" code is producing an error message, while "lrm.fit" and "glm" do not. In particular I would welcome a solution to ensure I can produce a nomogram. Kind regards, Laura Dr Laura Bonnett NIHR Post-Doctoral Fellow Department of Biostatistics, Waterhouse Building, Block F, 1-5 Brownlow Street, University of Liverpool, Liverpool, L69 3GL 0151 795 9686 l.j.bonn...@liverpool.ac.uk [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Print All Warnings that Occurr in All Parallel Nodes
Dear R Users, I have developed the following code for importing a series of zipped CSV by parallel computing. My problems are that: A) Some ZIP Files (Which contain CSVs inside) are corrupted, and cannot be opened. B) After executing parRapply I can only see the last.warning variable error, for knowing which CSV have failed in each node, but I cannot see all warnings, only 1 at a time. So: * For showing a list of all warnings in all nodes, I was thinking of using the following function in the code: warnings(DISPOIN_CSV_List <- parRapply(c1, DISPOIN_DIR_REL, parRaplly_Function)) Would this work? * And also, How could I check that a CSV can be opened before applying the function, and create an empty data.frame for those CSV. Thank you, Juan CODE ## DISPOIN Data Import Into MariaDB ## - ## Packages ## - # update.packages("RODBC") # update.packages("tidyverse") ## - ## Libraries ## - suppressMessages(require(RODBC)) suppressMessages(require(tidyverse)) suppressMessages(require(parallel)) ## - ## CMD: Command for DISPOIN's Directory Acquisition ## - # shell(cmd = 'pushd "\\srvdiscsv\data" && dir *AL*.zip /b /s > D:\DISPOIN_Data_Directories.csv && popd') ## - ## RODBC ## - ## A) MariaDB Connection String con <- odbcConnect("MariaDB_Tornado24") invisible(sqlQuery(con, "USE dispoin;")) # B) Import R Data Directories from MariaDB DISPOIN_DIR_REL <- as_tibble(sqlFetch(con, "dispoin.t_DISPOIN_DIR_REL")) odbcClose(con) # C) Import Zipped CSV data into List of Dataframes, which latter on are compiled as a single dataframe by #means of rbind # C.1) parRapply Function Initialization: parRaplly_Function <- function (DISPOIN_CSV_Row) { return(read_csv2( file = DISPOIN_CSV_Row, col_names = c( "SCADA", "TAG", "ID_del_AEG", "Descripcion", "Time_ON", "Time_OFF", "Delta_Time", "Comentario", "Es_Alarma", "Es_Ultima", "Comentarios"), col_types = cols( "SCADA" = "c", "TAG" = "c", "ID_del_AEG" = "c", "Descripcion" = "c", "Time_ON" = "c", "Time_OFF" = "c", "Delta_Time" = "c", "Comentario" = "c", "Es_Alarma" = "c", "Es_Ultima" = "c", "Comentarios" = "c"), locale = default_locale(), na = c("", " "), quoted_na = TRUE, quote = "\"", comment = "", trim_ws = TRUE, skip = 0, n_max = Inf, guess_max = min(1000, n_max), progress = FALSE)) } # C.2) parallel Package: Environment Settings no_cores <- detectCores() c1 <- makeCluster(no_cores) invisible(clusterEvalQ(c1, library(readr))) setDefaultCluster(c1) # C.3) parRapply Function Application: DISPOIN_CSV_List <- parRapply(c1, DISPOIN_DIR_REL, parRaplly_Function) suppressWarnings(stopCluster(c1)) # D) List's Tibbles Compilation into a single Tibble: DISPOIN_CSV <- do.call(rbind, DISPOIN_CSV_List) # E) Write Compiled Table into CSV: write_csv( DISPOIN_CSV, path = file.path("D:/MySQL/R", "DISPOIN_CSV.csv"), na = "\\N", append = FALSE, col_names = TRUE) # F) Data Cleaning: Environment Variable Removal rm(list=ls()) [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Help understanding why glm and lrm.fit runs with my data, but lrm does not
Dear all, I am using the publically available GustoW dataset. The exact version I am using is available here: https://drive.google.com/open?id=0B4oZ2TQA0PAoUm85UzBFNjZ0Ulk I would like to produce a nomogram for 5 covariates - AGE, HYP, KILLIP, HRT and ANT. I have successfully fitted a logistic regression model using the "glm" function as shown below. library(rms) gusto <- spss.get("GustoW.sav") fit <- glm(DAY30~AGE+HYP+factor(KILLIP)+HRT+ANT,family=binomial(link="logit"),data=gusto,x=TRUE,y=TRUE) However, my review of the literature and other websites suggest I need to use "lrm" for the purposes of producing a nomogram. When I run the command using "lrm" (see below) I get an error message saying: Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : Unable to fit model using "lrm.fit" My code is as follows: gusto2 <- gusto[,c(1,3,5,8,9,10)] gusto2$HYP <- factor(gusto2$HYP, labels=c("No","Yes")) gusto2$KILLIP <- factor(gusto2$KILLIP, labels=c("1","2","3","4")) gusto2$HRT <- factor(gusto2$HRT, labels=c("No","Yes")) gusto2$ANT <- factor(gusto2$ANT, labels=c("No","Yes")) var.labels=c(DAY30="30-day Mortality", AGE="Age in Years", KILLIP="Killip Class", HYP="Hypertension", HRT="Tachycardia", ANT="Anterior Infarct Location") label(gusto2)=lapply(names(var.labels),function(x) label(gusto2[,x])=var.labels[x]) ddist = datadist(gusto2) options(datadist='ddist') fit1 <- lrm(DAY30~AGE+HYP+KILLIP+HRT+ANT,gusto2) Error in lrm(DAY30 ~ AGE + HYP + KILLIP + HRT + ANT, gusto2) : Unable to fit model using "lrm.fit" Online solutions to this problem involve checking whether any variables are redundant. However, the results for my data suggest that none are. redun(~AGE+HYP+KILLIP+HRT+ANT,gusto2) Redundancy Analysis redun(formula = ~AGE + HYP + KILLIP + HRT + ANT, data = gusto2) n: 2188 p: 5nk: 3 Number of NAs: 0 Transformation of target variables forced to be linear R-squared cutoff: 0.9 Type: ordinary R^2 with which each variable can be predicted from all other variables: AGEHYP KILLIPHRTANT 0.028 0.032 0.053 0.046 0.040 No redundant variables I've also tried just considering "lrm.fit" and that code seems to run without error too: lrm.fit(cbind(gusto2$AGE,gusto2$KILLIP,gusto2$HYP,gusto2$HRT,gusto2$ANT),gusto2$DAY30) Logistic Regression Model lrm.fit(x = cbind(gusto2$AGE, gusto2$KILLIP, gusto2$HYP, gusto2$HRT, gusto2$ANT), y = gusto2$DAY30) Model Likelihood DiscriminationRank Discrim. Ratio Test Indexes Indexes Obs 2188LR chi2 233.59R2 0.273C 0.846 0 2053d.f. 5g1.642Dxy 0.691 1135Pr(> chi2) <0.0001gr 5.165gamma 0.696 max |deriv| 4e-09 gp 0.079tau-a 0.080 Brier0.048 Coef S.E. Wald Z Pr(>|Z|) Intercept -13.8515 0.9694 -14.29 <0.0001 x[1]0.0989 0.0103 9.58 <0.0001 x[2]0.9030 0.1510 5.98 <0.0001 x[3]1.3576 0.2570 5.28 <0.0001 x[4]0.6884 0.2034 3.38 0.0007 x[5]0.6327 0.2003 3.16 0.0016 I was therefore hoping someone would explain why the "lrm" code is producing an error message, while "lrm.fit" and "glm" do not. In particular I would welcome a solution to ensure I can produce a nomogram. Kind regards, Laura Dr Laura Bonnett NIHR Post-Doctoral Fellow Department of Biostatistics, Waterhouse Building, Block F, 1-5 Brownlow Street, University of Liverpool, Liverpool, L69 3GL 0151 795 9686 l.j.bonn...@liverpool.ac.uk [[alternative HTML version deleted]] __ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.