[R] poLCA problem
Good morning everyone,
I am running into errors with poLCA as follows:
Error in round(mf) : non-numeric argument to mathematical function.
Here is what I have. Both variables are coded as 1, 2, 3
df <- as.data.frame(data)
items <- c("x1", "x2") <- there are more variables but shortened for this
purpose
df2 <- df[items]
i <- cbind(x1, x2)~1
poLCA (i, df2, nclass=2, maxiter=100, nrep=10, verbose =TRUE)
It is after the poLCA that I get the error. Any thoughts on what is causing
this?
Thanks,
Scott
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[R] Quantitative Methods Workshops in May 2020
FORWARDED – The following message has been forwarded and is not related to the University of Guelph. Apologies for the cross-posting. Good morning everyone. We sincerely hope you are all keeping safe and healthy while we all endure this pandemic. As a result of the restrictions on public gatherings, we are going to be delivering our May 2020 workshops via online live streaming now as opposed to face-to-face at McMaster University. We will be providing materials ahead of time for all participants and will still include time for individual consultation regarding your own data (which can be scheduled at the live-stream workshop). Each workshop provides a hands-on opportunity to learn using both R (with R-Studio) and Mplus. For further information and to register, please go to: https://workshops.enablytics.com/ [1] Introduction to Structural Equation Modeling This one-day hands-on workshop covers various introductory topics in structural equation modeling with continuous and categorical variables. Topics include, assumptions and data considerations, model creation, identification, and evaluation, multiple regression vs path analysis, path analysis, testing direct and indirect effects, and confirmatory factor analysis. Examples will be demonstrated in both R (using R-Studio) and Mplus. Syntax and output for both programs will be provided for all examples covered in the workshop. [2] Advanced Structural Equation Modeling This one-day hands-on workshop covers various advanced topics in structural equation modeling with continuous and categorical variables. Topics include, model creation, identification, and evaluation, testing moderation, mediation and moderated mediation, multiple group modeling, handling missing data, measurement invariance and power analysis. Examples will be demonstrated in both R (using R-Studio) and Mplus. Syntax and output for both programs will be provided for all examples covered in the workshop. [3] Growth Modeling This one-day hands-on workshop covers various topics in growth modeling (longitudinal modeling) with continuous and categorical variables. Topics include, growth modeling without covariates, growth modeling with time invariant and varying covariates, centering points, piecewise growth modeling, missing data and power analysis. Examples will be demonstrated in both R (using R-Studio) and Mplus. Syntax and output for both programs will be provided for all examples covered in the workshop. [4] Multilevel Modeling This two day hands-on workshop covers various topics in multilevel modeling with continuous and categorical variables. Topics include, when multilevel analysis is necessary, multilevel regression, random slopes and cross-level effects, multilevel confirmatory factor analysis and the MIMIC model, multilevel path analysis, multilevel mediation and moderation, multilevel latent variable modeling, longitudinal data, and power analysis. Examples will be demonstrated in both R (using R-Studio) and Mplus. Syntax and output for both programs will be provided for all examples covered in the workshop. Thank you, Scott [[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] Creating hanging bar plot in r from dplyr
That is perfect. Thanks! -- Scott R. Colwell, PhD On 2019-04-20, 1:23 PM, "Jeff Newmiller" wrote: Not really sure I understand what you want. Here is some code to consider: library(ggplot2) library(dplyr) library(tidyr) dta <- read.table( text = "samp.N RSQMRB_uc MRB_sb MRB_bp 50 0.3 1.4237.6 37.6 50 0.4 8.6143.1 43.1 50 0.5 7.4131.6 31.6 50 0.6 5.0621.5 21.5 50 0.7 3.3814.1 14.1 50 0.8 -1.075.16 5.16 100 0.3 -6.4140.3 40.3 100 0.4 -10.621.0 21.0 100 0.5 -9.0213.2 13.2 100 0.6 -9.855.14 5.14 100 0.7 -7.942.08 2.08 100 0.8 -4.811.28 1.28 ", header = TRUE ) dta2 <- ( dta %>% mutate( samp.N = factor( samp.N ) , RSQ = factor( RSQ ) ) %>% gather( Measure, value, -c( samp.N, RSQ ) ) ) ggplot( dta2, aes( x = RSQ, y = value, fill = samp.N ) ) + geom_bar( stat = "identity", position = "dodge", colour = "black" ) + facet_wrap( ~ Measure, ncol = 1, scale = "free_y" ) + ylab( "" ) On Sat, 20 Apr 2019, Scott Colwell wrote: > I am trying to figure out how to create a hanging bar plot from dplyr. > I have used dplyr as follows: > table4 <- cr %>% > group_by(samp.N, RSQ) %>% > summarize( >MRB_uc = mean(CF.F1F2/0.40*100)-100, >MRB_sb = mean(SBC.F1F2.Alpha/0.40*100) - 100, >MRB_bp = mean(BPC.F1F2.Alpha/0.40*100) - 100 > ) > which provides me with this: > samp.N RSQ MRB_uc MRB_sb MRB_bp > > 1 50 0.3 1.42 37.6 37.6 > 2 50 0.4 8.61 43.1 43.1 > 3 50 0.5 7.41 31.6 31.6 > 4 50 0.6 5.06 21.5 21.5 > 5 50 0.7 3.38 14.1 14.1 > 6 50 0.8 -1.07 5.16 5.16 > 7100 0.3 -6.41 40.3 40.3 > 8100 0.4 -10.6 21.0 21.0 > 9100 0.5 -9.02 13.2 13.2 > 10100 0.6 -9.85 5.14 5.14 > 11100 0.7 -7.94 2.08 2.08 > 12100 0.8 -4.81 1.28 1.28 > What I want to do is create a hanging bar plot with the x-axis being samp.N value by RSQ value. The bars are then values of MRB_uc, MRB_sb, and MRB_bp. Given some values are negative, some bars will be above zero and others below (hence the hanging bar plot) > I don't have any code yet as I am completely unfamiliar with how to do this. Any suggestions would be really appreciated. > Thank you! > Scott > > > > > -- > Scott R. Colwell, PhD > > > [[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. > --- Jeff NewmillerThe . . Go Live... DCN:Basics: ##.#. ##.#. Live Go... Live: OO#.. Dead: OO#.. Playing Research Engineer (Solar/BatteriesO.O#. #.O#. with /Software/Embedded Controllers) .OO#. .OO#. rocks...1k --- __ 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] Creating hanging bar plot in r from dplyr
I am trying to figure out how to create a hanging bar plot from dplyr. I have used dplyr as follows: table4 <- cr %>% group_by(samp.N, RSQ) %>% summarize( MRB_uc = mean(CF.F1F2/0.40*100)-100, MRB_sb = mean(SBC.F1F2.Alpha/0.40*100) - 100, MRB_bp = mean(BPC.F1F2.Alpha/0.40*100) - 100 ) which provides me with this: samp.N RSQ MRB_uc MRB_sb MRB_bp 1 50 0.3 1.42 37.6 37.6 2 50 0.4 8.61 43.1 43.1 3 50 0.5 7.41 31.6 31.6 4 50 0.6 5.06 21.5 21.5 5 50 0.7 3.38 14.1 14.1 6 50 0.8 -1.07 5.16 5.16 7100 0.3 -6.41 40.3 40.3 8100 0.4 -10.6 21.0 21.0 9100 0.5 -9.02 13.2 13.2 10100 0.6 -9.85 5.14 5.14 11100 0.7 -7.94 2.08 2.08 12100 0.8 -4.81 1.28 1.28 What I want to do is create a hanging bar plot with the x-axis being samp.N value by RSQ value. The bars are then values of MRB_uc, MRB_sb, and MRB_bp. Given some values are negative, some bars will be above zero and others below (hence the hanging bar plot) I don't have any code yet as I am completely unfamiliar with how to do this. Any suggestions would be really appreciated. Thank you! Scott -- Scott R. Colwell, PhD [[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] 2019 Spring Workshops using R
Apologies for any cross-postings. Just a reminder that registration is open for four quantitative methods workshops in May of 2019. Each workshop features hands-on examples in Mplus and R, plus lots of opportunities to discuss the analysis for your own research. For more information and to register, please see https://enablytics.com/workshop_events/ [1] Introductory Structural Equation Modeling - May 5, 2019 141 Adelaide Street West Toronto, Ontario M5H 3L5 This one-day hands-on workshop covers various introductory topics in structural equation modeling with continuous and categorical variables. Topics include, assumptions and data considerations, model creation, identification, and evaluation, multiple regression vs path analysis, path analysis, testing direct and indirect effects, and confirmatory factor analysis. Syntax and output for both Mplus and R will be provided for all examples covered in the workshop. [2] Advanced Structural Equation Modeling - May 6, 2019 141 Adelaide Street West Toronto, Ontario M5H 3L5 This one-day hands-on workshop covers various advanced topics in structural equation modeling with continuous and categorical variables. Topics include, model creation, identification, and evaluation, testing moderation, mediation and moderated mediation, multiple group modeling, handling missing and messy data, measurement invariance and power analysis. Syntax and output for both Mplus and R will be provided for all examples covered in the workshop. [3] Multilevel Modeling - May 7 and 8, 2019 141 Adelaide Street West Toronto, Ontario M5H 3L5 This two day hands-on workshop covers various topics in multilevel modeling with continuous and categorical variables. Topics include, when multilevel analysis is necessary, multilevel regression, random slopes and cross-level effects, multilevel confirmatory factor analysis and the MIMIC model, multilevel path analysis, multilevel mediation and moderation, multilevel latent variable modeling, longitudinal data, and power analysis. Syntax and output for both Mplus and R will be provided for all examples covered in the workshop. [4] Growth Modeling - May 9 and 10, 2019 141 Adelaide Street West Toronto, Ontario M5H 3L5 This two-day hands-on workshop covers various topics in growth modeling (longitudinal modeling) with continuous and categorical variables. Topics include, growth modeling without covariates, growth modeling with time invariant and varying covariates, centering points, piecewise growth modeling, autoregressive latent trajectory modeling (ALT -- Scott R. Colwell, PhD, CStat, PStat [[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] R packages for Mac Users
Hello, Does anyone know if all the R packages that are available for Windows users are also available for Mac users? Thank you, Scott -- Scott R. Colwell, Ph.D. Associate Professor, Dept. of Mkt/Cons Studies Adjunct Professor, Dept. of Psychology University of Guelph Guelph, Ontario, Canada, N1G 2W1 __ 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] Looping and break
Hello,
I apologies for bringing up next and break in loops given that there is so
much on the net about it, but I've tried numerous examples found using
Google and just can't seem to get this to work.
This is a simple version of what I am doing with matrices but it shows the
issue. I need to have the loop indexed as n to perform a calculation on the
variable total. But if "total" is greater than 8, it goes to the next loop
indexed "a". For example, it does condition a = 1 for n = 1 to 50 but
within n if total is greater than 8 it goes to the next condition of a which
would be a = 2, and so on.
for (a in 1:3){
if (a == 1) { b <- c(1:5) }
if (a == 2) { b <- c(1:5) }
if (a == 3) { b <- c(1:5) }
for (n in 1:50){
if (n > 15) next
total <- 2*b
if (total > 8) next
}
}
Any help would be greatly appreciated.
Thanks,
Scott
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Re: [R] Saving Mean Relative Difference from all.equal()
I think I have one solution. Not very pretty though. Relies on the text not
changing at all.
as.numeric(gsub("Mean relative difference: ", "",
all.equal(cov2cor(ITEMCOV),cor(item.data))[2]))
Is there a better way?
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[R] Saving Mean Relative Difference from all.equal()
Hello, Does anyone know how to save the numeric value of the "mean relative difference" when using the all.equal() command? For example this: all.equal(cov2cor(ITEMCOV),cor(item.data)) Gives: [1] "Attributes: < Length mismatch: comparison on first 1 components >" [2] "Mean relative difference: 0.01523708" I'd like to save the value 0.01523708 in a numeric format. Thanks, -- View this message in context: http://r.789695.n4.nabble.com/Saving-Mean-Relative-Difference-from-all-equal-tp4703905.html Sent from the R help mailing list archive at Nabble.com. __ 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] Extracting Factor Pattern Matrix Similar to Proc Factor
Thanks everyone -- View this message in context: http://r.789695.n4.nabble.com/Extracting-Factor-Pattern-Matrix-Similar-to-Proc-Factor-tp4703704p4703904.html Sent from the R help mailing list archive at Nabble.com. __ 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] Extracting Factor Pattern Matrix Similar to Proc Factor
Thanks David. What do you do when the input is a covariance matrix rather than a dataset? -- View this message in context: http://r.789695.n4.nabble.com/Extracting-Factor-Pattern-Matrix-Similar-to-Proc-Factor-tp4703704p4703719.html Sent from the R help mailing list archive at Nabble.com. __ 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] Extracting Factor Pattern Matrix Similar to Proc Factor
Hello, I am fairly new to R and coming from SAS IML. I am rewriting one of my MC simulations in R and am stuck on extracting a factor pattern matrix as would be done in IML using Proc Factor. I have found the princomp() command and read through the manual but can't seem to figure out how to save the factor pattern matrix. I am waiting for the R for SAS Users book to arrive. What I would use in SAS IML to get at what I am looking for is: PROC FACTOR Data=MODELCOV15(TYPE=COV) NOBS=1 N=16 CORR OUTSTAT=FAC.FACOUT15; RUN; DATA FAC.PATTERN15; SET FAC.FACOUT15; IF _TYPE_='PATTERN'; DROP _TYPE_ _NAME_; RUN; Would any SAS IML to R converts be able to help me with this? Thanks, Scott Colwell, PhD -- View this message in context: http://r.789695.n4.nabble.com/Extracting-Factor-Pattern-Matrix-Similar-to-Proc-Factor-tp4703704.html Sent from the R help mailing list archive at Nabble.com. __ 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.
