[R] Bifactor model and infit statistics?
Goodafternoon, I amcurrently in the process of calibrating an item bank using a GPCM model. So, amI right to assume that the bifactor model allows me to work with my generalfactor by assimilating it to a one-factor model, without taking into account groupfactors? That is, I can estimate my item parameters from my factor loadings onthe general factor only? If so, Ihave some questions about evaluating the fit of my model. The calculation of infitstatistics is specific to unidimensional models. Can I compute infit statisticsusing the general factor or do I have to do this separately for each of the groupfactors? Or is there a more appropriate method to evaluate the fit of my modelwhen calibration an item bank using a GPCM model? Thank youin advance. [[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] Linear regression with tranformed dependant variable
Dear all, I am trying to fit a multiple linear regression model with a transformed dependant variable (the normality assumption was not verified...). I have realised a sqrt(variable) transformation... The results are great, but I don't know how to interprete the beta coefficients... Is it possible to do another transformation to get interpretable beta coefficients to express the variations in the original untransformed dependant variable ? Thank you very much for your help!Noémie [[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] svyciprop object
Hi, I'd like to access to the different elements in a svyciprop object (to the confidence intervals in particular...). But none of the functions I know works.Thank you for your help ! > grr <- svyciprop(~temp==bzz, dclus1)> grr 2.5% > 97.5%temp == bzz 0.040719697 0.027622756 0.05965> attributes(grr)$names[1] > "temp == bzz" $var as.numeric(temp == bzz)as.numeric(temp == bzz) 6.42377038236e-05 $ci 2.5% 97.5% 0.0276227559667 0.0596454643748 $class[1] "svyciprop" > grr$ciErreur dans grr$ci : $ operator is invalid for atomic vectors> > grr["ci"] NA > ci(grr)Erreur : impossible de trouver la fonction "ci" [[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] problem of interpretation using mediation package
Dear all, I am using the package mediation in order to perform a parametric mediation analysis on survival data. I have 8 variables: - Mediator - Treat - time (days) - death (event) - X1-X4 (confounding variables) I ran the following code to estimate the causal mediation effects. med.m = lm(Mediator ~ Treat + X1 + X2 + X3 + X4) med.y = survreg(Surv(time, death) ~ Treat + X1 + X2 + X3 + Mediator + X4) med.out <- mediate(med.m, med.y, treat = "Treat", mediator = "Mediator") summary(med.out) Here are the output provided by this script: Causal Mediation Analysis Quasi-Bayesian Confidence Intervals Estimate 95% CI Lower 95% CI Upper p-value ACME (control) -3.68e+02 -1.27e+03 -3.34e+01 0.01 ACME (treated) -1.47e+02 -4.46e+02 -1.67e+01 0.01 ADE (control) -3.76e+03 -1.18e+04 -5.93e+02 0.00 ADE (treated) -3.54e+03 -1.14e+04 -5.53e+02 0.00 Total Effect -3.91e+03 -1.20e+04 -6.79e+02 0.00 Prop. Mediated (control) 9.56e-02 1.55e-02 2.36e-01 0.01 Prop. Mediated (treated) 3.82e-02 7.03e-03 1.49e-01 0.01 ACME (average) -2.57e+02 -8.36e+02 -2.46e+01 0.01 ADE (average) -3.65e+03 -1.16e+04 -5.78e+02 0.00 Prop. Mediated (average) 6.69e-02 1.17e-02 1.90e-01 0.01 Sample Size Used: 713 Simulations: 1000 My problem is that I do not understand how to interpret the value of the estimate obtained for the ACME (control) parameter. I know that when the response variable (Y) is binary, this estimate can be interpreted as the increase in terms of probability of the event for control subjects. What is the good interpretation when the response variable (Y) in the model is a survival object ? Does it indicates here a decrease expressed in number of days (368) ? According to the Prop. Mediated (average) value (i.e last row of the table), can I conclude that about 6.69% of the total effect of Treat on Y is explained by the indirect effect of Mediator ? Thanks for your consideration, Best regards, Kendejan [[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] mirt package error in ESTIMATION...
Hello everyone, I am trying to undertake an item bifactor analysis of graded response data from a questionnaire. I am using the mirt package, especially the bfactor function.My dataset is called data.items, it contains about 2000 observations and 31 variables (variables represent the items in the questionnaire). The items follow a Likert scale format and represent a level of satisfaction, missing values are coded as NA). I am having trouble at the beginning of my analysis, during the exploratory model fitting. The syntax and the error message are as follows: bfactor(data.items,model=9) Error in ESTIMATION(data = data, model = model, group = rep(all, nrow(data)), : index out of bounds I get the same error when i try with: bfactor(data.items,9,itemtype=graded) or bfactor(data.items,9,prev.cor=cor) where cor is the correlationmatrix that i compute without the missing values. If someone have an idea to suggest, do not hesitate. Thank you. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Triangular Test
Hello, I would like to perform triangular test for clinical trial with R. can you help me please ? Jan [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] compare means
Dear all, I would compare two means between cases and controls taking into account that I have matched 1 case to two controls. How i can do it with R. Thanks in advance Jan [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] CART with rpart
dear all, i want to keep in my data file the results of terminal nodes (groups) after CART analysis for performing other statisticals analysis by this groups. can you help me please? thanks. jan. [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Survival curves for case control and control
Hi, I want to perform Survival curves for case and control subjects in the propensity score-matched cohort that accounted for the clustering of matched pairs. How I can do it with R. Thanks for your help, Jan [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Transitions probability comparison
Hello, I am training to use the changeLOS package. Using data provided in this package (los.data), I want to compare transition probability P01 and P03 like the Kaplan-Meier Method.Can someone help me ? Thank you. Jan data(los.data) my.observ - prepare.los.data(x=los.data) my.model - msmodel(c(0,1,2,3),cens.name=cens) my.trans - trans(model=my.model,observ=my.observ) my.aj - aj(my.trans, s=0, t=80) plot(my.aj,c(0,0,0,0),c(0,1,2,3)) [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] changeLOS package use
Hello, I am training to use the changeLOS package. Using data provided in this package (los.data), I want to generate a new plot with overlaying 2 curves of transition probability P01 and P03 and also statistically compare the two curves like the Kaplan-Meier Method.Can someone help me ? Thank you. Jan data(los.data) my.observ - prepare.los.data(x=los.data) my.model - msmodel(c(0,1,2,3),cens.name=cens) my.trans - trans(model=my.model,observ=my.observ) my.aj - aj(my.trans, s=0, t=80) plot(my.aj,c(0,0,0,0),c(0,1,2,3)) [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Proportions comparison
Dear all, I want to compare two proportions of disease in two populations : group 1 (1200/15000) and group 2 (26/650). However I would take into account the number of physicians involved in each group G1 (1600 physicians) and G2 (1.6 million). Please can someone can help me ? Thanks [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Meta-analysis question
Dear all, I am trying to do meta-analysis of continuous outcome data. Twelve studies are selected but for six of them, i have only p-values and the six other means and standard deviation for the two groups (Experimental and Control). How can I do with R to take into account p-values and/or means and standard deviation to perform my meta-analysis. Thanks for your help Jan [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] binary logistic regression taking account clustering
Hello I would like to perform with R, a binary logistic regression analysis taking account clustering (A randomized trial into 2 groups, patients within 50 hospitals): y (0,1) is the outcome x1, x2 indivifdualâs characteristics x3,x4 hospitalsâ characteristics. Thanks in advance Jan [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Generate Numbers
Hello, How can I generate randomly in R a sample of skewed data with first quartile is 540 and third quartile is 715. I need a sample of 100 cases. Thank you for your help Jan [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Error in solve.default peforming Competing risk regression
Dear all, I am trying to use the crr function in the cmprsk package version 2.2 to analyse 198 observations.I have receive the error in solve.default. Can anyone give me some insights into where the problem is? Thanks here is my script : cov=cbind(x1,x2) z-crr(ftime,fstatus,cov)) and data file: x1x2fstatusftime 0.021263 0.031113 0.031523 0.03349 0.031278 0.031190 0.041472 0.041357 0.041219 0.0429349 0.051207 0.051166 0.06355 0.06217 0.071264 0.07267 0.07337 0.082190 0.08372 0.08236 0.08196 0.092136 0.097681273 0.11167 0.1276 0.13107 0.1371 0.11116 0.11262 0.111256 0.111385 0.111266 0.111174 0.113334 0.123270 0.12149 0.121733 0.131271 0.132160 0.131105 0.14256 0.14249 0.151266 0.15146 0.151112 0.151246 0.161371 0.161140 0.161279 0.161174 0.1617180 0.17173 0.17354 0.171320 0.171213 0.181215 0.181282 0.181263 0.18399 0.21266 0.2159 0.2097611659 0.211111 0.21162 0.221386 0.241249 0.24279 0.261199 0.26195 0.261270 0.261189 0.26706321 0.27183 0.27145 0.291154 0.291221 0.291174 0.29241112 0.31208 0.3231 0.32182 0.332152 1.341595 0.371182 0.381231 0.381282 1.38160 0.421181 0.441103 0.453251 0.452176 0.471234 0.49326 1.53142 0.5551152 0.561229 1.571179 0.58189 0.62187361 0.66332 1.69114 1.69171 0.7594190 1.9462391 0.951138 1.969191 11.13129 01.171145 11.26146 11.361187 01.36174 11.48169 01.61102 11.62174 11.92103 11.91152 12.11159 12.161238 12.201140 02.231124 12.42161 12.451283 12.47142 12.582291 12.591182 12.73328 12.731459 13.0071179 13.04152 13.167385 13.2451559 13.251320 13.2331482 13.275182 13.37141982 13.3652142 13.411184 13.4833187 13.5324131 13.58181374 13.629149 13.63681297 13.7193 13.781364 13.884156 13.88166 13.891160 13.9361205 14.042161 14.38299 14.3921311 14.411257 14.48197 14.53176 14.510194 14.5721568 14.6196 14.664159 14.825136 15.151459 15.1852111 15.333158 15.32177 15.348338 15.473277 15.5441453 15.7331363 15.866132 15.9157 15.9411870 16128 16.09153 16.302123 16.6671196 16.647245 16.7271132 16.304339 16.746161 16.786150 17125 17.044 1252 17.164242 17.6903134 17.802180 18.3981133 18.666132 18.687341 18.794129 18.887193 19.172348 19.7961275 111.361230 111.56147 112.12175 112.142168 112.411318 116.50167 117.211510 [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Competing risk regression error in solve.default
Dear all, I am trying to use the crr function in the cmprsk package version 2.2 to analyse 198 observations.I have receive the error in solve.default. Can anyone give me some insights into where the problem is? Thanks here is my script and in attached file my data. cov=cbind(x1,x2) z-crr(ftime,fstatus,cov)) __ R-help@r-project.org mailing list 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] Competing Risks Regression with qualitative predictor with more than 2 categories
Hello,
I have a question regarding competing risk regression using cmprsk package
(function crr()). I am using R2.9.1. How can I do to assess the effect of
qualitative predictor (gg) with more than two categories (a,b,c) categorie c is
the reference category. See above results, gg is considered like a ordered
predictor !
Thank you for your help
Jan
# simulated data to test
set.seed(10)
ftime - rexp(200)
fstatus - sample(0:2,200,replace=TRUE)
gg - factor(sample(1:3,200,replace=TRUE),1:3,c('a','b','c'))
cov - matrix(runif(600),nrow=200)
dimnames(cov)[[2]] - c('x1','x2','x3')
cov2=cbind(cov,gg)
print(z - crr(ftime,fstatus,cov2))
convergence: TRUE
coefficients:
x1 x2 x3 gg
0.2624 0.6515 -0.8745 -0.1144
standard errors:
[1] 0.3839 0.3964 0.4559 0.1452
two-sided p-values:
x1x2x3gg
0.490 0.100 0.055 0.430
summary(z)
Competing Risks Regression
Call:
crr(ftime = ftime, fstatus = fstatus, cov1 = cov2)
coef exp(coef) se(coef) z p-value
x1 0.262 1.3000.384 0.683 0.490
x2 0.652 1.9180.396 1.643 0.100
x3 -0.874 0.4170.456 -1.918 0.055
gg -0.114 0.8920.145 -0.788 0.430
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[R] Cuminc Plot
Dear All,
here is the example for cumulative incidence analysis with cmprsk package:
set.seed(2)
ss - rexp(100)
gg - factor(sample(1:3,100,replace=TRUE),1:3,c('a','b','c'))
cc - sample(0:2,100,replace=TRUE)
strt - sample(1:2,100,replace=TRUE)
print(xx - cuminc(ss,cc,gg,strt))
plot(xx,lty=1,color=1:6)
When I
perform this example, I have 6 curves.
a 1
b 1
c 1
a 2
b 2
c 2
I would
like to plot only 3 first curves of risk cc=1 not all of 6 curves.
a 1
b 1
c 1
How I can
do this with R ?
Many thanks
Jan
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[R] Error in Comprting Risks Regression
Dear All, I am trying to run the following function (a CRR=Competing Risks Regressionmodel) and receive the error in solve.default. Can anyone give me some insights into where the problem is? Thanks print(z-crr(J3500,CD3500,cov)) Error in solve.default(v[[1]]) : Lapack routine dgesv : system is exactly singular [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] Bland-Altman method to measure agreement with repeated measures
Dear all, I want to use the Bland-Altman method to measure agreement with repeated measures collected over period of time (seven periods). How can I do this with R Many thanks _ o.fr [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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] spline
dear all, I have a problem about spline, when I send this: library(mgcv) attach(SG2) modele3 - gam(J15STATUS~SWANG1+s(AGE)+ s (SP2),data=SG2,family=binomial) it doesen't work et it says: Erreur dans get(.Random.seed,envir=.GlobalEnv) variable.Random.seed introuvable Thanks a lot __ ble contre les messages non sollicités [[alternative HTML version deleted]] __ R-help@r-project.org mailing list 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.
