longitudinal

问题 I'm trying to estimate child work based on a lagged variable on children's school aspirations. I'm deciding whether I should use glm or clogit to run my models (need fixed effect logits). When I run my glm, my coefficients are very different from my clogit. model1 <- glm(chldwork~lag_aspgrade_binned+age+as.factor(childid), data=finaletdtlag, family='binomial') GLM Output: Call: glm(formula = chldwork ~ lag_aspgrade_binned + age + as.factor(childid), family = "binomial", data = finaletdtlag)

问题 Is it possible to estimate a repeated measures random effects model with a nested structure using plm() from the plm package? I know it is possible with lmer() from the lme4 package. However, lmer() rely on a likelihood framework and I am curious to do it with plm() . Here's my minimal working example , inspired by this question. First some required packages and data, # install.packages(c("plm", "lme4", "texreg", "mlmRev"), dependencies = TRUE) data(egsingle, package = "mlmRev") the data-set

问题 Is it possible to estimate a repeated measures random effects model with a nested structure using plm() from the plm package? I know it is possible with lmer() from the lme4 package. However, lmer() rely on a likelihood framework and I am curious to do it with plm() . Here's my minimal working example , inspired by this question. First some required packages and data, # install.packages(c("plm", "lme4", "texreg", "mlmRev"), dependencies = TRUE) data(egsingle, package = "mlmRev") the data-set

问题 Is it possible to estimate a repeated measures random effects model with a nested structure using plm() from the plm package? I know it is possible with lmer() from the lme4 package. However, lmer() rely on a likelihood framework and I am curious to do it with plm() . Here's my minimal working example , inspired by this question. First some required packages and data, # install.packages(c("plm", "lme4", "texreg", "mlmRev"), dependencies = TRUE) data(egsingle, package = "mlmRev") the data-set

问题 The kml package implements k-means for longitudinal data. The clustering works just fine. Now I'm wondering how I can show the 'structure' of the clusters, for example, by coloring them. A most simple example from the docs (help file of the clusterLongData function..): library(kml) traj <- matrix(c(1,2,3,1,4, 3,6,1,8,10, 1,2,1,3,2, 4,2,5,6,3, 4,3,4,4,4, 7,6,5,5,4),6) myCld <- clusterLongData( traj=traj, idAll=as.character(c(100,102,103,109,115,123)), time=c(1,2,4,8,15), varNames="P", maxNA=3

问题 I just started working with the kml package to perform longitudinal k-means clustering in R . By default the kml function uses the Calinski Harabatz Sorted criterion to choose the 'best' clustering. So by accessing the 'best' clustering you will always see the Calinski Harabatz Sorted criterion. How can we choose another quality criterion ? A minimal example: library(kml) # some data cld <- generateArtificialLongData(25) # perform clustering kml(cold) # choose the 'best' clustering: choice

问题 I just started working with the kml package to perform longitudinal k-means clustering in R . By default the kml function uses the Calinski Harabatz Sorted criterion to choose the 'best' clustering. So by accessing the 'best' clustering you will always see the Calinski Harabatz Sorted criterion. How can we choose another quality criterion ? A minimal example: library(kml) # some data cld <- generateArtificialLongData(25) # perform clustering kml(cold) # choose the 'best' clustering: choice

问题 I have longitudinal data with level 1 and level 2 variables in R my dataframe (df) : ID Year Gender Race MathScore DepressionScore MemoryScore 1 1999 M C 80 15 80 1 2000 M C 81 25 60 1 2001 M C 70 50 75 2 1999 F C 65 15 99 2 2000 F C 70 31 98 2 2001 F C 71 30 99 3 1999 F AA 92 10 90 3 2000 F AA 89 10 91 3 2001 F AA 85 26 80 I've tried these: summary(fix <- lme(MathScore ~ Gender+Race+DepressionScore+MemoryScore, random= Year|ID, data=df, na.action="na.omit") summary(fix2 <- lme(MathScore ~ 1