Dear Community,
I am struggling with a growth curve estimation problem. It is a classic BMI change with age calculation. I am not an expert of this field but have some statistical experience in other fields.
Of course I started reading classical papers related to the topic and understood the concept of the LMS method. After that I thought it will be a "piece of cake", R must have a package related to the topic, so I just need the data and I am just a few lines of code from the results.
I encountered some problems:
- found at least three packages related to LMS: gamlss, VGAM and an old one lmsqreg (I did not try this because it does not support my R version (3.0.1.))
- it was relatively easy to get plots of percentiles in both packages, although they were far not the same (I also tried an other software, LMSchartmaker it gave different results from the previous ones)
- I tried to get tables of predicted values (with the predict function in VGAM and with centiles.pre in gamlss) but without any success.
- I tried to use the function gamlss() instead of lms() in gamlss but I could not force them to give the same (or very similar results), but the centiles.pred() function did work as expected for the model resulted from galmss()
- lms gives really different results if k is specified different ways, which is "best"?
Also I have a general question: some publications state they estimated the centiles so that aroun 18 years of age the curves pass through certain points. How is that possible?
Thank you for any suggestions or insights about the methods or preferred package!
Here is my code (without data):
#####gamlss
library(gamlss)
library(VGAM)
library(foreign)
adatok <- read.spss("MDSZ adatok.sav", to.data.frame=TRUE)
adatok_fiu <- subset(adatok, adatok$gender == "Fi??k")[,2:3]
row.names(adatok_fiu) <- NULL
adatok_lany <- subset(adatok, adatok$gender == "L??nyok")[,2:3]
row.names(adatok_lany) <- NULL
m1 <- lms(BMI,age,data=adatok_fiu, cent=c(3,10,25,50,75,90,97), families="BCCG")
fittedPlot(m1, x=adatok_fiu$age)
m1 <- lms(BMI,age,data=adatok_fiu, cent=c(3,10,25,50,75,90,97), families="BCCG", method.pb="GAIC", k=log(1455))
fittedPlot(m1, x=adatok_fiu$age)
m1 <- lms(BMI,age,data=adatok_fiu, cent=c(3,10,25,50,75,90,97), families="BCCG", method.pb="GAIC")
fittedPlot(m1, x=adatok_fiu$age)
m2 <- lms(BMI,age,data=adatok_lany, cent=c(3,10,25,50,75,90,97), families="BCCG")
m2 <- lms(BMI,age,data=adatok_lany, cent=c(3,10,25,50,75,90,97), families="BCCG", method.pb="GAIC", k=log(1144))
m2 <- lms(BMI,age,data=adatok_lany, cent=c(3,10,25,50,75,90,97), families="BCCG", method.pb="GAIC")
m3 <- gamlss(BMI~age, family=BCT, data=adatok_fiu)
centiles(m3,xvar=adatok_fiu$age, cent=c(3,10,25,50,75,90,97))
newx <- seq(12,20,.5)
centiles.pred(m1, xname="age", xvalues=newx)
centiles.pred(m3, xname="age", xvalues=newx)
centiles(m1,adatok_fiu$age)
#####VGAM
library(foreign)
library(VGAM)
library(VGAMdata)
adatok <- read.spss("MDSZ adatok.sav", to.data.frame=TRUE)
adatok_fiu <- subset(adatok, adatok$gender == "Fi??k")
adatok_lany <- subset(adatok, adatok$gender == "L??nyok")
fit1 <- vgam(BMI ~ s(age), lms.bcn(percentiles = c(3, 10, 25, 50, 75, 90, 97)), adatok_fiu)
fit2 <- vgam(BMI ~ s(age), lms.bcn(percentiles = c(3, 10, 25, 50, 75, 90, 97)), adatok_lany)
qtplot(fit1, percentiles = c(3, 10, 25, 50, 75, 90, 97), xlim = c(10.5, 20.5), ylim=c(13,34),
las = 1, ylab = "BMI", lcol = 4, pch=NA)
qtplot(fit2, percentiles = c(3, 10, 25, 50, 75, 90, 97), xlim = c(10.5, 20.5), ylim=c(13,34),
las = 1, ylab = "BMI", lcol = 3, add=TRUE, pch=NA, label=FALSE)
Thank you:
Daniel
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