sam() and sampling weights

[Kyle Cox]

Hi all,

I'm looking into SAM approaches that incorporate sampling weights. It seems like the sam() function accommodates sampling weights but I want to confirm this is accurate. Specifically, if the default LSAM approach accommodates sampling weights and how. I couldn't find any publication/presentation that described the method the sam() function uses to incorporate the sampling weights.

Thanks,

Kyle Cox

[Felipe Vieira]

Hi Kyle, 

As far as I know, you can pass the weights through the argument "sampling.weights" (it gets forwarded through ... ). For example, see documentation pages 4 (and 47 for some information on normalization). However, from my current understanding of the code, I am not entirely sure how the SEs incorporate that information/work in that case.

A simple example:

pop.model <- '
  f1 =~ 0.1*x1 + 0.2*x2 + 0.3*x3
  f2 =~ 0.1*y1 + 0.2*y2 + 0.3*y3
  f2 ~ 0.4*f1
'

Data <- simulateData(pop.model, sample.nobs = 1000, seed = 1234)
Data$wt <- ifelse(Data$y1 > 0, 2, 1)

model <- '
  f1 =~ x1 + x2 + x3
  f2 =~ y1 + y2 + y3
  f2 ~ f1
'
fit_nowt <- sam(model, data = Data); fit_wt   <- sam(model, data = Data, sampling.weights = "wt")
coef(fit_nowt); coef(fit_wt)

Best, 

Felipe. 

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[Felipe Vieira]

Hi Kyle, 

As far as I know, you can pass the weights through the argument "sampling.weights" (it gets forwarded through ... ). For example, see documentation pages 4 (and 47 for some information on normalization). However, from my current understanding of the code, I am not entirely sure how the SEs incorporate that information/work in that case.

A simple example:

pop.model <- '
  f1 =~ 1*x1 + 2*x2 + 3*x3
  f2 =~ 1*y1 + 2*y2 + 3*y3
  f2 ~ 4*f1
'

Data <- simulateData(pop.model, sample.nobs = 1000, seed = 1234)
Data$wt <- ifelse(Data$y1 > 0, 2, 1)

model <- '
  f1 =~ x1 + x2 + x3
  f2 =~ y1 + y2 + y3
  f2 ~ f1
'
fit_nowt <- sam(model, data = Data); fit_wt   <- sam(model, data = Data, sampling.weights = "wt")
coef(fit_nowt); coef(fit_wt)

Best, 

Felipe.

On Thu, Apr 16, 2026 at 9:57 PM Kyle Cox <kyle...@gmail.com> wrote:

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