Obtaining standardized betas from a svyglm() weighted regression

[cholo.trem]

Hi! I have a svyglm() weighted linear regression model and would like to obtain the standardized betas of the regression coefficients. I have tried to get them with the lm.beta() function, however when I do this the p-values of the coefficients change. I have also tried with the effectsize:standardize_parameters() function, but this returns the unstandardized coefficients (the same ones as in the original svyglm() model). How can I obtain the standardized coefficients of the svyglm() model with the correct p-values? Any help would be greatly appreciated. Here is the code for the model:

library(tidyverse)
library(lm.beta)
library(survey)
library(sjstats)
dat <- read.csv("https://raw.githubusercontent.com/LucasTremlett/questions/master/questiondata.csv")
dat.weighted<- svydesign(ids = ~1, data = dat, weights = dat$weight)
model.weighted<- svyglm(DV~IV1+IV2+IV3, design=dat.weighted)
summary(model.weighted)

Here is the code for the two methods that have not worked for me

##Withlmbeta()
model.weighted.lm.beta <- lm.beta(model.weighted)
summary(model.weighted.lm.beta)

##Witheffectsize:standardize_parameters()
effectsize::standardize_parameters(model.weighted)

[StatSteph]

What do you mean when you say they don't work? It "works" for me in that there are no errors or problems with the code.

library(tidyverse)
library(lm.beta)
library(survey)
#> Loading required package: grid
#> Loading required package: Matrix
#> 
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#> 
#>     expand, pack, unpack
#> Loading required package: survival
#> 
#> Attaching package: 'survey'
#> The following object is masked from 'package:graphics':
#> 
#>     dotchart
library(sjstats)
#> 
#> Attaching package: 'sjstats'
#> The following object is masked from 'package:survey':
#> 
#>     cv
dat <- read.csv("https://raw.githubusercontent.com/LucasTremlett/questions/master/questiondata.csv")
dat.weighted<- svydesign(ids = ~1, data = dat, weights = dat$weight)
model.weighted<- svyglm(DV~IV1+IV2+IV3, design=dat.weighted)
summary(model.weighted)
#> 
#> Call:
#> svyglm(formula = DV ~ IV1 + IV2 + IV3, design = dat.weighted)
#> 
#> Survey design:
#> svydesign(ids = ~1, data = dat, weights = dat$weight)
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)  7.32065    0.97863   7.481 1.79e-13 ***
#> IV1          0.57412    0.50718   1.132 0.257956    
#> IV2         -0.01029    0.01510  -0.681 0.495991    
#> IV3         -0.53752    0.15370  -3.497 0.000494 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> (Dispersion parameter for gaussian family taken to be 14.14836)
#> 
#> Number of Fisher Scoring iterations: 2

##Withlmbeta()
model.weighted.lm.beta <- lm.beta(model.weighted)
summary(model.weighted.lm.beta)
#> 
#> Call:
#> svyglm(formula = DV ~ IV1 + IV2 + IV3, design = dat.weighted)
#> 
#> Weighted Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -19.7331  -2.1371  -0.7513   1.9734  13.7321 
#> 
#> Coefficients:
#>              Estimate Standardized Std. Error t value Pr(>|t|)    
#> (Intercept)  7.320655     0.000000   0.689727  10.614  < 2e-16 ***
#> IV1          0.574116     0.066581   0.275336   2.085   0.0373 *  
#> IV2         -0.010288    -0.040034   0.008166  -1.260   0.2081    
#> IV3         -0.537517    -0.161916   0.121259  -4.433 1.05e-05 ***
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#> 
#> Residual standard error: 3.768 on 879 degrees of freedom
#>   (809 observations deleted due to missingness)
#> Multiple R-squared:  0.02851,    Adjusted R-squared:  0.02519 
#> F-statistic: 8.597 on 3 and 879 DF,  p-value: 1.253e-05

##Witheffectsize:standardize_parameters()
effectsize::standardize_parameters(model.weighted)
#> Parameter   | Coefficient (std.) |      1e+02% CI
#> -------------------------------------------------
#> (Intercept) |               7.32 | [ 5.40,  9.24]
#> IV1         |               0.57 | [-0.42,  1.57]
#> IV2         |              -0.01 | [-0.04,  0.02]
#> IV3         |              -0.54 | [-0.84, -0.24]
#> 
#> # Standardization method: Refit
effectsize::standardize_parameters(model.weighted.lm.beta)
#> Parameter   | Coefficient (std.) |      1e+02% CI
#> -------------------------------------------------
#> (Intercept) |               7.32 | [ 5.40,  9.24]
#> IV1         |               0.57 | [-0.42,  1.57]
#> IV2         |              -0.01 | [-0.04,  0.02]
#> IV3         |              -0.54 | [-0.84, -0.24]
#> 
#> # Standardization method: Refit

^{Created on 2020-10-05 by the reprex package (v0.3.0)}

[cholo.trem]

"Don't work" was a confusing way to put it, sorry about that. Basically, the model that lm.beta() produces has different standard errors than the one svygml() produces. I don't know why this is the case.