repglm: Generalized Linear Models with Replicate Weights

In ILSAstats: Statistics for International Large-Scale Assessments (ILSA)

Generalized Linear Models with Replicate Weights

Description

Fits a generalized linear model using glm for replicate weights. For a detailed explanation on how the standard errors are estimated see repse.

Usage

repglm(
  formula,
  family = stats::gaussian,
  pvs = NULL,
  relatedpvs = TRUE,
  quiet = FALSE,
  summarize = TRUE,
  setup = NULL,
  df,
  wt,
  repwt,
  group = NULL,
  exclude = NULL,
  na.action = getOption("na.action"),
  method = c("TIMSS", "PIRLS", "ICILS", "ICCS", "PISA", "TALIS")
)

Arguments

formula	an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under ‘Details’.
family	a description of the error distribution and link function to be used in the model. For glm this can be a character string naming a family function, a family function or the result of a call to a family function. For glm.fit only the third option is supported. (See family for details of family functions.)
pvs	if plausible values are not used, this should be NULL. Otherwise it is a list indicating which variables from formula should be replaced by which plausible values variables. For more details check the examples.
relatedpvs	a logical value indicating if pvs are drawn from the same model. If TRUE (default), a total of n estimations will be done, where n is the number of plausible values for each plausible value variable. If FALSE, a total of n_1 \times n_2 \times n_... estimations will be done, where n_i is the number of plausible values in each plausible value variable.
quiet	a logical value indicating if progress status should be shown while estimating models by group. Default is FALSE.
summarize	a logical value indicating if lm objects should be converted to summary.lm or summary.glm objects and stripped from certain elements to reduce the size of the output object. Default is TRUE.
setup	an optional list produced by repsetup.
df	a data frame.
wt	a string specifying the name of the column (within df) with the total weights.
repwt	a string indicating the common names for the replicate weights columns (within df), or a data frame with the replicate weights.
group	a string specifying the variable name (within df) to be used for grouping. Categories in group are treated as independent, e.g., countries.
exclude	a vector indicating which groups (in the same format as group) should be excluded from the pooled and composite estimates.
na.action	a function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The ‘factory-fresh’ default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
method	a string indicating the name of the large-scale assessment to determine the replication method to use. Available options are: "TIMSS", "PIRLS", "ICILS", "ICCS", "PISA", and "TALIS". Note that "TIMSS" and "PIRLS" refer to the method used from 2016 onwards. Their method has not yet been implemented for previous cycles.

Value

a list with the standard errors and the total weights models.

Examples

# Less data for shorter example
repdata2 <- repdata[1:1000,]

RW <- repcreate(df = repdata2, # the data frame with all the information
                 wt = "wt", # the total weights column name
                 jkzone = "jkzones", # the jkzones column name
                 jkrep = "jkrep", # the jkreps column name
                 repwtname = "REPWT", # the desired name for the rep weights
                 reps = 50, # the number of replications
                 method = "ICILS") # the name of the method aka the study name

### No groups ----

# Simple regression - weights within df
replm(formula = Math1 ~ 1 + GENDER,
      wt = "wt", # Name of total weight column within df
      repwt = "REPWT", # Common names of replicate weights within df
      df = cbind(repdata2,RW), # Data frame
      method = "ICILS") # the name of the method aka the study name

# Simple regression - weights as a separate data frame
replm(formula = Math1 ~ 1 + GENDER,
       wt = "wt", # Name of total weight column within df
       repwt = RW, # Data frame of weights
       df = repdata2, # Data frame
       method = "ICILS") # the name of the method aka the study name

# Multiple regression
replm(formula = Math1 ~ 1 + GENDER + Reading1,
       wt = "wt", # Name of total weight column within df
       repwt = RW, # Data frame of weights
       df = repdata2, # Data frame
       method = "ICILS") # the name of the method aka the study name

# Multiple regression - with PVs
## Named list, with element names matching formula variables
pvs = list(Math = paste0("Math",1:3))
pvs

replm(formula = Math ~ 1 + GENDER + Reading1, # Math1 now is "Math"
       wt = "wt", # Name of total weight column within df
       repwt = RW, # Data frame of weights
       df = repdata2, # Data frame
       pvs = pvs, # Named list
       method = "ICILS") # the name of the method aka the study name

# Multiple regression - with more than one related PV variable
## Named list, with element names matching formula variables
pvs = list(Math = paste0("Math",1:3),
           Reading = paste0("Reading",1:3))
pvs

replm(formula = Math ~ 1 + GENDER + Reading, # Reading1 now is "Reading"
       wt = "wt", # Name of total weight column within df
       repwt = RW, # Data frame of weights
       df = repdata2, # Data frame
       pvs = pvs, # Named list
       method = "ICILS") # the name of the method aka the study name

# Multiple regression - with more than UNrelated PV variables
## Named list, with element names matching formula variables
pvs = list(Math = paste0("Math",1:3),
           Reading = paste0("Reading",1:3))
pvs

replm(formula = Math ~ 1 + GENDER + Reading, # Reading1 now is "Reading"
       wt = "wt", # Name of total weight column within df
       repwt = RW, # Data frame of weights
       df = repdata2, # Data frame
       pvs = pvs, # Named list
       relatedpvs = FALSE, # Unrelated PVs
       method = "ICILS") # the name of the method aka the study name


### Groups ----

# Simple regression - weights within df
replm(formula = Math1 ~ 1 + GENDER,
      wt = "wt", # Name of total weight column within df
      repwt = "REPWT", # Common names of replicate weights within df
      df = cbind(repdata2,RW), # Data frame
      group = "GROUP",
      method = "ICILS") # the name of the method aka the study name

# Simple regression - weights as a separate data frame
replm(formula = Math1 ~ 1 + GENDER,
      wt = "wt", # Name of total weight column within df
      repwt = RW, # Data frame of weights
      df = repdata2, # Data frame
      group = "GROUP",
      method = "ICILS") # the name of the method aka the study name

# Multiple regression
replm(formula = Math1 ~ 1 + GENDER + Reading1,
      wt = "wt", # Name of total weight column within df
      repwt = RW, # Data frame of weights
      df = repdata2, # Data frame
      group = "GROUP",
      method = "ICILS") # the name of the method aka the study name

# Multiple regression - with PVs
## Named list, with element names matching formula variables
pvs = list(Math = paste0("Math",1:3))
pvs

replm(formula = Math ~ 1 + GENDER + Reading1, # Math1 now is "Math"
      wt = "wt", # Name of total weight column within df
      repwt = RW, # Data frame of weights
      df = repdata2, # Data frame
      pvs = pvs, # Named list
      group = "GROUP",
      method = "ICILS") # the name of the method aka the study name

# Multiple regression - with more than one related PV variable
## Named list, with element names matching formula variables
pvs = list(Math = paste0("Math",1:3),
           Reading = paste0("Reading",1:3))
pvs

replm(formula = Math ~ 1 + GENDER + Reading, # Reading1 now is "Reading"
      wt = "wt", # Name of total weight column within df
      repwt = RW, # Data frame of weights
      df = repdata2, # Data frame
      pvs = pvs, # Named list
      group = "GROUP",
      method = "ICILS") # the name of the method aka the study name

# Multiple regression - with UNrelated PV variables
## Named list, with element names matching formula variables
pvs = list(Math = paste0("Math",1:3),
           Reading = paste0("Reading",1:3))
pvs

replm(formula = Math ~ 1 + GENDER + Reading, # Reading1 now is "Reading"
      wt = "wt", # Name of total weight column within df
      repwt = RW, # Data frame of weights
      df = repdata2, # Data frame
      pvs = pvs, # Named list
      relatedpvs = FALSE, # Unrelated PVs
      group = "GROUP",
      method = "ICILS") # the name of the method aka the study name

ILSAstats documentation built on April 4, 2025, 12:05 a.m.