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Common Data Processing

Source: R/process_3_in_1.R
common_processes.Rd

For the process_data argument. It do three types of processing in one step: Creating ordinal variables, generating missing values, and computing scales scores.

Usage

common_processes(
  data,
  cut_patterns = NULL,
  cuts = NULL,
  missing_values_args = list(),
  var_patterns = NULL,
  vars_in_waves = NULL,
  attrition_rate = NULL,
  prop = NULL,
  mech = "MCAR",
  method = c("none", "mean", "sum"),
  na.rm = FALSE
)

Arguments

data

A data frame.

cut_patterns

A named vector. The names are the names of the latent variables for which indicator scores will be converted. Each value must be the name of one of the built-in patterns (call cut_patterns() to list the patterns and their names). Can be used with cuts but a latent variable should appear only either in cut_patterns or cuts, not both. If both cut_patterns and cuts are set to NULL, then the original data (data) will be returned unchanged.

cuts

A named list. The names are the names of the latent variables for which indicator scores will be converted. Each element is a vector of the thresholds for the conversion. -Inf and Inf will be automatically included during the conversion. Can be used with cut_patterns but a latent variable should appear only either in cut_patterns or cuts, not both. If both cut_patterns and cuts are set to NULL, then the original data (data) will be returned unchanged.

missing_values_args

A named list of optional arguments to be passed to mice::ampute(). Note that prop and mech will override the values set for them in missing_values_args, if any.

var_patterns

A list of named list. Used to specify the pattern at the latent variable level. Each list has two elements. vars is the vector of latent variables names. The pattern will consist of all indicators of these latent variables. prop is the proportion of missing for this pattern. If the values of prop summed to one, then the values will be used as freq in mice::ampute(). If they do not summed to one and prop is not set manually, the sum will be used in prop, and prop divided by the sum will be used as freq.

vars_in_waves

A list of character vectors. Each vector denotes the latent variables or non-indicator observed variables in a wave. For example, list("x", c("m1", "m2"), "y") means that x is collected in Wave 1, m1 and m2 in Wave 2, and y in Wave 3.

attrition_rate

The probability that a case will drop out from one wave to another.

prop

The proportion of missingness. Default is 0.5, about 50% of the cases have missing data. If set to NULL, then the original data (data) will be returned unchanged.

mech

The missing data mechanism. Default is "MCAR" (missing completely at random). Other possible values are "MAR" (missing at random) and "MNAR" (missing not at random). Please refer to the help of mice::ampute() for details.

method

The method to be used to compute the scale scores. Can be "mean" or "sum". Implemented by mean() and sum(). If set to "none", then the original data (data) will be returned unchanged.

na.rm

How missing value (NA) are handled. Default is FALSE, the same default value for mean() and sum().

Value

The function returns a data frame with the processed data.

Details

This function is to be used in the process_data argument of power4test().

This function is simply a wrapper of the following three functions:

Please refer to these functions on how the raw data is processed.

See also

power4test(). See also ordinal_variables(), missing_values, and scale_scores for the processes employed.

Examples


# Specify the model

mod <-
"
m ~ x
y ~ m + x
"

# Specify the population values

mod_es <-
"
y ~ m: l
m ~ x: m
y ~ x: n
"

# Specify the numbers of indicators and reliability coefficients

k <- c(y = 3,
       m = 4,
       x = 5)
rel <- c(y = .70,
         m = .70,
         x = .70)

# Simulate the data

out <- power4test(
         nrep = 2,
         model = mod,
         pop_es = mod_es,
         n = 200,
         number_of_indicators = k,
         reliability = rel,
         process_data = list(
           fun = common_processes,
           args = list(
                     prop = .75,
                     cut_patterns = c(x = "-ma3", y = "-ma3", m = "-ma3")
                   )
           ),
         test_fun = test_parameters,
         test_args = list(op = "~"),
         parallel = FALSE,
         iseed = 1234)
#> Recommend setting 'parallel' to TRUE for faster analysis
#> Simulate the data:
#> Fit the model(s):
#> Do the test(s): test_parameters: CIs (op: ~) 

dat <- pool_sim_data(out)
head(dat, 50)
#>    y1 y2 y3 m1 m2 m3 m4 x1 x2 x3 x4 x5
#> 1   2  1  1  1  1 NA  1  1  2  1  3  2
#> 2   3  2  3  3 NA  1  2  2  2  2  2  1
#> 3   1  2  2  3  3  2  2  2  1 NA  2  2
#> 4   2  3  2 NA  1  2  1  2  2  1  2  3
#> 5   2  2  3  3  3  2  3  1 NA  1  2  3
#> 6   2  3  1  3  3  2  2 NA  2  2  2  2
#> 7   3  2  3  2  2  3  1  2  1  2  2  3
#> 8   3  2  2  2  2  1  1  2  2  2  1 NA
#> 9  NA  2  2  2  2  1  2  2  2  3  2  2
#> 10  2  1  2  2  2  1  1  1  2  2  2 NA
#> 11  2  3  3  1  2 NA  1  1  1  2  2  2
#> 12  2  2  3  1  1  2  1  1  3 NA  2  2
#> 13  3  2 NA  1  2  2  2  2  2  2  2  1
#> 14  1  1 NA  2  2  1  1  2  3  3  1  2
#> 15  1  3  2  3  2  2  3  1  2  2  2  3
#> 16  2  2  2  2  2  1  3  3  3  2  2  3
#> 17  3  2 NA  2  3  3  2  3  3  3  3  3
#> 18  2  2  1  3  2 NA  2  2  2  2  2  2
#> 19  1  1  2  2  3  1  1  3  2 NA  2  3
#> 20 NA  2  2  3  3  3  3  2  2  3  2  1
#> 21  3  2  2  3 NA  2  2  2  2  2  2  2
#> 22  3 NA  2  2  2  3  1  2  3  2  3  3
#> 23  2  2  3  2  2  2  1  2  1  2  2 NA
#> 24  3  3  2  3  2  3  3  3  3 NA  2  2
#> 25  2  3  2  2  1  2  1  2  2  2  3 NA
#> 26  2  2  1  1  1  2  1  2  3  2  3  1
#> 27 NA  3  3  3  3  2  3  2  3  2  2  3
#> 28  2  2  3  2  2  2  1  2  2 NA  2  2
#> 29  2  1  2  2 NA  1  2  2  2  1  1  2
#> 30  2  2  1  2  2  2  3  1  2  3  1  2
#> 31  3  2  3  3  3  1  3  1  2  1  1  1
#> 32  2  2  1  2  2  2  2  3 NA  1  1  1
#> 33  1  1  1 NA  2  1  1  1  2  1  1  2
#> 34 NA  2  2  1  2  1  2  1  2  2  3  2
#> 35  1  2 NA  2  1  1  1  2  1  2  3  2
#> 36  1  2  1  1  1  1  3  2  3  3 NA  3
#> 37  1  2  1  2  2  2  3 NA  2  3  3  3
#> 38  2  2  2  2  2  2  2  3  2  3  3  1
#> 39  2  2  2  2  1  2  2  2  2  2  2  1
#> 40  3 NA  3  1  2  2  2  1  1  3  3  2
#> 41  3  2  3  3  3  3  3  3  3  3  2 NA
#> 42  2  2  1  2  2  2  1  2  2  2  1  3
#> 43  1  1  1  3  2  2  1  1  1 NA  3  1
#> 44  2  3  3  2  2 NA  2  2  1  2  1  1
#> 45  2  2  3 NA  1  2  1  2  2  1  1  1
#> 46  1  1  3  1  1  3  2  2  1  3  2  3
#> 47  2  2  3  2  2  3  1  2  2  2  3  3
#> 48  2  1  2  2  2  2  1  3  2  1  2  3
#> 49  2  3  1 NA  2  2  2  3  3  2  2  1
#> 50  2  1  2  2  2 NA  3  2  3  3  3  2