Useful short cut for poLCA::poLCA. — poLCA

you get a result table for reporting with the other functions like poLCA_check_class or poLCA_BLRT.

Usage

poLCA_result(
  formula,
  data,
  nclass,
  maxiter = 5000,
  nrep = 1,
  reorder_with = "class prop",
  reorder_outcome = 1,
  reorder_decreasing = TRUE,
  verbose = TRUE
)

Arguments

formula: a formula object directly passed to poLCA::poLCA.
data: a data frame.
nclass: a numeric vector of number of latent class to estimate. For example, if you want to estimate 2 class model to 5 class model for comparing model fitting of them, input nclass = 2:5.
maxiter: The maximum number of iterations through which the estimation algorithm will cycle.
nrep: Number of times to estimate the model, using different values of probs.start. The default is one. Setting nrep>1 automates the search for the global—rather than just a local—maximum of the log-likelihood function. poLCA returns the parameter estimates corresponding to the model with the greatest log-likelihood.
reorder_with: 'class_prop' or one variable name. If 'class_prop', the order of latent class is determined by the proportion of each class. If one variable name, the order of latent class is determined by the conditional probability of the variable. The default is 'class_prop'.
reorder_outcome: a numeric value of a category of manifest variable. You can reorder latent classes with reference to the category of manifest variable. You must specify a manifest variable in reorder_with argment. The default is 1.

Examples

# example data
data('carcinoma', package = 'poLCA')

# estimate 2 class model to 3 class model
result <- poLCA_result(
  formula = as.matrix(carcinoma) ~ 1,
  data = carcinoma,
  nclass = 2:3,
  maxiter = 6000,
  nrep = 1,
  verbose = TRUE
)
#> Conditional item response (column) probabilities,
#>  by outcome variable, for each class (row) 
#>  
#> $A
#>            Pr(1)  Pr(2)
#> class 1:  0.0000 1.0000
#> class 2:  0.8835 0.1165
#> 
#> $B
#>            Pr(1)  Pr(2)
#> class 1:  0.0169 0.9831
#> class 2:  0.6456 0.3544
#> 
#> $C
#>            Pr(1)  Pr(2)
#> class 1:  0.2391 0.7609
#> class 2:  1.0000 0.0000
#> 
#> $D
#>            Pr(1)  Pr(2)
#> class 1:  0.4589 0.5411
#> class 2:  1.0000 0.0000
#> 
#> $E
#>            Pr(1)  Pr(2)
#> class 1:  0.0214 0.9786
#> class 2:  0.7771 0.2229
#> 
#> $F
#>            Pr(1)  Pr(2)
#> class 1:  0.5773 0.4227
#> class 2:  1.0000 0.0000
#> 
#> $G
#>            Pr(1)  Pr(2)
#> class 1:  0.0000 1.0000
#> class 2:  0.8835 0.1165
#> 
#> Estimated class population shares 
#>  0.5012 0.4988 
#>  
#> Predicted class memberships (by modal posterior prob.) 
#>  0.5 0.5 
#>  
#> ========================================================= 
#> Fit for 2 latent classes: 
#> ========================================================= 
#> number of observations: 118 
#> number of estimated parameters: 15 
#> residual degrees of freedom: 103 
#> maximum log-likelihood: -317.2568 
#>  
#> AIC(2): 664.5137
#> BIC(2): 706.0739
#> G^2(2): 62.36543 (Likelihood ratio/deviance statistic) 
#> X^2(2): 92.64814 (Chi-square goodness of fit) 
#>  
#> Conditional item response (column) probabilities,
#>  by outcome variable, for each class (row) 
#>  
#> $A
#>            Pr(1)  Pr(2)
#> class 1:  0.0000 1.0000
#> class 2:  0.9427 0.0573
#> class 3:  0.4872 0.5128
#> 
#> $B
#>            Pr(1)  Pr(2)
#> class 1:  0.0191 0.9809
#> class 2:  0.8621 0.1379
#> class 3:  0.0000 1.0000
#> 
#> $C
#>            Pr(1)  Pr(2)
#> class 1:  0.1425 0.8575
#> class 2:  1.0000 0.0000
#> class 3:  1.0000 0.0000
#> 
#> $D
#>            Pr(1)  Pr(2)
#> class 1:  0.4138 0.5862
#> class 2:  1.0000 0.0000
#> class 3:  0.9424 0.0576
#> 
#> $E
#>            Pr(1)  Pr(2)
#> class 1:  0.0000 1.0000
#> class 2:  0.9449 0.0551
#> class 3:  0.2494 0.7506
#> 
#> $F
#>            Pr(1)  Pr(2)
#> class 1:  0.5236 0.4764
#> class 2:  1.0000 0.0000
#> class 3:  1.0000 0.0000
#> 
#> $G
#>            Pr(1)  Pr(2)
#> class 1:  0.0000 1.0000
#> class 2:  1.0000 0.0000
#> class 3:  0.3693 0.6307
#> 
#> Estimated class population shares 
#>  0.4447 0.3736 0.1817 
#>  
#> Predicted class memberships (by modal posterior prob.) 
#>  0.4322 0.3729 0.1949 
#>  
#> ========================================================= 
#> Fit for 3 latent classes: 
#> ========================================================= 
#> number of observations: 118 
#> number of estimated parameters: 23 
#> residual degrees of freedom: 95 
#> maximum log-likelihood: -293.705 
#>  
#> AIC(3): 633.41
#> BIC(3): 697.1357
#> G^2(3): 15.26171 (Likelihood ratio/deviance statistic) 
#> X^2(3): 20.50335 (Chi-square goodness of fit) 
#>

Useful short cut for `poLCA::poLCA`.

Usage

Arguments

See also

Examples