poLCA.predcell: Predicted cell percentages in a latent class model In poLCA: Polytomous variable Latent Class Analysis

Description

Calculates the predicted cell percentages from a latent class model, for specified values of the manifest variables.

Usage

 `1` ``` poLCA.predcell(lc,y) ```

Arguments

 `lc` A model object estimated using the `poLCA` function. `y` A vector or matrix containing series of responses on the manifest variables in `lc`.

Details

The parameters estimated by a latent class model can be used to produce a density estimate of the underlying probability mass function across the cells in the multi-way table of manifest variables. This function calculates cell percentages for that density estimate, corresponding to selected sets of responses on the manifest variables, `y`.

Value

A vector containing cell percentages corresponding to the specified sets of responses `y`, based on the estimated latent class model `lc`.

`poLCA`

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```data(carcinoma) f <- cbind(A,B,C,D,E,F,G)~1 lca3 <- poLCA(f,carcinoma,nclass=3) # log-likelihood: -293.705 # Only 20 out of 32 possible response patterns are observed lca3\$predcell # Produce cell probabilities for one sequence of responses poLCA.predcell(lc=lca3,y=c(1,1,1,1,1,1,1)) # Estimated probabilities for a cell with zero observations poLCA.predcell(lc=lca3,y=c(1,1,1,1,1,1,2)) # Cell probabilities for both cells at once; y entered as a matrix poLCA.predcell(lc=lca3,y=rbind(c(1,1,1,1,1,1,1),c(1,1,1,1,1,1,2))) ```

Example output

```Loading required package: scatterplot3d
Conditional item response (column) probabilities,
by outcome variable, for each class (row)

\$A
Pr(1)  Pr(2)
class 1:  0.4872 0.5128
class 2:  0.0000 1.0000
class 3:  0.9427 0.0573

\$B
Pr(1)  Pr(2)
class 1:  0.0000 1.0000
class 2:  0.0191 0.9809
class 3:  0.8621 0.1379

\$C
Pr(1)  Pr(2)
class 1:  1.0000 0.0000
class 2:  0.1425 0.8575
class 3:  1.0000 0.0000

\$D
Pr(1)  Pr(2)
class 1:  0.9424 0.0576
class 2:  0.4138 0.5862
class 3:  1.0000 0.0000

\$E
Pr(1)  Pr(2)
class 1:  0.2494 0.7506
class 2:  0.0000 1.0000
class 3:  0.9449 0.0551

\$F
Pr(1)  Pr(2)
class 1:  1.0000 0.0000
class 2:  0.5236 0.4764
class 3:  1.0000 0.0000

\$G
Pr(1)  Pr(2)
class 1:  0.3693 0.6307
class 2:  0.0000 1.0000
class 3:  1.0000 0.0000

Estimated class population shares
0.1817 0.4447 0.3736

Predicted class memberships (by modal posterior prob.)
0.1949 0.4322 0.3729

=========================================================
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)

A B C D E F G observed expected
1  1 1 1 1 1 1 1       34   33.849
2  1 1 1 1 2 1 1        2    1.973
3  1 2 1 1 1 1 1        6    6.323
4  1 2 1 1 1 1 2        1    1.548
5  1 2 1 1 2 1 1        4    3.045
6  1 2 1 1 2 1 2        5    4.660
7  2 1 1 1 1 1 1        2    2.058
8  2 1 2 1 2 1 2        1    0.186
9  2 2 1 1 1 1 1        2    1.284
10 2 2 1 1 1 1 2        1    1.630
11 2 2 1 1 2 1 1        2    2.892
12 2 2 1 1 2 1 2        7    6.494
13 2 2 1 1 2 2 2        1    1.446
14 2 2 1 2 1 1 2        1    0.100
15 2 2 1 2 2 1 2        2    2.552
16 2 2 1 2 2 2 2        3    2.049
17 2 2 2 1 2 1 2       13    9.563
18 2 2 2 1 2 2 2        5    8.701
19 2 2 2 2 2 1 2       10   13.550
20 2 2 2 2 2 2 2       16   12.328
[,1]
[1,] 0.2868564
[,1]
[1,] 7.196555e-22
[,1]
[1,] 2.868564e-01
[2,] 7.196555e-22
```

poLCA documentation built on May 29, 2017, 5:59 p.m.