confint.RSA | R Documentation |

Computes confidence intervals for RSA parameters, standard or bootstrapped (using a percentile bootstrap)

## S3 method for class 'RSA' confint( object, parm, level = 0.95, ..., model = "full", digits = 3, method = "standard", R = 5000 )

`object` |
An RSA object |

`parm` |
Not used. |

`level` |
The confidence level required. |

`...` |
Additional parameters passed to the bootstrapLavaan function, e.g., |

`model` |
A string specifying the model; defaults to "full" |

`digits` |
Number of digits the output is rounded to; if NA, digits are unconstrained |

`method` |
"standard" returns the CI for the lavaan object as it was computed. "boot" computes new percentile bootstrapped CIs. |

`R` |
If |

There are two ways of getting bootstrapped CIs and p-values in the RSA package If you use the option `se="boot"`

in the `RSA`

function, `lavaan`

provides CIs and p-values based on the bootstrapped standard error (*not* percentile bootstraps). If you use `confint(..., method="boot")`

, in contrast, you get CIs and p-values based on percentile bootstrap.

`RSA`

## Not run: set.seed(0xBEEF) n <- 300 err <- 2 x <- rnorm(n, 0, 5) y <- rnorm(n, 0, 5) df <- data.frame(x, y) df <- within(df, { diff <- x-y absdiff <- abs(x-y) SD <- (x-y)^2 z.sq <- SD + rnorm(n, 0, err) }) r1 <- RSA(z.sq~x*y, df, models="SSQD") (c1 <- confint(r1, model="SSQD")) # Dummy example with 10 bootstrap replications - better use >= 5000! (c2 <- confint(r1, model="SSQD", method="boot", R=10)) # multicore version confint(r1, model="SSQD", R=5000, parallel="multicore", ncpus=2) ## End(Not run)

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