Calculates various GOF statistics for model object including global chi-squared test statistic and AIC. Extract model-specific mean and variance structure, residuals and various predicitions.
Usage
gof(object, ...)
# S3 method for class 'lvmfit'
gof(object, chisq=FALSE, level=0.90, rmsea.threshold=0.05,all=FALSE,...)
moments(x,...)
# S3 method for class 'lvm'
moments(x, p, debug=FALSE, conditional=FALSE, data=NULL, latent=FALSE, ...)
# S3 method for class 'lvmfit'
logLik(object, p=coef(object),
data=model.frame(object),
model=object$estimator,
weights=Weights(object),
data2=object$data$data2,
...)
# S3 method for class 'lvmfit'
score(x, data=model.frame(x), p=pars(x), model=x$estimator,
weights=Weights(x), data2=x$data$data2, ...)
# S3 method for class 'lvmfit'
information(x,p=pars(x),n=x$data$n,data=model.frame(x),
model=x$estimator,weights=Weights(x), data2=x$data$data2, ...)Arguments
- object
Model object
- ...
Additional arguments to be passed to the low level functions
- x
Model object
- p
Parameter vector used to calculate statistics
- data
Data.frame to use
- latent
If TRUE predictions of latent variables are included in output
- data2
Optional second data.frame (only for censored observations)
- weights
Optional weight matrix
- n
Number of observations
- conditional
If TRUE the conditional moments given the covariates are calculated. Otherwise the joint moments are calculated
- model
String defining estimator, e.g. "gaussian" (see
estimate)- debug
Debugging only
- chisq
Boolean indicating whether to calculate chi-squared goodness-of-fit (always TRUE for estimator='gaussian')
- level
Level of confidence limits for RMSEA
- rmsea.threshold
Which probability to calculate, Pr(RMSEA<rmsea.treshold)
- all
Calculate all (ad hoc) FIT indices: TLI, CFI, NFI, SRMR, ...
Examples
m <- lvm(list(y~v1+v2+v3+v4,c(v1,v2,v3,v4)~x))
set.seed(1)
dd <- sim(m,1000)
e <- estimate(m, dd)
gof(e,all=TRUE,rmsea.threshold=0.05,level=0.9)
#>
#> Number of observations = 1000
#> BIC = 14585.57
#> AIC = 14468.26
#> log-Likelihood of model = -7216.128
#>
#> log-Likelihood of saturated model = -7212.5
#> Chi-squared statistic: q = 7.254653 , df = 7
#> P(Q>q) = 0.4028559
#>
#> RMSEA (90% CI): 0.006 (0;0.0397)
#> P(RMSEA<0.05)=0.9916145
#> TLI = 0.9998998
#> CFI = 0.9999532
#> NFI = 0.9986715
#> SRMR = 0.008682085
#>
#> rank(Information) = 18 (p=18)
#> condition(Information) = 10.37525
#> mean(score^2) = 4.216767e-09
set.seed(1)
m <- lvm(list(c(y1,y2,y3)~u,y1~x)); latent(m) <- ~u
regression(m,c(y2,y3)~u) <- "b"
d <- sim(m,1000)
e <- estimate(m,d)
rsq(e)
#> $`R-squared`
#> y1 y2 y3 u
#> 6.714238e-01 5.109812e-01 5.276472e-01 1.221245e-15
#>
#> $`Variance explained by 'u'`
#> y1 y2 y3
#> 0.3697894 0.5109812 0.5276472
#>
##'
rr <- rsq(e,TRUE)
rr
#>
#> R-squared:
#>
#> Estimate Std.Err 2.5% 97.5% P-value
#> y1 0.6666507 0.02449714 0.6186372 0.7146642 4.506818e-163
#> y2 0.5062724 0.02751655 0.4523409 0.5602038 1.342309e-75
#> y3 0.5319590 0.02627482 0.4804613 0.5834567 3.855758e-91
estimate(rr,contrast=rbind(c(1,-1,0),c(1,0,-1),c(0,1,-1)))
#> Estimate Std.Err 2.5% 97.5% P-value
#> [y1] - [y2] 0.16038 0.04040 0.08119 0.23956 7.197e-05
#> [y1] - [y3] 0.13469 0.03884 0.05857 0.21081 5.244e-04
#> [y2] - [y3] -0.02569 0.02786 -0.08029 0.02891 3.565e-01
#>
#> Null Hypothesis:
#> [y1] - [y2] = 0
#> [y1] - [y3] = 0
#> [y2] - [y3] = 0
#>
#> chisq = 16.2844, df = 2, p-value = 0.000291