div_gof: Divergence Tests of Goodness of Fit

In termehs/netropy: Statistical Entropy Analysis of Network Data

Divergence Tests of Goodness of Fit

Description

Tests of various hypothetical structural models p0 against the general model p which is estimated using empirical data.

Usage

div_gof(dat, var1, var2, var_cond = NULL)

Arguments

dat	dataframe with rows as observations and columns as variables. Variables must all be observed or transformed categorical with finite range spaces.
var1	variable name as character in dat specified to be independent to those specified in var2 under p0
var2	variable name as character in dat specified to be independent to those specified in var1
var_cond	character of variable name in dat to condition the independence specification on, must be different variables than those specified in var1 and var2). Default = empty (no conditioning).

Details

this function is currently implemented to only test goodness of fit of models specified as X independent of Y, or X independent of Y given Z.

Value

Message indicating whether the hypothesis with the specified independence model can be rejected or not on approximately 5% level of significance.

summary

Dataframe including the value of the divergence D and its degrees of freedom

Author(s)

Termeh Shafie

References

Frank, O., & Shafie, T. (2016). Multivariate entropy analysis of network data. Bulletin of Sociological Methodology/Bulletin de Méthodologie Sociologique, 129(1), 45-63.

See Also

joint_entropy, assoc_graph, entropy_trivar

Examples

# Data editing and creation of dyad variables:
data(lawdata)
adj.advice <- lawdata[[1]]
adj.friend <- lawdata[[2]]
adj.cowork <-lawdata[[3]]
df.att <- lawdata[[4]]
att.var <-
data.frame(
 status   = df.att$status-1,
 gender   = df.att$gender,
 office   = df.att$office-1,
 years    = ifelse(df.att$years<=3,0,
            ifelse(df.att$years<=13,1,2)),
 age  = ifelse(df.att$age<=35,0,
        ifelse(df.att$age<=45,1,2)),
 practice = df.att$practice,
 lawschool= df.att$lawschool-1)
dyad.status    <- get_dyad_var(att.var$status, type = 'att')
dyad.gender    <- get_dyad_var(att.var$gender, type = 'att')
dyad.office    <- get_dyad_var(att.var$office, type = 'att')
dyad.years     <- get_dyad_var(att.var$years, type = 'att')
dyad.age       <- get_dyad_var(att.var$age, type = 'att')
dyad.practice  <- get_dyad_var(att.var$practice, type = 'att')
dyad.lawschool <- get_dyad_var(att.var$lawschool, type = 'att')
dyad.cwk    <- get_dyad_var(adj.cowork, type = 'tie')
dyad.adv    <- get_dyad_var(adj.advice, type = 'tie')
dyad.frn    <- get_dyad_var(adj.friend, type = 'tie')
dyad.var <-
data.frame(cbind(status    = dyad.status$var,
                 gender    = dyad.gender$var,
                 office    = dyad.office$var,
                 years     = dyad.years$var,
                 age       = dyad.age$var,
                 practice  = dyad.practice$var,
                 lawschool = dyad.lawschool$var,
                 cowork    = dyad.cwk$var,
                 advice    = dyad.adv$var,
                 friend    = dyad.frn$var)
                 )
# To test whether friend is independent of cowork given advice:
div_gof(dat = dyad.var, var1 = "friend", var2 = "cowork", var_cond = "advice")

termehs/netropy documentation built on Oct. 28, 2024, 6:04 p.m.