netropy (version 0.2.0)

div_gof: 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)

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

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).

Author

Termeh Shafie

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.

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")