value_stability/cfa.R at master · flowersteam/value_stability · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
# install.packages("lavaan")
# install.packages("jsonlite")

library(lavaan)
library(jsonlite)

args = commandArgs(trailingOnly=TRUE)


if (length(args) != 3) {
  stop("Exactly three arguments are required: 1) data path, 2) questionnaire type (PVQ or SVS). 3) high level value")
}

datapath = args[1]
questionnaire_type = args[2]
high_level_value = args[3]

# PVQ models
pvq_model_conservation <- '
  sec =~ item5 + item14 + item21 + item31 + item35
  conf =~ item7 + item16 + item28 + item36
  trad =~ item9 + item20 + item25 + item38
'

pvq_model_openness_to_change <- '
  selfdir =~ item1 + item11 + item22 + item34
  stim =~ item6 + item15 + item30
  hed =~ item10 + item26 + item37
'
pvq_model_self_transcendence <- '
  ben =~ item12 + item18 + item27 + item33
  uni =~ item3 + item8 + item19 + item23 + item29 + item40
'
pvq_model_self_enhancement <- '
  ach =~ item4 + item13 + item24 + item32
  pow =~ item2 + item17 + item39
'

## SVS model (example model, replace with actual SVS model definition)
svs_model <- '
  selfdir =~ item5 + item16 + item31 + item41 + item53
  stim =~ item9 + item25 + item37
  hed =~ item4 + item50 + item57
  ach =~ item34 + item39 + item43 + item55
  pow =~ item3 + item12 + item27 + item46
  sec =~ item8 + item13 + item15 + item22 + item56
  conf =~ item11 + item20 + item40 + item47
  trad =~ item18 + item32 + item36 + item44 + item51
  ben =~ item33 + item45 + item49 + item52 + item54
  uni =~ item1 + item17 + item24 + item26 + item29 + item30 + item35 + item38
'
## SVS models split into four parts (example models, replace with actual SVS model definitions)
svs_model_conservation <- '
  sec =~ item8 + item13 + item15 + item22 + item56
  conf =~ item11 + item20 + item40 + item47
  trad =~ item18 + item32 + item36 + item44 + item51
'
svs_model_openness_to_change <- '
  selfdir =~ item5 + item16 + item31 + item41 + item53
  stim =~ item9 + item25 + item37
  hed =~ item4 + item50 + item57
'
svs_model_self_transcendence <- '
  ben =~ item33 + item45 + item49 + item52 + item54
  uni =~ item1 + item17 + item24 + item26 + item29 + item30 + item35 + item38
'
svs_model_self_enhancement <- '
  ach =~ item34 + item39 + item43 + item55
  pow =~ item3 + item12 + item27 + item46
'

# Select the appropriate model based on the questionnaire type
# model <- ifelse(questionnaire_type == "SVS", svs_model, pvq_model)

if (questionnaire_type == "SVS") {
  "SVS"
  model_conservation <- svs_model_conservation
  model_openness_to_change <- svs_model_openness_to_change
  model_self_transcendence <- svs_model_self_transcendence
  model_self_enhancement <- svs_model_self_enhancement
} else {
  "PVQ"
  model_conservation <- pvq_model_conservation
  model_openness_to_change <- pvq_model_openness_to_change
  model_self_transcendence <- pvq_model_self_transcendence
  model_self_enhancement <- pvq_model_self_enhancement
}

if (high_level_value == "conservation") {
 model <- model_conservation
} else if (high_level_value == "openness_to_change") {
 model <- model_openness_to_change
} else if (high_level_value == "self_transcendence") {
 model <- model_self_transcendence
} else if (high_level_value == "self_enhancement") {
 model <- model_self_enhancement
}

# Load your data (assuming it is in a data frame named 'data')
data <- read.csv(datapath)

fit <- cfa(model, data = data, std.lv = TRUE)

cov_lv <- lavInspect(fit, "cov.lv")
off_diag_vals <- cov_lv[upper.tri(cov_lv)]

# print(inspect(fit, "estimates"))
# print(lavInspect(fit, "cov.lv"))
# var_table <- lavInspect(fit, "est")$theta
# print(diag(var_table))  # Check for negative variances

if (any(off_diag_vals >= 1)) {
    warning("Covariance between latent variables is >= 1.")
    # this means that the data does not distinguish between the factors - and the model is not fit
    return(-1)
}


fit_measures <- fitMeasures(fit)
cfi <- fit_measures["cfi"]
tli <- fit_measures["tli"]
srmr <- fit_measures["srmr"]
rmsea <- fit_measures["rmsea"]


# Combine the measures into a list
results <- list(cfi=cfi, tli=tli, srmr=srmr, rmsea=rmsea)

# Convert the results to JSON and print
cat(toJSON(results, pretty=TRUE))