jamovi/jmv-i18n — Dutch @ Hosted Weblate: data <- lavaan::HolzingerSwineford1939 jmv::cfa( data = data, factors = list( …

Translation

English

data <- lavaan::HolzingerSwineford1939

jmv::cfa(
data = data,
factors = list(
list(label="Visual", vars=c("x1", "x2", "x3")),
list(label="Textual", vars=c("x4", "x5", "x6")),
list(label="Speed", vars=c("x7", "x8", "x9"))),
resCov = NULL)

#
# CONFIRMATORY FACTOR ANALYSIS
#
# Factor Loadings
# ─────────────────────────────────────────────────────────────────
# Factor Indicator Estimate SE Z p
# ─────────────────────────────────────────────────────────────────
# Visual x1 0.900 0.0832 10.81 < .001
# x2 0.498 0.0808 6.16 < .001
# x3 0.656 0.0776 8.46 < .001
# Textual x4 0.990 0.0567 17.46 < .001
# x5 1.102 0.0626 17.60 < .001
# x6 0.917 0.0538 17.05 < .001
# Speed x7 0.619 0.0743 8.34 < .001
# x8 0.731 0.0755 9.68 < .001
# x9 0.670 0.0775 8.64 < .001
# ─────────────────────────────────────────────────────────────────
#
#
# FACTOR ESTIMATES
#
# Factor Covariances
# ──────────────────────────────────────────────────────────────
# Estimate SE Z p
# ──────────────────────────────────────────────────────────────
# Visual Visual 1.000 ᵃ
# Textual 0.459 0.0635 7.22 < .001
# Speed 0.471 0.0862 5.46 < .001
# Textual Textual 1.000 ᵃ
# Speed 0.283 0.0715 3.96 < .001
# Speed Speed 1.000 ᵃ
# ──────────────────────────────────────────────────────────────
# ᵃ fixed parameter
#
#
# MODEL FIT
#
# Test for Exact Fit
# ────────────────────────
# χ² df p
# ────────────────────────
# 85.3 24 < .001
# ────────────────────────
#
#
# Fit Measures
# ───────────────────────────────────────────────
# CFI TLI RMSEA Lower Upper
# ───────────────────────────────────────────────
# 0.931 0.896 0.0921 0.0714 0.114
# ───────────────────────────────────────────────
#

Dutch

data <- lavaan::HolzingerSwineford1939

jmv::cfa(
    data = data,
    factors = list(
        list(label="Visual", vars=c("x1", "x2", "x3")),
        list(label="Textual", vars=c("x4", "x5", "x6")),
        list(label="Speed", vars=c("x7", "x8", "x9"))),
    resCov = NULL)

#
#  BEVESTIGENDE FACTORANALYSE
#
#  Factorladingen
#  ─────────────────────────────────────────────────────────────────
#    Factor     Indicator    Schatting   St. fout  Z        p
#  ─────────────────────────────────────────────────────────────────
#    Visueel    x1              0.900    0.0832    10.81    < .001
#               x2              0.498    0.0808     6.16    < .001
#               x3              0.656    0.0776     8.46    < .001
#    Tekst      x4              0.990    0.0567    17.46    < .001
#               x5              1.102    0.0626    17.60    < .001
#               x6              0.917    0.0538    17.05    < .001
#    Snelheid   x7              0.619    0.0743     8.34    < .001
#               x8              0.731    0.0755     9.68    < .001
#               x9              0.670    0.0775     8.64    < .001
#  ─────────────────────────────────────────────────────────────────
#
#
#  FACTORSCHATTINGEN
#
#  Factor-covarianties
#  ──────────────────────────────────────────────────────────────
#                          Schatting   St. fout  Z       p
#  ──────────────────────────────────────────────────────────────
#    Visueel    Visueel     1.000 ᵃ
#               Tekst       0.459      0.0635    7.22    < .001
#               Snelheid    0.471      0.0862    5.46    < .001
#    Tekst      Tekst       1.000 ᵃ
#               Snelheid    0.283      0.0715    3.96    < .001
#    Snelh. Snelheid    1.000 ᵃ
#  ──────────────────────────────────────────────────────────────
#    ᵃ vaste parameter
#
#
#  MODELVORM
#
#  Toets voor exacte passende vorm
#  ────────────────────────
#    χ²      df    p
#  ────────────────────────
#    85.3    24    < .001
#  ────────────────────────
#
#
#  Vormmeting
#  ───────────────────────────────────────────────
#    CFI      TLI      RMSEA     Laag      Hoog
#  ───────────────────────────────────────────────
#    0.931    0.896    0.0921    0.0714    0.114
#  ───────────────────────────────────────────────
#

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	English	Dutch	Actions
	Cut-off plot	Grenswaarde grafiek
	Cut-off value	Grenswaarde
	dat <- data.frame( `1st survey` = c('Approve', 'Approve', 'Disapprove', 'Disapprove'), `2nd survey` = c('Approve', 'Disapprove', 'Approve', 'Disapprove'), `Counts` = c(794, 150, 86, 570), check.names=FALSE) contTablesPaired(formula = Counts ~ `1st survey`:`2nd survey`, data = dat) # # PAIRED SAMPLES CONTINGENCY TABLES # # Contingency Tables # ──────────────────────────────────────────────── # 1st survey Approve Disapprove Total # ──────────────────────────────────────────────── # Approve 794 150 944 # Disapprove 86 570 656 # Total 880 720 1600 # ──────────────────────────────────────────────── # # # McNemar Test # ───────────────────────────────────────────────────── # Value df p # ───────────────────────────────────────────────────── # χ² 17.4 1 < .001 # χ² continuity correction 16.8 1 < .001 # ───────────────────────────────────────────────────── # # Alternatively, omit the left of the formula (`Counts`) from the # formula if each row represents a single observation: contTablesPaired(formula = ~ `1st survey`:`2nd survey`, data = dat)
	dat <- data.frame(x=c(8, 15)) propTest2(dat, vars = x, areCounts = TRUE) # # PROPORTION TEST (2 OUTCOMES) # # Binomial Test # ─────────────────────────────────────────────────────── # Level Count Total Proportion p # ─────────────────────────────────────────────────────── # x 1 8 23 0.348 0.210 # 2 15 23 0.652 0.210 # ─────────────────────────────────────────────────────── # Note. Hₐ is proportion ≠ 0.5 #
	Data	Gegevens
	data <- lavaan::HolzingerSwineford1939 jmv::cfa( data = data, factors = list( list(label="Visual", vars=c("x1", "x2", "x3")), list(label="Textual", vars=c("x4", "x5", "x6")), list(label="Speed", vars=c("x7", "x8", "x9"))), resCov = NULL) # # CONFIRMATORY FACTOR ANALYSIS # # Factor Loadings # ───────────────────────────────────────────────────────────────── # Factor Indicator Estimate SE Z p # ───────────────────────────────────────────────────────────────── # Visual x1 0.900 0.0832 10.81 < .001 # x2 0.498 0.0808 6.16 < .001 # x3 0.656 0.0776 8.46 < .001 # Textual x4 0.990 0.0567 17.46 < .001 # x5 1.102 0.0626 17.60 < .001 # x6 0.917 0.0538 17.05 < .001 # Speed x7 0.619 0.0743 8.34 < .001 # x8 0.731 0.0755 9.68 < .001 # x9 0.670 0.0775 8.64 < .001 # ───────────────────────────────────────────────────────────────── # # # FACTOR ESTIMATES # # Factor Covariances # ────────────────────────────────────────────────────────────── # Estimate SE Z p # ────────────────────────────────────────────────────────────── # Visual Visual 1.000 ᵃ # Textual 0.459 0.0635 7.22 < .001 # Speed 0.471 0.0862 5.46 < .001 # Textual Textual 1.000 ᵃ # Speed 0.283 0.0715 3.96 < .001 # Speed Speed 1.000 ᵃ # ────────────────────────────────────────────────────────────── # ᵃ fixed parameter # # # MODEL FIT # # Test for Exact Fit # ──────────────────────── # χ² df p # ──────────────────────── # 85.3 24 < .001 # ──────────────────────── # # # Fit Measures # ─────────────────────────────────────────────── # CFI TLI RMSEA Lower Upper # ─────────────────────────────────────────────── # 0.931 0.896 0.0921 0.0714 0.114 # ─────────────────────────────────────────────── #	data <- lavaan::HolzingerSwineford1939 jmv::cfa( data = data, factors = list( list(label="Visual", vars=c("x1", "x2", "x3")), list(label="Textual", vars=c("x4", "x5", "x6")), list(label="Speed", vars=c("x7", "x8", "x9"))), resCov = NULL) # # BEVESTIGENDE FACTORANALYSE # # Factorladingen # ───────────────────────────────────────────────────────────────── # Factor Indicator Schatting St. fout Z p # ───────────────────────────────────────────────────────────────── # Visueel x1 0.900 0.0832 10.81 < .001 # x2 0.498 0.0808 6.16 < .001 # x3 0.656 0.0776 8.46 < .001 # Tekst x4 0.990 0.0567 17.46 < .001 # x5 1.102 0.0626 17.60 < .001 # x6 0.917 0.0538 17.05 < .001 # Snelheid x7 0.619 0.0743 8.34 < .001 # x8 0.731 0.0755 9.68 < .001 # x9 0.670 0.0775 8.64 < .001 # ───────────────────────────────────────────────────────────────── # # # FACTORSCHATTINGEN # # Factor-covarianties # ────────────────────────────────────────────────────────────── # Schatting St. fout Z p # ────────────────────────────────────────────────────────────── # Visueel Visueel 1.000 ᵃ # Tekst 0.459 0.0635 7.22 < .001 # Snelheid 0.471 0.0862 5.46 < .001 # Tekst Tekst 1.000 ᵃ # Snelheid 0.283 0.0715 3.96 < .001 # Snelh. Snelheid 1.000 ᵃ # ────────────────────────────────────────────────────────────── # ᵃ vaste parameter # # # MODELVORM # # Toets voor exacte passende vorm # ──────────────────────── # χ² df p # ──────────────────────── # 85.3 24 < .001 # ──────────────────────── # # # Vormmeting # ─────────────────────────────────────────────── # CFI TLI RMSEA Laag Hoog # ─────────────────────────────────────────────── # 0.931 0.896 0.0921 0.0714 0.114 # ─────────────────────────────────────────────── #
	Data Reduction	Gegevensreductie
	Data set is currently unweighted	Gegevensset is momenteel ongewogen
	Data Summary	Gegevenssamenvatting
	data('birthwt', package='MASS') dat <- data.frame( low = factor(birthwt$low), age = birthwt$age, bwt = birthwt$bwt) logRegBin(data = dat, dep = low, covs = vars(age, bwt), blocks = list(list("age", "bwt")), refLevels = list(list(var="low", ref="0"))) # # BINOMIAL LOGISTIC REGRESSION # # Model Fit Measures # ─────────────────────────────────────── # Model Deviance AIC R²-McF # ─────────────────────────────────────── # 1 4.97e-7 6.00 1.000 # ─────────────────────────────────────── # # # MODEL SPECIFIC RESULTS # # MODEL 1 # # Model Coefficients # ──────────────────────────────────────────────────────────── # Predictor Estimate SE Z p # ──────────────────────────────────────────────────────────── # Intercept 2974.73225 218237.2 0.0136 0.989 # age -0.00653 482.7 -1.35e-5 1.000 # bwt -1.18532 87.0 -0.0136 0.989 # ──────────────────────────────────────────────────────────── # Note. Estimates represent the log odds of "low = 1" # vs. "low = 0" # #
	data('birthwt', package='MASS') dat <- data.frame( race = factor(birthwt$race), age = birthwt$age, low = factor(birthwt$low)) logRegMulti(data = dat, dep = race, covs = age, factors = low, blocks = list(list("age", "low")), refLevels = list( list(var="race", ref="1"), list(var="low", ref="0"))) # # MULTINOMIAL LOGISTIC REGRESSION # # Model Fit Measures # ────────────────────────────────────── # Model Deviance AIC R²-McF # ────────────────────────────────────── # 1 360 372 0.0333 # ────────────────────────────────────── # # # MODEL SPECIFIC RESULTS # # MODEL 1 # # Model Coefficients # ─────────────────────────────────────────────────────────────── # race Predictor Estimate SE Z p # ─────────────────────────────────────────────────────────────── # 2 - 1 Intercept 0.8155 1.1186 0.729 0.466 # age -0.1038 0.0487 -2.131 0.033 # low: # 1 – 0 0.7527 0.4700 1.601 0.109 # 3 - 1 Intercept 1.0123 0.7798 1.298 0.194 # age -0.0663 0.0324 -2.047 0.041 # low: # 1 – 0 0.5677 0.3522 1.612 0.107 # ─────────────────────────────────────────────────────────────── # #

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Translation added

English

Dutch 2280 characters edited Current translation Translated

data <- lavaan::HolzingerSwineford1939

jmv::cfa(
data = data,
factors = list(
list(label="Visual", vars=c("x1", "x2", "x3")),
list(label="Textual", vars=c("x4", "x5", "x6")),
list(label="Speed", vars=c("x7", "x8", "x9"))),
resCov = NULL)

#
# BEVESTIGENDE FACTORANALYSE
#
# Factorladingen
# ─────────────────────────────────────────────────────────────────
# Factor Indicator Schatting St. fout Z p
# ─────────────────────────────────────────────────────────────────
# Visueel x1 0.900 0.0832 10.81 < .001
# x2 0.498 0.0808 6.16 < .001
# x3 0.656 0.0776 8.46 < .001
# Tekst x4 0.990 0.0567 17.46 < .001
# x5 1.102 0.0626 17.60 < .001
# x6 0.917 0.0538 17.05 < .001
# Snelheid x7 0.619 0.0743 8.34 < .001
# x8 0.731 0.0755 9.68 < .001
# x9 0.670 0.0775 8.64 < .001
# ─────────────────────────────────────────────────────────────────
#
#
# FACTORSCHATTINGEN
#
# Factor-covarianties
# ──────────────────────────────────────────────────────────────
# Schatting St. fout Z p
# ──────────────────────────────────────────────────────────────
# Visueel Visueel 1.000 ᵃ
# Tekst 0.459 0.0635 7.22 < .001
# Snelheid 0.471 0.0862 5.46 < .001
# Tekst Tekst 1.000 ᵃ
# Snelheid 0.283 0.0715 3.96 < .001
# Snelh. Snelheid 1.000 ᵃ
# ──────────────────────────────────────────────────────────────
# ᵃ vaste parameter
#
#
# MODELVORM
#
# Toets voor exacte passende vorm
# ────────────────────────
# χ² df p
# ────────────────────────
# 85.3 24 < .001
# ────────────────────────
#
#
# Vormmeting
# ───────────────────────────────────────────────
# CFI TLI RMSEA Laag Hoog
# ───────────────────────────────────────────────
# 0.931 0.896 0.0921 0.0714 0.114
# ───────────────────────────────────────────────
#

10 months ago

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a year ago

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English	Dutch
factor	factor	jamovi
Factor
Upper	Rechtergrens	jamovi

String information

Flags

ignore-multiple-failures, ignore-same

Source string location

cfa/options.description.R.usage

String age

a year ago

Last updated

a year ago

Source string age

2 years ago

Translation file

nl.po, string 526