README.md
In stp4/stp25stat: Functions for statistical computations
stp25stat
Wolfgang Peter
2021-02-23
Functions for Statistical Computations
which_output()
## [1] "markdown_html"
#' # set_my_options(style_mean = list(line_break = '<br>'))
#' # set_my_options(prozent = list(null_percent_sign = '.'))
#' # set_my_options(mittelwert = list(mean.style = 2, median.style = "IQR"),
#' # prozent=list(style=2))
#' # set_my_options(caption= "include.n")
Deskriptive Tabellen mit sig-Test
#' effect_size
#'
#' Cohen's d d=(x1-x2)/s psych::cohen.d
#' Hedges' g g= (x1-x2)/s1s2 ( pooled standard deviation)
#' g enspricht x<-scale(x) (mean(x1) - mean(x2))
#'
#' Generalized Log Odds Ratios for Frequency Tables vcd::oddsratio
#'
#' @param x vector
#' @param by factor
#' @param measure intern c("mean", "median", "numeric", "factor", "logical")
#' @param measure.test, test intern c("cattest", "contest", "notest", "ttest", ...)
#' @export
#'
#' @examples
#'
#' effect_size(c(2, 3, 4, 2, 3, 4, 3, 6,
#' 7, 6, 8, 9, 4, 5, 6, 7) ,
#' gl(2, 8, labels = c("Control", "Treat")))
#'
#' effect_size(factor(c(2, 2, 2, 2, 1, 1, 1, 1,
#' 2, 2, 2, 2, 1, 2, 1, 2)) ,
#' gl(2, 8, labels = c("Control", "Treat")))
#'
#'
effect_size2 <- function(x,
by,
measure,
...
) {
dat <- na.omit(data.frame(x = x, by = by))
n <- nrow(dat)
es <- rslt <- "n.a."
if (n > 10) {
if (measure %in% c("mean", "median", "numeric")) {
es <- psych::cohen.d(dat$x, dat$by)
es <- stp25rndr::Format2(es$cohen.d)
es <- paste0(es[2], " [", es[1], ", ", es[3], "] ES")
}
else if (measure %in% c("factor", "logical")) {
# Generalized Log Odds Ratios for Frequency Tables
if (measure == "factor" & nlevels(dat$x) != 2) {
es <- "n.a."
}
else{
es <- vcd::oddsratio(table(dat$x, dat$by), log = FALSE)
if (coef(es) < 0.01)
es <- "n.a."
else if (coef(es) > 100)
es <- "n.a."
else{
es <- stp25rndr::rndr_ods(c(coef(es) , confint(es)))
es <- paste0(es[1], " [", es[2], ", ", es[3], "] OR")
}
}
}
}
cbind('Odds Ratio/Effect Size' = es)
}
# Zu Auto-Test existiiert eine eigene Funktion unter stp25stat::auto_test
auto_test2 <- function(x,
by,
measure,
measure.test,
type = 2) {
dat <- na.omit(data.frame(x = x, by = by))
rslt <- NULL
contest <- stp25stat:::contest
cattest <- stp25stat:::cattest
if (measure.test == "notest") {
rslt <- ""
}
else if (measure.test == "contest") {
if (inherits(x, "factor")) {
dat$x <- as.numeric(dat$x)
}
rslt <- stp25stat:::conTest(x ~ by, dat)
}
else if (measure.test == "cattest") {
rslt <- stp25stat:::catTest( ~ x + by, dat)
}
else if (measure.test %in% contest) {
if (inherits(x, "factor")) {
dat$x <-
as.numeric(dat$x)
}
rslt <- stp25stat:::conTest(x ~ by, dat, measure.test)
}
else if (measure.test %in% cattest) {
rslt <- stp25stat:::catTest( ~ x + by, dat, measure.test)
}
if (type == 1) {
cbind('Statistics' = rslt)
}
else if (type == 2) {
rslt <- strsplit(rslt, ', p')[[1]]
cbind("Test Statistics" = rslt[1],
"p-value" = gsub("=", "", rslt[2]))
} else if (type == 3) {
cbind('Statistics' = gsub(", p", ",<BR>p", rslt))
}
}
DF %>% Tbll_desc(
age[median, 1],
height[1, mean, ttest],
sex,
smoker[fisher],
iq[0],
likert[2, mean],
by = ~ died,
include.custom = effect_size2,
include.test=TRUE,
include.n=TRUE,
include.total=TRUE
) %>% Output()
## Warning in `[[<-.factor`(`*tmp*`, i, value = 4): invalid factor level, NA
## generated
Tab 1: died
Item
Total
FALSE
TRUE
Odds Ratio/Effect Size
Test Statistik
(N)
30
15
15
Age (median)
10.0 (IQR 2.0)
10.0 (IQR 2.0)
10.0 (IQR 2.5)
-0.15 \[-0.86, 0.57\] ES
F(1, 28)=0.11, p=.739
Height (mean)
52.2 (8.7)
52.8 (9.4)
51.7 (8.3)
-0.13 \[-0.85, 0.59\] ES
T(28)=0.35, p=.729
Sex
0.76 \[0.18, 3.24\] OR
X2(1)=0.14, p=.713
Male
13 (43%)
6 (40%)
7 (47%)
Female
17 (57%)
9 (60%)
8 (53%)
Smoker true
20 (67%)
10 (67%)
10 (67%)
1.00 \[0.22, 4.56\] OR
OR=1.00, p=1.000
IQ (mean)
99 (11)
99 (13)
100 (9)
0.13 \[-0.59, 0.84\] ES
F(1, 28)=0.65, p=.427
Likert (mean)
3.07 (1.41)
3.13 (1.41)
3.00 (1.46)
-0.05 \[-0.78, 0.68\] ES
F(1, 28)=0.06, p=.804
Wilcoxon-Test, T-Test, Pearson Chi-squared, Fisher Exact Test
population.model <- '
Sex =~ sex
Age =~ age
Bildungszertifikat =~ edu
Beruf =~ beruf
Education ~ 1.04*Bildungszertifikat + 2.76* Beruf
single~ 1*age + 2*edu
Openness =~ o1+o2+o3+o4+o5
Conscientiousness =~ c1+c2+c3+c4+c5
Extraversion =~ e1+e2+e3+e4+e5
Neuroticism =~ n1+n2+n3+n4+n5
Agreeableness =~ a1+a2+a3+a4+a5
Einkommen ~ 1.25*Sex + (-0.31)*Age + .12*Openness + .23*Conscientiousness + (-0.91)*Extraversion + 2.3*Neuroticism + (-0.541)*Agreeableness
'
DF <- lavaan::simulateData(population.model, sample.nobs = 100)
DF<- transform(DF,
sex=cut(sex, 2, c("m", "f")),
age=scale2(age, 18, 55),
edu=cut(edu,3, c("low", "med", "hig")),
beruf= cut(edu,2, c("blue", "withe")),
single =cut(single, 2, c("yes", "no")),
Einkommen = (4-log(Einkommen-min(Einkommen)+1))*1000
)
Mittelwerte und Prozent
#APA_Correlation(~o1+o2+o3+o4+o5+c1+c2+c3, DF)
Tbll_corr(~o1+o2+o3+o4+o5+c1+c2+c3, DF) %>% Output()
Tab 2:
Quelle
\(1\)
\(2\)
\(3\)
\(4\)
\(5\)
\(6\)
\(7\)
\(8\)
(1) o1
1
.62\*\*\*
.53\*\*\*
.58\*\*\*
.55\*\*\*
.09
-.01
-.02
(2) o2
1
.48\*\*\*
.63\*\*\*
.45\*\*\*
-.03
-.00
.05
(3) o3
1
.56\*\*\*
.46\*\*\*
.13
.11
.10
(4) o4
1
.42\*\*\*
.12
.06
.08
(5) o5
1
.06
.09
.17
(6) c1
1
.37\*\*\*
.35\*\*\*
(7) c2
1
.50\*\*\*
(8) c3
1
pearson
PCA
vars <- c(
"o1", "o2", "o3", "o4", "o5", "c1", "c2", "c3",
"c4", "c5", "e1", "e2", "e3", "e4", "e5", "n1",
"n2", "n3", "n4", "n5", "a1", "a2", "a3", "a4", "a5"
)
APA_PCA(DF[vars], 5, cut=.35, include.plot = FALSE)
## R was not square, finding R from data
Tab 3: Standardized loadings (pattern matrix) based upon correlation
matrix
Item
Nr
PC1
PC2
PC3
PC4
PC5
h2
o1
1
0.83
0.71
o4
4
0.81
0.67
o2
2
0.81
0.67
o3
3
0.76
0.59
o5
5
0.72
0.55
a2
22
0.84
0.74
a4
24
0.79
0.65
a3
23
0.77
0.64
a5
25
0.75
0.60
a1
21
0.74
0.59
n2
17
0.81
0.70
n5
20
0.78
0.62
n4
19
0.78
0.62
n1
16
0.77
0.62
n3
18
0.76
0.62
e1
11
0.79
0.63
e4
14
0.79
0.62
e3
13
0.76
0.65
e5
15
0.75
0.58
e2
12
0.74
0.56
c4
9
0.79
0.63
c3
8
0.77
0.62
c2
7
0.74
0.56
c5
10
0.68
0.53
c1
6
0.64
0.45
Tab 4: Standardized loadings (pattern matrix) based upon correlation
matrix
Measures
Test Statistik
n.obs
100
Mean item complexity
1.1
RMSR
0.06
empirical chi square
X2=184.22, p=.502
Tab 5: Standardized loadings (pattern matrix) based upon correlation
matrix
Measures
Statistic
Kaiser-Meyer-Olkin Measure
0.71
Bartlett’s test of sphericity
X2(300)=1038.49, p<.001
Reliability
Openness <- Reliability(~o1+o2+o3+o4+o5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
Conscientiousness <- Reliability(~ c1+c2+c3+c4+c5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
Extraversion <-Reliability(~ e1+e2+e3+e4+e5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
Neuroticism <- Reliability(~ n1+n2+n3+n4+n5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
Agreeableness <- Reliability(~ a1+a2+a3+a4+a5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
DF$O<- Openness$index
DF$C<- Conscientiousness$index
DF$E<- Extraversion$index
DF$N<- Neuroticism$index
DF$A<- Agreeableness$index
Alpha2(Openness,Conscientiousness,Extraversion,Neuroticism,Agreeableness)
Tab 6:
Quelle
Item
n
M
SD
Alpha
Range
Skew
Kurtosi
Shapiro.Test
Openness
5
100
0.11
1.24
0.85
-3.96; 4.02
0.17
-0.17
W=0.99, p=.543
Conscientiousness
5
100
0.02
0.94
0.78
-4.47; 3.50
-0.07
-0.12
W=0.99, p=.938
Extraversion
5
100
-0.11
1.06
0.83
-4.51; 4.12
-0.12
0.15
W=0.99, p=.703
Neuroticism
5
100
-0.09
1.10
0.84
-4.11; 4.85
0.30
-0.43
W=0.99, p=.356
Agreeableness
5
100
0.00
1.15
0.84
-4.54; 4.23
0.13
-0.29
W=0.99, p=.769
Test des Regressionsmodels
Mittelwerte und Effecte
fit<- lm(Einkommen ~ sex + age + O + C + E + N + A, DF)
#Tabelle2 gibt die Tabellen formatiert aus.
mean_sd<- Tabelle(fit )
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
mean_sd$sex %>% Output( "Mittelwerte")
Tab 7: Mittelwerte
Einkommen
sex
n
M
SD
m
31
2303.33
566.81
f
69
2172.44
462.23
#mean_sd$O %>% Output(output="md")
APA2(effects::Effect("sex", fit))
Tab 8: Effekte: Effect
sex
fit
lower
upper
m
2379.71
2253.87
2505.56
f
2138.12
2055.28
2220.96
APA2(visreg::visreg(fit, "sex", plot=FALSE), digits=1)
Tab 9: Einkommen
sex
age
O
C
E
N
A
fit
ci
m
44.6420180327509
-0.0399716297517768
0.0278381187668443
0.0288540142655275
-0.177365131342206
-0.0727364994640318
2419.9
\[2292.4, 2547.5\]
f
44.6420180327509
-0.0399716297517768
0.0278381187668443
0.0288540142655275
-0.177365131342206
-0.0727364994640318
2178.3
\[2095.0, 2261.7\]
Regressionsmodel
APA_Table(fit, include.se=FALSE, include.ci=TRUE)
Tab 10:
Quelle
b
conf
Parameter
(Intercept)
2326\*\*\*
\[2659, 1992\]
sexf
-242\*\*
\[-87.9, -395\]
age
0.866
\[7.92, -6.19\]
O
-16.5
\[41.4, -74.3\]
C
-4.98
\[69.5, -79.5\]
E
106\*\*
\[ 172, 40.2\]
N
-321\*\*\*
\[-254, -387\]
A
69.5\*
\[ 131, 8.41\]
Goodness of fit
R2
0.56
adj. R2
0.53
AIC
1460.0
BIC
1483.4
RMSE
327.30
Obs
100
GOF
APA_Validation(fit)
Tab 11: Testing Regression Models
Test
statistic
F-Statistic
F(7, 92)=16.96, p<.001
Deviance Residuals
10712573.9
R2
0.56
adj. R2
0.53
Heteroskedasticity (Breusch-Pagan)
BP(7)=10.57, p=.158
Autocorrelation (Durbin-Watson)
DW=2.02, p=.536
Shapiro-Wilk normality test
W=0.93, p<.001
AIC
1460.0
BIC
1483.4
Var: Residual
116441.02
Obs
100
library(units)
## udunits system database from C:/Users/wpete/OneDrive/Dokumente/R/win-library/4.0/units/share/udunits
dat<- data.frame(
spd1 = set_units(1:5, m/s),
spd2 = set_units(1:5, km/h))
dat<- Label(dat, spd1="Ultra", spd2="Half")
dat
## spd1 spd2
## 1 1 [m/s] 1 [km/h]
## 2 2 [m/s] 2 [km/h]
## 3 3 [m/s] 3 [km/h]
## 4 4 [m/s] 4 [km/h]
## 5 5 [m/s] 5 [km/h]
dat %>% Tabelle(spd1, spd2)
## Item value
## 1 Ultra [m/s] 3.00 (SD 1.58, range 1.00 to 5.00)
## 2 Half [km/h] 3.00 (SD 1.58, range 1.00 to 5.00)
Test
Count Data
stats::chisq.test Pearson's Chi-squared Test for Count Data
stats::fisher.test Fisher's Exact Test for Count Data
stats::mcnemar.test McNemar's Chi-squared Test for Count Data
stats::pairwise.prop.test Pairwise comparisons for proportions
stats::prop.test Test of Equal or Given Proportions
stats::prop.trend.test Test for trend in proportions
stats::binom.test Exact Binomial Test
stats::cor.test Test for Association/Correlation Between Paired Samples
stats::ansari.test Ansari-Bradley Test
T-Test
stats::t.test Student's t-Test
stats::pairwise.t.test Pairwise t tests
Hmisc::t.test.cluster t-test for Clustered Data
stats::oneway.test Test for Equal Means in a One-Way Layout
Nicht-parametrische Tests
stats::kruskal.test Kruskal-Wallis Rank Sum Test
stats::wilcox.test Wilcoxon Rank Sum and Signed Rank Tests
stats::pairwise.wilcox.test Pairwise Wilcoxon rank sum tests
Hmisc::biVar Bivariate Summaries Computed Separately by a Series of Predictors
stats::mantelhaen.test Cochran-Mantel-Haenszel Chi-Squared Test for Count Data
stats::friedman.test Friedman Rank Sum Test
stats::ks.test Kolmogorov-Smirnov Tests
stats::shapiro.test Shapiro-Wilk Normality Test
survival
survival::cox.zph Test the Proportional Hazards Assumption of a Cox Regression
survival::plot.cox.zph Graphical Test of Proportional Hazards
survival::survdiff Test Survival Curve Differences
survival::survival-internal Internal survival functions
survival::survobrien O'Brien's Test for Association of a Single Variable with Survival
survival::survregDtest Verify a survreg distribution
Power calculations
stats::power.anova.test Power calculations for balanced one-way analysis of variance tests
stats::power.prop.test Power calculations two sample test for proportions
stats::power.t.test Power calculations for one and two sample t tests
Hmisc::bpower Power and Sample Size for Two-Sample Binomial Test
Hmisc::ciapower Power of Interaction Test for Exponential Survival
Hmisc::cpower Power of Cox/log-rank Two-Sample Test
Hmisc::spower Simulate Power of 2-Sample Test for Survival under Complex Conditions
Sobel
multilevel::sobel Estimate Sobel's (1982) Test for Mediation
multilevel::sobel.lme Estimate Sobel's (1982) Test for Mediation in Two-Level lme Model
GOF
stats::bartlett.test Bartlett Test of Homogeneity of Variances
stats::mauchly.test Mauchly's Test of Sphericity
stats::var.test F Test to Compare Two Variances
vcd::coindep_test Test for (Conditional) Independence
vcd::goodfit Goodness-of-fit Tests for Discrete Data
stats::poisson.test Exact Poisson tests
Andere
stats::Box.test Box-Pierce and Ljung-Box Tests
stats::fligner.test Fligner-Killeen Test of Homogeneity of Variances
stats::mood.test Mood Two-Sample Test of Scale
stats::PP.test Phillips-Perron Test for Unit Roots
stats::quade.test Quade Test
stp4/stp25stat documentation built on Sept. 17, 2021, 2:03 p.m.
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stp25stat
Wolfgang Peter 2021-02-23
Functions for Statistical Computations
which_output()
## [1] "markdown_html"
#' # set_my_options(style_mean = list(line_break = '<br>'))
#' # set_my_options(prozent = list(null_percent_sign = '.'))
#' # set_my_options(mittelwert = list(mean.style = 2, median.style = "IQR"),
#' # prozent=list(style=2))
#' # set_my_options(caption= "include.n")
Deskriptive Tabellen mit sig-Test
#' effect_size
#'
#' Cohen's d d=(x1-x2)/s psych::cohen.d
#' Hedges' g g= (x1-x2)/s1s2 ( pooled standard deviation)
#' g enspricht x<-scale(x) (mean(x1) - mean(x2))
#'
#' Generalized Log Odds Ratios for Frequency Tables vcd::oddsratio
#'
#' @param x vector
#' @param by factor
#' @param measure intern c("mean", "median", "numeric", "factor", "logical")
#' @param measure.test, test intern c("cattest", "contest", "notest", "ttest", ...)
#' @export
#'
#' @examples
#'
#' effect_size(c(2, 3, 4, 2, 3, 4, 3, 6,
#' 7, 6, 8, 9, 4, 5, 6, 7) ,
#' gl(2, 8, labels = c("Control", "Treat")))
#'
#' effect_size(factor(c(2, 2, 2, 2, 1, 1, 1, 1,
#' 2, 2, 2, 2, 1, 2, 1, 2)) ,
#' gl(2, 8, labels = c("Control", "Treat")))
#'
#'
effect_size2 <- function(x,
by,
measure,
...
) {
dat <- na.omit(data.frame(x = x, by = by))
n <- nrow(dat)
es <- rslt <- "n.a."
if (n > 10) {
if (measure %in% c("mean", "median", "numeric")) {
es <- psych::cohen.d(dat$x, dat$by)
es <- stp25rndr::Format2(es$cohen.d)
es <- paste0(es[2], " [", es[1], ", ", es[3], "] ES")
}
else if (measure %in% c("factor", "logical")) {
# Generalized Log Odds Ratios for Frequency Tables
if (measure == "factor" & nlevels(dat$x) != 2) {
es <- "n.a."
}
else{
es <- vcd::oddsratio(table(dat$x, dat$by), log = FALSE)
if (coef(es) < 0.01)
es <- "n.a."
else if (coef(es) > 100)
es <- "n.a."
else{
es <- stp25rndr::rndr_ods(c(coef(es) , confint(es)))
es <- paste0(es[1], " [", es[2], ", ", es[3], "] OR")
}
}
}
}
cbind('Odds Ratio/Effect Size' = es)
}
# Zu Auto-Test existiiert eine eigene Funktion unter stp25stat::auto_test
auto_test2 <- function(x,
by,
measure,
measure.test,
type = 2) {
dat <- na.omit(data.frame(x = x, by = by))
rslt <- NULL
contest <- stp25stat:::contest
cattest <- stp25stat:::cattest
if (measure.test == "notest") {
rslt <- ""
}
else if (measure.test == "contest") {
if (inherits(x, "factor")) {
dat$x <- as.numeric(dat$x)
}
rslt <- stp25stat:::conTest(x ~ by, dat)
}
else if (measure.test == "cattest") {
rslt <- stp25stat:::catTest( ~ x + by, dat)
}
else if (measure.test %in% contest) {
if (inherits(x, "factor")) {
dat$x <-
as.numeric(dat$x)
}
rslt <- stp25stat:::conTest(x ~ by, dat, measure.test)
}
else if (measure.test %in% cattest) {
rslt <- stp25stat:::catTest( ~ x + by, dat, measure.test)
}
if (type == 1) {
cbind('Statistics' = rslt)
}
else if (type == 2) {
rslt <- strsplit(rslt, ', p')[[1]]
cbind("Test Statistics" = rslt[1],
"p-value" = gsub("=", "", rslt[2]))
} else if (type == 3) {
cbind('Statistics' = gsub(", p", ",<BR>p", rslt))
}
}
DF %>% Tbll_desc(
age[median, 1],
height[1, mean, ttest],
sex,
smoker[fisher],
iq[0],
likert[2, mean],
by = ~ died,
include.custom = effect_size2,
include.test=TRUE,
include.n=TRUE,
include.total=TRUE
) %>% Output()
## Warning in `[[<-.factor`(`*tmp*`, i, value = 4): invalid factor level, NA
## generated
population.model <- '
Sex =~ sex
Age =~ age
Bildungszertifikat =~ edu
Beruf =~ beruf
Education ~ 1.04*Bildungszertifikat + 2.76* Beruf
single~ 1*age + 2*edu
Openness =~ o1+o2+o3+o4+o5
Conscientiousness =~ c1+c2+c3+c4+c5
Extraversion =~ e1+e2+e3+e4+e5
Neuroticism =~ n1+n2+n3+n4+n5
Agreeableness =~ a1+a2+a3+a4+a5
Einkommen ~ 1.25*Sex + (-0.31)*Age + .12*Openness + .23*Conscientiousness + (-0.91)*Extraversion + 2.3*Neuroticism + (-0.541)*Agreeableness
'
DF <- lavaan::simulateData(population.model, sample.nobs = 100)
DF<- transform(DF,
sex=cut(sex, 2, c("m", "f")),
age=scale2(age, 18, 55),
edu=cut(edu,3, c("low", "med", "hig")),
beruf= cut(edu,2, c("blue", "withe")),
single =cut(single, 2, c("yes", "no")),
Einkommen = (4-log(Einkommen-min(Einkommen)+1))*1000
)
Mittelwerte und Prozent
#APA_Correlation(~o1+o2+o3+o4+o5+c1+c2+c3, DF)
Tbll_corr(~o1+o2+o3+o4+o5+c1+c2+c3, DF) %>% Output()
PCA
vars <- c(
"o1", "o2", "o3", "o4", "o5", "c1", "c2", "c3",
"c4", "c5", "e1", "e2", "e3", "e4", "e5", "n1",
"n2", "n3", "n4", "n5", "a1", "a2", "a3", "a4", "a5"
)
APA_PCA(DF[vars], 5, cut=.35, include.plot = FALSE)
## R was not square, finding R from data
Reliability
Openness <- Reliability(~o1+o2+o3+o4+o5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
Conscientiousness <- Reliability(~ c1+c2+c3+c4+c5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
Extraversion <-Reliability(~ e1+e2+e3+e4+e5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
Neuroticism <- Reliability(~ n1+n2+n3+n4+n5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
Agreeableness <- Reliability(~ a1+a2+a3+a4+a5, DF, check.keys=TRUE)
## Number of categories should be increased in order to count frequencies.
DF$O<- Openness$index
DF$C<- Conscientiousness$index
DF$E<- Extraversion$index
DF$N<- Neuroticism$index
DF$A<- Agreeableness$index
Alpha2(Openness,Conscientiousness,Extraversion,Neuroticism,Agreeableness)
Test des Regressionsmodels
Mittelwerte und Effecte
fit<- lm(Einkommen ~ sex + age + O + C + E + N + A, DF)
#Tabelle2 gibt die Tabellen formatiert aus.
mean_sd<- Tabelle(fit )
## Registered S3 methods overwritten by 'lme4':
## method from
## cooks.distance.influence.merMod car
## influence.merMod car
## dfbeta.influence.merMod car
## dfbetas.influence.merMod car
mean_sd$sex %>% Output( "Mittelwerte")
#mean_sd$O %>% Output(output="md")
APA2(effects::Effect("sex", fit))
APA2(visreg::visreg(fit, "sex", plot=FALSE), digits=1)
Regressionsmodel
APA_Table(fit, include.se=FALSE, include.ci=TRUE)
GOF
APA_Validation(fit)
library(units)
## udunits system database from C:/Users/wpete/OneDrive/Dokumente/R/win-library/4.0/units/share/udunits
dat<- data.frame(
spd1 = set_units(1:5, m/s),
spd2 = set_units(1:5, km/h))
dat<- Label(dat, spd1="Ultra", spd2="Half")
dat
## spd1 spd2
## 1 1 [m/s] 1 [km/h]
## 2 2 [m/s] 2 [km/h]
## 3 3 [m/s] 3 [km/h]
## 4 4 [m/s] 4 [km/h]
## 5 5 [m/s] 5 [km/h]
dat %>% Tabelle(spd1, spd2)
## Item value
## 1 Ultra [m/s] 3.00 (SD 1.58, range 1.00 to 5.00)
## 2 Half [km/h] 3.00 (SD 1.58, range 1.00 to 5.00)
Test
Count Data
stats::chisq.test Pearson's Chi-squared Test for Count Data
stats::fisher.test Fisher's Exact Test for Count Data
stats::mcnemar.test McNemar's Chi-squared Test for Count Data
stats::pairwise.prop.test Pairwise comparisons for proportions
stats::prop.test Test of Equal or Given Proportions
stats::prop.trend.test Test for trend in proportions
stats::binom.test Exact Binomial Test
stats::cor.test Test for Association/Correlation Between Paired Samples
stats::ansari.test Ansari-Bradley Test
T-Test
stats::t.test Student's t-Test
stats::pairwise.t.test Pairwise t tests
Hmisc::t.test.cluster t-test for Clustered Data
stats::oneway.test Test for Equal Means in a One-Way Layout
Nicht-parametrische Tests
stats::kruskal.test Kruskal-Wallis Rank Sum Test
stats::wilcox.test Wilcoxon Rank Sum and Signed Rank Tests
stats::pairwise.wilcox.test Pairwise Wilcoxon rank sum tests
Hmisc::biVar Bivariate Summaries Computed Separately by a Series of Predictors
stats::mantelhaen.test Cochran-Mantel-Haenszel Chi-Squared Test for Count Data
stats::friedman.test Friedman Rank Sum Test
stats::ks.test Kolmogorov-Smirnov Tests
stats::shapiro.test Shapiro-Wilk Normality Test
survival
survival::cox.zph Test the Proportional Hazards Assumption of a Cox Regression
survival::plot.cox.zph Graphical Test of Proportional Hazards
survival::survdiff Test Survival Curve Differences
survival::survival-internal Internal survival functions
survival::survobrien O'Brien's Test for Association of a Single Variable with Survival
survival::survregDtest Verify a survreg distribution
Power calculations
stats::power.anova.test Power calculations for balanced one-way analysis of variance tests
stats::power.prop.test Power calculations two sample test for proportions
stats::power.t.test Power calculations for one and two sample t tests
Hmisc::bpower Power and Sample Size for Two-Sample Binomial Test
Hmisc::ciapower Power of Interaction Test for Exponential Survival
Hmisc::cpower Power of Cox/log-rank Two-Sample Test
Hmisc::spower Simulate Power of 2-Sample Test for Survival under Complex Conditions
Sobel
multilevel::sobel Estimate Sobel's (1982) Test for Mediation
multilevel::sobel.lme Estimate Sobel's (1982) Test for Mediation in Two-Level lme Model
GOF
stats::bartlett.test Bartlett Test of Homogeneity of Variances
stats::mauchly.test Mauchly's Test of Sphericity
stats::var.test F Test to Compare Two Variances
vcd::coindep_test Test for (Conditional) Independence
vcd::goodfit Goodness-of-fit Tests for Discrete Data
stats::poisson.test Exact Poisson tests
Andere
stats::Box.test Box-Pierce and Ljung-Box Tests
stats::fligner.test Fligner-Killeen Test of Homogeneity of Variances
stats::mood.test Mood Two-Sample Test of Scale
stats::PP.test Phillips-Perron Test for Unit Roots
stats::quade.test Quade Test
R Package Documentation
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