Minci2
aengarD
06/06/2006
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Describe baza_de_date (tbl_df, tbl, data.frame):
data.frame: 52 obs. of 6 variables
Nr ColName Class NAs Levels
1 Sex factor 6 (11.5%) (2): 1-F, 2-M
2 ICE (da/nu) factor . (2): 1-da, 2-nu
3 Timp de procedura numeric .
4 Doza de radiatii numeric .
5 Timp scopie numeric .
6 Tip FiA factor . (2): 1-Paroxistica,
2-Persistenta
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1 - Sex (factor - dichotomous)
length n NAs unique
52 46 6 2
88.5% 11.5%
freq perc lci.95 uci.95'
F 13 28.3% 17.3% 42.5%
M 33 71.7% 57.5% 82.7%
' 95%-CI Wilson
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2 - ICE (da/nu) (factor - dichotomous)
length n NAs unique
52 52 0 2
100.0% 0.0%
freq perc lci.95 uci.95'
da 26 50.0% 36.9% 63.1%
nu 26 50.0% 36.9% 63.1%
' 95%-CI Wilson
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3 - Timp de procedura (numeric)
length n NAs unique 0s mean meanCI
52 52 0 14 0 189.42 175.44
100.0% 0.0% 0.0% 203.41
.05 .10 .25 median .75 .90 .95
120.00 141.00 160.00 180.00 220.00 258.00 294.50
range sd vcoef mad IQR skew kurt
240.00 50.23 0.27 29.65 60.00 0.38 0.34
lowest : 60.0, 100.0, 120.0 (3), 140.0, 150.0 (3)
highest: 240.0 (4), 260.0, 280.0, 290.0, 300.0 (3)
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4 - Doza de radiatii (numeric)
length n NAs unique 0s mean
52 52 0 51 0 14'050.02
100.0% 0.0% 0.0%
.05 .10 .25 median .75 .90
4'607.05 6'744.00 8'501.50 12'925.00 17'334.33 22'270.00
range sd vcoef mad IQR skew
35'276.00 7'530.38 0.54 6'668.22 8'832.83 1.12
meanCI
11'953.54
16'146.49
.95
27'656.48
kurt
1.29
lowest : 2'724.0, 4'044.0, 4'073.0, 5'044.0, 6'218.0
highest: 22'514.0, 25'200.70, 30'658.0, 35'421.60, 38'000.0
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5 - Timp scopie (numeric)
length n NAs unique 0s mean meanCI
52 52 0 47 0 35.47 31.40
100.0% 0.0% 0.0% 39.53
.05 .10 .25 median .75 .90 .95
13.86 15.86 24.52 35.25 42.82 54.70 60.00
range sd vcoef mad IQR skew kurt
60.00 14.61 0.41 15.42 18.30 0.07 -0.61
lowest : 5.5, 6.4, 13.7, 14.0 (2), 15.6
highest: 55.0, 58.0, 60.0 (2), 65.0, 65.5
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6 - Tip FiA (factor - dichotomous)
length n NAs unique
52 52 0 2
100.0% 0.0%
freq perc lci.95 uci.95'
Paroxistica 40 76.9% 63.9% 86.3%
Persistenta 12 23.1% 13.7% 36.1%
' 95%-CI Wilson
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Sex ~ ICE..da.nu.
Summary:
n: 46, rows: 2, columns: 2
Pearson's Chi-squared test (cont. adj):
X-squared = 1, df = 1, p-value = 0.2
Fisher's exact test p-value = 0.2
McNemar's chi-squared = 5, df = 1, p-value = 0.03
estimate lwr.ci upr.ci'
odds ratio 0.3571 0.0951 1.3416
rel. risk (col1) 0.6044 0.2900 1.2597
rel. risk (col2) 1.6923 0.9074 3.1560
Phi-Coefficient 0.229
Contingency Coeff. 0.223
Cramer's V 0.229
ICE..da.nu. da nu Sum
Sex
F freq 5 8 13
perc 10.9% 17.4% 28.3%
p.row 38.5% 61.5% .
p.col 19.2% 40.0% .
M freq 21 12 33
perc 45.7% 26.1% 71.7%
p.row 63.6% 36.4% .
p.col 80.8% 60.0% .
Sum freq 26 20 46
perc 56.5% 43.5% 100.0%
p.row . . .
p.col . . .
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' 95% conf. level
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Timp.de.procedura ~ ICE..da.nu.
Summary:
n pairs: 52, valid: 52 (100.0%), missings: 0 (0.0%), groups: 2
da nu
mean 181.538 197.308
median 180.000 180.000
sd 50.334 49.844
IQR 20.000 55.000
n 26 26
np 50.000% 50.000%
NAs 0 0
0s 0 0
Kruskal-Wallis rank sum test:
Kruskal-Wallis chi-squared = 0.9, df = 1, p-value = 0.3
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Doza.de.radiatii ~ ICE..da.nu.
Summary:
n pairs: 52, valid: 52 (100.0%), missings: 0 (0.0%), groups: 2
da nu
mean 11'839.600 16'260.435
median 10'166.800 13'405.350
sd 6'100.566 8'264.501
IQR 9'028.500 9'040.575
n 26 26
np 50.000% 50.000%
NAs 0 0
0s 0 0
Kruskal-Wallis rank sum test:
Kruskal-Wallis chi-squared = 4, df = 1, p-value = 0.04
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Timp.scopie ~ ICE..da.nu.
Summary:
n pairs: 52, valid: 52 (100.0%), missings: 0 (0.0%), groups: 2
da nu
mean 28.004 42.931
median 27.550 41.600
sd 12.549 12.731
IQR 13.750 15.000
n 26 26
np 50.000% 50.000%
NAs 0 0
0s 0 0
Kruskal-Wallis rank sum test:
Kruskal-Wallis chi-squared = 14, df = 1, p-value = 0.0002
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Tip.FiA ~ ICE..da.nu.
Summary:
n: 52, rows: 2, columns: 2
Pearson's Chi-squared test (cont. adj):
X-squared = 0, df = 1, p-value = 1
Fisher's exact test p-value = 1
McNemar's chi-squared = 6, df = 1, p-value = 0.01
estimate lwr.ci upr.ci'
odds ratio 1.000 0.275 3.634
rel. risk (col1) 1.000 0.525 1.906
rel. risk (col2) 1.000 0.525 1.906
Phi-Coefficient 0.000
Contingency Coeff. 0.000
Cramer's V 0.000
ICE..da.nu. da nu Sum
Tip.FiA
Paroxistica freq 20 20 40
perc 38.5% 38.5% 76.9%
p.row 50.0% 50.0% .
p.col 76.9% 76.9% .
Persistenta freq 6 6 12
perc 11.5% 11.5% 23.1%
p.row 50.0% 50.0% .
p.col 23.1% 23.1% .
Sum freq 26 26 52
perc 50.0% 50.0% 100.0%
p.row . . .
p.col . . .
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' 95% conf. level
da.vars | da.n | da.mean | da.sd | da.median | da.trimmed | da.mad | da.min | da.max | da.range | da.skew | da.kurtosis | da.se |
1 | 26.000 | 1.808 | 0.402 | 2.000 | 1.864 | 0.000 | 1.000 | 2.000 | 1.000 | -1.472 | 0.179 | 0.079 |
2 | 26.000 | 1.000 | 0.000 | 1.000 | 1.000 | 0.000 | 1.000 | 1.000 | 0.000 | 0.000 | ||
3 | 26.000 | 181.538 | 50.334 | 180.000 | 181.364 | 29.652 | 60.000 | 290.000 | 230.000 | 0.098 | 0.402 | 9.871 |
4 | 26.000 | 11839.600 | 6100.566 | 10166.800 | 11647.618 | 6961.845 | 2724.000 | 22514.000 | 19790.000 | 0.325 | -1.267 | 1196.419 |
5 | 26.000 | 28.004 | 12.549 | 27.550 | 27.709 | 11.268 | 5.500 | 55.000 | 49.500 | 0.167 | -0.566 | 2.461 |
6 | 26.000 | 1.231 | 0.430 | 1.000 | 1.182 | 0.000 | 1.000 | 2.000 | 1.000 | 1.205 | -0.565 | 0.084 |
nu.vars | nu.n | nu.mean | nu.sd | nu.median | nu.trimmed | nu.mad | nu.min | nu.max | nu.range | nu.skew | nu.kurtosis | nu.se |
1 | 20.000 | 1.600 | 0.503 | 2.000 | 1.625 | 0.000 | 1.000 | 2.000 | 1.000 | -0.378 | -1.947 | 0.112 |
2 | 26.000 | 2.000 | 0.000 | 2.000 | 2.000 | 0.000 | 2.000 | 2.000 | 0.000 | 0.000 | ||
3 | 26.000 | 197.308 | 49.844 | 180.000 | 195.000 | 44.478 | 120.000 | 300.000 | 180.000 | 0.677 | -0.305 | 9.775 |
4 | 26.000 | 16260.435 | 8264.501 | 13405.350 | 15238.486 | 5143.139 | 6739.000 | 38000.000 | 31261.000 | 1.205 | 0.607 | 1620.802 |
5 | 26.000 | 42.931 | 12.731 | 41.600 | 43.041 | 13.195 | 18.200 | 65.500 | 47.300 | 0.002 | -0.767 | 2.497 |
6 | 26.000 | 1.231 | 0.430 | 1.000 | 1.182 | 0.000 | 1.000 | 2.000 | 1.000 | 1.205 | -0.565 | 0.084 |
1 Rezultate
baza_de_date %>% make_summary_table("ICE (da/nu)", g.rows = c("mean_sd", "med_range"))Factor | Levels | da | nu | Total | Statistics |
ICE (da/nu) | 26 (50.0%) | 26 (50.0%) | 52 | ||
Sex | F | 5 (19.2%) | 8 (40.0%) | 13 (28.3%) | OR=0.36 [0.10, 1.34] (p=0.187) |
M | 21 (80.8%) | 12 (60.0%) | 33 (71.7%) | ||
Timp de procedura | Mean ±SD | 181.54 ±50.3 | 197.31 ±49.8 | 189.42 ±50.2 | MW: p=0.348 |
*M(R) | 180 (60:290) | 180 (120:300) | 180 (60:300) | ||
Doza de radiatii | Mean ±SD | 11839.60 ±6100.6 | 16260.43 ±8264.5 | 14050.02 ±7530.4 | MW: p=0.041 |
*M(R) | 10166.8 (2724:22514) | 13405.35 (6739:38000) | 12925 (2724:38000) | ||
Timp scopie | Mean ±SD | 28.00 ±12.5 | 42.93 ±12.7 | 35.47 ±14.6 | T-test: p<0.001 |
*M(R) | 27.55 (5.5:55) | 41.6 (18.2:65.5) | 35.25 (5.5:65.5) | ||
Tip FiA | Paroxistica | 20 (76.9%) | 20 (76.9%) | 40 (76.9%) | OR=1.00 [0.28, 3.63] (p=1.000) |
Persistenta | 6 (23.1%) | 6 (23.1%) | 12 (23.1%) | ||
*M(R) = Mediana (min:max); MW = Test Mann-Whitney; OR/RR = odds-ratio / risc relativ [cu IC 95%] și p calculat prin testul Fisher); V = Cramer V (p calculat prin testul Chi²). | |||||
t.test(baza_de_date$`Timp de procedura`~baza_de_date$`ICE (da/nu)`)
Welch Two Sample t-test
data: baza_de_date$`Timp de procedura` by baza_de_date$`ICE (da/nu)`
t = -1, df = 50, p-value = 0.3
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-43.7 12.1
sample estimates:
mean in group da mean in group nu
182 197 t.test(baza_de_date$`Doza de radiatii`~baza_de_date$`ICE (da/nu)`)
Welch Two Sample t-test
data: baza_de_date$`Doza de radiatii` by baza_de_date$`ICE (da/nu)`
t = -2, df = 46, p-value = 0.03
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-8476 -366
sample estimates:
mean in group da mean in group nu
11840 16260 t.test(baza_de_date$`Timp scopie`~baza_de_date$`ICE (da/nu)`)
Welch Two Sample t-test
data: baza_de_date$`Timp scopie` by baza_de_date$`ICE (da/nu)`
t = -4, df = 50, p-value = 0.00009
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-21.97 -7.89
sample estimates:
mean in group da mean in group nu
28.0 42.9 1.1 x=Timp de procedura, y=Doza de radiatii
C0 <- cor.test(baza_de_date$`Timp de procedura`, baza_de_date$`Doza de radiatii`, method = "spearman")
Cda <- cor.test(baza_de_date$`Timp de procedura`[1:20], baza_de_date$`Doza de radiatii`[1:20], method = "spearman")
Cnu <- cor.test(baza_de_date$`Timp de procedura`[21:40], baza_de_date$`Doza de radiatii`[21:40], method = "spearman")
C0.p <- cor.test(baza_de_date$`Timp de procedura`, baza_de_date$`Doza de radiatii`, method = "pearson")
Cda.p <- cor.test(baza_de_date$`Timp de procedura`[1:20], baza_de_date$`Doza de radiatii`[1:20], method = "pearson")
Cnu.p <- cor.test(baza_de_date$`Timp de procedura`[21:40], baza_de_date$`Doza de radiatii`[21:40], method = "pearson")
baza_de_date %>% ggplot(aes(x=`Timp de procedura`, y=`Doza de radiatii`)) +
geom_smooth(method="lm", se=F, color="black", fullrange=T)+
geom_smooth(method="lm", se=F, fullrange=T, aes(color=`ICE (da/nu)`), linetype="dashed")+
geom_point(aes(color=`ICE (da/nu)`), alpha=0.5, size=3)
print(paste("Overall: Spearman R =", C0$estimate, ", p =", C0$p.value))[1] "Overall: Spearman R = 0.508791871448474 , p = 0.000117424223618335"print(paste("ICE = da: Spearman R =", Cda$estimate, ", p =", Cda$p.value))[1] "ICE = da: Spearman R = 0.733711074581487 , p = 0.000231538690377559"print(paste("ICE = nu: Spearman R =", Cnu$estimate, ", p =", Cnu$p.value))[1] "ICE = nu: Spearman R = 0.45829458719531 , p = 0.0421322559878773"print(paste("Overall: Pearson R =", C0.p$estimate, ", p =", C0.p$p.value))[1] "Overall: Pearson R = 0.471067597656773 , p = 0.000424052780045767"print(paste("ICE = da: Pearson R =", Cda.p$estimate, ", p =", Cda.p$p.value))[1] "ICE = da: Pearson R = 0.638939913997892 , p = 0.00242449139421406"print(paste("ICE = nu: Pearson R =", Cnu.p$estimate, ", p =", Cnu.p$p.value))[1] "ICE = nu: Pearson R = 0.537430017091023 , p = 0.0145344143871273"1.2 x=Timp de procedura, y=Timp scopie
C0 <- cor.test(baza_de_date$`Timp de procedura`, baza_de_date$`Timp scopie`, method = "spearman")
Cda <- cor.test(baza_de_date$`Timp de procedura`[1:20], baza_de_date$`Timp scopie`[1:20], method = "spearman")
Cnu <- cor.test(baza_de_date$`Timp de procedura`[21:40], baza_de_date$`Timp scopie`[21:40], method = "spearman")
C0.p <- cor.test(baza_de_date$`Timp de procedura`, baza_de_date$`Timp scopie`, method = "pearson")
Cda.p <- cor.test(baza_de_date$`Timp de procedura`[1:20], baza_de_date$`Timp scopie`[1:20], method = "pearson")
Cnu.p <- cor.test(baza_de_date$`Timp de procedura`[21:40], baza_de_date$`Timp scopie`[21:40], method = "pearson")
baza_de_date %>% ggplot(aes(x=`Timp de procedura`, y=`Timp scopie`)) +
geom_smooth(method="lm", se=F, color="black", fullrange=T)+
geom_smooth(method="lm", se=F, fullrange=T, aes(color=`ICE (da/nu)`), linetype="dashed")+
geom_point(aes(color=`ICE (da/nu)`), alpha=0.5, size=3)
print(paste("Overall: Spearman R =", C0$estimate, ", p =", C0$p.value))[1] "Overall: Spearman R = 0.487663343241216 , p = 0.000245489143882031"print(paste("ICE = da: Spearman R =", Cda$estimate, ", p =", Cda$p.value))[1] "ICE = da: Spearman R = 0.650169571155871 , p = 0.00191276101664954"print(paste("ICE = nu: Spearman R =", Cnu$estimate, ", p =", Cnu$p.value))[1] "ICE = nu: Spearman R = 0.0998743915856213 , p = 0.675258215630004"print(paste("Overall: Pearson R =", C0.p$estimate, ", p =", C0.p$p.value))[1] "Overall: Pearson R = 0.538950007038622 , p = 0.0000375453191823553"print(paste("ICE = da: Pearson R =", Cda.p$estimate, ", p =", Cda.p$p.value))[1] "ICE = da: Pearson R = 0.596051679435744 , p = 0.00554510144854163"print(paste("ICE = nu: Pearson R =", Cnu.p$estimate, ", p =", Cnu.p$p.value))[1] "ICE = nu: Pearson R = 0.36129516267341 , p = 0.117551403955669"1.3 x=Doza de radiatii, y=Timp scopie
C0 <- cor.test(baza_de_date$`Doza de radiatii`, baza_de_date$`Timp scopie`, method = "spearman")
Cda <- cor.test(baza_de_date$`Doza de radiatii`[1:20], baza_de_date$`Timp scopie`[1:20], method = "spearman")
Cnu <- cor.test(baza_de_date$`Doza de radiatii`[21:40], baza_de_date$`Timp scopie`[21:40], method = "spearman")
C0.p <- cor.test(baza_de_date$`Doza de radiatii`, baza_de_date$`Timp scopie`, method = "pearson")
Cda.p <- cor.test(baza_de_date$`Doza de radiatii`[1:20], baza_de_date$`Timp scopie`[1:20], method = "pearson")
Cnu.p <- cor.test(baza_de_date$`Doza de radiatii`[21:40], baza_de_date$`Timp scopie`[21:40], method = "pearson")
baza_de_date %>% ggplot(aes(x=`Doza de radiatii`, y=`Timp scopie`)) +
geom_smooth(method="lm", se=F, color="black", fullrange=T)+
geom_smooth(method="lm", se=F, fullrange=T, aes(color=`ICE (da/nu)`), linetype="dashed")+
geom_point(aes(color=`ICE (da/nu)`), alpha=0.5, size=3)
print(paste("Overall: Spearman R =", C0$estimate, ", p =", C0$p.value))[1] "Overall: Spearman R = 0.585535586816233 , p = 0.00000512295038064821"print(paste("ICE = da: Spearman R =", Cda$estimate, ", p =", Cda$p.value))[1] "ICE = da: Spearman R = 0.7930777263912 , p = 0.0000303083334519293"print(paste("ICE = nu: Spearman R =", Cnu$estimate, ", p =", Cnu$p.value))[1] "ICE = nu: Spearman R = 0.142857142857143 , p = 0.546534964823574"print(paste("Overall: Pearson R =", C0.p$estimate, ", p =", C0.p$p.value))[1] "Overall: Pearson R = 0.652176597392501 , p = 0.000000162422123085522"print(paste("ICE = da: Pearson R =", Cda.p$estimate, ", p =", Cda.p$p.value))[1] "ICE = da: Pearson R = 0.740641433117388 , p = 0.000187801792243598"print(paste("ICE = nu: Pearson R =", Cnu.p$estimate, ", p =", Cnu.p$p.value))[1] "ICE = nu: Pearson R = 0.588110416176511 , p = 0.00638443365482919"