This step loads packages with functions needed for analysis and that are not present in base R.
#install.packages(RVAideMemoire)
library(RVAideMemoire)
## Warning: package 'RVAideMemoire' was built under R version 3.6.3
## *** Package RVAideMemoire v 0.9-78 ***
#install.packages(gdata)
library(gdata)
## Warning: package 'gdata' was built under R version 3.6.3
## gdata: Unable to locate valid perl interpreter
## gdata:
## gdata: read.xls() will be unable to read Excel XLS and XLSX files
## gdata: unless the 'perl=' argument is used to specify the location of a
## gdata: valid perl intrpreter.
## gdata:
## gdata: (To avoid display of this message in the future, please ensure
## gdata: perl is installed and available on the executable search path.)
## gdata: Unable to load perl libaries needed by read.xls()
## gdata: to support 'XLX' (Excel 97-2004) files.
##
## gdata: Unable to load perl libaries needed by read.xls()
## gdata: to support 'XLSX' (Excel 2007+) files.
##
## gdata: Run the function 'installXLSXsupport()'
## gdata: to automatically download and install the perl
## gdata: libaries needed to support Excel XLS and XLSX formats.
##
## Attaching package: 'gdata'
## The following object is masked from 'package:stats':
##
## nobs
## The following object is masked from 'package:utils':
##
## object.size
## The following object is masked from 'package:base':
##
## startsWith
#install.packages(gmodels)
library(gmodels)
## Warning: package 'gmodels' was built under R version 3.6.3
#install.packages(ggplot2)
library(ggplot2)
## Warning: package 'ggplot2' was built under R version 3.6.3
##Load database
norse = read.csv("C:\\Users\\crist\\Desktop\\NORSE dataset.csv")
##Variable transformation and description
dim(norse)
## [1] 40 30
head(norse)
## cases classification Age Sex Prodromes Prior.fever Fever.onset Nb.AED
## 1 1 PF 9.25 1 1 1 1 1
## 2 2 NF 10.25 0 0 0 0 2
## 3 3 F 1.10 1 0 0 1 2
## 4 5 PF 3.80 1 1 1 1 4
## 5 7 F 1.60 0 1 0 1 2
## 6 8 PF 8.60 1 1 1 1 3
## Nb.cont KD Plasmaph STEROIDS IVIG ImTh X FLAIR DWI MRI CSF.WC CSF.prot CSF
## 1 2 0 NA 1 1 1 NA 1 1 0 1 NA 1
## 2 0 0 NA 1 NA 1 NA 1 NA 0 1 0 0
## 3 0 0 NA NA NA 0 NA NA NA 1 1 1 1
## 4 4 1 1 1 1 1 NA 1 1 0 0 0 0
## 5 0 0 NA NA NA 0 NA NA NA 1 0 1 0
## 6 3 1 NA 1 1 1 NA NA NA 1 0 0 0
## sporadic.discharges EEG.intericatl.period EEG.Sz EEG ICUdur Convulsdur SEdur
## 1 0 0 1 0 42 18 1200
## 2 1 0 0 0 2 120 24
## 3 0 0 0 1 3 135 36
## 4 0 1 0 0 49 72000 NA
## 5 0 0 0 1 2 240 8
## 6 1 1 0 0 57 12960 672
## AE Outcome
## 1 1 0
## 2 0 1
## 3 0 1
## 4 1 0
## 5 1 1
## 6 1 0
##Variables class transformation and description
#Age
norse$Age<-as.numeric(norse$Age)
summary(norse$Age)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 1.100 2.250 4.357 6.875 15.300
#Description of age by subgroup
summary(norse[which(norse$classification== "F"),]$Age)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.200 0.950 1.200 2.058 1.750 13.200
norse[which(norse$classification== "F"),]$Age
## [1] 1.10 1.60 0.60 1.25 13.20 1.20 0.90 0.75 1.90 1.00 3.30 0.90
## [13] 1.20 0.20 1.10 1.00 4.50 1.20 2.20
norse[which(norse$classification== "PF"),]$Age
## [1] 9.25 3.80 8.60 10.60 15.30 5.40 6.30 5.20 1.80 2.50 0.25 0.80
## [13] 8.80 2.30 5.10 11.70
#SE duration
norse$SEdur<-as.numeric(norse$SEdur)
summary(norse$SEdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 6.00 19.00 273.03 96.75 2784.00 8
#Description of seizure duration by subgroup
summary(norse[which(norse$classification== "PF"),]$SEdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 1.0 103.5 684.0 821.7 1158.0 2784.0 6
summary(norse[which(norse$classification== "F"),]$SEdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 4.125 10.250 25.894 33.000 144.000 1
#Outcome
norse$Outcome<-as.factor(as.character(norse$Outcome))
class(norse$Outcome)
## [1] "factor"
CrossTable(norse$Outcome)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 15 | 25 |
## | 0.375 | 0.625 |
## |-----------|-----------|
##
##
##
##
#Classification (subgroups by fever)
norse$classification<-as.character(norse$classification)
CrossTable(norse$classification)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | F | NF | PF |
## |-----------|-----------|-----------|
## | 19 | 5 | 16 |
## | 0.475 | 0.125 | 0.400 |
## |-----------|-----------|-----------|
##
##
##
##
#Presence of fever at SE onset
norse$Fever.onset<-as.character(norse$Fever.onset)
CrossTable(norse$Fever.onset)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 9 | 31 |
## | 0.225 | 0.775 |
## |-----------|-----------|
##
##
##
##
#EEG
norse$EEG<-as.character(norse$EEG)
CrossTable(norse$EEG)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 16 | 20 |
## | 0.444 | 0.556 |
## |-----------|-----------|
##
##
##
##
#CSF
norse$CSF<-as.character(norse$CSF)
CrossTable(norse$CSF)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 12 | 23 |
## | 0.343 | 0.657 |
## |-----------|-----------|
##
##
##
##
#ICU lenght of stay
norse$ICUdur<-as.numeric(norse$ICUdur)
summary(norse$ICUdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 1.50 3.00 19.85 33.00 100.00 1
#Number of AEDs
norse$Nb.AED<-as.numeric(norse$Nb.AED)
summary(norse$Nb.AED)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.750 2.000 2.225 2.250 8.000
#MRI
norse$MRI<-as.character(norse$MRI)
CrossTable(norse$MRI)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 31
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 16 | 15 |
## | 0.516 | 0.484 |
## |-----------|-----------|
##
##
##
##
#Ketogenic diet
norse$KD<-as.character(norse$KD)
CrossTable(norse$KD)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 31 | 9 |
## | 0.775 | 0.225 |
## |-----------|-----------|
##
##
##
##
#Immunotherapy
norse$ImTh<-as.character(norse$ImTh)
CrossTable(norse$ImTh)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 28 | 12 |
## | 0.700 | 0.300 |
## |-----------|-----------|
##
##
##
##
#Antiseizure medications
norse$AE<-as.character(norse$AE)
CrossTable(norse$AE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 23 | 17 |
## | 0.575 | 0.425 |
## |-----------|-----------|
##
##
##
##
#Create variable prodromes
norse$Prodromes <- ifelse(norse$Prodromes=="1", 1, 0)
CrossTable(norse$Prodromes)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 17 | 23 |
## | 0.425 | 0.575 |
## |-----------|-----------|
##
##
##
##
#Create variable for subgroup "no fever"
norse$No.fever <- ifelse(norse$Prior.fever=="0" & norse$Fever.onset=="0", 1, 0)
CrossTable(norse$No.fever)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | 0 | 1 |
## |-----------|-----------|
## | 35 | 5 |
## | 0.875 | 0.125 |
## |-----------|-----------|
##
##
##
##
##Univariate analysis + Bonferroni correction
# Qualitative variables (comparison of NORSE subgroups)
fisher.multcomp(table(norse$classification, norse$Prodromes),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$Prodromes)
##
## F NF
## NF 1.000e+00 -
## PF 5.627e-05 0.002506
##
## P value adjustment method: bonferroni
fisher.multcomp(table(norse$classification, norse$Fever.onset),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$Fever.onset)
##
## F NF
## NF 7.058e-05 -
## PF 1.043e-01 0.01858
##
## P value adjustment method: bonferroni
fisher.multcomp(table(norse$classification, norse$EEG),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$EEG)
##
## F NF
## NF 1.00000 -
## PF 0.01154 0.8694
##
## P value adjustment method: bonferroni
fisher.multcomp(table(norse$classification, norse$MRI),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$MRI)
##
## F NF
## NF 0.026374 -
## PF 0.001911 1
##
## P value adjustment method: bonferroni
fisher.multcomp(table(norse$classification, norse$CSF),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$CSF)
##
## F NF
## NF 0.894737 -
## PF 0.006161 1
##
## P value adjustment method: bonferroni
fisher.multcomp(table(norse$classification, norse$KD),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$KD)
##
## F NF
## NF 1.0000000 -
## PF 0.0004861 0.1353
##
## P value adjustment method: bonferroni
fisher.multcomp(table(norse$classification, norse$ImTh),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$ImTh)
##
## F NF
## NF 6.250e-01 -
## PF 3.141e-05 0.3582
##
## P value adjustment method: bonferroni
fisher.multcomp(table(norse$classification, norse$Outcome),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$Outcome)
##
## F NF
## NF 1.0000000 -
## PF 0.0004314 0.1424
##
## P value adjustment method: bonferroni
fisher.multcomp(table(norse$classification, norse$AE),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$classification,norse$AE)
##
## F NF
## NF 1.0000000 -
## PF 0.0004629 0.07534
##
## P value adjustment method: bonferroni
fires <- subset(norse, norse$classification=="PF")
table(fires$classification, fires$Prodromes)
##
## 1
## PF 16
# Quantitative variables (comparison of NORSE subgroups)
kruskal.test(norse$Age, norse$classification)
##
## Kruskal-Wallis rank sum test
##
## data: norse$Age and norse$classification
## Kruskal-Wallis chi-squared = 13.029, df = 2, p-value = 0.001482
pairwise.wilcox.test(norse$Age, norse$classification, p.adj = "bonf", paired=F)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: norse$Age and norse$classification
##
## F NF
## NF 0.0344 -
## PF 0.0047 1.0000
##
## P value adjustment method: bonferroni
pairwise.wilcox.test(norse$SEdur, norse$classification, p.adj = "bonf", paired=F)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: norse$SEdur and norse$classification
##
## F NF
## NF 1.000 -
## PF 0.015 0.268
##
## P value adjustment method: bonferroni
pairwise.wilcox.test(norse$ICUdur, norse$classification, p.adj = "bonf", paired=F)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: norse$ICUdur and norse$classification
##
## F NF
## NF 1.000 -
## PF 6.8e-06 0.005
##
## P value adjustment method: bonferroni
pairwise.wilcox.test(norse$Nb.cont, norse$classification, p.adj = "bonf", paired=F)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: norse$Nb.cont and norse$classification
##
## F NF
## NF 1.0000 -
## PF 0.0026 0.1016
##
## P value adjustment method: bonferroni
pairwise.wilcox.test(norse$Nb.AED, norse$classification, p.adj = "bonf", paired=F)
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
## Warning in wilcox.test.default(xi, xj, paired = paired, ...): cannot compute
## exact p-value with ties
##
## Pairwise comparisons using Wilcoxon rank sum test
##
## data: norse$Nb.AED and norse$classification
##
## F NF
## NF 1.0000 -
## PF 0.0053 0.3134
##
## P value adjustment method: bonferroni
# Comparison of outcome and MRI findings
CrossTable(norse$Outcome, norse$MRI)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 31
##
##
## | norse$MRI
## norse$Outcome | 0 | 1 | Row Total |
## --------------|-----------|-----------|-----------|
## 0 | 11 | 4 | 15 |
## | 1.371 | 1.463 | |
## | 0.733 | 0.267 | 0.484 |
## | 0.688 | 0.267 | |
## | 0.355 | 0.129 | |
## --------------|-----------|-----------|-----------|
## 1 | 5 | 11 | 16 |
## | 1.285 | 1.371 | |
## | 0.312 | 0.688 | 0.516 |
## | 0.312 | 0.733 | |
## | 0.161 | 0.355 | |
## --------------|-----------|-----------|-----------|
## Column Total | 16 | 15 | 31 |
## | 0.516 | 0.484 | |
## --------------|-----------|-----------|-----------|
##
##
fisher.test(norse$Outcome, norse$MRI)
##
## Fisher's Exact Test for Count Data
##
## data: norse$Outcome and norse$MRI
## p-value = 0.03195
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.027761 38.629542
## sample estimates:
## odds ratio
## 5.66321
#Create 2 groups by age (> or < 6 years)
norse$age_less6[norse$Age <= 6] <- 1
norse$age_less6[norse$Age > 6] <- 0
class(norse$age_less6)
## [1] "numeric"
norse$age_less6 <- as.character(as.numeric(norse$age_less6))
norse$age_less6 <- as.factor(as.character(norse$age_less6))
class(norse$age_less6)
## [1] "factor"
#Univariate analysis (comparison of the two age groups)
#Cualitative variables
CrossTable(norse$age_less6, norse$classification)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$classification
## norse$age_less6 | F | NF | PF | Row Total |
## ----------------|-----------|-----------|-----------|-----------|
## 0 | 1 | 3 | 7 | 11 |
## | 3.416 | 1.920 | 1.536 | |
## | 0.091 | 0.273 | 0.636 | 0.275 |
## | 0.053 | 0.600 | 0.438 | |
## | 0.025 | 0.075 | 0.175 | |
## ----------------|-----------|-----------|-----------|-----------|
## 1 | 18 | 2 | 9 | 29 |
## | 1.296 | 0.728 | 0.583 | |
## | 0.621 | 0.069 | 0.310 | 0.725 |
## | 0.947 | 0.400 | 0.562 | |
## | 0.450 | 0.050 | 0.225 | |
## ----------------|-----------|-----------|-----------|-----------|
## Column Total | 19 | 5 | 16 | 40 |
## | 0.475 | 0.125 | 0.400 | |
## ----------------|-----------|-----------|-----------|-----------|
##
##
fisher.multcomp(table(norse$age_less6, norse$classification),p.method=c("bonferroni"))
##
## Pairwise comparisons using Fisher's exact test for count data
##
## data: table(norse$age_less6,norse$classification)
##
## F:NF F:PF NF:PF
## 0:1 0.05505 0.03898 1
##
## P value adjustment method: bonferroni
norse$Feveronset24[norse$classification == "F"] <- 1
norse$Feveronset24[norse$classification == "PF" | norse$classification == "NF"] <- 0
CrossTable(norse$age_less6, norse$Feveronset24)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$Feveronset24
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 10 | 1 | 11 |
## | 3.091 | 3.416 | |
## | 0.909 | 0.091 | 0.275 |
## | 0.476 | 0.053 | |
## | 0.250 | 0.025 | |
## ----------------|-----------|-----------|-----------|
## 1 | 11 | 18 | 29 |
## | 1.172 | 1.296 | |
## | 0.379 | 0.621 | 0.725 |
## | 0.524 | 0.947 | |
## | 0.275 | 0.450 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 21 | 19 | 40 |
## | 0.525 | 0.475 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$Feveronset24)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$Feveronset24
## p-value = 0.003923
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.760096 745.241485
## sample estimates:
## odds ratio
## 15.30593
CrossTable(norse$age_less6, norse$Sex)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$Sex
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 5 | 6 | 11 |
## | 0.104 | 0.115 | |
## | 0.455 | 0.545 | 0.275 |
## | 0.238 | 0.316 | |
## | 0.125 | 0.150 | |
## ----------------|-----------|-----------|-----------|
## 1 | 16 | 13 | 29 |
## | 0.039 | 0.044 | |
## | 0.552 | 0.448 | 0.725 |
## | 0.762 | 0.684 | |
## | 0.400 | 0.325 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 21 | 19 | 40 |
## | 0.525 | 0.475 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$Sex)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$Sex
## p-value = 0.7271
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.1314107 3.3975583
## sample estimates:
## odds ratio
## 0.6837744
CrossTable(norse$age_less6, norse$Prodromes)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$Prodromes
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 4 | 7 | 11 |
## | 0.097 | 0.072 | |
## | 0.364 | 0.636 | 0.275 |
## | 0.235 | 0.304 | |
## | 0.100 | 0.175 | |
## ----------------|-----------|-----------|-----------|
## 1 | 13 | 16 | 29 |
## | 0.037 | 0.027 | |
## | 0.448 | 0.552 | 0.725 |
## | 0.765 | 0.696 | |
## | 0.325 | 0.400 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 17 | 23 | 40 |
## | 0.425 | 0.575 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$Prodromes)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$Prodromes
## p-value = 0.7298
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.123637 3.562499
## sample estimates:
## odds ratio
## 0.7094504
CrossTable(norse$age_less6, norse$Fever.onset)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$Fever.onset
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 6 | 5 | 11 |
## | 5.020 | 1.458 | |
## | 0.545 | 0.455 | 0.275 |
## | 0.667 | 0.161 | |
## | 0.150 | 0.125 | |
## ----------------|-----------|-----------|-----------|
## 1 | 3 | 26 | 29 |
## | 1.904 | 0.553 | |
## | 0.103 | 0.897 | 0.725 |
## | 0.333 | 0.839 | |
## | 0.075 | 0.650 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 9 | 31 | 40 |
## | 0.225 | 0.775 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$Fever.onset)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$Fever.onset
## p-value = 0.006681
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.486298 80.933854
## sample estimates:
## odds ratio
## 9.584265
CrossTable(norse$age_less6, norse$Prior.fever)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$Prior.fever
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 4 | 7 | 11 |
## | 1.024 | 1.536 | |
## | 0.364 | 0.636 | 0.275 |
## | 0.167 | 0.438 | |
## | 0.100 | 0.175 | |
## ----------------|-----------|-----------|-----------|
## 1 | 20 | 9 | 29 |
## | 0.389 | 0.583 | |
## | 0.690 | 0.310 | 0.725 |
## | 0.833 | 0.562 | |
## | 0.500 | 0.225 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 24 | 16 | 40 |
## | 0.600 | 0.400 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$Prior.fever)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$Prior.fever
## p-value = 0.07996
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.04477997 1.36459611
## sample estimates:
## odds ratio
## 0.2668994
table(norse$age_less6, norse$No.fever)
##
## 0 1
## 0 8 3
## 1 27 2
CrossTable(norse$age_less6, norse$No.fever)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$No.fever
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 8 | 3 | 11 |
## | 0.274 | 1.920 | |
## | 0.727 | 0.273 | 0.275 |
## | 0.229 | 0.600 | |
## | 0.200 | 0.075 | |
## ----------------|-----------|-----------|-----------|
## 1 | 27 | 2 | 29 |
## | 0.104 | 0.728 | |
## | 0.931 | 0.069 | 0.725 |
## | 0.771 | 0.400 | |
## | 0.675 | 0.050 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 35 | 5 | 40 |
## | 0.875 | 0.125 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$No.fever)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$No.fever
## p-value = 0.1171
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.01485633 2.14018928
## sample estimates:
## odds ratio
## 0.2077226
CrossTable(norse$age_less6, norse$EEG)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 36
##
##
## | norse$EEG
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 8 | 3 | 11 |
## | 1.980 | 1.584 | |
## | 0.727 | 0.273 | 0.306 |
## | 0.500 | 0.150 | |
## | 0.222 | 0.083 | |
## ----------------|-----------|-----------|-----------|
## 1 | 8 | 17 | 25 |
## | 0.871 | 0.697 | |
## | 0.320 | 0.680 | 0.694 |
## | 0.500 | 0.850 | |
## | 0.222 | 0.472 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 16 | 20 | 36 |
## | 0.444 | 0.556 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$EEG)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$EEG
## p-value = 0.03351
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.9629193 40.1782859
## sample estimates:
## odds ratio
## 5.372581
CrossTable(norse[which(norse$EEG==0),]$age_less6, norse[which(norse$EEG==0),]$EEG.Sz)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 16
##
##
## | norse[which(norse$EEG == 0), ]$EEG.Sz
## norse[which(norse$EEG == 0), ]$age_less6 | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## 0 | 4 | 4 | 8 |
## | 0.333 | 0.200 | |
## | 0.500 | 0.500 | 0.500 |
## | 0.667 | 0.400 | |
## | 0.250 | 0.250 | |
## -----------------------------------------|-----------|-----------|-----------|
## 1 | 2 | 6 | 8 |
## | 0.333 | 0.200 | |
## | 0.250 | 0.750 | 0.500 |
## | 0.333 | 0.600 | |
## | 0.125 | 0.375 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 6 | 10 | 16 |
## | 0.375 | 0.625 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
CrossTable(norse[which(norse$EEG==0),]$age_less6, norse[which(norse$EEG==0),]$EEG.intericatl.period)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 16
##
##
## | norse[which(norse$EEG == 0), ]$EEG.intericatl.period
## norse[which(norse$EEG == 0), ]$age_less6 | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## 0 | 3 | 5 | 8 |
## | 0.100 | 0.045 | |
## | 0.375 | 0.625 | 0.500 |
## | 0.600 | 0.455 | |
## | 0.188 | 0.312 | |
## -----------------------------------------|-----------|-----------|-----------|
## 1 | 2 | 6 | 8 |
## | 0.100 | 0.045 | |
## | 0.250 | 0.750 | 0.500 |
## | 0.400 | 0.545 | |
## | 0.125 | 0.375 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 5 | 11 | 16 |
## | 0.312 | 0.688 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
CrossTable(norse[which(norse$EEG==0),]$age_less6, norse[which(norse$EEG==0),]$sporadic.discharges)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 14
##
##
## | norse[which(norse$EEG == 0), ]$sporadic.discharges
## norse[which(norse$EEG == 0), ]$age_less6 | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## 0 | 2 | 6 | 8 |
## | 0.257 | 0.143 | |
## | 0.250 | 0.750 | 0.571 |
## | 0.400 | 0.667 | |
## | 0.143 | 0.429 | |
## -----------------------------------------|-----------|-----------|-----------|
## 1 | 3 | 3 | 6 |
## | 0.343 | 0.190 | |
## | 0.500 | 0.500 | 0.429 |
## | 0.600 | 0.333 | |
## | 0.214 | 0.214 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 5 | 9 | 14 |
## | 0.357 | 0.643 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
CrossTable(norse$age_less6, norse$MRI)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 31
##
##
## | norse$MRI
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 8 | 2 | 10 |
## | 1.561 | 1.665 | |
## | 0.800 | 0.200 | 0.323 |
## | 0.500 | 0.133 | |
## | 0.258 | 0.065 | |
## ----------------|-----------|-----------|-----------|
## 1 | 8 | 13 | 21 |
## | 0.743 | 0.793 | |
## | 0.381 | 0.619 | 0.677 |
## | 0.500 | 0.867 | |
## | 0.258 | 0.419 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 16 | 15 | 31 |
## | 0.516 | 0.484 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$MRI)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$MRI
## p-value = 0.0538
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.8995616 73.4269797
## sample estimates:
## odds ratio
## 6.100051
CrossTable(norse[which(norse$MRI==0),]$age_less6, norse[which(norse$MRI==0),]$FLAIR)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 14
##
##
## | norse[which(norse$MRI == 0), ]$FLAIR
## norse[which(norse$MRI == 0), ]$age_less6 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|
## 0 | 6 | 6 |
## | 0.429 | |
## -----------------------------------------|-----------|-----------|
## 1 | 8 | 8 |
## | 0.571 | |
## -----------------------------------------|-----------|-----------|
## Column Total | 14 | 14 |
## -----------------------------------------|-----------|-----------|
##
##
CrossTable(norse[which(norse$MRI==0),]$age_less6, norse[which(norse$MRI==0),]$DWI)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 9
##
##
## | norse[which(norse$MRI == 0), ]$DWI
## norse[which(norse$MRI == 0), ]$age_less6 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|
## 0 | 4 | 4 |
## | 0.444 | |
## -----------------------------------------|-----------|-----------|
## 1 | 5 | 5 |
## | 0.556 | |
## -----------------------------------------|-----------|-----------|
## Column Total | 9 | 9 |
## -----------------------------------------|-----------|-----------|
##
##
CrossTable(norse$age_less6, norse$CSF)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 35
##
##
## | norse$CSF
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 6 | 5 | 11 |
## | 1.317 | 0.687 | |
## | 0.545 | 0.455 | 0.314 |
## | 0.500 | 0.217 | |
## | 0.171 | 0.143 | |
## ----------------|-----------|-----------|-----------|
## 1 | 6 | 18 | 24 |
## | 0.604 | 0.315 | |
## | 0.250 | 0.750 | 0.686 |
## | 0.500 | 0.783 | |
## | 0.171 | 0.514 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 12 | 23 | 35 |
## | 0.343 | 0.657 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$CSF)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$CSF
## p-value = 0.1297
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.6254279 20.8881847
## sample estimates:
## odds ratio
## 3.455321
CrossTable(norse[which(norse$CSF==0),]$age_less6, norse[which(norse$CSF==0),]$CSF.WC)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 12
##
##
## | norse[which(norse$CSF == 0), ]$CSF.WC
## norse[which(norse$CSF == 0), ]$age_less6 | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## 0 | 3 | 3 | 6 |
## | 0.500 | 1.500 | |
## | 0.500 | 0.500 | 0.500 |
## | 0.333 | 1.000 | |
## | 0.250 | 0.250 | |
## -----------------------------------------|-----------|-----------|-----------|
## 1 | 6 | 0 | 6 |
## | 0.500 | 1.500 | |
## | 1.000 | 0.000 | 0.500 |
## | 0.667 | 0.000 | |
## | 0.500 | 0.000 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 9 | 3 | 12 |
## | 0.750 | 0.250 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
CrossTable(norse[which(norse$CSF==0),]$age_less6, norse[which(norse$CSF==0),]$CSF.prot)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 12
##
##
## | norse[which(norse$CSF == 0), ]$CSF.prot
## norse[which(norse$CSF == 0), ]$age_less6 | 0 | 1 | Row Total |
## -----------------------------------------|-----------|-----------|-----------|
## 0 | 6 | 0 | 6 |
## | 0.500 | 1.500 | |
## | 1.000 | 0.000 | 0.500 |
## | 0.667 | 0.000 | |
## | 0.500 | 0.000 | |
## -----------------------------------------|-----------|-----------|-----------|
## 1 | 3 | 3 | 6 |
## | 0.500 | 1.500 | |
## | 0.500 | 0.500 | 0.500 |
## | 0.333 | 1.000 | |
## | 0.250 | 0.250 | |
## -----------------------------------------|-----------|-----------|-----------|
## Column Total | 9 | 3 | 12 |
## | 0.750 | 0.250 | |
## -----------------------------------------|-----------|-----------|-----------|
##
##
CrossTable(norse$age_less6, norse$KD)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$KD
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 7 | 4 | 11 |
## | 0.273 | 0.940 | |
## | 0.636 | 0.364 | 0.275 |
## | 0.226 | 0.444 | |
## | 0.175 | 0.100 | |
## ----------------|-----------|-----------|-----------|
## 1 | 24 | 5 | 29 |
## | 0.103 | 0.356 | |
## | 0.828 | 0.172 | 0.725 |
## | 0.774 | 0.556 | |
## | 0.600 | 0.125 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 31 | 9 | 40 |
## | 0.775 | 0.225 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$KD)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$KD
## p-value = 0.2268
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.05985895 2.42596346
## sample estimates:
## odds ratio
## 0.3752061
CrossTable(norse$age_less6, norse$ImTh)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$ImTh
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 5 | 6 | 11 |
## | 0.947 | 2.209 | |
## | 0.455 | 0.545 | 0.275 |
## | 0.179 | 0.500 | |
## | 0.125 | 0.150 | |
## ----------------|-----------|-----------|-----------|
## 1 | 23 | 6 | 29 |
## | 0.359 | 0.838 | |
## | 0.793 | 0.207 | 0.725 |
## | 0.821 | 0.500 | |
## | 0.575 | 0.150 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 28 | 12 | 40 |
## | 0.700 | 0.300 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$ImTh)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$ImTh
## p-value = 0.05632
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.03827155 1.23112409
## sample estimates:
## odds ratio
## 0.2274606
CrossTable(norse[which(norse$ImTh==1),]$age_less6, norse[which(norse$ImTh==1),]$IVIG)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 9
##
##
## | norse[which(norse$ImTh == 1), ]$IVIG
## norse[which(norse$ImTh == 1), ]$age_less6 | 1 | Row Total |
## ------------------------------------------|-----------|-----------|
## 0 | 4 | 4 |
## | 0.444 | |
## ------------------------------------------|-----------|-----------|
## 1 | 5 | 5 |
## | 0.556 | |
## ------------------------------------------|-----------|-----------|
## Column Total | 9 | 9 |
## ------------------------------------------|-----------|-----------|
##
##
CrossTable(norse[which(norse$ImTh==1),]$age_less6, norse[which(norse$ImTh==1),]$STEROIDS)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 11
##
##
## | norse[which(norse$ImTh == 1), ]$STEROIDS
## norse[which(norse$ImTh == 1), ]$age_less6 | 1 | Row Total |
## ------------------------------------------|-----------|-----------|
## 0 | 6 | 6 |
## | 0.545 | |
## ------------------------------------------|-----------|-----------|
## 1 | 5 | 5 |
## | 0.455 | |
## ------------------------------------------|-----------|-----------|
## Column Total | 11 | 11 |
## ------------------------------------------|-----------|-----------|
##
##
CrossTable(norse[which(norse$ImTh==1),]$age_less6, norse[which(norse$ImTh==1),]$Plasmaph)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 3
##
##
## | norse[which(norse$ImTh == 1), ]$Plasmaph
## norse[which(norse$ImTh == 1), ]$age_less6 | 1 | Row Total |
## ------------------------------------------|-----------|-----------|
## 0 | 1 | 1 |
## | 0.333 | |
## ------------------------------------------|-----------|-----------|
## 1 | 2 | 2 |
## | 0.667 | |
## ------------------------------------------|-----------|-----------|
## Column Total | 3 | 3 |
## ------------------------------------------|-----------|-----------|
##
##
CrossTable(norse$age_less6, norse$Outcome)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$Outcome
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 8 | 3 | 11 |
## | 3.640 | 2.184 | |
## | 0.727 | 0.273 | 0.275 |
## | 0.533 | 0.120 | |
## | 0.200 | 0.075 | |
## ----------------|-----------|-----------|-----------|
## 1 | 7 | 22 | 29 |
## | 1.381 | 0.828 | |
## | 0.241 | 0.759 | 0.725 |
## | 0.467 | 0.880 | |
## | 0.175 | 0.550 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 15 | 25 | 40 |
## | 0.375 | 0.625 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$Outcome)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$Outcome
## p-value = 0.009013
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 1.411104 59.170969
## sample estimates:
## odds ratio
## 7.855526
CrossTable(norse$Outcome, norse$age_less6)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$age_less6
## norse$Outcome | 0 | 1 | Row Total |
## --------------|-----------|-----------|-----------|
## 0 | 8 | 7 | 15 |
## | 3.640 | 1.381 | |
## | 0.533 | 0.467 | 0.375 |
## | 0.727 | 0.241 | |
## | 0.200 | 0.175 | |
## --------------|-----------|-----------|-----------|
## 1 | 3 | 22 | 25 |
## | 2.184 | 0.828 | |
## | 0.120 | 0.880 | 0.625 |
## | 0.273 | 0.759 | |
## | 0.075 | 0.550 | |
## --------------|-----------|-----------|-----------|
## Column Total | 11 | 29 | 40 |
## | 0.275 | 0.725 | |
## --------------|-----------|-----------|-----------|
##
##
CrossTable(norse$age_less6, norse$AE)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 40
##
##
## | norse$AE
## norse$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 4 | 7 | 11 |
## | 0.855 | 1.156 | |
## | 0.364 | 0.636 | 0.275 |
## | 0.174 | 0.412 | |
## | 0.100 | 0.175 | |
## ----------------|-----------|-----------|-----------|
## 1 | 19 | 10 | 29 |
## | 0.324 | 0.439 | |
## | 0.655 | 0.345 | 0.725 |
## | 0.826 | 0.588 | |
## | 0.475 | 0.250 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 23 | 17 | 40 |
## | 0.575 | 0.425 | |
## ----------------|-----------|-----------|-----------|
##
##
fisher.test(norse$age_less6, norse$AE)
##
## Fisher's Exact Test for Count Data
##
## data: norse$age_less6 and norse$AE
## p-value = 0.153
## alternative hypothesis: true odds ratio is not equal to 1
## 95 percent confidence interval:
## 0.05282248 1.57151322
## sample estimates:
## odds ratio
## 0.310545
fires <- subset(norse, norse$classification=="PF")
CrossTable(fires$age_less6, fires$Prodromes)
##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 16
##
##
## | fires$Prodromes
## fires$age_less6 | 1 | Row Total |
## ----------------|-----------|-----------|
## 0 | 7 | 7 |
## | 0.438 | |
## ----------------|-----------|-----------|
## 1 | 9 | 9 |
## | 0.562 | |
## ----------------|-----------|-----------|
## Column Total | 16 | 16 |
## ----------------|-----------|-----------|
##
##
table(fires$age_less6, fires$Prodromes)
##
## 1
## 0 7
## 1 9
CrossTable(fires$age_less6, fires$Outcome)
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##
## Cell Contents
## |-------------------------|
## | N |
## | Chi-square contribution |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 16
##
##
## | fires$Outcome
## fires$age_less6 | 0 | 1 | Row Total |
## ----------------|-----------|-----------|-----------|
## 0 | 7 | 0 | 7 |
## | 0.583 | 1.750 | |
## | 1.000 | 0.000 | 0.438 |
## | 0.583 | 0.000 | |
## | 0.438 | 0.000 | |
## ----------------|-----------|-----------|-----------|
## 1 | 5 | 4 | 9 |
## | 0.454 | 1.361 | |
## | 0.556 | 0.444 | 0.562 |
## | 0.417 | 1.000 | |
## | 0.312 | 0.250 | |
## ----------------|-----------|-----------|-----------|
## Column Total | 12 | 4 | 16 |
## | 0.750 | 0.250 | |
## ----------------|-----------|-----------|-----------|
##
##
#Cuantitative variables
nobs(norse$SEdur)
## [1] 32
summary(norse$SEdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 6.00 19.00 273.03 96.75 2784.00 8
summary(norse[which(norse$age_less6==1),]$SEdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 4.25 8.50 105.18 40.00 1032.00 6
summary(norse[which(norse$age_less6==0),]$SEdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 9 24 52 702 1200 2784 2
wilcox.test(norse[which(norse$age_less6 == 0), ]$SEdur, norse[which(norse$ age_less6 == 1), ]$SEdur)
## Warning in wilcox.test.default(norse[which(norse$age_less6 == 0), ]$SEdur, :
## cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: norse[which(norse$age_less6 == 0), ]$SEdur and norse[which(norse$age_less6 == 1), ]$SEdur
## W = 166.5, p-value = 0.00878
## alternative hypothesis: true location shift is not equal to 0
nobs(norse$ICUdur)
## [1] 39
summary(norse$ICUdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.00 1.50 3.00 19.85 33.00 100.00 1
summary(norse[which(norse$age_less6==1),]$ICUdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.000 1.000 3.000 12.739 6.275 100.000 1
summary(norse[which(norse$age_less6==0),]$ICUdur)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.50 3.50 42.00 37.95 62.00 93.00
wilcox.test(norse[which(norse$age_less6 == 0), ]$ICUdur, norse[which(norse$ age_less6 == 1), ]$ICUdur)
## Warning in wilcox.test.default(norse[which(norse$age_less6 == 0), ]$ICUdur, :
## cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: norse[which(norse$age_less6 == 0), ]$ICUdur and norse[which(norse$age_less6 == 1), ]$ICUdur
## W = 233, p-value = 0.01401
## alternative hypothesis: true location shift is not equal to 0
nobs(norse$Nb.cont)
## [1] 40
summary(norse$Nb.cont)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.000 1.025 1.000 4.000
summary(norse[which(norse$age_less6==1),]$Nb.cont)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.8276 1.0000 4.0000
summary(norse[which(norse$age_less6==0),]$Nb.cont)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 1.000 1.000 1.545 2.500 4.000
wilcox.test(norse[which(norse$age_less6 == 0), ]$Nb.cont, norse[which(norse$ age_less6 == 1), ]$Nb.cont)
## Warning in wilcox.test.default(norse[which(norse$age_less6 == 0), ]$Nb.cont, :
## cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: norse[which(norse$age_less6 == 0), ]$Nb.cont and norse[which(norse$age_less6 == 1), ]$Nb.cont
## W = 218.5, p-value = 0.05885
## alternative hypothesis: true location shift is not equal to 0
nobs(norse$Nb.AED)
## [1] 40
summary(norse$Nb.AED)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.750 2.000 2.225 2.250 8.000
summary(norse[which(norse$age_less6==1),]$Nb.AED)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 2.000 2.000 2.207 2.000 8.000
summary(norse[which(norse$age_less6==0),]$Nb.AED)
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.500 2.000 2.273 3.000 5.000
wilcox.test(norse[which(norse$age_less6 == 0), ]$Nb.AED, norse[which(norse$ age_less6 == 1), ]$Nb.AED)
## Warning in wilcox.test.default(norse[which(norse$age_less6 == 0), ]$Nb.AED, :
## cannot compute exact p-value with ties
##
## Wilcoxon rank sum test with continuity correction
##
## data: norse[which(norse$age_less6 == 0), ]$Nb.AED and norse[which(norse$age_less6 == 1), ]$Nb.AED
## W = 172, p-value = 0.6945
## alternative hypothesis: true location shift is not equal to 0