Displaying 5 results from an estimated 5 matches for "sample5".
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2010 May 21
2
Data reconstruction following PCA using Eigen function
....1944390 -0.25181541 0.8882743 0.8404783 0.2531209
Sample3 1.7575174 0.03851425 -0.87537424 1.82811160 0.8342636
-0.8155942 -0.90893068 -0.5529098 -1.6586626 0.1761717
Sample4 0.2731749 0.24045167 -0.39913821 -0.48525909 0.1448994
-2.0173360 -0.01073639 -0.3219478 -1.1536431 -0.2521545
Sample5 -0.2014241 -1.04646151 0.28101160 0.74348390 0.1738312
-0.8431262 -0.08842512 1.2909658 1.2013136 0.6706926
Sample6 0.1743534 -1.70657357 -0.09170187 -0.55605031 -0.2940946
1.4525891 -0.39068509 -0.3373913 -0.0533732 0.9658389
Sample7 -0.8533191 -0.34438091 -1.23890437 -0.77360636 0.59...
2011 Jan 20
1
Problems with ecodist
...844 2407.1921 2818.8222 10046.113 1983.5246 2092.9254 8568.467
3518.9508 1182.826 6572.264 0.000 3647.8307
Loc12 3112.680 1314.6927 1063.8092 10253.957 1670.4862 1558.8962 8654.392
631.5406 3397.490 7497.861 3647.831 0.0000
> distancematrix
sample1 sample2 sample3 sample4 sample5 sample6 sample7 sample8
sample9 sample10 sample11 sample12
sample1 0.0000 0.0229 0.0258 0.0394 0.0295 0.0337 0.0269 0.0345
0.0314 0.0418 0.0577 0.0853
sample2 0.0229 0.0000 0.0219 0.0373 0.0337 0.0352 0.0320 0.0310
0.0275 0.0508 0.0533 0.0739
sample3 0.0258 0.0219...
2009 Mar 11
0
problem with rfImpute (package randomForest)
...y(mlbench)
library(randomForest)
data(BreastCancer)
summary(BreastCancer)
data=BreastCancer[,-1]
data=data[!is.na(data[,"Bare.nuclei"]),]
summary(data)
is.factor(data$Cl.thickness)# OK
##########selection of missing values######
x=1:nrow(data)
sample1=sample(x,70)
sample3=sample(x,70)
sample5=sample(x,70)
##########replace by missing values#########
data_missing=data
data_missing[sample1,1]=NA
data_missing[sample3,3]=NA
data_missing[sample5,5]=NA
summary(data_missing)
is.factor(data_missing$Cl.thickness)# OK
########imputation by random forest########
data_imputed <- rfImpute(C...
2012 Nov 07
0
generic question about differences between PCA and DMFA
...n about my case study.
I'm analizing a dataset of 9 experiments and 15 variables with the aim to
highlight the variables that can majorly explain the variance between the
experiments.
This is an example with only 3 rows and 5 variables
var1 var2 var3 var4 var5 sample5 0,067
0,005 0,008 0,100 0,005 sample6 0,069 0,001 0,011 0,084 0,005 sample7 -7
-5 -1 34 4
My problem is that in some experiments (like in sample7) the measures
related to my variables are measured as delta values (initial condition -
final condition). In the other cases the variables are measur...
2011 Mar 23
1
Function to crop p-values from multiple Anovas
...;"))
> rownames(example.df) <- paste("sample", 1:8, sep="")
> example.df
age treatment gene1 gene2 gene3 gene4
sample1 young drug 392 878 908 740
sample2 young control 167 263 711 392
sample3 young drug 155 252 242 547
sample4 young control 333 348 295 300
sample5 old drug 392 878 908 740
sample6 old control 167 263 711 392
sample7 old drug 155 252 242 547
sample8 old control 333 348 295 300
Now I would like to define a function that will crop the p-values from an Anova (so that I can use the function with a 'for loop' later on to go through all...