Fstat {SAGx} | R Documentation |
Calculates F statistic.
Fstat(indata = M, formula1 = ~as.factor(g), formula0 = "mean", design1 = NULL, design0 = NULL, B = NULL)
indata |
The data matrix |
formula1 |
a formula descibing the alternative linear model |
formula0 |
a formula describing the nullmodel. Use linear models syntax, except for one-way ANOVA ("mean") |
design1 |
the alternaive design matrix. If not NULL it overrides the formula argument |
design0 |
the null design matrix. If not NULL it overrides the formula argument |
B |
the number of bootstrap replicates |
A list with the components
Fstat |
the value of the F statistic |
fnum |
the numerator degrees of freedom |
fdenom |
the denominator degrees og freedom |
design1 |
the alternative design matrix |
design0 |
the null design matrix |
SS1 |
the sum of squares in the denominator of the F-statistic |
SS0 |
the sum of squares in the numerator of the F-statistic |
pvalue |
the p-value for testing the alternative vs the null model |
Per Broberg
## Annette Dobson (1990) "An Introduction to Generalized Linear Models". ## Page 9: Plant Weight Data. ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14) trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69) group <- gl(2,10,20, labels=c("Ctl","Trt")) weight <- c(ctl, trt) anova(lm.D9 <- lm(weight ~ group)) # Analysis of Variance Table # Response: weight # Df Sum Sq Mean Sq F value Pr(>F) #group 1 0.6882 0.6882 1.4191 0.249 #Residuals 18 8.7292 0.4850 Fstat(indata = rbind(weight,weight),formula1=~group) # Fstat will need at least two genes to work with # #$Fstat # weight weight #1.419101 1.419101 #$fnum #[1] 18 #$fdenom #[1] 1 #$design1 # (Intercept) groupTrt #1 1 0 #2 1 0 #3 1 0 #4 1 0 #5 1 0 #6 1 0 #7 1 0 #8 1 0 #9 1 0 #10 1 0 #11 1 1 #12 1 1 #13 1 1 #14 1 1 #15 1 1 #16 1 1 #17 1 1 #18 1 1 #19 1 1 #20 1 1 #attr(,"assign") #[1] 0 1 # $design0 # NULL # $SS1 # weight weight #8.72925 8.72925 #$SS0 # weight weight #0.688205 0.688205