Dear all
I have data related to cell count across time in 2 different types of
cells. I have transformed the count data using a log
and I want to test the H0: B cell_ttype1=Bcell_type2 across time
for that I am fitting the following model
fit_all<-lm(data$count~data$cell_type+data$time+data$cell_type*data$time)
the output is
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 1.0450021 0.0286824 36.434 <2e-16 ***
data$cell_typeOV -0.0456669 0.0405631 -1.126 0.271
data$time 0.0115620 0.0004815 24.015 <2e-16 ***
data$cell_typeOV:data$time -0.0009764 0.0006809 -1.434 0.164
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.06318 on 26 degrees of freedom
Multiple R-squared: 0.9764, Adjusted R-squared: 0.9737
F-statistic: 358.8 on 3 and 26 DF, p-value: < 2.2e-16
inspite the fact that the p-value of he interaction is >0.05 may I still
conclude that B cell_ttype1 is different from Bcell_type2 because the
p-value of the fit is lower<0.05?
Thanks in advance for your help.
With kind regards,
Andreia
--
---------------------------------------------------------------------------------------------
Andreia J. Amaral, PhD
BioFIG - Center for Biodiversity, Functional and Integrative Genomics
Instituto de Medicina Molecular
University of Lisbon
Tel: +352 217500000 (ext. office: 28253)
email:andreiaama...@fm.ul.pt ; andreiaama...@fc.ul.pt
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