I have import data on deseq2 using tximport from rsem results.

After create the rlog object I  try to use mroast and camera on  some pathway. This is the script I use:

files <- list.files(path=path, pattern="*.ensembl.csv", full.names=T, recursive=FALSE)
tipo<-list()
result_camera<-data.frame("Ngenes"=numeric(),"Direction"=character(),"PValue"=numeric(),"name"=character(),stringsAsFactors = F)

result_mroast<-data.frame("NGenes" =numeric(),"PropDown"=numeric(),"PropUp"=numeric(),"Direction"=character(),"PValue"=numeric(),
                       "FDR"=numeric(),"PValue.Mixed"=numeric(), "FDR.Mixed" =numeric(),stringsAsFactors = F)
for(i in 1:length(files)){
  ao<-strsplit(files[i],"/")[[1]]
  name<-ao[9]
  listaGeni<- read.table(files[i],header=F,sep="\t")
  id=ids2indices(listaGeni$V1,rownames(v))
  condition<-factor(metadata$CONDITION)
  
  design<-model.matrix(formula(~ condition))
  gst.camera<-camera(v,id,design = design)
  
  pi<-mroast(v,index=id,design,nrot = 99)
  result_mroast<-rbind(result_mroast,data.frame(pi))
  result_camera<-rbind(result_camera,data.frame(gst.camera$NGenes,gst.camera$Direction,gst.camera$PValue,name))
}

There is some difference For camera for istance

for the mroast:

NGenes   PropDown     PropUp Direction PValue  FDR PValue.Mixed FDR.Mixed
Set1       28 0.00000000 0.25000000        Up   0.34 0.34         0.17      0.17

 sessionInfo()
R version 3.3.2 (2016-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.2 LTS

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=it_IT.UTF-8       
 [4] LC_COLLATE=en_US.UTF-8     LC_MONETARY=it_IT.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=it_IT.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=it_IT.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods  
[9] base     

other attached packages:
 [1] GSEABase_1.36.0            graph_1.52.0               annotate_1.52.1           
 [4] XML_3.98-1.9               AnnotationDbi_1.36.2       limma_3.30.13             
 [7] clusterProfiler_3.2.14     DOSE_3.0.10                biomaRt_2.30.0            
[10] pheatmap_1.0.8             tximportData_1.2.0         tximport_1.2.0            
[13] DESeq2_1.14.1              SummarizedExperiment_1.4.0 Biobase_2.34.0            
[16] GenomicRanges_1.26.4       GenomeInfoDb_1.10.3        IRanges_2.8.2             
[19] S4Vectors_0.12.2           BiocGenerics_0.20.0       

loaded via a namespace (and not attached):
 [1] tidyr_0.6.3         bit64_0.9-7         splines_3.3.2       Formula_1.2-1      
 [5] DO.db_2.9           latticeExtra_0.6-28 blob_1.1.0          RSQLite_2.0        
 [9] backports_1.1.0     lattice_0.20-35     digest_0.6.12       RColorBrewer_1.1-2
[13] XVector_0.14.1      checkmate_1.8.2     qvalue_2.6.0        colorspace_1.3-2   
[17] htmltools_0.3.6     Matrix_1.2-10       plyr_1.8.4          pkgconfig_2.0.1    
[21] genefilter_1.56.0   zlibbioc_1.20.0     xtable_1.8-2        GO.db_3.4.0        
[25] scales_0.4.1        BiocParallel_1.8.2  htmlTable_1.9       tibble_1.3.3       
[29] ggplot2_2.2.1       nnet_7.3-12         lazyeval_0.2.0      survival_2.41-3    
[33] magrittr_1.5        memoise_1.1.0       foreign_0.8-69      tools_3.3.2        
[37] data.table_1.10.4   stringr_1.2.0       munsell_0.4.3       locfit_1.5-9.1     
[41] cluster_2.0.6       rlang_0.1.1         grid_3.3.2          RCurl_1.95-4.8     
[45] htmlwidgets_0.8     igraph_1.0.1        bitops_1.0-6        base64enc_0.1-3    
[49] gtable_0.2.0        DBI_0.7             reshape2_1.4.2      gridExtra_2.2.1    
[53] knitr_1.16          bit_1.1-12          fastmatch_1.1-0     Hmisc_4.0-3        
[57] fgsea_1.0.2         stringi_1.1.5       GOSemSim_2.0.4      Rcpp_0.12.11       
[61] geneplotter_1.52.0  rpart_4.1-11        acepack_1.4.1  

As Gordon already pointed out, mroast and camera do completely different tests! The right method to choose is the one that answers the biological question you are asking, not the one that gives you a significant result. If you are confused as to the difference, you should read the Goeman and Buhlmann paper that is referenced in both of the help files. Or you could simply read the help files for each of the functions you are using. You will find that you get better help if people think you are at least attempting to understand things for yourself, rather than expecting people on this forum to explain things that you could easily find for yourself. From ?mroast:

     'roast' and 'mroast' test whether any of the genes in the set are
     differentially expressed. They can be used for any microarray
     experiment which can be represented by a linear model. The design
     matrix for the experiment is specified as for the 'lmFit'
     function, and the contrast of interest is specified as for the
     'contrasts.fit' function. This allows users to focus on
     differential expression for any coefficient or contrast in a
     linear model. If 'contrast' is not specified, then the last
     coefficient in the linear model will be tested.

and from ?camera:

     'camera' and 'interGeneCorrelation' implement methods proposed by
     Wu and Smyth (2012). 'camera' performs a _competitive_ test in the
     sense defined by Goeman and Buhlmann (2007). It tests whether the
     genes in the set are highly ranked in terms of differential
     expression relative to genes not in the set. It has similar aims
     to 'geneSetTest' but accounts for inter-gene correlation. See
     'roast' for an analogous _self-contained_ gene set test.